SECTION 1 BIO-MEDICAL / BIOENGINEERING · BIO-MEDICAL / BIOENGINEERING . ASME District F - ECTC...

SECTION 1

BIO-MEDICAL / BIOENGINEERING

ASME District F - ECTC 2013 Proceedings - Vol. 12 1

ASME District F - Early Career Technical Conference Proceedings ASME District F - Early Career Technical Conference, ASME District F – ECTC 2013

November 2 – 3, 2013 - Birmingham, Alabama USA

NON-INVASIVE EVALUATION OF ENERGY LOSS IN THE PULMONARY ARTERIES USING 4D PHASE CONTRAST MRI MEASUREMENT

Namheon Lee Mechanical Engineering, School of Dynamics Systems

University of Cincinnati, Cincinnati, OH, USA

Michael D. Taylor The Heart Institute, Cincinnati Children’s Hospital

Medical Center, Cincinnati, OH, USA

Kan N. Hor Pediatric Cardiology, The Heart Center

, Nationwide Children’s Hospital, Columbus, OH, USA

Rupak K. Banerjee

Mechanical Engineering, School of Dynamics Systems University of Cincinnati, Cincinnati, OH, USA

ABSTRACT

Invasive cardiac catheterization is often required for

accurate cardiac hemodynamic measurements in support of

clinical decision making for congenital heart disease (CHD)

patients. In this research, we used non-invasively measured

4D phase contrast magnetic resonance imaging (PC MRI) data,

i.e. dynamic three directional velocity data, to determine blood

flow and pressure drop in the branch pulmonary arteries (PAs)

of CHD patients. This enabled us to calculate energy loss in

the branch PAs non-invasively.

4D PC MRI was performed for a CHD patient with

abnormal right ventricle (RV)-PA physiology, including severe

pulmonary regurgitation and PA stenosis, and a normal subject.

The spatially averaged blood velocity, flow rate, and pressure

drop were obtained from 4D PC MRI data. Using these data,

energy loss in the branch PAs was calculated.

The average pressure drop in the branch PAs over the

cardiac cycle for the patient was -2.0 mmHg/s and -0.6

mmHg/s for the RPA and LPA, respectively, and was larger

(order of magnitude) than the control. Similarly, the average

total energy loss in the branch PAs for the patient, -106.8 mJ/s

and -28.3 mJ/s, for the RPA and LPA, respectively, was larger

than the control.

The pressure drop and energy loss in the branch PAs,

calculated from non-invasive measurements for the patient

were significantly different from those for the normal subject.

Thus, we believe that the status of PA hemodynamics of a CHD

patient can be evaluated non-invasively by pressure drop and

energy loss computed using 4D PC MRI data.

INTRODUCTION With the excellent survival rate of palliated congenital

heart disease (CHD) patients, monitoring residual lesions has

become increasingly important [1, 2]. The status of post-

operative sequelae of CHD, such as pulmonary regurgitation

and stenosis in this study, has been assessed by cardiac MRI

and catheterization in current clinical setting. Recently,

energy-based endpoints have been proposed for more

comprehensive assessment on physiological status of CHD

patient [3, 4, 5, 6, 7]. Particularly, body-surface-area indexed

RV stroke work (SWI) and energy transfer ratio (eMPA) between

the RV and main PA (MPA) were proposed to quantify the

status of abnormal RV-PA physiology for CHD patients [5, 6,

7].

However, energy-based endpoints mentioned above require

accurate measurement of functional data, such as blood

velocity, flow, and pressure data. Consequently, invasive

cardiac catheterization is required for those measurements.

Alternatively, we attempted to assess functional data non-

invasively using 4D phase contrast magnetic resonance imaging

(PC MRI), to compute energy-based endpoint, energy loss in

the branch PAs [8]. We hypothesize that the level of pressure

drop and energy loss in the abnormal RV-PA physiology would

be significantly different from those in normal RV-PA

physiology.

METHODS 4D PC MRI was performed for a CHD patient with

abnormal RV-PA physiology, such as severe pulmonary

regurgitation and PA stenosis, and a control subject with normal

RV-PA physiology. The spatially averaged blood velocity and

flow rate were measured at the respective plane for the PAs

(Fig. 1). The transient pressure gradient field (∇P =

) was

computed with 4D velocity data obtained from 4D PC MRI

using the Navier-Stokes equation (Eq. 1).


-9

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∆P,RPA (Patient) ∆P,LPA (Patient) ∆P,RPA (Control) ∆P,LPA (Control)

Control

(1)

The streamlines originating from the MPA were created at

the systolic phase of the cardiac cycle for both subjects (Fig. 1).

The multiple streamlines (3×3) were used to compute pressure

drop between the MPA and branch PAs (=Pbranch PA–PMPA). The

pressure drop was calculated by integrating ∇P along the

streamline. Using blood flow data and pressure drop, energy

loss in the branch PAs was calculated for both subjects.

Figure 1. The planes of PAs for the flow computation and streamlines originating from the MPA for the pressure

drop calculation are shown with the magnitude image for subjects: (A) control subject and (B) patient. The velocity

vectors on the inlet and outlet planes are also shown.

RETULTS AND DISCUSSION The blood flow rate in the PAs over the cardiac cycle for

the subjects is shown in Fig. 2. The control had the average

blood flow volumes of 88.6 ml/s, 47.7 ml/s, and 56.3 ml/s for

the MPA, RPA, and LPA, respectively. The small amount of

reverse blood flow in the MPA was observed during the early

and late diastole phases, and no reverse flow was observed in

the branch PAs, RPA and LPA (Fig. 2A). The time average

blood flow volumes for the patient were 96.0 ml/s, 48.5 ml/s,

and 44.9 ml/s for the MPA, RPA, and LPA, respectively. As

shown in Fig. 2B, the large amount of reverse blood flow in the

MPA and the LPA was observed during the diastole phase.

Figure 2. The blood flow rate versus time curves for the subjects, A) the control and B) the patient.

As shown in Fig. 3, the average pressure drop computed

was minimal in the control (-0.1 mmHg/s and -0.04 mmHg/s,

for the RPA and LPA, respectively) compared to that of the

patient (-2.0 mmHg/s and -0.6 mmHg/s, for the RPA and LPA,

respectively). Similarly, the peak pressure drop was smaller

for the control (-0.5 mmHg and –0.2 mmHg, for the RPA and

LPA, respectively) than that for the patient (-7.1 mmHg and -

3.0 mmHg, for the RPA and LPA, respectively).

Figure 3. The pressure drop in the branch PAs versus time curves for the control and patient.

LPA RPA

MPA

MPA

RPA LPA

A B

A

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Control

In Fig. 4, the average total energy loss was larger for the

patient (-106.8 mJ/s and -28.3 mJ/s, for the RPA and LPA,

respectively) compared to that for the control (-5.5 mJ/s and -

2.9 mJ/s, for the RPA and LPA, respectively). Also, the net

total energy loss in the branch PAs over the cardiac cycle was

larger for the patient (-77.6 mJ and -20.6 mJ, for the RPA and

LPA, respectively) than that for the control (-4.0 mJ and -2.1

mJ, for the RPA and LPA, respectively).

Figure 4. The energy loss in the branch PAs versus

time curves for the control and patient.

The patient in this study underwent the Ross procedure,

which requires aortic valve autograft with pulmonary

homograft, resulting in the replacement of the pulmonary valve

with a conduit. As a result, lack of a competent valve caused

pulmonary regurgitant flow (pulmonary regurgitation fraction,

a ratio between the backward and forward blood volumes, = 36.

8%) and adverse RV-PA physiology.

Figure 5. 3D representative of gadolinium-enhanced magnetic resonance angiography (Gd-MRA) of PA for the patient showing stenosis at the RPA origin and the dilated MPA that caused an uneven PA flow

distribution.

In addition, the stenosis at the RPA origin (Fig. 5) caused

large pressure drop in the RPA leading to a highly uneven PA

flow distribution. As confirmed in Fig. 2B, the MPA-LPA

flow was much higher than the MPA-RPA flow during the early

systole phase. Most of the blood volume from the MPA

rapidly flowed into the LPA during early systole, whereas the

MPA-RPA flow increased with time until the late systole.

Thus, the flow in the MPA-RPA was out of phase with respect

to the MPA-LPA flow. The stenotic RPA and severe

regurgitation in the patient’s MPA caused MPA dilation (Fig. 5).

Consequently, the MPA-RPA flow of the patient became

irregular compared to that of the control (Fig. 2B). A lesser

volume of blood directly flowed to the RPA; whereas, most of

the blood flowing to the RPA became trapped, swirled, and

recirculated in the dilated MPA for a short period of time (30 ~

40 ms), then flowed into the RPA along a tortuous path.

However, the blood flow in the branch PA of the control was

evenly distributed as shown in Fig. 2A.

The standard care procedures for assessing blood flow and

velocity in the large arteries, e.g. aorta or pulmonary artery, are

echocardiography and 2D PC MRI in a clinical setting.

However, both modalities are limited by single directional

velocity measurement from a 2D plane of choice along the

artery. Thus, it may be insufficient to characterize the local

hemodynamic change in CHD patients since it is most likely a

patient specific and complex 3D blood flow in nature.

Therefore, the altered local blood flow in patient’s large vessels

may be impossible to capture in detail using current techniques.

Using 4D PC MRI clinicians are able to monitor carefully

3D cardiac volume of interest after images were taken. This can

fill the gap of information under current 2D based modalities.

Further, using fundamental fluid mechanics principles, the

pressure information in the large vessels can be also computed.

Together with all hemodynamic information from 4D PC MRI,

the comprehensive assessment on local hemodynamic changes

can be obtained in any cardiac region of interest.

CONCLUSION

Based on our data, the pressure drop and energy loss values

in the branch PAs for the abnormal RV-PA physiology were

significantly different (order of magnitude) from those for the

normal physiology. Thus, we believe that the status of RV-PA

patho-physiology of CHD patients can be evaluated non-

invasively with the pressure drop and energy loss computed

from 4D PC MRI data.

REFRERENCES

[1] Ooi A et al. Eur J Cardiothorac Surg. 2006; 30, pp 917-922.

[2] Murphy JG et al. N Engl J Med. 1993;329, pp 593-599.

[3] Dasi LP et al. J Biomech. 2008; 41, pp 2246-2252.

[4] Dasi LP et al. Ann Biomed Eng. 2009;37, pp 661-673.

[5] Das A et al. Ann Biomed Eng. 2010;38(12), pp 3674-3687.

Stenosis

RPA LPA

Dilated

MPA


[6] Lee N et al. Heart Vessels. 2011; DOI 10.1007/s00380-011-

0212-7.

[7] Lee N et al. Congenital Heart Disease. 2013; DOI: 10.1111

/chd. 12034

[8] Markl M et al. J Cardiovasc Magn Reson. 2011;13:7.

PMCID: 3025879.

BIBLIOGRAPHY

[1] Ebbers T et al. Magn Reson Med. 2001;45(5), pp 872-879




DESIGN AND EEG-BASED CONTROL OF A BIO-INSPIRED ANKLE FOOT ORTHOSIS

Daniel K. Olender and Yong Zhu

Department of Mechanical Engineering Georgia Southern University

Statesboro, GA 30458

ABSTRACT Ankle Foot Orthoses (AFO) are externally applied devices

that control the foot and ankle joint complex. Passive AFO cannot provide the necessary force to maintain a more natural gait cycle. An actively powered AFO will be designed and tested using an electroencephalogram (EEG) signal to control the timing and displacement. The approach is to: 1) Design a compact and lightweight AFO using a pneumatic artificial muscle to provide active power. 2) Develop an EEG-based controller that will coordinate pneumatic muscle movement. This research will potentially provide a low-cost actively powered lightweight device that can not only prevent foot drop but also assist in developing normal gaits. It will also demonstrate the potential of using EEG signals to command rehabilitation devices for the disabled.

INTRODUCTION A stroke is the result of blood flow obstruction within the

brain that can permanently damage the affected area. One of the many ill effects that impact stroke victims is the loss of mobility. It has been observed that the majority of victims with impacted muscle control lose sensation in their extremities, like hands and feet. Those who lose muscle control of their feet or ankle typically struggle to create the necessary force for both dorsiflexion (toes are brought closer to the shin) and plantar flexion (toes down). However, with the help of custom made orthoses, many victims can recover mobility but tend to remain dependent on the device.

There are many options when selecting an orthosis that are dependent on the patient's preference and budget. Most of these options can be categorized as either passive or active orthoses. Passive orthoses do not contain powered mechanisms. Instead, they strive to provide a better walking ability by using different materials and manufacturing techniques [1]. Passive orthoses are merely for support and limit natural motion. We visited a local branch of Hanger Prosthetics & Orthotic, a leading provider of prosthetics and orthopedics, to study some of the more widely used devices. A few of the passive orthoses that are currently on the market are shown in Figure 1.

On the other hand, active orthoses have portable or tethered source of power to actually assist the patient in

creating the necessary force for a more natural and normal gait cycle. Most of the active orthoses function under the same principles as springs, adding enough force to return the user’s foot to its neutral position to assist drop foot [2].

(a) (b) (c) Figure 1. Examples of passive foot orthoses utilizing (a) two compression springs, (b) two cantilever type springs

and (c) carbon fiber as a torsion/cantilever spring To actively control our device we will be utilizing

pneumatic artificial muscles, such as the one shown in Figure 2, which consists of a hollow elastomer cylinder embedded with aramid fibres. By controlling the pressure inside, pneumatic artificial muscles work as variable stiffness springs.

Figure 2. Pneumatic artificial muscle

Pneumatic artificial muscles are natural impedances with true mechanical compliance. Forces are controlled by manipulating the pressure in the membrane, and compliance is provided by the compressibility of air and the membrane. The


natural compliance can be controlled to offer a pneumatic artificial muscle the ability to mimic the biological muscle-tendon architecture of a human ankle joint complex [3][4]. Pneumatic orthoses have some intrinsic advantages. Galle et al. [5] showed that pneumatic ankle-foot exoskeletons can help reduce metabolic cost up to 16% in less than 24 minutes without EMG-based control algorithms. Dietz et al. [6] demonstrated similar bio-adaptation for Parkinsonian patients through leg muscle activation.

Pneumatic artificial muscles have been widely used in rehabilitation engineering as actuators for actively powered orthoses [7]. Chin et al. [8] designed a standalone self-powered ankle foot orthosis to prevent foot-drop by building a bellows pump and pneumatic circuit into the AFO to harvest fluid power. This device was designed for someone with loss of muscle mass or damaged muscle of the plantar flexor and dorsiflexor. The method used was a cam to provide stability and a pneumatic system consisting of bellows and one way values hidden in the sole that provided dorsiflexion assistance. When the user’s weight was shifted on to the ball of the foot, the bellow became compressed and provided enough force to engage the cam to prevent drop foot. The pressure would then be exhausted and the system would cycle new ambient air through a system of one way valves. The device proved that fluid power harvested from surrounding ambient pressure could power a lower limb orthosis. The device fluctuated between operating extremes of 0-170 kPa. It would appear that the device restricts natural ankle motion and limits freedom of rotation about the ankle.

Shorter et al. [9] focused on a mobile active prosthesis powered by a CO2 canister. The device targets those who have weak or no muscle to create plantar flexion or dorsiflexion. The device is powered by a compressed canister of CO2 that provides pressure to a bidirectional rotary actuator which acts as an ankle joint. Pressure from the CO2 tank is regulated to mimic the forces a healthy ankle is capable of producing. Two solenoid values controlled the rotary actuator by reversing the flow of compressed air, resulting in dorsiflexion or plantar flexion. The type of flexion was governed by readouts of two load cells, one located in the heel and the other in the ball of the foot. A microcontroller was used to interoperate the signals from the load cell and fire the corresponding solenoid. An angle sensor was included as a safety protocol to prevent over rotation. The device functioned as anticipated, but it appears that the nature of the closed loop system caused inherent difficulties with signal processing when reading the two load cells. The CO2 bottle also added an additional eight pounds to the already bulky device.

Sawicki and Ferris [10] focused on the design of a type of exoskeleton that provided support for the entire leg, including the upper/lower knee, the calf and ankle. The device utilized eight pneumatic muscles, four for the thigh and four for the calf/ankle. The device targeted those who suffer from loss of muscle control due to spinal injury or poor nervous system response. The method used to power this device was a stationary compressed air source which would provide

pressurized air to each muscle through a series of proportional pressure regulators and electric actuators. Signals to allow for air flow were controlled by the users’ own electromyography (EMG) surface impulses. The device was designed to read the distorted or weak electro-muscular signals, amplify those signals and then assist the user with movement. It was found that EMG could be used to control a robotic muscular system. However extensive testing and samples are needed for each individual to develop a personal and effective algorithm.

Ferris et al. [11][12] also designed pneumatically powered knee-ankle-foot orthoses using pneumatic artificial muscles. The focus of their study was providing comparable knee and ankle torques. Park et al. [13] designed an active soft orthotic device for ankle foot pathologies using entirely soft materials. Three pneumatic artificial muscles are used to mimic three anterior muscles for assisting dorsiflexor motion.

A pneumatic artificial muscle powered AFO has promising clinical and basic science applications. Mechanical performances of pneumatic artificial muscles can mimic natural gait movements due to the low compliance and high backdrivability [14]. In general the literature contains a gap when it comes to utilizing EEG-based non-invasive signals to command pneumatic artificial muscle actuated ankle foot orthoses for natural gait rehabilitation [15][16][17].

Through literature review and conversing with local therapists, we came to the conclusion that weak dorsiflexion is the greater challenge for most patients with impaired lower extremities. Foot drop is usually associated with weak dorsiflexion. Therefore, the major goal of this design is to use a single pneumatic artificial muscle to provide dorsiflexion to prevent foot drop and hopefully also be able to provide plantar flexion to generate forward speed. We envision a simple and lightweight device that can not only prevent foot drop but also assist in developing normal gaits.

PROTOTYPE DESIGN This is an attempt to create a sturdy but light and

comfortable ankle foot orthosis powered by a pneumatic artificial muscle. The approach was to design prototypes using SolidWorks and then print them in ABS plastic using a 3D printer. 3D renderings of the two major components attached to the calf and the shoe are shown in Figure 3.

(a) (b) Figure 3. 3D rendering of the component attached (a) to

the calf and (b) to the bottom of shoe


The two components shown in Figure 3 are connected by a pneumatic artificial muscle. The pneumatic muscle is ideal for this application, because it allows for more flexibility than standard actuators. Also the Festo pneumatic muscle is manufactured in an array of sizes and lengths that will allow for greater customization of each orthotic. The operating pressure range for the muscle will also allow for precision control of ankle angle, providing the patient with sufficient forces to achieve a natural gait cycle. The assembled SolidWorks model and the prototype are shown in Figure 4.

Figure 4. 3D rendering in Solidworks and prototype of the

AFO assembly

The device shown above is designed to be worn around the patient’s shoe and strapped to the shoe and calf via two or three Velcro bands. Using a pneumatic muscle, this AFO is able to create forces necessary for both dorsiflexion and plantar flexion control while allowing for a more fluid motion and flexibility of the ankle joint.

(a) (b) Figure 5. 3D rendering of the component attached (a) to the calf and (b) to the top of shoe, for the revised design

The second prototype shown in Figures 5 and 6 is a revised

design that is supposed to have a greater ability to assist with dorsiflexion. This design is intended to have the top component worn on the front of the shin, connecting just below the knee, and the bottom component interlaced into the tongue on the top of the shoe. The design will allow for greater control of

dorsiflexion and is able to remain rigid enough to provide propulsive forces during plantar flexion. The control of the device will operate similarly to the first prototype for testing, and will eventually include a microcontroller and external compressed gas tank.

Figure 6. 3D rendering in Solidworks and prototype of the

revised design CONTROL

The prototype was tested using the rapid prototyping real -time control system shown in Figure 7. It uses a host-target control architecture to expedite control system development and maintain fast real time control. The communication between the host computer and the target machine is via an Ethernet cable. Simulink is a graphical front-end for programming software that will be used to control the device. Simulink models are compiled into C/C++ code and downloaded to the real time target machine for execution. xPC Target run this code in real-time on a target PC with guaranteed cycle time. This platform is widely used in both industry and university for model based control system design and hardware-in-the-loop simulations. The final embedded code ran by a microcontroller will be generated using Matlab Simulink Embedded Coder.

Figure 7. Real time control system using xPC target


Figure 8. NeuroSky Mindwave mobile headset

A physiologically-inspired controller was implemented to test the prototype AFO. A NeuroSky Mindwave Mobile headset shown in Figure 8 was configured to provide the raw electroencephalogram (EEG) data at 200Hz. The raw EEG data is generally quite noisy, since it uses a single sensor at the forehead, which picks up brainwaves and muscle movements as well. The software that came with the headset provides two proprietary indices: attention and meditation.

The reason for using an EEG signal to detect and control the AFO is based on motor learning theory in Neuroengineering. The EEG signal is ideal for muscle control in stoke victims because it encourages and promotes growth and recovery of the nervous system by amplifying weak or sporadic control signals from the brain. This application not only helps rehabilitate the muscle but also the mind-muscle interface that is damaged during a stroke. Based on our current observation, it appears that the device is consistently responsive to eye movements such as blinking. A sample of raw EEG data is shown in Figure 9. It can be clearly seen that the two eye blinking movements are very easily detected.

Figure 9. Raw EEG signal of two eye blinks

The EEG signal was used to control the timing and displacement of the pneumatic muscle actuator as shown in Figure 10.

Figure 10. EEG signal based control

EXPERIEMNTAL RESULTS The experimental setup is shown in Figure 11, testing the

first prototype. The EEG signal was provided by the NeuroSky Mindwave headset. The pneumatic artificial muscle (Festo DMSP-20-150N-RM-CM) has an inside diameter of 20 mm. For testing and calibration purposes, a pressure sensor (Festo SPTW-P25R-G14-VD-M12) is attached to the muscle as well a proportional valve (Festo MPYE-5-M5-010-B) to control the fluid flow. A linear potentiometer (Midori LP-100F) with 100 mm maximum travel is used to measure the elongation of the pneumatic muscle. Control is provided by an xPC real time target machine with an analog-to-digital A/D card (National Instruments PCI-6221) that controls the proportional valve through one analog output channel. The device is tested by connecting to a regulated (<600 kPa) compressed air supply line. The target machine uses input EEG signals to control the AFO, which corresponds to the increase or decrease of pressure within the muscle, and therefore the force created by the expansion/contraction of the pneumatic artificial muscle.

Figure 11. Real time control experimental setup

Initial testing of the AFO shown in Figure 12 using a

sinusoidal command on our healthy volunteer required the pneumatic muscle to operate at its highest cycle pressure, 320 kPa (gage pressure), and shortest length to achieve plantar flexion. The muscle could then also provide dorsiflexion torque by decreasing the pressure to 50 kPa and increasing the muscle length without losing rigidity of the muscle. The neutral ankle

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position would then correspond to a pressure of roughly 190 kPa allowing the back calf support to rest just below the subject’s mid-calf.

Figure 12. Pressure change in the pneumatic artificial

muscle with sinusoidal control command

Without considering the work consumed by membrane deformation, the pneumatic muscle’s tension force F can be represented by pressure P , volume V and length L as,

dLdVPF (1)

The force can be further expressed as a function of the pressure and the braid angle [16],

)1cos3(4/

/ 22

0

PD

ddLddVPF (2)

where 0D is the initial diameter of the pneumatic muscle

actuator. If 0L is the initial length of the actuator, Eq. (2) can be

linearized as [18],

)( 0LLPKF g (3)

where dPdKKg is defined as the stiffness ( K ) per unit

pressure and is approximately a constant unless the actuator is extremely short or extremely long. Based on the manufacturer’s product manual, plugging P =320 kPa, 0LL =0.02 m and F=200 N (according to tension-contraction ratio curve) into Eq. (3) yields 125.3gK cm. Therefore, the stiffness at

kPaP 320 is about 10,000 N/m; the stiffness at kPaP 50

is about 1,500 N/m, which is still comparable to the stiffness of biological muscle tendons (nominal muscle-tendon stiffness is 2,500 N/m [19]). That is why you can see from the Figure 13(b) that the pneumatic actuator starts to bend, but can still hold the foot at a fixed position, instead of dropping the foot.

(a) Contracted

(b) Relaxed

Figure 13. AFO control demonstrating contracted and relaxed states.

The synchronized EEG signal and the corresponding

pressure and displacement change are shown in Figure 14. It can be seen that the pneumatic muscle actuator pressure and the corresponding elongation change can be very well controlled by EEG signal, specifically eye blinks here.

Figure 14. EEG signal based control of the AFO.

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DISCUSSION A pneumatic, artificial muscle actuated, actively powered

ankle foot orthosis was designed and tested. The preliminary results of EEG-based control were also demonstrated. Powered by a single pneumatic artificial muscle, the proposed design preserves the natural compliance of biological muscle-tendon architecture to provide both dorsiflexion and plantar flexion. The design takes advantage of the natural compliance of a pneumatic artificial muscle to create a more reliable and less cumbersome device compared with other similar active ankle foot orthoses.

Based on motor learning theory in Neuroengineering, the proposed AFO combines an EEG signal with the control of a rehabilitation device. This not only helps rehabilitate the muscle but also the mind-muscle interface that may also be damaged during a stroke. The EEG-based control demonstrated the potential of using EEG signal as a non-invasive brain-machine interface to command rehabilitation device for disabled without motor functions.

Furthermore, it has come to our attention that the active AFO will add about 8 millimetres of height to the overall length of the test subject’s leg upon wearing this unit, which will cause a balance problem between two legs. This problem is not addressed in the current initial prototype design phase. In the future gait study, we could either add a shoe lift to the other leg or modify the device so that it can be clamped to the back of the shoe.

In the future, the device will be fitted with an external CO2 tank, or other compressed gas source, and a microcontroller to make the platform portable. Furthermore, the prototype and EEG-based control strategy will be tested to accomplish practical tasks such as standing up from wheelchair, and walking with ease by changing to rough terrains and climbing stairs. It will potentially provide a low cost actively powered AFO with reliable EEG signal control to assist elderly or injured people.

CONCLUSION The goal of this research was to design and test an actively

powered ankle foot orthosis using an EEG-based control signal. The preliminary test showed that a single pneumatic artificial muscle was able to provide both dorsiflexion and plantar flexion while simultaneously preserving the natural compliance of the ankle joint complex. The simple and natural design and the electro-biological signal based control are the two main features of this device, which would potentially lead to more reliable, less cumbersome and more intuitively controlled ankle foot orthoses. REFERENCES [1] Faustini, M. C., Neptune, R. R., Crawford, R. H., and

Stanhope, S. J., 2008, “Manufacture of Passive Dynamic Ankle-Foot Orthoses Using Selective Laser Sintering,” IEEE Trans. Biomedical Eng., 55(2), pp. 784-790.

[2] Weinberg, B., Nikitczuk, J., Patel, S., Patritti, B., Mavroidis, C., Bonato, P., and Canavan, P., 2007, “Design,

Control and Human Testing of an Active Knee Rehabilitation Orthotic Device,” Proc. IEEE Int. Conf. on Robotics and Automation, pp. 4126-4133.

[3] Yeh, T.-J., Wu, M.-J., Lu, T.-J., Wu, F.-K., and Huang, C.-R., 2010, “Control of McKibben Pneumatic Muscles for a Power-assist, Lower-limb Orthosis”, Mechatronics, 20(6), pp. 686-697.

[4] Waycaster, G. C., 2010, “Design of a Powered Above Knee Prosthesis using Pneumatic Artificial Muscles,” Master’s thesis, Mechanical Engineering, University of Alabama, Tuscaloosa, AL.

[5] Galle, S., Malcolm, P., Derave, W., and De Clercq, D., 2013, “Adaptation to Walking with an Exoskeleton that Assists Ankle Extension,” Gait & Posture, 38(3), pp. 495-499.

[6] Dietz, V., Zijlstra, W., Prokop, T., and Berger, W., 1995, “Leg Muscle Activation during Gait in Parkinson’s Disease: Adaptation and Interlimb Coordination,” Electroencephalography and Clinical Neurophyslology, 97(4), pp. 408-415.

[7] Nascimento, B., Vimieiro, C., Nagem, D., and Pinotti, M., 2008, “Hip Orthosis Powered by Pneumatic Artificial Muscle: Voluntary Activation in Absence of Myoelectrical Signal,” Artificial Organs, 32(4), pp. 317-322.

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[9] Shorter, K. A., Kogler, G. F., Loth, E., Durfee, W. K., and Hsiao-Wecksler, E. T., 2011, “A Portable Powered Ankle-Foot Orthosis for Rehabilitation,” J. of Rehabilitation Research & Development, 48(4), pp. 459-472.

[10] Sawicki, G. S., and Ferris, D. P., 2009, “A Pneumatically Powered Knee-Ankle-Foot Orthosis (KAFO) with Myoelectric Activation and Inhibition,” J. of Neuroengineering and Rehabilitation, 6(23).

[11] Ferris, D. P., Czerniecki, J. M., and Hannaford, B., 2005, “An Ankle-Foot Orthosis Powered by Artificial Pneumatic Muscles,” J. of Applied Biomechanics, 21(2), pp. 189-197.

[12] Ferris, D. P., Gordon, K. E., Sawicki, G. S., and Peethambaran, A., 2006, “An Improved Powered Ankle-Foot Orthosis using Proportional Myoelectric Control,” Gait & Posture, 23(4), pp. 425-428.

[13] Park, Y.-L., Chen, B.-R., Young, D., Stirling, L., Wood, R. J., Goldfield, E., and Nagpal R., 2011, “Bio-inspired Active Soft Orthotic Device for Ankle Foot Pathologies,” Proc. IEEE/RSJ Int. Conf. on Intelligent Robots and Systems, pp. 4488-4495.

[14] Gordona, K. E., Sawickia, G. S., and Ferris, D. P., 2006, “Mechanical Performance of Artificial Pneumatic Muscles to Power an Ankle-Foot Orthosis,” J. of Biomechanics, 39(10), pp. 1832-1841.

[15] Kanna, S., and Heng J., 2009, “Quantitative EEG Parameters for Monitoring and Biofeedback during


Rehabilitation after Stroke,” IEEE Int. Conf. on Advanced Intelligent Mechatronics, pp. 1689-1694.

[16] Raichur, A., Wihardjo G., Banerji S., and Heng J., 2009, “A Step towards Home-based Robotic Rehabilitation: An Interface Circuit for EEG/SEMG actuated orthosis,” IEEE Int. Conf. on Advanced Intelligent Mechatronics, pp.1998-2003.

[17] Wang, C., Phua, K. S., Ang, K. A., Zhang, H., Lin, R., Chua, S. G., Ang, B. T., and Kuah, C.W.K., 2009, “A Feasibility Study of Non-invasive Motor-imagery BCI-

based Robotic Rehabilitation for Stroke Patients,” 4th Int. IEEE Conf. on Neural Engineering, pp. 271-274.

[18] Chou, C.-P., and Hannaford, B., 1994, “Static and Dynamic Characteristics of McKibben Pneumatic Artificial Muscle,” IEEE Int. Conf. on Robotics and Automation, San Diego, CA.

[19] Richards, C.T., and Sawicki, G.S., 2012, “Elastic Recoil Can Either Amplify or Attenuate Muscle-Tendon Power, Depending on Inertial vs. Fuid Dynamic Loading,” J of Theoretical biology, 313(21), pp. 68-78.




DESIGN AND TESTING OF A CENTER-OF-PRESSURE OSCILLATION MEASUREMENT DEVICE FOR ASSESSMENT OF POSTURAL CONTROL

RECOVERY

Yong Zhu and Adetayo Faminu Department of Mechanical Engineering

Georgia Southern University Statesboro, GA 30458

Li Li Department of Health and Kinesiology

Georgia Southern University Statesboro, GA 30458

ABSTRACT A concussion is a mild traumatic brain injury as a result of

a blow to the head. From battlefields to football fields, concussions are unavoidable risks that occur on a daily basis. The goal of this research is to design and test the prototype of a low-cost, center of pressure (COP) measurement device to determine if one has fully recovered postural control after injury. Approximate entropy (ApEn) values reflecting the amount of randomness contained in COP oscillations will be used to calculate the index. The prototype will be designed by modifying a bathroom scale and using the four half-bridge load cells located at the four corners of the scale. The test subjects will be asked to stand on the device for 30 seconds with their eyes closed. The center-of-pressure oscillations will then be recorded. Based on the oscillation measurements, the entropy index will be calculated to indicate the state of postural control.

INTRODUCTION A concussion is a mild traumatic brain injury as a result of

a blow to the head. From the battlefield to the football (or any other contact sport) field, concussions are an unavoidable risk. According to the Centers for Disease Control and Prevention (CDC), at least 1.7 million traumatic brain injuries occur every year in US. About 75% of those injuries are concussions or other forms of mild brain injuries.

It is a very complex and challenging task to assess the short term and long term impact of concussions. Usually it would involve considering both subjective symptoms and objective measures (such as cognitive testing). The goal of this research is to design and test a portable low cost device that can serve as an objective measure for quick sideline assessment of concussion or mild brain injury. ApEn is a good indicator of the irregularity of a complex time series. The central hypothesis of this study is that ApEn can be used as an effective indicator of subtle postural control impairment after mild brain injury. Before complete recovery, the ApEn value of center of pressure oscillations would be lower to indicate the reduction of randomness of the COP oscillations. After complete recovery, the ApEn value should return to a pre-injury level to indicate a

full recovery. Therefore the device can be used as a low cost, simple, and quick indicator of whether or not the subject has recovered from mild brain injuries.

Most soldiers or athletes recover from concussions without long-term impairments. However, before the first injury has healed, even a small additional blow to the head can not only cause a slow recovery, but it also increases the likelihood of long-term impairments. Conventional CT or MRI scans of the brain are usually not recommended if symptoms are mild and dissipate within a week. Consequently, determining whether or not the mild symptoms have cleared after an initial concussion is vital. Since human judgment is fundamentally subjective and can be easily affected by one’s emotions or personality, it is essential to evaluate the recovery condition based on a low-cost portable device with a clinically proven index that represents the degree of recovery.

Many researchers have studied the balance impairment of athletes following concussions. Cripps [1] tried to find the relationship between visual processing deficits and balance impairment caused by concussions. It was concluded that within ten days of a sports related concussion, athletes can expect to experience functional impairment in balance. To determine if a player can return to play following mild head injury, Gustiewicz et al. [2] proposed two measures for assessment: postural stability and cognitive function. The study shows that postural stability measure is potentially a good assessment measure for postural stability recovery following mild head injury, although the effect of postural stability deficits on increasing likelihood of future injury remains unknown. Cavanaugh et al. [3] demonstrated that the linear and nonlinear combined measurement approach based on ApEn is a promising measure to detect the immediate, short term effect on postural control. Borg and Laxaback [4] provided further evidence that ApEn is a variable that may be used to measure the deficiencies of balance control. Cavanaugh et al. [5] also found out that athletes who demonstrate normal postural stability after concussion nonetheless display changes in ApEn, which further proves that ApEn analysis of COP oscillations may be a valuable assessment protocol for mild head injury. Deffeyes et al. [6] suggested that ApEn can be used to assess


sitting postural sway of infants with development delay. They also suggested that spectral analysis can be used to select lag parameters for ApEn analysis. Due to the complexity associated with a concussion, Guskiewicz’s study [7] suggested that balance is only one small piece of a large puzzle in the assessment of a concussion, although balance assessment does seem to be useful in identifying neurologic impairment a few days following a concussion. Riemann and Guskiewicz [8] proposed the Balance Error Scoring System as an alternative method to make the return-to-play decisions for athletes with mild head injury if a force-platform is not available due to the high cost and impracticality for sideline use. However, a further study [9] also suggested that the Balance Error Scoring System can be greatly affected by the effects of fatigue since these tests are mostly administered on the sidelines immediately following the injury. Cavanaugh et al. [10] further demonstrated that an ApEn value is clearly not a measure of postural stability, but a theoretically distinct measurement that can be used as a valuable supplement to postural stability measures. Lee Hong et al. [15] used both ApEn and CrossApEn as indices to study stance and sensory feedback influence on postural dynamics. The results demonstrated that ApEn is indeed very sensitive to postural stability.

The ultimate goal of this research is to bridge this gap by designing and testing a portable low cost force platform that can assist clinicians in making return-to-play decisions following concussion based on ApEn value.

The paper is organized as follows. First, the hardware design of the COP measurement device will be presented. Then, the center of pressure will be derived and its verification will be presented. After that, approximate entropy based index will be presented and discussions will be carried out to demonstrate the consistency of the proposed index as being able to capture the subtle difference in postural control movement. Conclusions are drawn at the end.

PROTOTYPE DESIGN A force plate is standard biomechanics equipment. Good

force plates cost $10K or more and require a specially trained person to set up the hardware and software and to calibrate it before it can produce reliable readings. It is neither a cost-effective nor a feasible approach (due to software installation on a computer) to design a rugged low cost portable device based on a commercial force plate.

Our first prototype was designed by modifying a low cost bathroom scale. The four half-bridge load cells located at the four corners of the scale are the measurement devices that we will be using. As shown in Figure 1, the prototype consists of a low cost kitchen scale, two National Instruments (NI) SCC-SG24 2-channel load cell input full-bridge modules and a NI SCC-68 I/O connector board. Data acquisition is provided by an Intel Core i7 computer with an A/D card NI PCIe-6321 plugged into its motherboard, which acquires the four load cell signals through four analog input channels.

Figure 1. First prototype modified based on a kitchen scale and its data acquisition system.

More details of the data acquisition system are shown in

Figure 2. The outputs of the four load cells are filtered and amplified one hundred times by the two NI SCC-SG24 modules, which are plugged into a NI SCC-68 I/O connector board. The NI SCC-68 interfaces with the data acquisition card PCIe-6321 in the computer through a connector block (NI SCB-68A) and a shielded cable (SHC68-68-EPM).

Figure 2. Schematic diagram of the signal generation and

data acquisition system

A LabVIEW Virtual Instrument (VI) model is built to analyze the load cell signals. The individual readings of the four load cells and the summation are displayed in the form of five vertical indicator bars. The time varying trajectory of the COP is shown displayed in real time. The computer and the LabVIEW VI window are shown in Figure 3. The test subjects will be asked to stand on the device for 30 seconds with their eyes closed. The COP oscillations will then be recorded and,


based on the oscillation measurements, the ApEn index, usually a number between 0 and 2, will be calculated and displayed.

Figure 3. LabVIEW VI window

CENTER OF PRESSURE Human postural control is done through a closed loop of

body sway, sensory input, motor command, and muscle activation [4]. A three dimensional Cartesian coordinate system of the kitchen scale is shown in Figure 4, where x is the medial-lateral direction, y is the anterior-posterior direction, and z is the vertical direction.

Figure 4. COP calculation in a 3D Cartesian coordinate system.

The COP can be calculated using the feedback of the four

load cells at the four corners of the scale. The moment about y

axis can be represented as wFFM y )( 43 , which is

equal to the product of COP x coordinate copx and the

summation of forces in the z direction

4

1iiF [11]. Therefore,

the COP coordinate in the x direction can be calculated in Eq. 1. The COP coordinate in the y direction can be calculated in the same way using Eq. 2.

4

1

43 )(

ii

cop

F

wFFx (1)

4

1

32 )(

ii

cop

F

lFFy (2)

Two initial tests are carried out to verify how sensitive the

device is in terms of capturing the subtle postural movement. Two random experimental results are included in Figures 5 and 6 below to show that there is a clear distinction between standing as still as possible versus the body moving slightly and slowly in a quasi-circular pattern. It can be explicitly seen that the device is capable of capturing the irregularities of the uncontrollable postural movement in Figure 5 although one tries to stand as still as possible. It is also clear that the device is capable of capturing the regularities or predictabilities of the intentional postural movements in Figure 6. The original data collected in LabVIEW was sampled at 1000 Hz. To reduce the effect of unwanted noise, the data shown in Figures 5 and 6 are down sampled at 100 Hz, a frequency which is still fast enough to capture subtle postural movements.

Figure 5. x_COP vs. y_COP time series (eyes closed and

standing as still as possible).

100 105 110 115 120 12578

80

82

84

86

88

90

92xcop vs. ycop

xcop(mm)

ycop

(mm

)


Figure 6. x_COP vs. y_COP time series (eyes closed and

body slowly moving in a quasi-circular pattern).

By comparing the two random tests in Figures 5 and 6, one can see that the device is capable of capturing the subtle regularity of intentional postural movement and randomness of natural postural balance. The excursions during slow motion (Figure 6) are more than three times larger than the excursions during quite standing (Figure 5). The irregularity in Figure 5 may be seen as a characteristic of a normal response to keep balance, which will surely lead to a greater ApEn index. Although only two cases were presented here, the trend is generally the same for all of the observed cases. APPROXIMATE ENTROPY

ApEn was first proposed by Pincus [12] as a measure of changing system complexity based on the evaluation of time series data collected from the system. The entropies approximate the expression ln(1/P), where P is the conditional probability that if two sets zi, zj of m consecutive data points are close to each other [5]. Mathematically, ApEn is defined as

1

))(ln())(ln(),,(

1

mNrr

rmNApEnmm

(3)

where N is the total number of data points, m is the series length of data points, and r is the tolerance threshold for accepting similar patterns between the neighbouring segments. For our case, the ),2,300( rApEn algorithm applies a moving

window of two elements to determine the probability that short sequences are repeated for time series of thirty seconds (down sampling frequency is at 10 Hz).

The selection of r is critical. Chon et al. [14] recently suggested that the widely recommended value for r (e.g. 0.1-0.2 times the standard deviation of the signal) can lead to an incorrect assessment of the complexity of a time series. Instead,

it was suggested that maximum ApEn leads to better prediction of a signal’s complexity with added computational burden. For our case, we choose r between 0.02 and 0.8 with a step size of 0.02. The computational burden appears to be still acceptable. The most appropriate threshold value is the maximum ApEn value among those tolerance threshold values shown in Figure 7 as an example to illustrate the importance of selecting r and how it could lead to incorrect ApEn values if not chosen properly, which seems to be almost impossible since the time series is not pre-known. As shown in Figure 7, choosing the maximum ApEn appears to predict a signal’s complexity more consistently.

Figure 7. Comparision of ApEn values corresponding to series of r values.

ApEn is commonly used in physiological applications as a

supplemental tool for measuring changes in postural control, especially in circumstances where subtle abnormality may increase the likelihood of subsequent injury [13]. For a given time series, ApEn is usually a real number between 0 and 2 [5]. Zero values indicate that the time series is perfectly repeatable and predictable, such as an ideal sinusoidal wave shown in Figure 8. Values of 2 indicate that the time series occur totally randomly, such as the random white noise shown in Figure 8. The ApEn value is larger if the time series is more complex and irregular.

The x_COP ApEn values of the two time series shown in Figures 5 and 6 are calculated using the proposed maximum ApEn algorithm and are found to be 0.91 and 0.51, respectively. To illustrate the trend of using the proposed ApEn index to predict complexity of a time series, their x_COP time series are compared with sinusoidal wave (most predictable and least complex) and computer generated random white noise (least predictable and most complex). The four time series with their randomness in ascending order together with their maximum ApEn values are shown in Figure 8.

90 100 110 120 130 140 15075

80

85

90

95

100

105

110

115

120

125xcop vs. ycop

xcop(mm)

ycop

(mm

)

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.80

0.2

0.4

0.6

0.8

1

1.2

1.4

r

ApE

n

Approximate Entropy(ApEn)

Sinusoid

x__COPy__COP

White Noise


Figure 8. Four time series in ascending order of

randomness and their calculated maximum approximate entropy.

To further provide the evidence that our maximum ApEn

value is consistent, 25 30-second time series samples were collected and their maximum ApEn are shown in Figure 9. The statistical analysis was carried out and the results are shown in Table 1. Table 1. Statistical Analysis of the Maximum ApEn of 25

COP time series. Min Max Mean Standard

Deviation 99 percentile

x_COP max ApEn

0.62 0.87 0.76 0.06 0.58-0.94

y_COP max ApEn

0.67 0.86 0.76 0.05 0.61-0.91

It can be clearly seen that the 99 percentile for both x_COP

and y_COP ApEn values are greater than the ApEn when body is intentionally moving (ApEn = 0.51), which is a reasonable indication that the maximum ApEn value is capable of consistently predicting the irregularities of natural postural movement.

Figure 9. ApEn of 25 time series samples (Subject #1 baseline, eyes closed, stand as still as possible)

DISCUSSION

To provide guidance for testing the device with athletes who are one or two days after suffering a concussion, two more reference subjects from two different age groups were tested. For each subject, ten 30-second time series were collected and the maximum ApEn values were computed, shown in Figure 10 and 11 below. Figure 10 shows the results from an age group twenty years older than the results demonstrated in Figure 9. Figure 11 shows the results from an age group fifteen years younger than the results demonstrated in Figure 9. Assuming that subjects from different age groups would demonstrate different postural control, the prototype device is able to capture the subtle difference of postural movement among different age groups, which is promising for testing the device on athletes before and after concussion.

That being said, however it is rather difficult to draw the conclusion as whether younger or elder subject should demonstrate higher ApEn values[4] since more randomness could be a sign of deficient postural control (as the elder subject would demonstrate) or a successful vigilant strategy to maintain balance (as the younger subject would demonstrate). Since the device is sensitive enough to capture the subtle differences among different age groups, we certainly hope it would be able to capture the difference of postural control before and after a mild head injury.

0 5 10 150

5

10

cm

Sine Wave

0 5 10 1510

12

14

cm

x COP (eyes closed, body still)

0 5 10 155

10

15

cm

Random Noise

Time(seconds)

0 5 10 155

10

15

cm

x COP (eyes closed, body moving)

ApEn__max=0.91

ApEn__max=0

ApEn__max=1.81

ApEn__max=0.51

0.65 0.7 0.75 0.8 0.85 0.9 0.950.65

0.7

0.75

0.8

0.85

0.9

ApEn of x__COP

ApE

n of

y__

CO

P

ApEn of 25 Time Series (x__COP stdev = 0.06, y__COP stdev = 0.05)


Figure 10. ApEn of 10 time series samples (Subject #2, older age group, eyes closed, stand as still as possible)

Figure 11. ApEn of 10 time series samples (Subject #3,

younger age group, eyes closed, stand as still as possible)

CONCLUSIONS

A center of pressure oscillation measurement device has been designed and tested to assess postural control recovery after mild brain injuries. The device is capable of detecting the increased regularity in COP oscillations. It appears that the approximate entropy index can be used as an indicator of mild brain injury recovery with a relatively low cost. If it is proven to be effective, it would have great potential to be used as a low cost, quick, and simple indictor to evaluate mild head injuries from contact sport fields to battle fields.

REFERENCES [1] A. E. Cripps, “Alterations in Visual Processing and its

Impact on Upright Postural Stability in Athletes Following Sport-related Concussion,” Ph.D. dissertation, University of Kentucky, Theses and Dissertations, Rehabilitation Sciences, 2013.

[2] K. M. Guskiewicz, B. Riemann, D. Perrin, and L. Nashner, “Alternative Approaches to the Assessment of Mild Head Injury in Athletes,” Medicine and Science in Sports and Exercise, 29, pp. 213-221, 1997.

[3] J. T. Cavanaugh, V. S. Mercer, and N. Stergiou, “Approximate Entropy Detects the Effect of a Secondary Cognitive Task on Postural Control in Healthy Young Adults: a Methodological Report,” Journal of NeuroEngineering and Rehabilitation, 42(4), 2007.

[4] F. G. Borg and G. Laxaback, “Entropy of Balance - Some Recent Results,” Journal of NeuroEngineering and Rehabilitation, 38(7), 2010.

[5] J. T. Cavanaugh, K. M. Guskiewicz, C. Giuliani, S. Marshall, V. Mercer, and N. Stergiou, “Detecting Altered Postural Control after Cerebral Concussion in Athletes with Normal Postural Stability,” British Journal of Sports Medicine, 39, pp. 805-811, 2005.

[6] J. E. Deffeyes, R. T. Harbourne, W. A. Stuberg, and N. Stergiou, “Approximate Entropy Used to Assess Sitting Postural Sway of Infants with Developmental Delay,” Infant Behavior Development, 34(1), pp. 81-99, 2011.

[7] K. M. Guskiewicz, “Balance Assessment in the Management of Sport-Related Concussion,” Clinics in Sports Medicine, 30, pp. 89-102, 2011.

[8] B. L. Riemann and K. M. Guskiewicz, “Effects of Mild Head Injury on Postural Stability as Measured Through Clinical Balance Testing,” Journal of Athletic Training, 35(1), pp. 19-25, 2000.

[9] J. C. Wilkins, T. C. Valovich McLeod, D. H. Perrin, and B. M. Gansneder, “Performance on the Balance Error Scoring System Decreases After Fatigue,” Journal of Athletic Training, 39(2), pp. 156-161, 2004.

[10] J. T. Cavanaugh, K. M. Guskiewicz, C. Giuliani, S. Marshall, V. S. Mercer and N. Stergiou, “Recovery of Postural Control After Cerebral Concussion: New Insights Using Approximate Entropy,” Journal of Athletic Training, 41(3) pp. 305-313, 2006.

[11] G. Robertson, G. Caldwell, J. Hamill, G. Kamen and S. Whittlesey, Research Methods in Biomechanics, Human Kinetics, 2004.

[12] S. M. Pincus, “Approximate entropy as a measure of system complexity,” Proceedings of the National Academy of Science USA, 88, pp. 2297–2301, 1991.

[13] M. Hassan, J. T. Terrien, C. Marque, and B. Karlsson, “Comparison between approximate entropy, correntropy and time reversibility: Application to uterine electromyogram signals,” Medical Engineering & Physics, 2011.

[14] K. H. Chon, C. G. Scully and S. Lu, “Approximate Entropy for All Signals,” IEEE Engineering in Medicine and Biology Magazine, pp. 18-23, November/December 2009.

[15] S. Lee Hong, B. Manor and L. Li, “Stance and Sensory Feedback Influence on Postural Dynamics,” Neuroscience Letters, 423, pp. 104-108, 2007.

0.65 0.7 0.75 0.8 0.85 0.9 0.950.65

0.7

0.75

0.8

0.85

0.9

0.95

1

1.05

ApEn of x__COP

ApE

n of

y__

CO

P


0.64 0.66 0.68 0.7 0.72 0.74 0.76

0.62

0.64

0.66

0.68

0.7

0.72

0.74

0.76

0.78

0.8

ApEn of x__COP

ApE

n of

y__

CO

P





DETERMINATION OF PARTICLE AERODYNAMIC SIZE DISTRIBUTION AND VIABILITY OF AEROSOLIZED H1N1 VIRUS

Mohammed Ali Department of Technology Jackson State University

Jackson, Mississippi, USA Phone: (601) 979-0327

Fax: (601) 979-4110 Email: [email protected]

Essam A. Ibrahim Department of Mechanical Engineering

The University of Texas of the Permian Basin Midland, Texas, USA

Phone: (432) 552-3217 Fax: (432) 552-2433

Email: [email protected]

ABSTRACT This study was performed to evaluate the aerodynamic size

distribution and viability of the aerosolized H1N1 virus particles. Freeze-dried H1N1 was suspended in filtered sterile deionized water to a concentration of 34x105 PFU/ml (plaque forming unit per milliliter) as the virus stock suspension and incubated at 4oC. The virus solution was aerosolized at air flow rates of 2, 6, 12 L/min by using a Collison nebulizer. The aerodynamic particle sizer spectrometer was used to measure the virus aerosol particle size profile at intervals of 1, 15, 30, 45, and 60 minutes. Aerosolized samples in triplicate were trapped in a quenching medium for viable enumeration. The data indicated that the number frequency mode for approximately 35 nm size particles for the 1 min of duration, was slightly less than the individual H1N1 virus size (~50 nm). However, the virus particle size spectrum produced by the Collison nebulizer exhibited an increase in fragments, as deduced from a 20% rise in modal frequency, when operated at an air flow rate of 12 L/min and over 60 min of continuous virus aerosol generation. In addition, measurements of H1N1 virus viability revealed a 40% loss for the samples collected at 60 min intervals, which can be explained as the effects of shear and impact stresses on biological agents (H1N1) as well as carrier fluid reuse commonly encountered in a Collison nebulizer. These trends are in good agreement with previous studies for aerosolization of bacterial agents using a Collison nebulizer.

INTRODUCTION Bioaerosols are airborne biological agents, including virus,

bacteria, fungi, atmospheric environmental pollutants, and pulmonary drug particles suspended in a gas or air. Its generation has numerous applications in pharmaceutical inhalers, air filtration and sanitization, and in contamination,

infection, toxicological, and immunological research. Recent concerns for improving inhaled drug delivery mythologies and indoor air quality, protecting hospital patients and staffs from spreading infection, and installing counter-bioterrorism measures have fueled interest in bioaerosol research. The capability of lab-generated bioaersols to simulate organisms in the environment under consideration is of utmost importance for the validity and applicability of the research results. Therefore, it is desirable to use bioaerosol generation techniques that enable control over significant aerosol parameters such as suspension concentration, particle size and count, output stability, as well as survivability of microorganisms.

The Collison nebulizer has dominated aerosol generation research since its invention in 1932 by W. E. Collison [1-9]. In principle, the Collison nebulizer is a pneumatic aerosol generation device that uses a compressed air jet to atomize particle suspensions or solutions into drops [10]. May [1] has given a detailed description of the operation and performance of the Collison nebulizer and reported that most of these drops are further broken down by impacts with the internal walls of the glass vessel.

The “quality” of the bioaerosol produced by a Collison nebulizer has recently come under scrutiny. Ulevicius et al. [11] hypothesized that the liquid, dispersed by the device’s high-velocity air streams and solid wall impingement is exposed to elevated shear and impact forces, respectively. Furthermore, in his classical paper May [1] pointed out that inside a Collison nebulizer with 20 mL of water in its flask, most of the water is recirculated roughly every 6 seconds. Thus additional shear forces are imparted on the suspension liquid due to being repeatedly “recycled.” These large shear and impact stresses are suspects in the increase of metabolic injury of bacteria and viruses over time when aerosolized with a Collison nebulizer [12-22]. It has also been shown that Collison nebulizers yield a


wide range of droplet sizes that vary over time and that vary across the classes of generators called “Collison nebulizer”; this renders aerosol characterization and inter-experimental comparison difficult [1,20,21,23]. Moreover, additional sample preparation, such as dialysis or centrifugation, may be needed due to foaming of propagation media during aerosolization with a Collison nebulizer [24]. Mainelis et al. [6] inferred that the high electrical charge carried by the bioaerosols emanating from Collison nebulizer could also be associated with a compromised structure integrity originating from nebulization-induced mechanical stresses.

Tuttle et al. [25] evaluated a nose-only inhalation exposure system for studies of aerosolized viable H5N1 viruses in ferrets. Their system comprised a Bioaerosol Nebulizing Generator (BANG) developed by BGI Inc., Waltham, Massachusetts, USA. In the BANG nebulizer, a high velocity air stream creates a venturi effect that siphons liquid through a tube from the nebulizer reservoir containing the pathogen suspension. As the liquid exits the tube at the top of the nebulizer, the air stream interacts with the liquid and shears it, creating an aerosol. This particular device was selected in Tuttle et al.’s [25] experiments based on the claim that it minimized the potential damage to the agent, minimized clumping of viruses, and provided uniformity of droplet size distribution, and efficiency (lower use rate and volume of virus suspension than that required by similar bio-aerosol generators). However, these claims were not substantiated by data, and the BANG nebulizer operation appears to closely resemble that of the Collison nebulizer.

Reports on aerosolized virus and drop size and viability measurements remain scarce in the literature, perhaps due to their ultrafine size (25-400 nm) and elaborate preparation and safe handling protocols. Nevertheless, airborne viruses, such as poxviruses and influenza are of particular concern because of their ability for rapid infection via the respiratory system. Hogan et al. investigated aerosolization and collection methodologies of MS2 and T3 viruses [24]. They argued that airborne virus particle size distributions were rarely available in the literature because samplers commonly used to collect virus particles were designed for the collection of micrometer-sized particles [26, 27]. Hence, Hogan et al. developed a method to determine the size distribution function of viable virus containing particles utilizing differential mobility selection [24]. Most of the published viral aerosol research focused on biological particle penetration through respiratory filters challenged with MS2 bacteriophage [28-30]. Extrapolation of results obtained with MS2 to other viruses e.g., H1N1, could be misleading due to the differences in size and other characteristics. For example, the mS2 is a nonenveloped, icosahedron-shaped, single-stranded RNA with single-capsid size of 27.5 nm.

The ability to generate a highly viable and mono-dispersed pathogenic aerosol is essential in evaluating the efficiency of

various airborne micro-organism collection and control methods. In addition, it is desirable to diminish the formation of residue particles, resulting from fragmentation of the microorganisms by any stresses associated with the aerosolization process, since these residues can only be enumerated by the most-size sensitive particle size spectrometers [31]. Furthermore, the implementation of H1N1 virus as pathogen in the present investigation is of primary interest due to the current concern of potential H1N1 influenza pandemic [32]. The ability to perform well-controlled studies of aerosolized viable H1N1 may contribute to an improved comprehension of factors responsible for the acquisition of H1N1 infections by humans and the virulence/lethality relative to route of transmission.

In this investigation a Collison nebulizer was employed as the aerosol generating device due to its widespread use in the research laboratories and industry. Therefore, the objectives of this work were: 1) To investigate the possibility of generating an aerosol of highly viable and mono-dispersed with H1N1 particles using Collison nebulizer; 2) To determine the production performance of this device in terms of particle aerodynamic size distributions and the viability of these biological agents upon generation and while suspended in the aerosol cloud.

MATERIALS AND METHODS

Experimental Setup and Procedure Figure 1 depicts the Laboratory-Scale Aerosol Tunnel

(LSAT) used to conduct the experiments. The LSAT consists of a test chamber with a circular cross-section duct with a diverging conical entrance, containing 3 equally-spaced mixing screens, and a converging conical outlet. The duct section is wider near its outlet and is separated into two identical parts, each plumbed with an access port, which are clamped and sealed to enable installation of test filtration media, if needed. Compressed air is supplied to the LSAT via a compressor that is fitted with a pressure regulator. The compressed air is allowed to pass through a HEPA filter and a humidifier before being split to feed the nebulizer and the porous tube dilution unit. The combined nebulizer bioaerosol and dilution air streams then enter a Kr85 charge neutralizer (TSI, Inc., Shoreview, MN, USA). Two overflow valves are located near the entrance of the measuring chamber to allow air to be diverted out of the LSAT through a HEPA filter. Three air pressure gauges (Dwyker, Michigan City, IN, USA) separately monitored the pressure of the air entering the nebulizer, the dilution unit, and the test chamber. The compressed air lines to the nebulizer and dilution unit are fitted with valves for air flow control. A digital flow meter (4000 series, TSI, Inc., Shoreview, MN, USA) measured the air volume flow rate exiting the test chamber. The exhaust air temperature and humidity are recorded by an integrated hygrometer/thermocouple probe (VWR, San Diego, CA, USA) before being collected at a flow


rate of 12.5 L/min, for 5 minutes to one of 10 AGI-30 impinger sampler (Ace Glass Inc., Vineland, New Jersey, USA), each filled with 20 mL of collection fluid.

Though the LSAT was originally designed for determining

viable filtration efficiency of filtration media or energetic devices, it is also adaptable to pathogen enumeration and viability experiments. The virus solution was loaded into a six-jet Collison nebulizer (Model MRE CN24/25, BGI Inc., Waltham, MA, USA). The LSAT was configured to direct the aerosol to the overflow. Compressed air was applied to the Collison nebulizer, and the system was operated for 10 minutes to bring it to steady state. The LSAT overflow valves were readjusted to direct the aerosol to the measuring chamber for 10 minutes. Following each completed test, the LSAT overflow valves were reconfigured to divert the aerosol back to overflow. The average relative humidity and temperature conditions for all the tests were 38% ± 5% and 22 ºC ± 2 ºC, respectively.

Test Parameters and Methodology

The H1N1 Influenza A/PR/8/34 VR-1469 (ATCC VR-95H1N1) virus was propagated in embryonic chicken eggs using standard protocols; the methodology for tissue culture of infectious dose to determine virus titers was demonstrated elsewhere [33]. The resulting concentration in the viral suspension was 34 x 105 PFU/ml (plaque forming unit per milliliter). A test matrix was charted to obtain experimental data on virus viability and aerosol drop size distribution simultaneously generated by the Collison nebulizer. The compressed air line connected to the Collison nebulizer was set to 20 psi. At this pressure the Collison nebulizer inlet air flow rate was measured as 2-, 6-, and 12-L/min when operated in one-, three-, and six-jet mode, respectively. By using the overflow valves, the total aerosol and filtered air flow rate in

the LSAT was kept at 85 L/min to comply with NIOSH requirements [34]. Prior to the beginning of each test, a 50 mL of virus stock solution was placed into the Collison nebulizer. To minimize fluctuations in the concentration of test particles, the liquid suspension sample was continuously mixed with a

magnetic stirrer. A standard Collison nebulizer recycles 20 mL/min of suspension approximately every 6 s, thus constantly mixing the suspension [1]. Therefore, no additional mixing of the Collison nebulizer suspension was performed. The size and concentration of test particles in the test chamber were isokinetically measured by connecting a Scanning Mobility Particle Sizer (SMPS) spectrometer (TSI Inc., Shoreview, MN, USA) to the test chamber access port. This instrument measures particles in the size range 2.5 nm to 1000 nm and concentration range of 2 to 107 particle/cm3 in up to 64 channels at an inlet flow rate of 0.6 L/min.

In order to assess changes in culturability of viruses over time due to aerosol generation process, aliquots wee were sampled before and at 1, 15, 30, 45, and 60 min intervals after aerosolization, and then plated along with the fluids from the reference and quality control blanks. Aliquot Western Equine Encephalitis (aliquots wee) is a method of measuring ingredients below the sensitivity of a scale by proportional dilution with inactive ingredients. Each sampled solution was serially diluted 10-fold, and three dilutions were plated in triplicate by using trpticase soy agar (TSA) plates and incubated at 28 C as recommended by Forney et al. [4]. Plates containing 30 to 300 colony-forming units (cfu) were counted after 96 h. Water, TSB, and PBS were evaluated as collection media.

All experiments were conducted in a class 2 Biological Safety Cabinet (Class II Type A/B3, NaUAIRE, Inc., Plymouth, MN, USA). Overall, each test at specific conditions was run at least three different times and the data presented here are represented by average values and standard deviations of those measurements.

Figure 1. Schematic Representation of Experimental Setup


RESULTS AND DISCUSSION

Particle Size Distribution Figure 2 depicts virus particle count concentration in the

aerosol with respect to the aerodynamic particle size at intervals of 1 min and 60 min after steady state is reached. It should be noted that the steady state was achieved within about 10 minutes from the aerosolization starting point in the Collison nebulizer. The data presented in Fig. 2 represent the aerosolization at an air pressure of 20 psi and air flow rate of 12 L/min, which correspond to the operating conditions of all 6 jets of Collison nebulizer.

The viral particle concentration frequency mode is

approximately 34 nm. Since an individual H1N1 virus measures about 50 nm in size, any smaller size particle is considered to be a fragment. It is likely that the number frequency mode may be dominated by the water particle size rather than the virus. However, it was found that the concentration of particles of modal suspension inside the Collison nebulizer increased by about 20% after 60 minutes of continuous aerosolization. The possible explanations of these phenomena are: 1) The virus suspension liquid loses its concentration inside the Collison nebulizer upon generation of aerosol for a while and the background lysis causes additional counts of viruses to the water i.e., rise in virus fragmentation contributes to the rise in concentration. 2) There can be a constant recycling of suspension taking place, and the viruses may be subjected to the shear and impact stresses inside the Collison nebulizer. Mainelis et al. studied P. fluorescens bacteria and showed that the number frequency mode is doubled and the concentration of fragments emitted from the Collison nebulizer increased 3-3.5 times after 90 minutes of continuous generation [21]. Comparing their work with the present work, it is evident that the large increase in frequency mode and ratio of fragments in Mainelis et al.’s experiments

may be attributed to the longer aerosolization period (90 min vs. 60 min) and high sensitivity of the microorganism, P. fluorescens bacteria. The P. fluorescens is a common Gram-negative and rod-shaped bacterium. With respect to these two characteristics, the H1N1 virus appears to be more resilient than the P. fluorescens bacteria.

Figure 3 presents the measurements of virus concentration

mode against aerosolization air flow rate for Collison nebulizer. Symbols represent averages of three repeats, and error bars represent standard deviation. The data have been fitted with second order polynomials with R2=1. It is observed that the modal concentration of the viral aerosol decreased as the air flow rate increased. This may be explained by an amplification of virus fragmentation stemming from amplified recirculation, shear, and impact stresses at the higher air flow rate, which coincides with utilizing more aerosolization jets in the Collison nebulizer. The change of the viral concentration modal frequency with time is found to be statistically insignificant (not shown) for the data range considered in Fig. 3.

Total Particle Concentration Figure 4 plots the aerosol particle concentration against

aerosolization time and air flow rate for the Collison nebulizer. Symbols represent averages of three repeats, and error bars represent standard deviation. The data have been fitted with second order polynomials with R2 = 1. It is evident that the concentration of the viral aerosol increased as time progressed. This may be expected since, as discussed earlier, the virus suspension concentration inside the Collison nebulizer was increased due to the continuous liquid recycling. This trend is magnified at larger aersolization flow rates. In addition, a higher air flow rate leads to a significant rise in the aerosol output due to enhancement of the aerosolization process by the use of 6 jets compared to the 3 jet or 1 jet Collison nebulizers.

0.0E+00

5.0E+05

1.0E+06

1.5E+06

2.0E+06

2.5E+06

3.0E+06

3.5E+06

0 100 200 300 400

t = 1 m in

t = 60 min

Par

ticle

Con

cent

ratio

n, #

/mL

Aerodynamic Particle size, nm

Figure 2. Changes in particle size over time for aerosol generated with Collison nebulizer at an air

pressure of 20 psi and air flow rate of 12 L/min.

Figure 3. Variation in virus concentration mode with aerosolization air flow rate and

time for Collison, nebulizer.

Aerosolization Air Flow Rate, L/min

30

32

34

36

38

40

0 5 10 15

t = 1 min

t = 60 min

Con

cent

ratio

n M

ode,

nm


This result is in good agreement with Mainelis et al. work, which used PSL (polystyrene latex sphere) particles of different sizes to evaluate the particle output from a Collison nebulizer at variable air flow rates.

Virus Viability Figure 5 exhibits H1N1 virus viability data, as measured

by plating for the Collison nebulizer, collected at intervals of 1, 30, and 60 minutes from steady state conditions. The experimental data were adjusted for volume of air passed through the sampler and volume of consumed liquid suspension to obtain culturability per volume of air, Ncfu in cfu/Lair according to the following equation:

tQ

VmLcfuN

samp

cliqcfu

/

where Ncfu is the colony forming units per L of air, cfu/mLliq is the colony forming units per mL of liquid, Vc = 20 mL is the consumed liquid volume in impinger, Qsamp = 12.5 L/min is the sample flow rate, and t = 5 min is the sampling interval.

It is explicable from the Fig. 5 that the virus viability decreased with the aerosolization time. The decrease in viability is approx. 40% over a 60 min period of continuous aerosolization. This trend is thought to be a consequence of liquid reuse as well as the shear and impact stresses experienced by the microorganism in the pneumatic aerosolization mechanism associated with Collison nebulizers. Mainelis et al. [21] reported 50% loss of pseudomonas fluorescens (P. fluorescens) bacteria’s viability over a period of 90 min when using a Collison nebulizer. Therefore, the present results are in line with Mainelis et al. work, taking into account the shorter aerosolization period employed here.

CONCLUSION The present study determined the performance of a

Collison nebulizer in assessing the generating capability of H1N1 bioaerosol’s particle size distribution and viability. The modal frequency of virus particles in the aerosolized form is about 34 nm, which is smaller than the naked H1N1 viruses size (~50 nm); this increased by 20% during the 60 min period of continuous aerosolization with an air flow rate of 12 L/min. Occurrence of this trend is due to the rise in virus fragmentation with residence time in the nebulization process. The culturability in terms of H1N1 virus viability of the nebulized fluid decreases about 40% over a period of 60 min. These results are consistent with the reported literature experimenting with Collison nebulizer and P. fluorescens bacteriophage, a similar pathogenic biological agent like H1N1. Suspension recycling which imposes shear and impact stresses in the Collison nebulizer can be the reason behind the diminishing particle size uniformity and virus viability over prolonged aerosolization periods.

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Par

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DU

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[19] Stewart, S. L., Grinshpun, S. A., Willeke, K., Terzieva, S., Ulevicius, V., Donnelly, J., 1995, Effect of Impact Stress on Microbial Recovery on an Agar Surface,” Applied and Environmental Microbiology, 61, pp. 1232-1239.

[20] Reponen, T., Willeke, K., Ulevicius, V., Grinshpun, S. A., Donnelly, J., 1997, “Techniques for Dispersion of Microorganisms into Air,” Aerosol Science and Technology, 27, pp. 405-421.

[21] Mainelis, G., Berry, D., An, H. R., Yao, M., Devoe, K., Fennell, D. E., and Jaeger, R., 2005, “Design and Performance of a Single-Pass Bubbling Bioaerosol Generator,” Atmospheric Environment, 39, pp. 3521-3533.

[22] Rule, A. M., Schwab, K. J., Kesavan, J., and Buckly, T. J., 2009, “Assessment of Bioaerosol Generation and Sampling Efficiency Based on Pantoea Agglomerans,” Aerosol Science and Technology, 43, pp. 620-628.

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[24] Hogan, C. J., Kettleson, E. M., Lee, M. –H., Ramaswami, B., Angnent, L. T., and Biswas, P., 2005, “Sampling Methodologies and Dosage Assessment Techniques for Submicrometer and Ultrafine Virus Particles,” Journal of Applied Microbiology, 99, pp. 1422-1434.

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[27] Willeke, K., Lin, X. J., and Grinshpun, S. A., 1998, “Improved Aerosol Collection by Combined Impaction and Centrifugal Motion,” Aerosol Sci. and Tech., 28, pp. 439-456.

[28] Balzay, A., Toivola, M., Adhikari, A., Sivasubramani, S., Reponen, T., and Grinshpun, S. A., 2006, “Do N95 Respirators


Provide 95% Protection Level against Airborne Viruses, and How Adequate are Surgical Masks?,” American Journal of Infection Control, 34, pp. 51-57.

[29] Eninger, R. M., Adhikari, A., Reponen, T., and Grinshpun, S. A., 2008, “Differentiating Between Physical and Viable Penetrations when Challenging Respirator Filters with Bioaerosols,” Clean – Soli, Air, Water: Special Issue on Bioaerosols, 36, pp. 615-621.

[30] Eninger, R., Honda, T., Adhikari, A., Heinonen-Tanski, H., Reponen, T., Grinshpun, S.A., 2008, “Filter performance of N99 and N95 facepiece respirators against viruses and ultrafine particles,” Annals of Occupational Hygiene, 52, pp. 385-396.

[31] Qian, Y., Willeke, K., Ulevicius, V., Grinshpun, S. A., and Donnelly, J., 1995, “Dynamic Size Spectrometry of Airborne Microorganisms: Laboratory Evaluation and Calibration,” Atmospheric Environment, 29, pp. 1123-1129.

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ELECTROPHORETIC FOCUSING AND NAVIGATION FOR INTRANASAL TARGET DRUG DELIVERY

Xiuhua Si and Rachel Gaide Department of Engineering, Calvin College

Grand Rapids, MI, 49546 USA

ABSTRACT Intranasal olfactory drug delivery provides a noninvasive

practical method of bypassing the BBB (Blood-Brain-Barrier) to directly deliver medications to the brain and spinal cord. However, a device designed for olfactory delivery has not yet been found. In this study, a new delivery method that utilizes electrophoretic force to focus and guide drug particles to the olfactory region is proposed. The feasibility of this method was numerically evaluated with three designs in an idealized 2-D nasal airway model. The influences of drug release positions at the nostrils were also studied. It was observed that applying electrophoretic forces significantly enhanced the dosage to the olfactory region. In this pilot 2-D study, electrophoretic-guided delivery achieved olfactory dosages of two orders higher than that without electrophoretic forces. Furthermore, releasing drugs at the upper half of the nostril (i.e., partial release) led to olfactory dosages two times higher than releasing drugs at the whole nostril. By combining the advantages of pointed drug release and appropriate electrophoretic guidance, more than 90% olfactory dosage could be achieved. Compared to the extremely low olfactory dosage (< 1%) of conventional inhaler devices, results of this study have important implications for developing new olfactory delivery devices for the treatment of neurological disorders.

INTRODUCTION The BBB has forestalled the use of many new genetically

engineered drugs for treating central nervous system (CNS) disorders such as Alzheimer’s disease and paraplegia [1, 2]. The need to develop a simple, safe, and effective way of delivering drugs for the treatment of CNS disorders is urgent. The olfactory region is the only site in the human body where the CNS is in direct contact with the outer environment. If a drug could be directly sent to this region through the nose, it could diffuse across this film and reach to CNS within a short time. Direct nose-to-brain drug delivery circumvents the BBB and has multiple advantages over conventional intravenous delivery [1].

However, demonstration of its clinical feasibility is still in adolescence due to the lack of devices that effectively deliver medications to the olfactory epithelium. While there are vast options of nasal delivery devices available on the market, devices designed for selective olfactory deposition have not yet been found [2]. Without better options, the limitations of conventional nasal devices are obvious; only a very small faction of therapeutic agents deposits in the olfactory region, which may possibly enter the brain via the extracellular epithelial pathway [3]. Previous studies [4, 5] have shown that less than 1% of nasal-inhaled particles could reach the olfactory nerves, which are secluded in the upmost nasal cavity (Fig. 1a). Therefore, it is critical to search for more effective drug-delivery strategies that can directly deliver drugs to the olfactory region.

The nasal cavity is composed of two relatively symmetric nasal passages separated by the nasal septum. Each nasal passage features three curved, fin-like airway protrusions known as the superior, middle and inferior meatus. The osseous tissues above each meatus are termed the superior, middle and inferior turbinates, respectively, and form the lateral wall of the main passage. The olfactory epithelium, or olfactory mucosa, is located at the very top of the nasal cavity between the superior turbinate and the cribriform plate of the ethmoid bone (Figs. 1a,b). It covers 10~12 cm2, or approximately 8% of the total nasal surface area. If drugs are delivered deep and high enough into the nasal cavity, olfactory mucosa may be reached and the drug will be transported into the brain via the olfactory receptor neurons. Factors affecting nasal drug delivery to the brain include molecular size, lipophilicity, charge, and most importantly, the ability to deliver drugs to the olfactory mucosa, which is the entry to the brain [2, 3]. It is of critical significance to search for drug-delivery strategies that can effectively deliver drugs to the olfactory region for establishing clinical feasibility of nose-to-brain drug delivery to treat neurological diseases.

The limitations of conventional inhalation drug delivery devices depend solely on the aerodynamics of the inhaled flow to direct therapeutical aerosols to the target site. The three major


forces acting on the particles are particle inertia, drag force, and gravity. There is no control on the trajectories of these particles once they have been released from an inhaler devices such as a nebulizer, powder inhaler, or pressurized metered-dosed inhaler. Accordingly, how far a particle travels, or where it ends up primarily depends on its initial velocity and the nasal airway structure. Due to this lack of control, a significant amount of medication is wasted in the upper airway and doesn’t reach the targeted receptor tissues.

The rationale of the newly proposed concept of intranasal drug delivery can be described by an analogy to guided ballistic missiles. They are able to reach their targeted destinations because they are under continuous control along a designed trajectory. What if there were a way to control the pharmaceutical particles/aerosols so that they behave much like guided missiles? The task seems challenging considering the tiny size of these aerosols (0.2-5 microns, compared to 70 microns of the thickness of a human hair) and the complicated inner nasal structure, which requires high accuracy of design of the trajectory.

In order to overcome these difficulties, the idea of electrostatic levitation and propulsion was applied here. The answer actually lies in the tiny particle size that enables remote electromagnetic controlling of the inhaled particles. There are two forces to balance in this problem: gravity and electromagnetic force. Each force dominates in its size domain and loses its influence beyond that. The tiny size of nanoparticles places them under the control of both electromagnetic force and gravity. The smaller the size, the more the aerosol particles are influenced by the electromagnetic force if the amount of charge is not changed. The novel aspect of this new delivery protocol is that charged or magnetized drug particles can be precisely focused and driven via electric forces.

The proposed delivery device will have three functions: (1) particle acceleration, (2) focusing, and (3) path-guidance. By carefully arranging multiple electrodes with different electric potentials around the nose, a path with zero electric field (E) intensity could be generated. The trajectories of charged drug particles will be defined by the zero-E-path because of the repulsive forces to the charged particles beyond this zero-E path, and therefore can be guided through the path without contacting the airway surface.

The objectives of this study are to (1) develop a computational fluid dynamics (CFD) model of electrophoretic-guided drug delivery; (2) numerically evaluate the feasibility of three electrophoretic delivery designs; (3) numerically evaluate the efficiency of the electrophoretic-guided drug delivery in a two-dimensional nose model; and (4) identify the optimal layout and operating parameters of the electrodes. The potential benefits from this study include (1) insight into the transport dynamics underling the delivery of intranasal neurological medications, and (2) specific guidelines for the development of effective drug delivery devices that target at olfactory epitheliums.

METHODS

NASAL AIRWAY MODEL A physiologically accurate airway model is a necessary

first step for reliable analysis of inhalation medication dosing. Image-based modeling represents a remarkable improvement over conventional cadaver casting, which is subject to large distortions due to the shrinkage of mucous membranes or insertion of casting materials. Head MRI scans of a healthy non-smoking 53-year-old male (weight 73 kg and height 173 cm) were used in this study to construct the nasal airway model (Fig. 1b). The multi-slice MRI scans were first segmented, using MIMICS (Materialise, Ann Arbor, MI), into a 3-D model, which was further converted to a set of contours that define the airway of interest. Based on these contours, an internal surface geometry was constructed in Gambit 2.4 (Fig. 1c). The resulting model is intended to faithfully represent the anatomy of the nasal airway with only minor surface smoothing. This model can either be manufactured into a solid cast by prototyping techniques for in vitro studies, or be meshed with high-quality computational elements for numerical analysis.

FLUID-PARTICLE TRANSPORT MODELS Because inhaled air quickly adjusts to human body

temperature, flows in this study are assumed to be isothermal and incompressible. For numerical simplicity, steady state or square waveform inhalation was assumed for all simulations [4, 6]. Under normal breathing conditions, the Reynolds number at the nostrils was around 1,413, and the flow regime inside human nasal airways is predominately laminar [4, 6, 7]. Therefore, the laminar flow model has been used in this study.

The trajectories of monodispersed particles were calculated on a Lagrangian basis by directly integrating an appropriate form of the particle transport equation. The particles are

Figure 1. Nasal airway physiology: (a) anatomy; (b) MRI scan; and (c) surface model geometry.


assumed spherical and range from 0.5 µm to 5 µm in diameter. Aerosols in this size range have very low Stokes numbers (Stk = pdp

2UCc /18μDh << 1), where p is the particle density (1.0 g/cm3), Cc the Cunningham slip correction factor, μ the fluid viscosity, U the mean fluid velocity, and Dh the hydraulic diameter of the nostril. The appropriate equations for spherical particle motion under these conditions can be expressed as

, , ,1ii i i i Brownian i lift i electrophoretic

p

dv f u v g f f fdt

(1)

where vi and ui are the components of the particle and local fluid velocity, and p (i.e., p dp

2/18) is the characteristic time required for a particle to respond to changes in the flow field.

The drag factor f, which represents the ratio of the drag coefficient CD to Stokes drag, is based on the expression of Morsi and Alexander [7] for aerosols greater than 1 µm. It also includes the Cunningham slip correction factor for sizes less than 1 µm. The force per unit particle mass due to gravity was included with the gravity vector oriented in the vertical direction. Saffman lift force was calculated for particles greater than 1 µm [8]. The effect of Brownian motion was considered for particles less than 1 µm. Because of the dilute concentration of drug particles, one-way coupling is implemented in this study.

ELECTRIC FIELDS AND ELECTROPHORETIC FORCE Figure 2 shows a diagram of the Ring design, which

employs a series of rings of varying sizes to accommodate the outer nose shape. To achieve electrophoretic focusing and guidance of the drug particles, each ring is composed of four pieces of metal (electrodes). Both direct and alternating voltages are implemented. The two positive charged poles and the two negative charged poles are arranged in positions such that a zero electric field is formed in the predefined region. The charged particles were pushed into the zero-force location which is in the zero-E channel by the symmetric electric forces

from the four different directions. The zero-electric field channel acts as a trap to those charged particles, which concentrates the particles together so called focusing. Once the particles are focused, they will travel freely along the predefined path with their initial velocity because the zero-electric field provides no force to charged particles in the direction tangent to the path. Figure 3 shows the other two designs that use plates and poles instead of rings as in Fig. 2.

Figure 3(a) is a simple 1-D design of quadrupole which can allow the charged particles to flow along a straight line, and Fig. 3(b) is a 2-D design, which shows that the trajectory of charged particles can change to different directions with the arrangement of electrodes. Fig 5 shows the change of the particle trajectories with different E-voltage arrangements. These results demonstrate that applying electrophoretic force to manage effective targeted drug delivery is feasible.

For the DC field, the electric potential, UDC, is obtained by

solving Poisson’s equation

0 0r U (2)

where ε0 is the permittivity of free space (F/m) and εr is the relative permittivity of free space. The zero on the right hand side of the above equation indicates that the space charge density is negligible. For the AC field, the conservation of electric currents is solved to compute the AC potential, V:

0 0rj V (3)

where σ is the electrical conductivity and ω is the angular frequency (Hz). The total electric field is obtained from the superposition of the DC and AC fields considering that the equations for DC and AC fields are linear.

The electrophoretic force is computed by

,i electrophoretic DC ACf ZeE Ze E E (4)

where Z is the dimensionless charge number and e is the elementary charge whose SI unit is Coulomb, EDC and EAC are electric field intensity which are computed as follows

( ); [ ]j tDC ACE U E real V e (5)

The symbol V denotes that the AC electric potential is a complex value.

In this study, electrode potentials less than 14 V were tested. This potential is great enough to provide the energy

Figure 2. Diagram of electrophoretic guided drug delivery (Ring design).

Figure 3. Alternative designs: (a) 1-D Quadruplate design; and (b) 2-D quadrupole design.


needed to guide and focus the aerosol to the targeted location, and at the same time, it falls well within the human safe range, which should be less than 36 V. The corresponding operating parameters such as optimal particle size, particle charge, drug release velocity/position, and electrode layout/strength will be explored. Comsol multiphysics with electromagnetic, fluid flow, and particle tracing modules were used to make the simulation.

RESULTS The feasibility of electrophoretic forcing was first tested in

the 1-D quadruplate design. The diameter and length of the quadruplates are 15 mm and 100 mm, respectively. The voltages applied are 12 V, and the particle size is 0.2 µm with a charge number of 200. The airflow and charged particles enter the quadruplate at the same end at a speed of 0.1 m/s. Figure 4a shows the electric field intensity inside the geometry. Blue is the negative potential, and red means positive potential. A zero field is achieved at the center of the structure. The airflow is laminar and has a parabolic profile (Fig. 4b). In Fig. 4c, the blue colored center is the trace of the trajectory of the charged particles, which is focused along the centerline with no discernible dispersion.

The electric field and particle trajectories in the 2-D

quadrupole design are shown in Fig. 5. In this trial, we tested the sensitivity of charged nanoparticles to the electrophoretic guidance by varying the electric fields in both direction and strength. In doing so, there are 8 groups of electrodes arranged along the curve so that different voltage combination can be specified. In Fig. 5(a), the opposite two electrodes have the same voltage (12 V or – 12 V), which resembles the design of the quadruplate in Fig. 4. Similarly, a zero E-field is achieved along the curved centerline. The particles are concentrated within this field and at the same time are guided toward the exit, maneuvering through the turns by overcoming the particle inertia.

In the second trial (Fig. 5b), we remove the voltage from the last two electrode groups to verify the indispensability of electrophoretic forces in particle focusing and navigation. As

expected, without the electrophoretic forces, particle trajectories depart from the centerline by inertial forces, indicating a break of force balance between the electrophoretic and inertial forces. A careful inspection of Figs. 5 (a,b) further reveals this delicate force balance, which is manifested in the wavelets or fluctuations of particle trajectories along the curved centerline.

The third trial tests the capacity of electrophoretic forces to refine particle trajectories by arranging the zero-E field off the centerline (Fig. 5c). In this test, the opposite two electrodes in the same group have different voltages (12 V, 6 V), with the neighboring two groups alternating the voltage arrangement (Fig. 5c). It is observed that the particle trajectories are indeed refined by following the specified zero-E field. This is highly desirable because it provides a tool in guiding the drug particles through the convoluted nasal passages by navigating them away from the intruding nasal conches in live scenarios. However, a disadvantage of particle-trajectory-refining is a lesser degree of focusing, as evidenced by the more erratic and dispersive particle traces.

The feasibility of the new delivery protocol was further

evaluated in a MRI-based 2-D nose model. Fig. 6(a) shows the airflow within the nasal passage. Flows are observed to be divided into three streams by the inferior and middle conchae (or turbinates) if there are no electrophoretic force

Figure 4. Electric field, velocity field, and particle trajectories within the quadruplate.

Figure 5. Electric field and particle trajectories within the quadrupole with varying electrode voltage layouts.

Figure 6. Airflow and electric potential filed in the nose.


interventions. The upper stream is further split by the superior concha, with only a minor portion of the flow stream being distributed to the olfactory region (the uppermost of the nasal cavity). Overall, the upper nasal cavity receives a minimal fraction of the airflow. Two recirculation zones are observed as a result of sudden area expansion, with one in nose tip and the other in the upper dorsal part of the nasopharynx.

To test the electrophoretic intranasal delivery protocol, four electrodes were arranged around the nose. The electric potentials are -3V, -8V, -12V, and 0V, respectively. The small voltage differential (3V) in the nasal vestibule is to provide a slight driving force toward the upper nasal cavity, while at the same time trying to avoid direct particle collisions with the vestibule. The voltage differential increases to 8V after the nasal valve region to further diverge the particles toward the olfactory region. The voltage differential increases to 12V right above the olfactory region to collect the drug aerosols. Figure 6(b) displays the electric potential field. The red color is zero voltage, and the blue color is negative potential. The E-field is observed to change from nearly 0V at the nostrils to nearly 12 V at the olfactory region.

Figure 7 shows snapshots of particle positions at varying

instants from their release at the nostrils. The particles considered are 0.2 µm in diameter with a density of 1g/cm3 and a charge number of 200. Both airflow and particles enter the whole nostrils at a speed of 0.1 m/s. It is observed that initially (t = 0.25 s) the particle profiles are similar with and without the electrophoretic force. The deviation of the electrophoretic particle profile from the baseline case increases thereafter. Due to the increasing upward electrophoretic force, a majority of the

particles impinges on the three nasal conchae (or turbinates). A small portion of particles (~14%) reach the olfactory region. In contrast, there is almost no deposition in the olfactory region without the electrophoretic guidance.

Based on the observations that conchae depositions are predominately from particles that are released at the lower half of the nostril, it is natural to conceive that avoiding the release of the drug from this region will decrease unwanted depositions in the nasal conchae. Figure 8a shows the scenarios of partial drug release from the upper nostrils only, which significantly reduces the deposition in the middle concha and almost eliminate the deposition in the inferior concha. In the case of electrophoretic guiding, partial release delivers about 45% of the drugs to the olfactory region, with the reminder (~55%) deposited on the superior concha. In light of the 55% waste, we further refined the drug release area so that all particles from that area could navigate through the nasal passages and reach the olfactory region. Figure 8b shows the particle dynamics one second after their release from a pointed region at the tip of the nares. Nearly all electrophoretic-driven particles from this region (~95%) deposit on the olfactory region. In contrast, only 0.77% particles from this region end up in the olfactory region, with the rest being inhaled into the lung.

The deposition fractions of drug particles at the olfactory

region (i.e., olfactory dosage) are shown in Fig. 9 using both logarithm and linear scales. Applying electrophoretic forces can remarkably enhance the olfactory dosage. In this pilot 2-D study, electrophoretic-guided delivery gives rise to olfactory dosages that are two orders of magnitude higher than those obtained without electrophoretic forces. Furthermore, releasing drugs at the upper half nostril (i.e., partial release) leads to an olfactory dosage two times higher than is obtained by releasing drugs at the whole nostrils (i.e., conventional release). The optimal release position with electrophoretic guidance is located at the top of the nostrils, which delivers 95% of the

Figure 7. Particle trajectories at different instants after release without and with electric forces.

Figure 8. Particle trajectories without and with electric forces: (a) partial release; (b) pointed release.


drugs to the olfactory region. Considering the extremely low olfactory delivery efficiency (0.11-0.77%) that is seen with traditional approaches, this new protocol is highly advantageous in the development of nose-to-brain drug delivery and topical intranasal target drug delivery.

DISCUSSION AND SUMMARY The feasibility of direct nose-to-brain drug delivery rests

on two factors: clinically relevant dosage to the CNS and no obvious systemic side effects. In this regard, the optimal olfactory dosage (95%) predicted in this study appears very effective. The highest voltage applied around the nose is only 12V, which is safe to the human body and can be easily achieved by using a 12V battery.

The novel aspect of this study is that charged nanoparticles can be precisely focused and navigated using appropriate electrophoretic forces. This protocol can provide many advantages that current inhaler devices cannot achieve. For example, electrophoresis-guided drug particles can be contact-free from the airway wall, which significantly reduces the drug loss and increases the olfactory dosage. As a result, it has the potential of alleviating or overcoming the nose-to-brain bottleneck posed by the extremely low olfactory deposition. Drug particles can be free of ferric magnetic materials, therefore minimizing side effects from metallic accumulations. Furthermore, delivery does not rely on inhalation maneuvers, which makes it suitable for seniors or patients with respiratory distresses. The proposed platform can also be easily adapted for targeted drug delivery at respiratory sites other than the olfactory region by modifying electrode layouts and voltage input frequencies.

The proposed device is envisioned to be applied immediately outside of the patient’s nose when administering drugs. A brace will be used to fix the device to the human head so that (1) there is no direct contact between the electrodes and skin, and (2) there is no relative movement between the device and the nose. Cushions will be applied wherever necessary to improve the comfort of the patients. This device should be applied in the environment where there is no strong electric or

magnetic fields existing. Most homes and hospitals satisfy the requirement.

This study systematically evaluated the feasibility and effectiveness of targeted drug delivery with electrophoretic forces in a 2-D nose model. The influences of electric fields-, drug-release positions, particle sizes and initial velocities were examined and compared. The results of this pilot study have implications for the development of effective intranasal drug delivery devices that target at olfactory epitheliums. Specific findings of this study are:

1. It is practical to focus and guide nanoparticles with a voltage that is safe to the human body (<12 V).

2. Applying electrophoretic forces can significantly increase the olfactory deposition of nanoparticles with diameters of 0.2 µm and below.

3. With appropriate electrophoretic guidance and selective drug release, more than 90% olfactory dosage could be achieved.

FUTURE STUDIES Limitations of this study include the assumption of steady

flows, rigid airway walls, limited electrode layouts/operating parameters, and neglect of outer nose structures. Each limitation influences the realism of the model predictions and should be addressed in future studies. Besides, this proof-of-concept study is limited to a two-dimensional nose model, and is merely computational. Future numerical studies with 3-D, anatomically accurate nose models, as well as complementary experimental studies (Fig. 10), are necessary to advance our knowledge of the feasibility and efficacy of this new drug delivery protocol. To achieve highly effective olfactory drug delivery, our future studies will address the three tasks:

1. To experimentally establish the proof-of-concept of particle focusing and navigation using electrophoresis in an ideal human nasal replica (Fig. 11b);

2. To numerically simulate the nanoparticle electropheresis in 3-D physiological realistic nasal models of different age groups such as children, adults, and senior populations. The predicted results will cross-validate the in vitro measurements and provide detailed information to optimize the olfactory delivery design.

3. To evaluate the performance of the platform in an image-based anatomically accurate nose model (Fig.

Figure 9. Comparison of olfactory dosages with and without electric forces in 2-D model at (a) logarithm

scale; and (b) linear scale.

Figure 10. Nasal casts for experimental studies.


11c), and tune the design parameters accordingly for effective delivery to the olfactory region.

REFERENCES [1] Mistry, A., Stolnik, S., and Illum, L., 2009, “Nanoparticles for Direct Nose-to-Brain Delivery of Drugs,” Int. J. Pharm., 379, pp. 146-157. [2] Hanson, L., and Frey, W., 2007, “Strategies for Intranasal Delivery of Therapeutics for the Prevention and Treatment of NeuroAIDS,” J. Neuroimmune Pharmacol., 2, pp. 81-86 [3] Misra, A., and Kher, G., 2012, “Drug Delivery Systems from Nose to Brain,” Curr. Pharm. Biotechnol., 13, pp. 2355-2379 [4] Shi, H., Kleinstreuer, C., and Zhang, Z., 2006, “Laminar Airflow and Nanoparticle or Vapor Deposition in a Human Nasal Cavity Model,” J. Biomech. Eng.- T ASME, 128, pp. 697-706 [5] Si, X., Xi, J., Kim, J. W., Zhou, Y., and Zhong, H., 2013, “Modeling of Release Position and Ventilation Effects on Olfactory Aerosol Drug Delivery,” Respir. Physiol. Neurobiol., 186(1), pp. 22-32. [6] Xi, J., and Longest, P.W., 2008, “Numerical Predictions of Submicrometer Aerosol Deposition in the Nasal Cavity using a Novel Drift Flux Approach,” Int. J. Heat Mass Tran., 51, pp. 5562-5577. [7] Morsi, S. A., and Alexander, A. J., 1972, “An Investigation of Particle Trajectories in Two-Phase Flow Systems,” J Fluid Mechanics, 55, pp. 193–208. [8] Xi, J., and Longest, P.W., 2009, “Characterization of Submicrometer Aerosol Deposition in Extrathoracic Airways during Nasal Exhalation,” Aerosol Sci. Tech., 43, pp. 808-827.




COUNTER-BALANCING MECHANISM FOR IMPROVING INDEPENDENCE WHEN USING AN EXOSKELETON

Drew A. Lathan

University of Alabama Tuscaloosa, AL USA

Beth A. Todd

University of Alabama Tuscaloosa, AL USA

ABSTRACT

An exoskeleton is a robotic device used in assisting

paraplegics with standing and walking. Existing designs use a

series of DC motors and brakes to move the different parts of

the device. Some exoskeletons mimic the musculoskeletal

system by sending signals to a computer that tells the motors to

rotate the knee, ankle, and hip joints appropriately for correct

forward movement of the device. Users of the devices have a

walker or crutches with controls to aid in balance. The goal of

this project is to provide complete independence by removing

the need for these walking aids. A new leg orthotic has been

designed that may be implemented on any exoskeleton design

to maintain balance in the fore-aft direction. A series of fast-

acting electric actuators respond to the individual’s movements.

If at any point the device begins to tip, the actuators engage in

such a way that the user’s leg is brought back to an up-right

position allowing balance to be recovered. As this movement

takes place, the normal actions of the device’s DC motors and

brakes are also engaged to avoid falling (the reactions from the

motors and brakes are already a feature of current exoskeleton

designs.) This is a counter-balancing mechanism and could

provide the gift of independence to paraplegics in the future.

INTRODUCTION “The dream of regaining the ability to stand up and walk

has come closer to reality for people paralyzed below the waist

who thought they would never take another step” [2, 6].

Imagine being paralyzed for 20 years and then, because of

some incredible ingenuity, being able to walk once again and

live a semi-traditional life. That is what the robotic exoskeleton

is providing some paraplegics around the world. The Ekso™ is

an example of a mechanical bodysuit invented to provide relief

for patients with spinal cord injuries. Ekso Bionics™ is one of

the leading companies to develop this piece of technology.

These devices can allow a patient to sit, stand-up, walk, and

climb stairs with a marginal range of independence.

Furthermore, it is light weight and its compact size allows easy

mobility. The military is already using it, and soon it will be

available for personal use once insurance companies create the

correct policies [1]. Some exoskeleton designs mimic the

musculoskeletal system found in the human body –different

parts of the exoskeleton can carry out the many functions

associated with the brain, muscles, and the sensory nerves. The

exoskeleton comes accompanied with a walker or crutches to

provide balance and stability. Some may say that this is its flaw

when it comes to complete independence for a user. The

objective of this research is to model a prototype device to

create balance stability in the sagittal plane without the use of

crutches.

BACKGROUND In order to provide an individual with an orthosis to

improve mobility, one must understand gait. Gait is the pattern

of movement of the limbs as an individual moves over a solid

surface. A person’s gait is defined by that individual’s footfall

pattern. Human gait is described as bipedal forward movement

of the center of gravity of the human body in which there are

curved movements of different sections of the body with the

least amount of expelled energy. Different gaits are related by

differences in limb motions, overall velocity, kinetic and

potential energy, forces, and fluctuations on the contact surface

[3, 4].

Farris et al. has developed a hybrid orthosis to aid in

restoring gait back to individuals with spinal cord injuries. The

device uses FES or functional electrical stimulation which

stimulates the quadriceps muscle group of the legs. Along with

FES, a controllable orthosis couples hip to knee flexion in the

sagittal plane, aids in hip and knee flexion with a spring assist,

and incorporates sensors and modulated friction brakes.

Combined, these elements help control joint and limb

trajectories. Because of unidirectional coupling, knee extension

does not generate hip extension. The purpose of the spring and

joint coupling is so the knee joint is biased toward an

equilibrium position where both the knee and hip joint are

flexed. This combination enables knee flexion, hip flexion, and

knee extension which all are from surface stimulation of the

quadriceps muscle group of both legs. The joint coupled

orthosis (JCO) also contains controllable friction brakes at both

knees and hips. These brakes can independently lock the joints


as well as modulate the resistive torque at each joint in order to

control limb motion. The JCO also contains sensors at both hips

and knees to detect angles of motion, providing feedback

control of limb motion. The last element of the JCO is its

ability to constrain motion along uncontrolled degrees of

freedom (hip adduction and ankle flexion) which will enhance

stability and control of an individual’s gait. Probably the most

important part of this device is the wafer disc brakes. The

brakes provide added safety via the normally locked design of

the knee brake, preventing the individual from falling if the

device loses power. Also, the brakes increase muscle efficiency

by locking joints during different phases of gait when they are

normally static. This action takes pressure of support off the leg

muscles which reduces muscle fatigue and extends walking

time. Finally, the brakes provide a smooth and controlled leg

trajectory for a more natural and repetitive gait by using the

brakes as dampers controlled in relation to joint angle feedback

[5].

From the earlier orthotic designs, full-body exoskeletons

have been developed for an individual paralyzed from the chest

down. These exoskeletons work using the same type of

elements (computers, sensors, actuators, etc.) to provide

outstanding mobility for paralyzed individuals. There are

approximately 200,000 spinal cord- injured (SCI) individuals in

the United States. Each year there are approximately 11,000

new SCI’s, most of all reporting a reduced quality of life [7].

The ReWalk® powered exoskeleton was created to

address this very issue. Their goal was to develop a device that

not only provided a means of mobility, but mobility over a

longer distance than most other lower-limb orthoses and FES

gait enhancing systems. The ReWalk® is a lower limb powered

exoskeleton that allows thoracic or lower level motor-complete

individuals with spinal cord injuries to improve their

independence of walking. The device has independently

controlled bilateral hip and knee joint motors, rechargeable

batteries, and a computerized control system that is carried by

means of a backpack. Users of the ReWalk® control their

walking and movements through trunk motion and changes in

center of gravity. The angle of the torso is determined by a tilt

sensor and generates a preset hip and knee displacement (angle

and time) that will result in a step. The ankles use a double-

action orthotic joint with limited motion and spring-assisted

dorsiflexion that is adjustable through screw tension. The

ReWalk® is adjustable in height and in width. In addition, the

device has padded interfaces for calves and thighs and the

limbs are linked via a rigid pelvic frame. An individual using

the exoskeleton is secured to the device by a waist belt, shoes,

and Velcro closures with pads. As with other devices, standing

stability for the ReWalk® is achieved by the use of crutches.

The user can remotely interact with the system with a user-

operated wrist pad controller that can command sit-to-stand,

stand-to-sit, and walk-activation. This unique design allows the

user to actively be involved with controlling the device. The

software algorithm was specifically designed to interpret

signals from the torso tilt sensor. Furthermore, this algorithm

then generates alternating limb-coordinated motion to produce

bipedal walking. One function of the ReWalk® is that it does

not allow two sequential steps to be taken by the same leg.

During training, an external computer may be used to adjust hip

and knee joint angle displacements to optimize the walking

characteristics or implement a training mode. Also equipped, is

a manual mode of operation that is used to trigger steps,

bypassing the tilt sensor. This same mode may be used to

trigger sit-stand-sit transfers. Walking using the ReWalk®

exoskeleton is similar to upright bipedal walking, offering a

way in which SCI’s can overcome some of the physical and

psychosocial problems that are common in these individuals.

ReWalk® researchers conducted tests on 13 individuals using

the device. The average range of speed was between 0.03 and

0.45 m/s. The test subjects were evaluated over a 10 m walk

although many subjects were able to walk much greater

distances (greater than 150m). One of the negative aspects of

the tests was several patients not being able to consistently and

comfortably use the remote wrist controller. These subjects

were still in control of their walking by self-triggering steps;

however, they were not able to consistently change from

walking and sitting routines by themselves as they were more

comfortable using the crutches for stability [8].

Another powered exoskeleton that has been developed is

known as the “Ekso™”, engineered by Ekso Bionics®. The

Ekso™ is a mechanical bodysuit invented to provide relief for

patients with spinal cord injuries. Ekso Bionics® is one of the

original companies to develop this piece of technology. The

Ekso™ can allow a patient to sit, stand-up, walk, and climb

stairs with a marginal range of independence. Furthermore, it is

light weight and its compact size allows easy mobility. Also,

the Ekso™ may contain embedded sensors that detect

differences in the patient’s hand movements, which are signals

of the wearer’s intention of what he or she would like to do.

The control system detects and translates the sensory elements

into a signal that moves the bionic legs. Finally, a motor

translates these signals into actual movements. For example, if

pressure is exerted on both crutches, this may tell the sensors

that the patient wants to stand up or turn. Moving one crutch

forward causes the opposite knee to flex which, in turn, causes

the opposite leg to move. The design of the sensors allows a

patient to walk over uneven ground and semi-difficult terrain.

An experienced user can get up from a wheelchair or other

sitting position and strapped into the Ekso™ in five minutes.

With the Ekso™, a user gets the choice of three walk modes. 1)

FirstStep™- a physical therapist actuates steps with a button

push. The user progresses from sit to stand and from using a

walker to walking with crutches, often in their first session of

training. 2) ActiveStep™- As they progress, users take control

of actuating their steps through buttons on the walker or

crutches. 3) ProStep™- The Ekso™ can sense the user’s

gestures and body shift when the patient is in the right position.

In other words, the device knows the user wants to take a step

and takes a step for them. The Ekso™ also has a training mode

where the Ekso™ can be set to provide audio feedback when

the patient has achieved the ideal positioning required for a

step. Training mode is used to determine the position targets for


a patient that wants to move to ProStep™. The Ekso™ is also

equipped with EksoPulse™- a data module that automatically

gathers and transmits statistics and device information during

walking sessions. What are the uses and limitations of the

Ekso™? The device may be used by people with lower limb

weakness or paralysis due to spinal injury or disease, multiple

sclerosis, and Guillain Barre Syndrome. The height range of the

device is 5’2” to 6’2” with a maximum weight of 220 pounds.

Also the maximum hip width is 16.5”. Some other requirements

include proper upper body strength to balance with crutches or

a walker, ability to transfer oneself from a wheelchair or other

chair, and a complete evaluation and screening by a medical

provider [9]. As with the ReWalk®, this device requires the

user to use crutches for balance as well as giving commands to

the Ekso™. So, what can one do to provide an individual with

even more independence while using an exoskeleton? The

purpose of this thesis is to show how a simple series of

actuators can be implemented on many existing exoskeleton

designs to provide further independence for an individual with

a SCI.

METHODS AND MATERIALS Designing a new exoskeleton was not a goal of this

research. Instead, the focus was creating a new design to the

lower limbs that could be implemented on any existing device.

A design goal was to keep the device relatively simple and

inexpensive. This led to a mechanical system that could run off

of the same power and signals as the rest of the exoskeleton,

performing the desired tasks that are interpreted as signals

being sent to the computer that is built in the many current

designs of the exoskeleton. It was determined that a series of

actuators could be used in an attempt to counter-balance an

individual who is using the exoskeleton. The preliminary

designs are shown below in Figure 1.

Figure 1: Preliminary Sketches of Counter-Balance

Design

The final design created on SolidWorks® is shown in

Figure 2.

Figure 2: Final Counter-Balance Design

The new orthotic presented operates in a similar fashion as

the previously mentioned orthotic and exoskeleton designs.

There are mounts at the ankle, knee, and hip joints to allow

motors and a braking system to be installed for functionality

that may be preferred by a company that builds exoskeletons. In

addition, a tilt sensor (not shown in figure) is located at the

bottom of the ankle bracket. If the foot plate is rotated past 15

degrees up or down in a vertical plane, the tilt sensor sends a

signal to the computer as a stumble flag. This flag is interpreted

as a signal and engages all actuators, bringing the user back to

an up-right position. As the actuators engage the user will gain

the sense of stability being exerted on his or her center of

gravity, allowing the user to regain control in an amount of time

before the exoskeleton makes the next step. In addition, if an

exoskeleton loses all power, the brakes automatically engage

and the actuators lock, preventing the user from falling and

risking injury.

The actuators chosen to fit the device are 18 inch stroke

fast-acting linear actuators, designed by Firgelli

Automations™. The specifications of the entire design are

shown in Table 1.

Table 1: Component Lengths Top Leg Link Length 20 inch

Bottom Leg Link Length 16 inch

Ankle Bracket Length 4 inch

Foot Plate Length 14 inch

Foot Plate Width 7.5 inch

Front/Rear Foot Plate Angle 15 degrees

It was determined that the centers of gravity (CG’s) for each

part of the leg needed to be found in order to show that the


device was balanced properly. The CG’s are listed below in

Table 2.

Table 2: CG Location Center of Gravity

Coordinate Top Leg

Link (in)

Bottom Leg

Link (in)

Foot

Plate (in)

X-coordinate

(from left)

2.94 3.0 5.5

y-coordinate

(from top)

16.4 11.1 6.0

The actuators used on the orthotic were 18 inch stroke, 12 Volt

DC, 200 lbs max force actuators produced by Firgelli

Automations®. The actuators are capable of movements as fast

as 1in/sec. This is a good reaction time because of the short rate

of time that signals are sent from the tilt sensors to the

computer, and back again to the actuators.

Further analysis was done to determine the loads that the

new exoskeleton legs would see. Static equilibrium formulas

were used to find the amount of force exerted on each joint of

the new orthotic. It was estimated that the average approximate

mass that would be evenly exerted on one leg is described

below in Table 3.

Table 3: Relative Masses and Loads Average Exoskeleton Mass 45 lbs

Max Mass Exo. Can Hold 220 lbs

Distrib. Force on Each Leg 132.5 lbf

Actuator Mass 3.3 lbs

Total Leg Mass 15 lbs

DISCUSSION AND RESULTS From the data in Table 3, it was determined that the

actuators chosen for the new orthotic would support the load

for an individual and provide the proper amount of force to help

keep a user from potentially falling and risking injury.

Another task that needed to be addressed was power

consumption. Most of the orthotics and exoskeleton designs

mentioned in the previous section have custom-made battery

packs capable of producing enough power and battery life for

up to 8 continuous hours of usage. The extra power that was

needed to run 4 actuators at maximum load was determined.

The following equation represents the amount of amp hours

(Ah) that is needed to run 4 actuators for 8 hours:

Idrawn= (5A*4)*8hrs=160 Ah at V=12 volts; where I is the

current in amps that is drawn by 4 actuators and thus is required

as a minimum from the battery source that will be powering a

full-size exoskeleton. The goal here was to show how many

extra amp hours are needed in order to be implemented and

properly function on an existing exoskeleton design.

CONCLUSION The orthotic has been modeled and the results showed that

the modification provides adequate force to prevent tipping in

the sagittal plane. Though the new mechanism that has been

proposed has not been tested thoroughly in a facility, it does

offer hope and a new idea for gaining the fore-mentioned

independence a user may want. There are fail-safes built into

the design, should the device lose power. As mentioned

previously if an exoskeleton loses all power, the brakes engage

as well as the actuators leaving the user stranded in what could

be an awkward position for an unknown amount of time,

however; this action may also keep the user from getting

injured. Also, if there is malfunctioning in the tilt sensor at any

point, the actuators may not react to signals from the computer

effectively resulting in un-proper gait of the exoskeleton. These

problems may need to be addressed once a full-scale test is

conducted.

REFERENCES [1] Ramachandran, Priya. "From Science Fiction to Reality:

Exoskeletons." National Spinal Cord Injury Association. United

Spinal Association, 2012 n.d. Web. 9 Feb. 2013.

[2] Vanderbilt University, 2012, Center for Intelligent

Mechatronics, <http://research.vuse.vanderbilt.edu/cim/>

[3] Chi and Schmitt, 2005, J Biomech. 2005 Jul; 38(7):1387-95.

Epub 2004 Nov 30. Mechanical energy and effective foot mass

during impact loading of walking and running., Department of

Biology, Duke University.

[4] BMLWalker V1.8, The Biomotion Lab. Gait animation.

Web. 3 Feb. 2013.

[5] Farris, Ryan J., Hugo A. Quintero, Thomas J. Withrow, and

Michael Goldfarb. "Design and Simulation of a Joint-coupled

Orthosis for Regulating FES-aided Gait." (2009): n. pag.

Design and Simulation of a Joint-... Preview & Related Info.

2009. Web. 18 Dec. 2012.

[6] Goldfarb M, Durfee W, Korkowski K, and Harrold B.

“Evaluation of a Controlled-Brake Orthosis for FES-Aided

Gait,” IEEE Transactions on Neural Systems and Rehabilitative

Engineering, vol. 11, no. 3, pp. 241-248, 2003

[7] CDC. Resources for entertainment education content

developers. <http://www.cdc.gov/healthcommunication/

ToolsTemplates/EntertainmentEd/Tips/SpinalCordInjury.html.

>Accessed June 16, 2013

[8] Esquenazi, Alberto, Mukul Talaty, Andrew Packel, and

Michael Saulino. "American Journal of Physical Medicine &

Rehabilitation." The ReWalk Powered Exoskeleton to Restore

Ambulatory Functio... :. N.p., 2012. Web. 9 Feb. 2013.

[9] EksoBionics. N.p.: EksoBionics, n.d. Ekso Bionics. Web. 9

Feb. 2013. <http://www.eksobionics.com/ekso>.


http://www.ncbi.nlm.nih.gov/pubmed/15922749?itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum&ordinalpos=3

http://www.ncbi.nlm.nih.gov/pubmed/15922749?itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_RVDocSum&ordinalpos=3

http://www.biomotionlab.ca/Demos/BMLwalker.html

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