IEEJ Journal of Industry ApplicationsVol.5 No.4 pp.347–354 DOI: 10.1541/ieejjia.5.347

Paper

IMU Sensor based Human Motion Detection and Its Application toBraking Control of Electric Wheeled Walker for Fall-prevention

Kiichi Hirota∗ Student Member, Toshiyuki Murakami∗ Senior Member

(Manuscript received Oct. 31, 2015, revised Jan. 29, 2016)

With the increase in the aging population of developed countries, the demand for walking assisting devices hasgrown. Walkers are widely used because they provide high mobility and safety. In this paper, an electric wheeledwalker (EWW) is used to prevent the user from falling down. This paper presents novel fall prevention systems thatare composed of a human motion detecting algorithm and a braking system. The motion detecting algorithm focuseson the reaction torque of the EWW and the acceleration of the user. The reaction torque is estimated by the reactiontorque observer (RTOB), and the acceleration is measured by using one IMU sensor. The braking system works byswitching commands on the basis of human motion. Four motions are considered, and if the user motion is detectedas dangerous, the command value switches, and braking is initiated in response to the user posture. Experiments areperformed to verify the effectiveness of the proposed algorithm and controller.

Keywords: fall prevention, walking assistance, motion detection, reaction torque observer (RTOB), compliance control, sensor

1. Introduction

Recently, developed countries have problems with the de-creasing birthrate and aging population. It is assumed thatmore and more elderlies need nursing. The number of wel-fare facilities and care workers are increasing, and the rate ofpeople who lives alone and aged over 65 is getting higher. So,assistive device for elderlies to live themselves is demanded.

Walking is one of the important element for elderlies to livetheir lives independently. Activity area of elderlies changeswhether they can walk by themselves or not. Maintainingthe ability to walk often make their quality of life (QOL)higher. A lot of people who cannot walk by themselves usea wheelchair. However, if people only use a wheelchair andwon’t try to walk, people are restricted to use their muscle.Then, the decline of muscular strength will be promoted andthat will lead to be bedridden. So it is important for elderliesto prevent the decline of their walking function.

Therefore, assistive devices which promote walking bythemselves have been gaining attention recently. Especially,walkers are popular among handicapped people, becausewalkers have both high mobility and safety. Recently, col-laboration between robot technology and welfare sector hasalso been paying attention. For instance, Murata Manufactur-ing Co., Ltd. developed electric walking assist device named”KeePace” in 2012. This machine uses gyro sensor, whichdetects the slope of the device, then control by itself not to falldown. Walkers are safe and have high mobility, however fatalaccidents sometimes happen. Main reason of the accident isfalling down. According to National Consumer Affairs Cen-ter of Japan (NCAC), 90 percent of the fatal accidents werederived from falling down when people use walkers. Once

∗ Keio University3-14-1, Hiyoshi, Kohoku-ku, Yokohama 223-8522, Japan

elderlies fall down, it will cause bedridden, then that makesthem difficult to walk independently.

To solve these problems, some researches have conductedwhich detect human motion by using sensors (1)–(6). Hirata etal. stopped walkers automatically detecting pitch, roll anglesof upper limb and distance between user and EWW by us-ing stereo camera (1). In this research, EWW detects user’sposture then put on the brake automatically when the useris in danger. Pei Di et al. presented cane robot and ZeroMoment Point (ZMP) stabilization (2). In this paper, on-shoesensor was proposed and detected ZMP to stabilize humanposture. EWW described Kikuchi et al. implements a Webcamera in front of EWW and detects obstacles, then gener-ates the trajectory to avoid (3). Cong Zong et al. installed 3Dcamera and infrared sensor to propose motion capturing sys-tem. The subject was detected whole body, then conversedto 3D data (4). A.K. Bourke et al. presented motion detectingalgorithm that distinguish between Activities of Daily Liv-ing (ADL) and falling down. Human motion was detectedby using gyroscope (5). Other researches have also conductedto prevent from falling down. K. Agrawal et al. proposed amethod to choose time varying control gain to assist walkingat a slope (7) (8).

However, there are two problems in conventional methods.First, only human motion or around environment are consid-ered. Nobody regarded both EWW and the user informationas element for preventing fall down. It is necessary to applymeasured parameter of both EWW and human to motion de-tecting algorithm. Second, it cost too much money to use alot of sensors. Sensors are used to measure human informa-tion and around environment. Therefore, new motion detect-ing algorithm and braking system by switching command tobrake safely are proposed. In this research, only one IMUsensor which can measure triple axis accelerations, Euler an-gles and Euler velocities are used to detect human motion.

c© 2016 The Institute of Electrical Engineers of Japan. 347

Human Motion Detection and Braking Control of EWW（Kiichi Hirota et al.）

This sensor is not expensive, so this research can be practi-cal. The purpose of this research is to prevent from fallingdown when using EWW. EWW brake by itself when EWWdetects the user is likely to fall down. Timing and velocityadjustment of braking are also considered in this research.

This paper is organized as follows. In section 2, the outlineof the system is introduced. Section 3 explains the model-ing of EWW and handle. Section 4 explains the algorithmto detect human motion by using EWW and IMU sensor. Insection 5, the controller design is introduced. In section 6,three experiments were conducted to verify the performanceof proposed method. Finally, this paper is concluded in sec-tion 7.

2. Outline of the System

In this research, EWW shown in Fig. 1 is used. And Fig. 2shows how to use EWW.2.1 Handle Some people who use a walker can-

not support their weight by themselves, because their mus-cle becomes weaker as they get old. So, the handle is at-tached to support their weight in this research. The handlealso helps users to steer easily. Most of walkers have non-holonomic constraints due to prevent sharp turn. However,non-holonomic constraints make turning difficult for users.Therefore, handle command steering direction. The handleand wheels are controlled independently, because the handleis actuated to support the user not to fall down, not only tocommand steering motion. The handle only moves steeringdirection.2.2 IMU Sensor The user attaches an IMU sensor at

the red point shown in Fig. 2. IMU sensor detects user’s mo-tion. In this research, IMU sensor detects walking state andstopping state by measuring acceleration of vertical direction.In my previous research, three IMU sensors are attached and

Fig. 1. EWW used in this paper

Fig. 2. Usage of EWW

four patterns of the user motion are detected (9). However,complicated algorithm is needed to judge motions of the user,therefore proposed algorithm is simpler and uses less infor-mation of the user measured by IMU sensors.

3. Modeling

EWW is composed of two parts. First one is the part of thecar and the other is the handle. The modeling of the car partis shown in Fig. 3 and the handle model is shown in Fig. 4respectively.3.1 Kinematics of Car Part The position, posture,

angle of the wheel are defined as X =[x0, y0, φ

]T , θw =[θr, θl]T . Kinematic equation can be derived as follows usingJacob matrix J .

X = J θw · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (1)⎡⎢⎢⎢⎢⎢⎢⎢⎣x0

y0

ψ

⎤⎥⎥⎥⎥⎥⎥⎥⎦ =⎡⎢⎢⎢⎢⎢⎢⎢⎢⎢⎣

R2 cosψ R

2 cosψR2 sinψ R

2 sinψRW − R

W

⎤⎥⎥⎥⎥⎥⎥⎥⎥⎥⎦[θr

θl

]· · · · · · · · · · · · (2)

3.2 Dynamics of Car Part Dynamic modeling ofEWW is derived from Lagrange’s equation shown in follow-ing equation.

τw = Mwθw · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (3)

where, Mw denotes inertia vector of EWW. Each parametersare composed of Eqs. (4)–(6).

Mw =

⎡⎢⎢⎢⎢⎢⎣mwR2

4 + Jw + JR2

W2mwR2

4 − JR2

W2

mwR2

4 − JR2

W2mwR2

4 + Jw + JR2

W2

⎤⎥⎥⎥⎥⎥⎦ · · · · · · · (4)

θw =

[θr

θl

]· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (5)

τw =

[τr

τl

]· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (6)

3.3 Dynamics of Handle Part In this paper, θh, Mh

Fig. 3. The modeling of EWW

Fig. 4. The modeling of handle part

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Human Motion Detection and Braking Control of EWW（Kiichi Hirota et al.）

are the angle and the inertia of the handle. Dynamic modelingof handle part can be written as Eq. (7).

τh = Mhθh · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (7)

4. Proposed Algorithm to Judge User’s Motion

In this section, the method of judging user’s motion by anIMU sensor and EWW is introduced. Because patterns of hu-man motion are various, only four motions are discussed inthis research. Four motions are introduced at first, and thenthe motion analyses are also presented to decide the param-eters which should be measured for fall-prevention. Finally,the algorithm to judge user’s motion is explained.4.1 Considered Human Motion The examples of

falling accidents caused by walkers are as follows.( 1 ) Moving a walker even though the user is in danger( 2 ) Crushing by steps( 3 ) Sharp turning

To save the user from dangerous situation, four motions thoseare walking, stopping, leaning, and sharp turning are con-sidered. These motions are detected by an IMU sensor andEWW, and then prevent falling down by actuating car partand handle part of EWW.4.2 Motion Analysis Motion analyses were con-

ducted to decide the parameters which should be measuredfor detecting human motion. Seven healthy subjects aged 22-78 years old performed two motions written as follows. Dur-ing these experiments, subjects used the walker which is soldon the market.4.2.1 Leaning Motion In this motion analysis, the

subject leaned on the walker, then stopped. Two force sensorsare attached on the handle and the load toward the walker ismeasured during the analysis. One of the results is shown inFig. 5. In this case, the subject started to lean on the walkerat about 6 second, then stopped leaning at 12 second. Thevalue of measured load rapidly increased and while the sub-ject keeps leaning, the value keeps large. From the results,the load can be the parameter which detects leaning motion.Moreover, this value can be measured by observer instead ofusing force sensor, because the handle part is actuated.4.2.2 Walking Motion The subject attached an IMU

sensor at chest during the analysis. Walking and stoppingmotions are considered. The subjects took five steps forwardand stop, then took five steps forward again. Figure 6 is anexample of the results. The subject started walking in straightdirection at 4 second and walk 5 steps, then stopped at around9 second. Then, subject started walking again at 12 secondand stopped at around 17 second. According to the results,the value of acceleration was approximately −0.8 g duringthe experiment caused by gravity. When the subject walkedmaintaining normal upright posture, acceleration was −1.0 g.However, the subject walked with gripping handle shown inFig. 2, the value was measured larger than −1.0 g. The accel-eration of vertical direction oscillated clearly when the swingleg was landed. Therefore, this can be the parameter whichdetects walking and stopping motions.4.3 Motion Detecting Algorithm From the motion

analysis, three motions, those are leaning, walking, and stop-ping motions are defined as Eqs. (8)–(10). In case of suddenleaning motion, it is detected by using the algorithm defined

Fig. 5. Load toward the walker

Fig. 6. Chest acceleration in vertical direction

Table 1. Parameter value in the algorithm

Name Description

F Estimated load forceac Chest acceleration in vertical directionθh Angular velocity of the handle○ (k) Current value○ (k − n) Value before n sampling times○ limit Threshold

as Eq. (11). Each parameter are presented in Table 1.

Leaning : F(k) > Flimit · · · · · · · · · · · · · · · · · · · · · · · · · · (8)

Walking : |ac (k) − ac (k − n)| ≥ alimit · · · · · · · · · · · · (9)

Stopping : |ac (k) − ac (k − n)| < alimit · · · · · · · · · · · (10)

Sharp turning :∣∣∣θh

∣∣∣ > θlimit · · · · · · · · · · · · · · · · · · · · · (11)

In Eq. (8), change of load value (left condition) representsthe beginning of leaning motion. Moreover, right conditiondefines leaning state. Walking motion is defined as Eq. (9),then once the subject is regarded as walking, an IMU sensorkeep detect them as walking until about 0.8 second after. Thistime means the average time of 1 step. If the subject is notdetected as leaning or walking he/she is detected as stopping.These four algorithms obviously classify four motions andaccording to the conditions, EWW is controlled to stop grad-ually and keep user safely as mentioned next section. Usu-ally, IMU sensor is suffered from noise and off-set, thereforesecond-order Butterworth filter is utilized to make accelera-tion value of vertical direction smooth shown in Eq. (12) (10).

acalc (k) = m0aget

c (k) + m1agetc (k − 1) + m2aget

c (k − 2)

+ n1acalc (k − 1) + n2acal

c (k − 2) · · · · · · · · (12)

where, acalc denotes acceleration of vertical direction after cal-

culation and agetc acceleration measured directly by IMU sen-

sor.

5. Control System

In this section, control system with EWW and handle are

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Human Motion Detection and Braking Control of EWW（Kiichi Hirota et al.）

explained. First, Reaction Torque Observer (RTOB) is intro-duced (11). Then, compliance control and the decision of steer-ing input are also introduced. The braking system in responseto the user’s motion is proposed. Finally, block diagram ofwhole system is described.5.1 Reaction Torque Observer (RTOB) Reaction

Torque Observer (RTOB) is conducted to estimate the reac-tion torque of pushing force by the user. Based on the esti-mated torque, command velocity of EWW and the handle aregenerated. In case of the wheels, the motion equation of eachwheels of EWW including disturbance is shown as Eq. (13).

Mwθresw = τ

re fw −

(τ f ricw + τdis

w

)· · · · · · · · · · · · · · · · · · · (13)

In Eq. (13), total input disturbance is τ f ricw + τdis

w . Disturbancetorque in the wheels is compensated by using disturbance ob-server (DOB). Disturbance torque caused by friction torqueτ f ricw is defined as Eq. (14).

τ f ricw = Fw + Dwθ

resw · · · · · · · · · · · · · · · · · · · · · · · · · · · · (14)

Fw is coefficient of static friction and Dw is coefficient ofdynamic friction. Reaction torque τreac

w without τ f ricw can be

estimated as Eq. (15).

τreacw = τre f

w − Mwθresw − τ f ric

w · · · · · · · · · · · · · · · · · · · · (15)

Equation (15) can be transformed as Eq. (16) by using pseudodifferentiator.

τreacw =

gr

s + gr

(τre fw + gr Mwθ

resw − τ f ric

w

)

− gr Mwθresw · · · · · · · · · · · · · · · · · · · · · · · · · · · · (16)

In the equation, s is Laplace operator and gr is cut off fre-quency for pseudo differentiator. Block diagram of RTOBis shown as Fig. 7. In this research, the value of cut-off fre-quency of low-pass filter was set small, because if the esti-mated value of reaction torque includes high frequency com-ponent, the motion of EWW becomes oscillative.

To transform τreacw into F

reacw , τreac

w is transformed asEq. (17) by using pseudo Jacobean. RTOB is also appliedto the motor of handle to obtain the estimated force whichcommand handle acceleration and velocity.

Freacw = J+τreac

w · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (17)

Fig. 7. Block diagram of RTOB

5.2 Compliance Control Compliance control is usedto generate EWW’s motion command of straight direction.The command is derived from Eq. (18).

Mcwdcmd + Dcwdcmd = Freacw · · · · · · · · · · · · · · · · · · · · · (18)

where, Freacw , Mcw, Dcw denote average value of estimated

reaction force of each wheeled motors, virtual mass anddamper. dcmd, dcmd are command acceleration and velocityof straight direction. In this research, EWW is controlledbased on velocity control. Adjusting Mcw, Dcw, it is possibleto generate the command synchronized to user’s force. Thecommand in handle part is generated by Eq. (19).

Mchθcmdh + Dchθ

cmdh = Freac

h · · · · · · · · · · · · · · · · · · · · · (19)

where, Freach , Mch, Dch represent estimated reaction force of

motor attached to the handle, virtual mass and damper of thehandle. If the subject is detected as walking, the commandsare generated based on the compliance control. Otherwise,the commands switches like following control system.5.3 Steering Control In this subsection, steering

control is introduced. The handle decides the command ofsteering angle of EWW. At first, weighting function w is de-signed as Eq. (20).

w = f (θh) =

⎧⎪⎪⎪⎪⎪⎪⎪⎪⎪⎨⎪⎪⎪⎪⎪⎪⎪⎪⎪⎩

1 θh > θhighθh−θlow

θhigh−θlowθhigh ≥ θh > θlow

0 θlow ≥ θh > −θlowθh+θlow

θhigh−θlow− θlow ≥ θh > −θhigh

−1 − θhigh ≥ θh

· · · · · (20)

where θh denotes the angle of the handle and θlow, θhigh areminimum, maximum angle to rotate. These angles are de-signed such that user operate, then find optimal values. Com-mand velocity in steering direction φ is derived from Eq. (21)based on w.

φcmd = Aw · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (21)

φ is the command acceleration in steering direction and φ isthe time derivative of φ. Acceleration of wheels are calcu-lated by Eqs. (22)(23) by using straight and steering direc-tions of command accelerations. Command velocity of thewheels are also derived from same equation by using φ andd, instead of φ and d.

θcmdl =

2dcmd − φcmdW2R

· · · · · · · · · · · · · · · · · · · · · · · · · (22)

θcmdr =

2dcmd + φcmdW2R

· · · · · · · · · · · · · · · · · · · · · · · · · (23)

Then, acceleration reference of each wheels can be obtainedfrom Eq. (24).

θre fw = Kdw(θ

cmdw − θres

w ) + θcmdw · · · · · · · · · · · · · · · · · · (24)

in this equation, w = l, r.Reference acceleration of the handle can be derived by PDcontroller written as Eq. (25)

θre fh = Kph(θcmd

h − θresh ) + Kdh(θcmd

h − θresh ) · · · · · · · · (25)

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Human Motion Detection and Braking Control of EWW（Kiichi Hirota et al.）

Fig. 8. Block diagram of whole system

5.4 Command Switching Based on User’s MotionEWW is actuated by compliance control and steering con-

trol in normal walking. However, the IMU sensor and EWWdetect that the user is leaning, stopping or sharply turning,the command value switches to brake EWW and handle forsafety. Equation (26) generates command in straight direc-tion.

d = dw − db · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (26)

db =Freacw

Mb· · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (27)

In Eqs. (26)(27), brake command is generated when the useris detected as leaning, stopping or sharply turning. The valueof brake acceleration changes by reaction force Freac

w . Whenthe user is in danger while turning, the value of weightingfunction w switches to 0, shown in Eq. (28).

w = 0, φcmd = 0 · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · · (28)

Since turning angle and velocity of EWW are small for me-chanical reasons, the command shown in Eq. (28) does notaffect abrupt changing of motions. Therefore, EWW doesnot turn though the user moves the handle when the user isin danger. Command switching of handle part is shown inEq. (29).

Mchθcmdh +Dchθ

cmdh +Kch(θcmd

h −θdes)=Freach · · · · · · · (29)

Where, Kch denotes virtual elastic. θdes is desired angle of thehandle. θdes is determined by response angle which is mea-sured just before command switches. This elastic term meansthat the handle is commanded to return to the desired anglegradually. In this research, if an unexpected error causes inthe RTOB computation, the command position and velocitybecomes 0 then EWW will stop and remain to the point. Therole of EWW is not only help user walking but also supportthe balance of user, therefore in case of the unexpected phe-nomenon in RTOB, EWW stops and support the balance ofthe user by staying on the point. When unexpected error oc-curs, the command position and velocity gradually becomes

0. After the detection, command velocity becomes −0.2 rad/suntil EWW stops. The value was determined by some ex-periment. In case of stopping motion, EWW stops gradu-ally according to the reaction force, however if RTOB valuehas large error, EWW should stop regardless of the value ofreaction force. Therefore, EWW stops automatically if theunexpected phenomenon in RTOB has been detected.5.5 Block Diagram of Whole System Figure 8

shows the block diagram of whole system. In this system,Reaction Torque Observer (RTOB) estimates reaction torqueof pushing force by the user. EWW and the IMU sensor de-tect user’s motion, and then command values switch in re-sponse to the user’s motion. EWW brakes when the user isregarded as stopping, leaning and sharp turning. When theuser walks, commands are generated based on compliancecontrol. To trace reference values, PD controller and Distur-bance Observer are used (12).

6. Experiment

In this section, experimental results are described to con-firm the validity of proposed algorithm and control. In thispaper, three experiments were conducted. The purpose ofeach experiments were to

( 1 ) Verify that the command values switch accordingto proposed motion detecting algorithm and that re-sponse value trace to the command.

( 2 ) Verify that the algorithm detects human motion.Figure 9 shows the situations of each experiment.6.1 Experiment A: Leaning Motion In this experi-

ment, part of the algorithm was evaluated. A subject attachesan IMU sensor and walked toward straight direction by us-ing EWW. At first, the subject walked then started to lean onEWW at around 7 second. The subject kept leaning until 15second. The value of threshold Flimit was 15.0 N. If commandvelocity generated by Eq. (24) is over 0.01 m/s backward, thecommand keeps 0.01 m/s for safety. Figure 10 shows esti-mated force of EWW. It can be seen that the value of es-timated force increased sharply at 7 second, then exceeded

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Human Motion Detection and Braking Control of EWW（Kiichi Hirota et al.）

(a) Leaning (b) Sudden turning (c) Stopping

Fig. 9. The situations of each experiment

Table 2. Parameter value in experiment

Name Unit Value

Cut off frequency of DOB of wheel part gdw rad/s 5.0

Cut off frequency of RTOB of wheel part grw rad/s 5.0

Cut off frequency of DOB of handle part gdh rad/s 30.0

Cut off frequency of RTOB of handle part grh rad/s 30.0

Velocity gain of wheel part Kdw 900.0Position gain of handle part Kph 900.0Velocity gain of handle part Kdh 60.0

Mass coefficient of wheel part (usual) Mcw kg 60.0Damping coefficient of wheel part (usual) Dcw Ns/m 50.0

Mass coefficient of handle part (usual) Mch kg 1.0Damping coefficient of handle part (usual) Dch Ns/m 2.0

Mass coefficient of wheel part (braking) Mb kg 100.0Mass coefficient of handle part (braking) Mch kg 5.0

Damping coefficient of handle part (braking) Dch Ns/m 10.0Elastic coefficient of handle part (braking) Kch N/m 5.0

Fig. 10. Estimated force of EWW

Flimit at about 8 second.Figure 11 shows the velocity of EWW in straight direc-

tion. Command switching was conducted at 8 second andEWW is braked as estimated force Freac increased. Esti-mated force increased sharply at 10 second, because EWWstarted to go backward and the load which the subject tookwas much transferred. And it can be considered that esti-mated force oscillated due to oscillation by the subject. Thesubject broke the balance when he leaned, therefore the os-cillation occurred.6.2 Experiment B: Sharp Turning Motion In this

subsection, sharp turning is considered. At first, the subjectwalked straight, then started to turn left side at around 3 sec-ond. At 8 second, the subject turned right side and EWWcollided with the chair shown in Fig. 9 at 10 second. Due tothe collision, the subject sharply moved the handle to rightside and broke the balance. In this experiment, leaning andstopping detections are not considered. To detect sharp turn-ing, the threshold θlimit was set 0.6 rad/s.

Fig. 11. Command and response velocity of EWW inexperiment A (straight direction)

Fig. 12. Angular velocity of the handle

Fig. 13. Angle of the handle

Figure 12 shows the handle velocity. It can be con-firmed that command angular velocity immediately switchedto brake handle motion. This means that handle was stopped,performed by proposed braking system. When the responsevalue exceeded θlimit, the command value rapidly changed, socertain error occurred.

The results of the handle angle is shown in Fig. 13. Fromthe result, it can be seen that the control system keeps goodtracking performance. After the collision, the angle is grad-ually back to the value θdes which was measured just beforethe collision. In this experiment, the angle was −0.3 rad whenthe subject started to turn sharply, therefore θdes written inEq. (29) was set at −0.3 rad. This means handle was com-manded to return the angle at −0.3 rad. It can be confirmedthat the angle returned gradually to −0.3 rad at the end of theexperiment. Experimental result of EWW velocity in straightdirection is shown in Fig. 14. In Fig. 14, EWW drove fasterduring 10.5–11.0 second, this was caused by sudden turn-ing. At 11.0 second, the subject was detected as turning sud-denly by handle velocity shown in Fig. 12. EWW was braked

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Human Motion Detection and Braking Control of EWW（Kiichi Hirota et al.）

Fig. 14. Command and response velocity of EWW inexperiment B (straight direction)

Fig. 15. Command and response velocity of EWW inexperiment B (turning direction)

relative to the value of estimated reaction force.Figure 15 shows experimental result of angular velocity of

EWW in turning direction. Command velocity converted to0 at 11.0 second. Due to the sharp change, response veloc-ity overshot at 11.5 second, however that did not affect thesafety.6.3 Experiment C: Stopping Motion In this exper-

iment, stopping and walking motions are considered. Thesubject attached one IMU sensor on chest and the accelera-tion of vertical direction (Y-axis) was measured. The subjectwalked five steps forward, and then stopped at 6.5 second.Leaning and turning motions were not considered in the ex-periment. The experimental result of acceleration in verticaldirection is shown in Fig. 16. Acceleration started to varysharply at 2.5 second, this means the subject took a first step.The value sharply changed at 3.5, 4.6, 5.5 and 6.2 second. Itis assumed that the subject took a step at these times. Dur-ing 6.5–9.0 second, acceleration kept −0.85 g and the subjectstopped as shown in Fig. 9.

Figure 17 shows EWW velocity in straight direction.EWW were moved forward until 7 second, then the subjectwas detected as stopping. Therefore, EWW started to brakegradually from 7.0 second and finally moved backward to re-turn to the subject safely at 8.4 second.

7. Conclusion

The purpose of this research is to prevent from fallingdown when elderly person uses walker. For the purpose, mo-tion detecting algorithm based on the measured value by us-ing EWW and one IMU sensor were proposed. Moreover,braking system that helps assisting their body was also pro-posed to brake EWW and handle safely. In this paper, fourmotions which included walking, stopping, leaning and sharp

Fig. 16. Chest acceleration in vertical direction

Fig. 17. Command and response velocity of EWW inexperiment C (straight direction)

turning were considered. To detect these four motions, reac-tion force which is estimated by Reaction Torque Observer(RTOB) was used and chest acceleration in vertical directionwas also measured. From the experimental results, appropri-ate human motion detecting was confirmed and smooth brak-ing was also confirmed.

AcknowledgmentThis work was supported in part by a Grant-in-Aid for Sci-

entific Research (15H02235).

References

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Human Motion Detection and Braking Control of EWW（Kiichi Hirota et al.）

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Kiichi Hirota (Student Member) received the B.E. degrees from KeioUniversity, Yokohama, Japan, in 2014. He is cur-rently a graduate student in Keio University.

Toshiyuki Murakami (Senior Member) (M’93) received the B.E.,M.E., and Ph.D. degrees in electrical engineeringfrom Keio University, Yokohama, Japan, in 1988,1990, and 1993, respectively. In 1993, he joined theDepartment of Electrical Engineering, Keio Univer-sity, where he is currently a Professor in the Depart-ment of System Design Engineering. From 1999 to2000, he was a Visiting Researcher with The Institutefor Power Electronics and Electrical Drives, AachenUniversity of Technology, Aachen, Germany. His re-

search interests are robotics, intelligent vehicles, mobile robots, and motioncontrol.

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