Vestibular humanoid postural controlcga/tmp-public/redfern1.pdf · ular sensor along with a...

Journal of Physiology - Paris 103 (2009) 178–194

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Journal of Physiology - Paris

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Vestibular humanoid postural control

Thomas Mergner *, Georg Schweigart, Luminous FennellNeurologie der Universität Freiburg, Neurozentrum, Breisacher Str. 64, 79106 Freiburg, Germany

a r t i c l e i n f o

Keywords:Reactive stabilizationUpright stanceHumansHumanoidsVestibular systemsBehavioral flexibilityFail safeSensory re-weightingEmergenceHardware-in-the-loop simulation

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* Corresponding author. Tel.: +49 761 270 5313; faE-mail addresses: [email protected] (T. M

uniklinik-freiburg.de (G. Schweigart), [email protected]

a b s t r a c t

Many of our motor activities require stabilization against external disturbances. This especially applies tobiped stance since it is inherently unstable. Disturbance compensation is mainly reactive, depending onsensory inputs and real-time sensor fusion. In humans, the vestibular system plays a major role. Whenthere is no visual space reference, vestibular-loss clearly impairs stance stability. Most humanoid robotsdo not use a vestibular system, but stabilize upright body posture by means of center of pressure (COP)control. We here suggest using in addition a vestibular sensor and present a biologically inspired vestib-ular sensor along with a human-inspired stance control mechanism. We proceed in two steps. First, in anintroductory review part, we report on relevant human sensors and their role in stance control, focusingon own models of transmitter fusion in the vestibular sensor and sensor fusion in stance control. In a sec-ond, experimental part, the models are used to construct an artificial vestibular system and to embed itinto the stance control of a humanoid. The robot’s performance is investigated using tilts of the supportsurface. The results are compared to those of humans. Functional significance of the vestibular sensor ishighlighted by comparing vestibular-able with vestibular-loss states in robot and humans. We show thata kinematic body-space sensory feedback (vestibular) is advantageous over a kinetic one (force cues) fordynamic body-space balancing. Our embodiment of human sensorimotor control principles into a robotis more than just bionics. It inspired our biological work (neurorobotics: ‘learning by building’, proof ofprinciple, and more). We envisage a future clinical use in the form of hardware-in-the-loop simulationsof neurological symptoms for improving diagnosis and therapy and designing medical assistive devices.

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1. Introduction

Many of our human motor activities such as reaching or orient-ing are reactively stabilized against external disturbances. This isespecially evident when we are standing, since biped stance isinherently unstable. External disturbances may then arise fromforce fields such as gravity, from contact forces such as a push orpull having impact on the body, or from motion of the body sup-port surface. There exist numerous clinical and experimentalinvestigations on human stance control (Horak and Macpherson,1996; Mergner, 2007). Clinical relevance is high, since many neu-rological diseases affect stance stability and thereby patients’activities. A well-known cause of instability and falls is acute fail-ure of the vestibular sensor (Brandt, 1999). This indicates that thevestibular system normally is an integral part of human stance sta-bilization. Even in the state of chronic vestibular loss is stance con-trol clearly affected (Mergner et al., 2009).

If one considers for comparison humanoid robots, one finds thatmost of them are not endowed with a vestibular sensor, i.e. a kine-

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x: +49 761 270 5391.ergner), georg.schweigart@

t (L. Fennell).

matics body-space sensor, with few exceptions (e.g. Löffler et al.,2003). They rely foremost on centre of pressure (COP) control interms of walking trajectory planning with or without online con-tinuous or discontinuous force feedback (Caballero et al., 2006;Prahlad et al., 2008). This may explain why their motor perfor-mance during stance stays clearly behind that of humans(Mahboobin et al., 2008). Robots from commercial companies(e.g. Honda, Sony) are involving gyros and/or translational acceler-ometers in their active balancing, but details are not provided bytheir creators and quantitative comparison to humans are not yetavailable. Here, we ask: Is it the vestibular sensor that makesstance control of humans better than that of humanoids in termsof performance, flexibility, and fail safe? And, what makes the ves-tibular sensor advantageous over force sensors in stance control?

The approach we chose to answer these questions was to buildand investigate a humanoid with an artificial vestibular systemand a vestibular-based stance control, compare its performanceto that of vestibular-able humans, and repeat the comparison forthe vestibular-loss state in both robot and humans. The compari-son focuses on body-space balancing on a moving support surface.

There has been considerable progress on the understanding ofhuman stance control over the last years in comparison to the ear-lier postural reflex concept, which we will briefly outline.

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T. Mergner et al. / Journal of Physiology - Paris 103 (2009) 178–194 179

Postural reflexes. In the past, sensorimotor physiologists consid-ered postural reflexes to be the basis of reactive stabilization. Oneof them is the vestibulo-spinal reflex that produces fast stereotypemotor responses upon stimulation of the vestibular sensor (Wilsonand Melvill Jones, 1979). The reflex is seen in reduced animal prep-arations (i.e. animals, in which higher brain functions are elimi-nated) or in newborns or brain damaged humans; it is evokedindependently of the behavioral context.

Overcoming the reflex concept. The reflex concept posed mainlytwo problems. One is purely theoretical and is called the ‘pos-ture-movement problem’ (Feldman, 2009): The reflexes werethought to stabilize a given (static) posture and, therefore, to re-quire reflex suppression during movements. Furthermore, duringmovements, effects of external disturbances would not be compen-sated. The problem does not occur, however, when the movementcommand uses the stabilizing feedback loop as a ‘servo’ (Mergner,2004).

The other problem was that the reflexes are not providing thebehavioral flexibility that is required in face of different behavioralsituations. It becomes more and more established during humanmotor development (Prechtl, 1977). In a solution from our labora-tory (Mergner et al., 2002, 2003; Mergner, 2004), we hypothesizedthat the flexibility evolves from inter-sensory interactions. Thesecome with maturation of additional sensory loops through highercenters. The low-level mechanisms, still serving as fast fail safes,may re-emerge following lesions of the higher centers. The flexibil-ity is achieved by internally reconstructing the external disturbancesthat have impact on the body. The disturbance estimates ratherthan the ‘raw’ sensory signals are then used for reactive disturbancecompensation.

Example of disturbance estimation and compensation. To estimatesupport surface tilt, it must be internally reconstructed from atleast two sensors (Mergner et al., 1997). With firm foot-supportcontact, support-space tilt can be estimated from foot-space tilt.This can be achieved by combining a vestibular body-space tilt sig-nal, bs, with a proprioceptive body-foot signal, bf (foot-space tiltangle, fs = bs � bf).

For tilt compensation, the fs estimate is fed into a propriocep-tive feedback loop for body-foot stabilization, transforming thisinto a body-space stabilization (note that a vertical body orienta-tion is advantageous in terms of fatigue, energy consumption,and working range). The addition of the fs signal to the feedbackadds a vestibular signal and thereby strengthens the overall vestib-ular contribution to the feedback loop (see ‘sensory re-weighting’,Section 1.4).

This article is structured in two parts, a review and an experi-mental part. The remainder of Introduction provides the reviewthat describes the biological background of our approach. First,we focus on sensors and sensory interaction in human stance con-trol (Section 1.1). Then we describe sensory disturbance estimation(Section 1.2), place this into the framework of our stance controlmodel (Section 1.3) and explain vestibular sensory re-weightingthat comes with the disturbance estimations (Section 1.4).

The experimental part starts with Methods, in which we explainconstruction of the artificial vestibular sensors (Section 2.1) and ofthe robot and its stance control, as well as experimental proce-dures and setups (Section 2.2). In Results, we present performanceof the artificial vestibular sensor (Section 3.1) and human and ro-bot responses to support surface tilts in vestibular-able (Section3.2) and vestibular-loss (Section 3.3) states. Discussion considersthe vestibular sensor (Section 4.1), its role in stance control (Sec-tion 4.2), consequences of vestibular loss (Section 4.3), sensoryre-weighting (Section 4.4), and the values of embodiment andhardware-in-the-loop (HIL) simulation (Section 4.5), before draw-ing conclusions on the findings and the neurorobotics approach(Section 4.6).

1.1. Review on sensor concept and sensors

1.1.1. Sensor conceptFor modeling of human sensorimotor functions, one needs to

identify and characterize the sensors that are used. When we speakof a joint position sense, there exists no corresponding sensor atthe level of a single receptor (transducer) type. Several sets ofinformation from different transducers are combined: musclelength (from muscle spindle receptors), muscle force (Golgi tendonorgans), skin stretch, and more. However, a joint position sense canbe identified and characterized at the level of human conscious per-ception using psychophysical experiments. We resorted to thiswhen we previously modeled human self-motion perception(overview Mergner, 2002). Here we assume that essentially thesame sensory information is used for sensorimotor control.

1.1.2. Sensors in human stance controlClinical and experimental studies suggest contributions from

visual, vestibular, joint angle proprioceptive, and force cues to hu-man stance control (Horak and Macpherson, 1996). Vision im-proves stance stability, but is dispensable for principleconsiderations (Mergner et al., 2005). We, therefore, omit it hereand proceed from the following three sensor types.

1.1.2.1. Joint position and velocity sensor (‘joint angle propriocep-tion’). The sensor delivers internal representations of body-foot an-gle BF and angular velocity _BF (internal signals: bf and _bf ).Contributions from muscle length transducers (muscle spindles)are well documented (Eklund, 1973; Hayashi et al., 1981; Kavou-noudias et al., 1999). Here we consider only a limited workingrange (<8�, <80�/s; frequencies, 0–1.6 Hz) and assume that goingbeyond this range calls for extra control strategies (such as a rescuereaction step). Within this range, the sensor’s frequency and ampli-tude characteristics are essentially ideal. The detection threshold issmall (psychophysical results, Mergner et al., 1991; Fitzpatrick andMcCloskey, 1994) and needs not to be considered here.

1.1.2.2. Force sensor (COP or ankle joint torque sensor). Golgi tendonorgans provide a signal that reflects joint torque (Duysens et al.,2000). In addition, during stance, ground reaction forces are regis-tered by pressure sensors in the feet. They contribute to a sense ofcentre of pressure (COP) shift that contributes to human stancecontrol (Magnusson et al., 1990; Maurer et al., 2000, 2001). COPis approximately proportional to ankle joint torque (van der Kooijet al., 2005). It is measurable using a force-transducing platform(clinically used to quantify spontaneous or evoked body sway).

1.1.3. Vestibular systemThe vestibular organs lie in the inner ears, encapsulated by bone

and, therefore, are affected only by field forces such as gravito-inertial force fields (Wilson and Melvill Jones, 1979). Each com-prises two receptor systems (for an engineering inspired review,see Mayne, 1974).

1.1.3.1. Receptor (transducer) systems.(a) Otolith organs. Otolith receptors register gravito-inertial

acceleration, aGi (3D; sensor coordinates). Conceptually, we com-bine here all otolith signals into a single internal representationof aGi (response characteristics, see Section 1.1.3.2).

(b) Semicircular canals. They consist of fluid filled circular tubes,one for each of the three rotational planes. During rotation, but nottranslation, fluid inertia exerts pressure on neural receptors andleads to a neural signal (Goldberg and Fernández, 1975). Mainlydue to mechanical factors (such as fluid adhesion), the canals codeangular acceleration during low frequency rotation and angularvelocity in the mid- to high-frequency range. This can be

Fig. 1. Canal-otolith signal fusion, complementary filter principles. (A) Simpletechnical solution, position variant. Tilt in x plane, ux, activates the accelerometer(ACC.) depending on sensor angle with respect to gravity. Low pass filter (LP)reduces high frequency signals (translational acceleration components) and noise.Derivative of input tilt, angular velocity, is transformed by gyro (GYRO) into angularvelocity signal x (subsequently converted into angle by integration,

R). The high

pass filter (HP), complementary to LP, reduces low frequency noise and drifts. Thesum of both signals yields a broad band pass estimate of tilt angle, ux . (B) Biologicalsolution, 3D velocity variant of A. For the fusion, an angular velocity signal (x0) isderived from accelerometers (through cross product of its gravito-inertial signal aGi

and the first derivative _aGi, jerk). The results of the fusion is a 3D estimate of angularrotation xx;y;z . Note from insets that translation and rotation conventions are fromvestibular physiology and aviation.

180 T. Mergner et al. / Journal of Physiology - Paris 103 (2009) 178–194

represented by a low pass filtered acceleration signal (T = 5 s; ‘ca-nal time constant’; Wilson and Melvill Jones, 1979). For didacticreasons, however, one may equally adopt the reverse view andconsider the canals angular velocity transducers that output a highpassed filtered (T = 5 s) 3D angular velocity vector, xCan.

The xCan signal contains noise with low frequency componentsthat, after velocity-position integration, stand out as fluctuatingdrifts (Mergner, 2007). The drifts limit the use of the canalsignal (see below; note that integration of white noise yields1/f noise).

1.1.3.2. Vestibular sensor (result of canal–otolith fusion). Numerousstudies suggest that the brain fuses otolith and canal signals forspatially oriented functions (Angelaki et al., 1999; Borah et al.,1988; Bos and Bles, 2002; Crane and Demer, 1999; Glasauer andMerfeld, 1997; Laurens and Droulez, 2007; Mayne, 1974; Merfeldet al., 1993; Mergner and Glasauer, 1999; Ormsby and Young,1977; Paige and Tomko, 1991; Zupan et al., 2002; compare Viévilleand Faugeras, 1989). The fusion yields (a) an improvement of thecanal time constant during rotation in the vertical planes, and (b)a decomposition of the otolith (accelerometer) signal aGi into esti-mates of sensor (or head) attitude and translational acceleration, gand ai. The fusion is not perfect, though, and may give rise to per-ceptual illusions. An example is the ‘catapult launch illusion’ (back-ward body ‘tilt illusion’ upon lasting very high accelerations duringaircraft take off, e.g. from a carrier ship). Biologists use them tomake inferences on how the brain performs the fusion. Here, wetry to provide a simple ‘recipe’ of the fusion.

For inspiration, we start with an engineering concept. Whenengineers build simple low cost gravito-inertial guiding systems,they often combine gyros and translational accelerometers (e.g.Günthner, 2008). The gyros used are angular rate sensors that out-put angular velocity signals. The function principle (e.g. Coriolisforce) differs from that of the canals. Yet, similar as canal signals,the gyro signals contain noise with strong low frequency compo-nents that lead to fluctuating drifts upon velocity-position integra-tion. These drifts can be reduced by high-pass filtering (see Section1.1.3.1). The accelerometer signal, on the other hand, tends to con-tain high frequency signal components (from translational acceler-ations) and noise that can be reduced by low pass filtering. This ledto a ‘‘complementary filter” solution as shown in Fig. 1A (Mayne,1974; Baerveldt and Klang, 1997; Günthner, 2008).

The accelerometer in Fig. 1A delivers upon rotation in the verti-cal planes (ux) an angular position signal that stems from gravity(the sensor is rotated with respect to gravitational vector). Transla-tional acceleration components, which tend to be transient andhigh frequency, are strongly reduced by the low pass filtering.The result is an inclinometer with low frequency transfer charac-teristics. The gyro, on the other hand, is used to obtain an inclinom-eter with high frequency transfer characteristics. It outputs anangular velocity signal that is integrated into another angular posi-tion signal. The integration emphasizes low frequency noise, lead-ing to fluctuating drifts, which are prevented by high-pass filtering.Given complementary filtering, the two transducer signals can becombined into an inclinometer that estimates the rotation angle(ux) with essentially ideal dynamics and reduced noise. However,some responsiveness to translational acceleration may remain,leading to ‘tilt illusions’ (see above; extent is determined by lowpass filtering of accelerometer signal). Implementation of this solu-tion in a 3D sensor is rather complicated (e.g. Günthner, 2008).

In a biological complementary filter solution that allows for arelatively simple 3D implementation (Mergner and Glasauer,1999), the gyro-accelerometer fusion is performed at the level ofangular velocity signals (Fig. 1B). To this end, aGi is used to obtainan angular velocity rather than a position signal, using a crossproduct of aGi and its derivative (jerk; _aGi; compare also step I in

Fig. 2A and B and Mergner and Glasauer, 1999; note that jerk iscontained in some otolith afferent signals; Hess, 2006). The result-ing x0, which reflects essentially the accelerometer’s rotationalvelocity with respect to the gravitational vector, contains only xand y components and no z component (because pure horizontalrotations are not registered by the accelerometers), unlike thegyro’s x signal that is 3D. Summing both after complementary fil-tering yields improvements of response dynamics and noise reduc-tion as before, and also the tendency for tilt illusions is similar. Butthis solution comes with the disadvantage that the output has lostits direct position referencing to the gravitational vector, which canbe regained, however, in a later fusion step (Section 2.1.3.3).

The complete biological model of canal-otolith fusion is shownin Fig. 2A. It includes in addition to the just described complemen-tary filter solution (steps I and II) the estimations of attitude (stepIII) and translational acceleration (step IV). In step III, x* is used toobtain the estimate of head attitude g using a ‘bootstrapping’ pro-cedure (see Section 2.1.3.3). In step IV, g is subtracted from the ori-ginal otolith signal aGi to estimate translational acceleration, ai. Themodel is thought to represent a stand-alone vestibular sensor forvarious functions, i.e. in addition to reactive body stabilization alsofor stabilization of gaze in space (e.g. eye, vestibulo-ocular reflex),perceptual spatial orientation, and autonomic nervous functions(Wilson and Melvill Jones, 1979). It served to construct our artifi-cial vestibular sensor (Section 2.1.3.3 and Fig. 2B).

1.2. Inter-sensor interactions and disturbance estimations

Our psychophysical studies suggested a number of sensoryinteraction rules (overview Mergner, 2002). One rule concernsthe fact that vestibular information is not only used for controllingthe head, where the sensor is located, but also other body parts byway of interaction with proprioceptive signals. These interactionsprovide for the coordinate transformation that is necessitated byhead-trunk movements, for example, if the vestibular signal from

Fig. 2. Canal-otolith fusion model. (A) ‘Neurological four-neuron’ version as suggested by Mergner and Glasauer (1999). Note that steps I and II correspond in essence toFig. 1B. They yield an estimate of angular velocity x. Step III yields an estimate of attitude g by ‘bootstrapping’ it by means of the improved x signal, x*. Step IV derives anestimate of translational acceleration ai by subtracting g from aGi. (B) Full version of model in A, which can be used for direct realization in simulation program. Note that theideal integrator (step III) is replaced by a leaky one (see text).


the head is to be used for stabilizing the trunk. The procedure canbe extended further down to the legs, feet, and ultimately to thebody support surface, yielding an estimate of platform motion(Mergner et al., 1997; compare above, foot-space tilt signal,fs = bs � bf).

Noticeably, subjects are not aware of these transformations. Forinstance, during passive motion, they directly attribute their sensa-tion of self-motion to the support-space motion. Thus, for con-scious perception, the external stimulus is reconstructed byinternal processing. This concept of internal reconstruction canbe extended to disturbance estimation in sensorimotor control.As mentioned above, we used it to replace the postural reflex con-cept, in which the ‘‘raw” sensory signals would stabilize the body.In the new concept, the sensory signals are used in the first place toestimate the disturbances acting on the body and these estimatesthen orchestrate the rejection of the disturbances. The conceptwas implemented into a model of human stance control.

1.3. Multisensory interaction model of human stance control

Our stance control model (Mergner et al., 2003; Maurer et al.,2006; Fig. 3A) considers anterior–posterior, a–p, body excursionsabout an axis through the ankle joints (‘inverted pendulum simpli-fication’; head, arms, trunk, and legs lumped together in one body).Control is based on a PID controller (proportional, integral, andderivative) and an ankle joint proprioceptive feedback loop (whichis transformed into a body-space loop; see below). It provides reac-tive stance stabilization using disturbance estimates and compen-sations. Estimation is performed through sensors and sensoryinteraction and compensation is performed at the level of body-space kinematics.

For the disturbances estimations, the multitude of externalevents that may have impact on the body’s stance stability aredecomposed into four external disturbances: support tilt and trans-lation, force fields, and contact forces. The three sensors describedabove (Section 1.1.2; proprioceptive, vestibular, force) suffice toperform the decomposition. We hold that this form of disturbanceestimation and rejection, although very simple, provides the model

with a remarkable flexibility in face of the multitude of externalsituations (see Section 1.4), with robustness in face of internalnoise, inaccuracies, and failures, and includes stabilization of voli-tional movements.

The model shown in Fig. 3A contains three of the four distur-bance estimates: external contact force (external torque estimate,bT ext), force fields such as gravity (gravitational torque estimate,bT grav), and foot (support)-space tilt (cFS; support translation is omit-ted for simplicity). Note that all three estimates involve vestibularinput. For brevity, several details of the model are exiled to Appen-dix A (also Maurer et al., 2006).

1.4. Sensory re-weighting in the model

The model in Fig. 3A covers autonomously (i.e. without externaladjustments) most of the experimental observations we havemade so far in humans (Mergner et al., 2003, 2005, 2009; Maureret al., 2006; Cnyrim et al., 2009). This includes that relative weightof the sensory feedbacks automatically adjusts to the stimulus sit-uation (automatic sensory re-weightings). One example is that smallbody-space excursions are less compensated than large ones,which the model accounts for by thresholds of the disturbanceestimations.

Another example is the initially described estimation offoot(support)-space tilt cFS that we now describe in more detail(Fig. 3B). It is derived by combining the body-space angular veloc-ity signal _bs (from the vestibular x signal) with the proprioceptivebody-foot velocity signal _bf (foot-space velocity, _fs ¼ _bs� _bf ). Afterfurther processing (gain factor, velocity threshold, mathematicalintegration), it is fed as fs signal into the feedback loop. With sta-tionary support, the fs signal is zero and the bs0 feedback signal isdetermined by the proprioceptive bf signal. This changes duringsupport tilt, when _fs becomes supra-threshold. Then bs0 becomesdetermined by the vestibular _bs signal, because the _bf constituent(note negative sign) tends to cancel bf.

To didactically illustrate the effect, a sine wave is injected intothe vestibular _bs signal, making it identifiable (Fig. 3C). Trace ashows _bs0, the differentiated bs0 signal (result of summing fs and

Fig. 3. Human stance control model. (A) Model architecture. It comprises the following four parts: ‘Controller’, passing error between set point signal ‘Voluntary Body-SpaceLean’ and negative sensory feedback signal through PDI constants (proportional, derivative, integral). ‘Physics/Biomechanics’, represented by human or robot actuated atankle joints and subjected to External Disturbances g, Text, and FS (gravity, external torque, and foot support-space tilt, respectively). ‘Sensors’: VEST, vestibular system;FORCE, force cues; PROP, joint angle and angular velocity proprioception. ‘Disturbance Estimations’, consisting of proprioceptive feedback loop, into which the disturbance

estimates bT ext , bT grav, and cFS are fed. (B) Model detail, used to explain the estimation of cFS (box contains gain element, detection threshold, and integration). TA, ankle torque

(internal representation: bT A); BF and _BF, body-foot angle and angular velocity (bf and _bf ); _bs, body-space angular velocity (after processing, body-space position signals are bs0

and bs0 0); fs and _fs, foot-space angle and angular velocity; Dt, time delays. (C) Simulations, demonstrating sensory re-weighting during tilt (see text). FS, foot-space angle; BS,body-space angle; _bs0 is derived from bs0 (in panel B).


bf). It shows the sine waves only during the time of the dynamicfoot(support)-space tilt (FS). As a crosscheck, when adding the sinewave to the proprioceptive signals (to _bf and, in integrated form, tobf), it shows up in the _bs0 signal during the static platform tilt and isstrongly diminished during dynamic tilt (trace b).

The threshold in the fs pathway is not only instrumental for thesensory re-weighting, but can also be viewed as a noise optimiza-tion that copes for the fact that the vestibular signal is noisier thanthe proprioceptive signal (see Mergner et al., 2001). Consider inFig. 3C the sine waves on _bs to be noise; it becomes effective onlyduring dynamic tilt (trace a). In the behaviorally prevailing situa-tion of stationary support it is suppressed. In stance control, thisis not quite realistic, though. Total suppression of noise appearsnot to be a goal; humans always show some spontaneous sway.It helps preventing sensory adaptation, nutrition of joint cartilage,etc. The sources of the sway are not exactly known.

The above outlined biological background (Sections 1.1–1.4) isembodied in the second half of this study into our ‘stance control’robot, PostuRob, for comparing its performance in vestibular-ableand vestibular-loss states with that of humans. Following the Oc-cam’s razor principle (Gibbs and Sugihara, 1996/1997) that callsfor parsimony, we focuse, as in our biological work, on sensorsand sensory interactions and simplify biomechanics and actuatoraspects.

2. Methods

2.1. Biologically inspired technical sensors

The above described biological sensor concepts (Sections 1.1.2–1.1.3) were embodied into the following robot sensors.

2.1.1. Proprioceptive joint angle sensorA rotatory potentiometer fixed to the robot’s ankle joint axis

provided a joint angle signal, its electronic differentiation an angu-lar velocity signal.

2.1.2. Force sensorPressure (normal force) signals under forefeet and heels of the

two legs were electronically collected using compression load cells(Model 8526, Burster, Germsbach, Germany). From these signals,we calculated a–p COP shift, low passed filtered the signal (cornerfrequency, 0.8 Hz), and used it as a measure of joint torque, TA

(compare Section 1.1.2.2 and Fig. 3B). Note that TA contains threecomponents: Active torques counteracting (i) external contactforces such as a pull or a push having impact on the body (Text),(ii) the gravitational torque of the centre of mass (COM; Tgrav;proportional to BS angle, see Appendix B), and (iii) the body’seigen-inertia (proportional to BS angular acceleration). Note that


the latter two components are eliminated in the estimation of Text,using vestibular signals (Maurer et al., 2006). Focusing the presentarticle on the vestibular sensor, the role of force sensors for stancecontrol is postponed to a future article, but some aspects related tovestibular-loss balancing are considered in Discussion (Section4.3).

2.1.3. Artificial vestibular sensor and its transducers2.1.3.1. Gyros. As a canal analog, we used gyros (ADXRS401, AnalogDevices, Norwood, USA; frequency range, DC = 0 Hz to 300 Hz).They were implemented on an electronic circuit board (only a–pplane was considered). To allow experimentally variation of thesignal-to-noise ratio, we used 16 gyros and derived from them fouroutput signals corresponding to the average of n = 1, 2, 4, or 16gyros (each doubling of n reduces stimulus-noise ratio by half, gi-ven the noise sources are independent of each other).

To mimic the canals, the gyro signal was first order high-passfiltering with the ‘canal time constant’ (5 s; corner frequency,0.032 Hz) and with an adaptation time constant (100 s; corner fre-quency, 0.0016 Hz) and fed through a velocity threshold (see Merg-ner, 2007; latter two steps not shown in Fig. 2A and B). Calibrationsand tests were performed using a motion platform (Section2.2.3.1). Minor technical sensor imperfections were consideredirrelevant (velocity-related translation response, �1.6[�/s]/[m/s];gravity response, �0.5[�/s]/g).

2.1.3.2. Accelerometers. As otolith analogs we used translationalaccelerometers (ADXL203, Analog Devices, Norwood, USA). Two ofthem were mounted, perpendicular to each other, on the electronicboard. They covered the range of ±1.7 g with a sensitivity of1000 mV/g (band width, <60 Hz). Low pass filtering was used to re-duce high-frequency noise (4th order Bessel filter; corner frequency,10 Hz). This allowed us to compute from the output aGI the first deriv-ative, jerk _aGI. Because of the filtering, the jerk signal had to be gain ad-justed to yield a correct cross product (processing step I in Fig. 2A).Static calibrations were performed with respect to earth vertical.

2.1.3.3. Gyro-accelerometer (canal–otolith) fusion. The gyro andaccelerometer signals were combined in a Matlab/Simulink model(MATLAB�, The MathWorks Inc., Natick, MA, USA). It is shown inFig. 2B. Compared to the schematic model in Fig. 2A, it carries anextension in step III, the g ‘bootstrapping’ part (the bootstrappingmetaphor literally means that one is pulling oneself on the ownbootstraps out of a swamp, for instance). It was added to allowfor a hardware realizations of the sensor, where internal noise,drifts, etc. may lead to large errors upon the integration in stepIII. We offset these effects by providing for continuous referencingof g to space. This is achieved by giving the integrator a ‘leak’ (de-cay time constant, T = 20 s) and filling this by adding a complemen-tary fraction of the ‘raw’ aGI signal to the integrator input (gainG = 1/20). Due to the integrator’s low pass filter properties, essen-tially only the gravity component of aGI is added. This updating wasinspired by a model of centrifuge experiments (Glasauer, 1993;also Mayne, 1974).

2.2. Structure and control of robot

2.2.1. RobotWe built a biped humanoid robot for stance control simulations,

using anthropometric measures and the ‘inverted pendulum sim-plification’ (‘‘PostuRob”; picture in Fig. 3A; details in AppendicesA and B; see also www.uniklinik-freiburg.de/neurologie/live/fors-chung/sensorfusion/PostuRob.html). The robot’s structure consistsof an aluminum frame where all body segments above the anklejoints are lumped into one ‘body’ segment and where the two feetare freely standing, while being pressed to the ground by the body

weight and held by foot sole-ground friction. Each leg carries frontand back pneumatic ‘muscles’ with ‘tendons’ (springs) for actuat-ing the ankle joint. Actuator (muscle) performance was essentiallyideal in the present context (see Appendix B).

2.2.2. Stance controlThe signals from the mechatronic sensor transducers (see Sec-

tion 2.1.3.1–2.1.3.3) were input to, and commands for actuatorcontrol were output from a real time PC (embedded-PC MOPSlcd7,Kontron, Deggendorf, Germany) working under the control of ahost PC. From the model in Fig. 3A the parts ‘Disturbance Estima-tions’ and ‘Controler’ were downloaded to the real time PC fromthe host PC using Simulink xPC Target. The system’s dead timeamounted to approximately 40 ms (mechatronic, 20 ms; muscle–tendon system, 20 ms; see Appendix B).

2.2.3. Testbed, experimental procedures, human subjects2.2.3.1. Testbed and stimuli. Experiments were performed in a neu-rological laboratory for postural control. Same hardware and soft-ware were used for humans and robot. These were standing on acomputer controlled 6D motion platform. It was tilted in a–p plane(x–z plane) under the control of a PC and custom-made software(Maurer et al., 2006). Visual space cues were absent. In additionto normal tilt, we applied body sway referenced platform (BSRP)tilt, where body-space angular position is measured and fed intothe input of the platform control together with the tilt command.The result is that the platform is coupled to body position duringspontaneous or evoked sway, thereby effectively opening theirproprioceptive feedback. In the general relation between foot-space (FS), body-space (BS), and body-foot (BF) angles(FS � BS = �BF), BS referencing to FS leads to FS = BS with the con-sequence that �BF = 0� on uncommanded platform, while �BF = FSwith externally commanded FS tilt. Human stance control withoutvision is known to depend critically on vestibular function in thiscondition (Nashner et al., 1982).

The following external stimuli were applied:

(1) Sine wave stimuli over a frequency range of 0.05–0.8 Hz(0.0125–6 Hz in software simulations). Tilt amplitude wasvaried from ±0.5� to ±4.0� (peak-to-peak, pp 1�–8�).

(2) Six cycles of a pseudorandom ternary sequence stimulus(PRTS: random sequence of 0, �n, +n; peak displacementsdeclining strongly, and velocities little over the frequencyrange of 0.0125–2.5 Hz; for details, see Peterka, 2002; anexample of a stimulus position curve is given in Fig. 4C).Each cycle of the PRTS stimulus lasted 60.5 s. Peak stimulusamplitudes were the same as in (1).

Stimulus application was in space (laboratory) coordinates. Inour software simulations (performed in Matlab/Simulink) thestimuli were in space coordinates and quaternions were used totransform them into sensor coordinates.

2.2.3.2. Data acquisition and analyses. Body sway measuring devicescomprised a force-transducing platform for recording COP (Kistler�,platform type 9286, Winterthur, Switzerland) that was mounted onthe motion platform. Furthermore, an optoelectronic device wasused for measuring shoulder, hip, and platform positions in space(Optotrak 3020�, Waterloo, Canada). Its 3 cameras tracked activemarkers, three of which were fixed on rigid plastic triangles thatwere attached to hip, shoulders, and platform. The triangles’ 3Dpositions were collected with a 200 Hz time resolution and usedto calculate on-line 3D translational and 3D angular positions. Fromfurther data processing we obtained platform-space angle as well asCOM-space angle as described by Winter (1990; body-space angle,BS) and shoulder-hip angle. In Results, we focus on BS.

http://www.uniklinik-freiburg.de/neurologie/live/forschung/sensorfusion/PostuRob.html

http://www.uniklinik-freiburg.de/neurologie/live/forschung/sensorfusion/PostuRob.html

Fig. 4. Gyro noise and vestibular sensor transfer characteristics. (A) Gyro noise represented as power spectral density (PSD) plots. Comparison across averages from n = 1, 2, 4,and 16 gyros, as indicated (note linear scales). (B) Gyro noise PSD plots (one gyro, signal integrated) showing effect of high-pass filtering (HP) and velocity thresholds(Threshold), as indicated. (C) Response of vestibular sensor (g, here equivalent to bs signal in Fig. 3B). PRTS tilt with an amplitude of pp 4� (inset); note that stimulus and gessentially coincide. Gain, phase, and coherence function are plotted over stimulus frequency (0.0165–2.23 Hz). They are essentially ideal (phase values are corrected fordelay in testbed). Note that coherence is not considerably corrupted by noise. Data from centric and eccentric rotations are superimposed and coincide (see text).


Omitting the first response in each experimental run, the datawere analyzed off-line using custom-made software programmedin MATLAB. The BS data was subjected to a spectral analysis usinga discrete Fourier transform. Fourier coefficients at stimulus fre-quency were used to calculate gain (peak response amplitude di-vided by peak stimulus amplitude) and phase (relative timingbetween response and stimulus). The BS gain measures have nounits with tilt stimuli (both, BS and tilt are in �). Zero gain(G = 0) indicates that the stimulus evoked no body excursion inspace (ideal stimulus compensation), while G > 0 indicates bodyexcursion into tilt direction (under-compensation; over-compen-sation did not occur). The degree of BS-tilt correlation as a func-tion of frequency was calculated in the form of coherencefunctions from the smoothed power spectra (see Maurer et al.,2006).

2.2.3.3. Human subjects. Six vestibular-able adults participated(age, 21–55 yrs, 3 females and 3 males; no history of vertigo andbalance problems) and two vestibular-loss patients (23 and25 yrs; diagnosis according to case history and neuro-otologicaltests; no other neurological symptoms apart from hearing prob-lems). In compliance with the Helsinki declaration (1964), all sub-jects gave their informed consent. The study was approved by thelocal Ethics Committee of the Freiburg University Clinics.

Stance control in subjects occurred essentially as ‘ankle strat-egy’ (trials with considerable hip or knee bending and forefoot orheel lift-off were discarded). In control experiments, hip and kneebending was mechanically prevented by a light-weight backboard;tilt responses in the low to mid-frequency range essentially resem-bled those without backboard (minor deviations in the high-fre-quency range).


3. Results

3.1. Response characteristics of vestibular sensor

3.1.1. Bio-inspired canal systemFig. 5A gives an example of a gyro raw signal obtained during a–

p sinusoidal tilt (pp 1�, 0.2 Hz), which shows considerable noise(panel a). Velocity-position integration yielded a signal with verystrong drifts. To obtain the illustration in panel b, we had to highpass filter it and started with the human adaptation time constant,T = 100 s (Mergner, 2007; we conceived that already the trans-ducer mechanisms show some hysteresis and thus requiring adap-tation). Further high-pass filtering with the canal time constant of5 s yields a more stable response (panel c). Two additional biolog-ically inspired steps were used to further improve the gyro signal.One was an averaging of several gyro signals. Fig. 4A shows noisepower spectral density (PSD) plots for averages across n = 1, 2, 4and 16 gyros. Note that the PSD maxima are located at low fre-quencies and become clearly reduced with increasing n (curvesrepresent 3rd order polynomials). The second step was to intro-duce a detection threshold (‘velocity threshold’, since the gyro out-puts angular velocity). Fig. 4B gives noise PSD plots of an integratedgyro signal (n = 1), showing the effects of the above two filters anda 0.025�/s threshold and, alternatively, of a 0.1�/s threshold.Noticeably, from the raw signal to the latter threshold, noise is re-duced by almost two orders of magnitude, more at low than at highfrequencies, using simple passive (non recursive) processing.

3.1.2. Bio-inspired otolith systemFig. 5Ba–c illustrates an accelerometer response evoked by pp

8� (±4�), 0.05 Hz tilt. Panel Ba gives the input stimulus in terms

Fig. 5. Hardware sensor signals and their fusion (time response data). (A) Gyro signal uposition integration of xCan yields pronounced drift that is attenuated by high-pass filtecanal time constant T = 5 s). (B) Sensor fusion of gyro and accelerometer signals during sinJerk derived from accelerometer signal. (c) Angular velocity signal xOto derived from cro(canal time constant, T = 5 s). (e) Fused angular velocity signals (step II, x*). (f) Estimate

of angular position and velocity. The corresponding accelerometeroutput aGi showed relatively little noise (similar to stimulus posi-tion signal; not shown), whereas its derivate shows pronouncedhigh-frequency noise ( _aGi, jerk; panel b). The noise is still reflectedin the angular velocity signal resulting from the cross product (stepI, Fig. 2A), although somewhat reduced by the complementary lowpass filtering of xOto (panel c).

3.1.3. Canal-otolith fusion and noise reductionIn Fig. 5Bc–e, xOto (panel c) and xCan (panel d) are summed,

yielding x* (panel e). The x* signal was used for the robot’s esti-mation of cFS ( _bs signal), despite its noise. Since low frequencynoise components were strongly reduced by the gyro filtering,the integration of _fs0 in the estimation (see Fig. 3B) did not leadto major drifts of cFS and the robot’s body excursions. And, it isthe latter integration that suppresses high-frequency noise in thefs signal.

The x* signal was furthermore used for the estimation of atti-tude, g (bs signal; panel f), which essentially equals stimulus posi-tion in panel a. How to explain its successful noise reduction? Thefact that bs shows no considerable drifts owes to the high-pass fil-tering of the gyro signal. In addition, filling the leak in the ‘boot-strapping’ integration (step III in Fig. 2B) by direct otolith inputcontributes to drift reduction. Finally, high-frequency noise com-ponents of g are reduced by the low-pass filtering after step Iand by the integration in step III.

3.1.4. Canal-otolith fusion and transfer characteristicsFig. 4C shows the results obtained for the vestibular sensor

when this was subjected to the a–p PRTS tilts on the motion plat-form. Shown is the attitude estimate g in terms of gain, phase, and

3

pon sinusoidal rotation (0.2 Hz, pp 1�). (a) Raw velocity signal (xCan). (b) Velocity-ring, T = 100 s decay time constant. (c) Result of further high-pass filtering (2nd HP,usoidal vertical rotations at 0.05 Hz, pp 8�. (a) Stimulus (angle u and velocity _u). (b)

ss product (step I in Fig. 2A) and low pass filtering. (d) High pass filtered gyro signalof sensor angle with respect to gravity, attitude, g (output of step III).


coherence curves. Gain and phase are essentially ideal. Similarcurves were obtained for x, while the ai estimates were zero(which also is correct). Noticeably, the results obtained for eccen-tric (1 m) rotations were essentially identical to those shown inFig. 4C. Note furthermore, that also the coherence function isessentially ideal, which indicates that sensor noise was small overthe investigated frequency range.

Corresponding tests with a–p translational acceleration (hori-zontal excursions, pp 14 cm) yielded approximately veridical ai

estimates above 0.2 Hz and underestimation at lower frequencies(not shown; compare Appendix C, and Fig. 10B). Tilt illusionsaffecting x and g were negligibly small.

Since the sensor was restricted to the a–p plane and since theplatform motions were limited in their dynamics, we performedin addition 3D software simulations of x, g, and g using sinusoidsover a frequency range of 0.0125–6 Hz (see Appendix C, andFig. 10). The results confirmed the validity of the fusion principlesin 3D, showed that the tilt illusion upon translational accelerationis small, and characterized the frequency characteristics of transla-tional acceleration. Furthermore, they showed that combination ofrotation and translational acceleration during eccentric rotationdoes not corrupt x and g to a considerable degree. This is achievedwith the time constant of 5 s in the complementary filter solution.Smaller time constant values considerably worsened the responsecharacteristics. Longer time constant values, in contrast, had noconsiderable effect on the response characteristics (but allow formore gyro drift at the output). These findings support our notionthat the biological 5 s time constant represents an optimum (opti-mal compromise) within the considered constraints.

3.2. Tilt responses in vestibular-able robot and humans

Fig. 6A shows gain, phase, and coherence curves of the robot’sresponses to the a–p PRTS tilt stimulus (pp 2�). The data of sixhealthy subjects are superimposed. Their gain and phase curves

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Fig. 6. Frequency characteristics of tilt responses, comparing robot with humans (no vis(gain G = 0, ideal compensation; G = 1, body-space excursion = support-space tilt). (A) vtolerated; not shown). (B) Vestibular-loss robot and humans (n = 2, one trial each; larger sis better than in vestibular-able humans.

resemble closely those of the robot. Gain tends to be below unity,meaning that body excursions were somewhat smaller than plat-form excursions. Larger tilt stimuli (up to pp 8�) yielded lower gainvalues and smaller ones yielded larger gains in humans and the ro-bot (not shown; gain non-linearity, see Peterka, 2002; Maureret al., 2006). Coherence of human and robot responses was closeto unity at low frequency and decreased in humans above 0.1 Hzand in the robot above 1 Hz.

Fig. 7A shows corresponding response plots of humans for tiltwith body sway referenced platform, BSRP. Gain and phase curvesare similar as before, but the drop of coherence above 0.1 Hz is morepronounced. The similarity in gain is surprising, since software sim-ulations indicated larger gains (excursions). The robot’s BSRP tilt re-sponses suggested a solution (Fig. 7B). They did show the expectedlarger body excursions. To make them ‘human-like’, i.e. smaller, wehad to increase the loop gain (a gain factor of feedback loop after allsumming junctions, not shown in Fig. 3A; increase from 1 in theprevious experiment via 1.3, 1.5 to 1.65; Fig. 7B). This also improvedcoherence that always was strikingly better than in humans.

Conceiving the possibility that the difference in coherence be-tween robot and humans may be due to smaller internal noise inthe robot, we chose a very noisy gyro and observed indeed somedeterioration in the coherence of the robot’s tilt responses at low fre-quency, and some further deterioration with BSRP tilt (not shown).Furthermore, worsening of coherence mainly in the mid- to high-frequency range was obtained by injecting white noise into the pro-prioceptive bf signal and the actuator command signal (not shown).We refrained from any such tentative explanation, however, since itwas foiled by the finding of an almost robot-like good coherence inthe responses of the vestibular-loss humans (below and Fig. 6B).

3.3. Effects of vestibular loss

Vestibular loss leaves humans with only the proprioceptive sen-sor and the force sensor when balancing with eyes closed on the

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ual input). PRTS stimulus, pp 2�. Gain, phase, and coherence curves over frequencyestibular-able robot and humans (n = 6, two trials each; stimuli up to pp 8� weretimuli were not tolerated). Note that coherence of robot and vestibular-loss humans

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Fig. 7. Frequency characteristics of tilt response on body sway referenced platform, BSRP. Otherwise as in Fig. 6. (A) Vestibular-able humans. (B) Vestibular-able robot. Loopgain (LG) had to be enhanced as indicated to make gain and phase curves human like.


tilting platform. All three disturbance estimates (bT grav, bT ext, cFS),normally receiving vestibular input, are invalidated (noticeably,they likely are restored upon eyes opening, though; see Section4.3). A model that comprehensively covers the findings on humanvestibular-loss stance control is still missing (Mergner et al., 2009).We, therefore, resorted for the robot to a preliminary model that isable to describe the tilt responses in a previous study (Schweigartand Mergner, 2008; also Section 4.3 and Fig. 9). With tilt stimuli inthe steady state, sensory feedback consists of the proprioceptiveloop with enhanced loop gain (from 1.0 to 1.5), into which low passfiltered force cue signals are fed (switch S in Fig. 9 closed; see Sec-tion 4.3). Note that the proprioceptive loop performs body-supportstabilization, while body-space stabilization requires addition ofthe force cues.

With this vestibular-loss stance control implemented into therobot, its responses to the PRTS tilt exhibited abnormally largegains in the low to mid-frequency range (compare Figs. 6B with6A). Yet, the gain and phase curves resemble those of the two ves-tibular-loss humans.

Surprisingly, the coherence functions of the vestibular-loss hu-mans were clearly better than those of vestibular-able humans, al-most resembling the robot data. Does this indicate that thevestibular system is the major source of noise in the human stancecontrol system? Not necessarily, since there are other possibleexplanations. For instance, a factor may be the abnormally large re-sponses of the vestibular-loss subjects (which may yield a bettersignal-to-noise ration). Also other factors can be imagined suchas an increase in passive stiffness by way of muscle co-contraction,overriding the non-linear reflexive stiffness. Thus, interpretation ofthe coherence function remains equivocal.

When increasing amplitude of the PRTS tilt above pp 2�, the ves-tibular-loss robot and humans lost balance, unlike the vestibular-able robot and humans. This indicates that vestibular loss is asso-ciated with a reduced working range of the balancing. Further-more, vestibular-loss subjects and robot were unable to stand on

BSRP (compare Nashner et al., 1982). This is similar to their inabil-ity to stand on compliant surfaces such as foam rubber (compareDiscussion, Section 4.3).

4. Discussion

Recent progress in understanding human stance control owes tothe use of engineering and mathematical tools (e.g. Johansson andMagnusson, 1991; Fitzpatrick et al., 1996; van der Kooij et al.,1999; van der Kooij and de Vlugt, 2007; Alexandrov et al., 2001;Peterka, 2002; Kiemel et al., 2002; Mergner et al., 2003; Maureret al., 2006). In this research, our laboratory focused on the roleof sensors and sensory interactions, with an emphasis on the ves-tibular system (Maurer et al., 2000, 2006; Mergner et al., 2003,2005, 2009; Blümle et al., 2006; Schweigart and Mergner, 2008).This allowed us to construct a bio-inspired artificial vestibular sys-tem and to implement it into the human-inspired stance control ofa robot. Comparing robot with human stance performance in thevestibular-able versus vestibular-loss state served as a proof-of-principle test for the vestibular sensor and the stance control mod-el. Based on the findings, we recommend a vestibular sensor also forhumanoids.

Human stance control appears to represent a conglomeration ofsubsystems, each relatively simple, limited, and imperfect – yet byway of smart and flexible combinations and under ecological per-spectives functionally adequate and powerful. Already the vestibu-lar sensor is an example of such an imperfect, yet efficient andmulti-purpose device that appears to represent a good compro-mise for both biological and physical constraints.

4.1. Vestibular sensor

What are the arguments suggesting that the vestibular systemrepresents such a good or even optimal solution? Our first argu-ment is an indirect one. Form and dimensions of this system have


essentially been maintained over hundreds of millions of yearsduring phylogenesis and are similar in extant vertebrate speciesacross body weight variations of several orders of magnitude(Spoor et al., 2002; Clarke, 2005). Focusing mainly on the canal sys-tem, previous work related its dimensions to the need for certainfrequency response characteristics and attributed minor differ-ences across species to differences in their dynamic behaviors(Thoss and Schwartze, 1975; Clarke, 2005). The dimensions werefurthermore considered under the aspect of optimal sensor sensi-tivity (Squires, 2004). Our work with the artificial vestibular sys-tem led us to postulate, more specifically, that the canaldynamics (time constant), and ultimately the canal dimensionsdetermining it, reflect an optimal solution in the context of thecomplementary filter fusion. It reflects the need to reduce low fre-quency noise (fluctuating drifts) that becomes emphasized by theacceleration-velocity and velocity-position integrations in the ca-nal system. This, in turn, needs to be reconciled with other con-straints such as the tilt illusion effect (see Section 1.1.3) and,related to it, a corruption of the vestibular outputs by tangentialand centrifugal accelerations during eccentric rotations.

This notion was corroborated by our hardware and softwaresimulations. In these, we proceeded from the biological time con-stant of 5 s. This yielded considerable reduction of drifts from thecanals/gyros and, less relevant, of high-frequency noise from theotoliths/accelerometers. Furthermore, _bs and bs signals (takenfrom x and g of the robot’s vestibular sensor) showed essentiallyideal frequency response characteristics. This also applies to eccen-tric rotations. Even an increase of eccentricity from 0.1 to 10 mworsened x only marginally (this in the mid- to high-frequencyrange, which may call for slower head rotations in very large ani-mals/robots). However, considerable corruption occurred whenwe shortened the time constant. In contrast, a prolongation yieldedno considerable effect (but tends to worsen the drift reduction).Together, this suggests that the 5 s is an optimal compromise be-tween drift reduction versus transfer characteristics.

Ecological feasibility is given despite remaining imperfectionsin noise reduction and decomposition of aGi. This also applies tothe non-ideal frequency characteristics of ai (see Appendix C). Notethat underestimation of translational accelerations at low fre-quency has only little impact on body stability, because the accel-erations tend to be small. For other vestibular functions such asself-motion perception, however, underestimation of slow transla-tions and rotations in the horizontal plane calls for compensationby other sensors such as vision.

Drift reduction could be achieved not only by filtering, but alsoby a velocity threshold (Section 3.1.1; Fig. 4B). It appears not to bea feature of the stand-alone sensor, though, but rather one of ves-tibular functions. In human stance control, we estimated thethreshold of the _bs signal (Fig. 3B) to amount to 0.17�/s (Maureret al., 2006). This is different from the 1�/s threshold in human ves-tibular self-motion perception, where it appears to serve completesuppression of autokinesia (illusion of self-motion that can occur atrest when visual cues are unavailable; Mergner et al., 1991). On theother hand, for the vestibulo-ocular reflex we found no evidencefor a velocity threshold whatsoever (Schweigart and Mergner,unpublished observations). Note that there occur also other modi-fications of vestibular signals at the level of the functions. Anexample would be the central time constant prolongation of thehorizontal rotational vestibulo-ocular reflex (VOR; �20 s, ‘behav-ioral time constant’; Wilson and Melvill Jones, 1979).

Our knowledge of the human canal-otolith fusion and vestib-ular functions is still limited, though. Researchers in biology, clin-ics, and aviation and space medicine have performed manydifferent tests to make inferences on the vestibular transducersand how the brain fuses them. An example would be the con-stant-velocity ‘off-vertical axis rotation’ test where the sensor

fails more and more over time (e.g. Denise et al., 1988; clinicalotolith test, Wiener-Vacher, 2001). The functional implication inthe present context with the robot is negligible, however, sincethese stimuli appear to have little behavioral relevance (they oc-cur only rarely, they tend to be actively avoided, etc.). Most ofthese errors can be related to those we find with the proposedcanal-otolith fusion.

Interestingly, the vestibular system, i.e. the sixth physiologicalsense or equilibrioception, was discovered in modern biologicalscience only about 160 years ago. This is possibly related to the factthat it registers gravito-inertial force fields, which are not visibleand not intuitive (yet, automatically and rapidly learned and there-with removed from the brain’s conscious experience), unlike con-tact forces such as a push against the body. The phylogeneticdevelopment of the sense is likely related to the fact that ourancestors lived in water, where gravity, because of buoyancy, andactive movements yield less reliable and weaker, if any, reactionforces than on land. Reasons for the persistence of the vestibularsensor on land remain to be named (see Section 4.3).

4.2. Vestibular-able disturbance compensation

Previous psychophysical findings suggested to us that humansuse vestibular information to internally reconstruct external phys-ics having impact on their bodies, which led to a model of reactivestance stabilization that is based on the principle of disturbancecompensation (Mergner et al., 1997, 2003; Mergner, 2004, 2007;Maurer et al., 2006). Its architecture is based on on-line multi-sen-sory disturbance estimations, combines the estimates for compen-sation in a common kinematic feedback that uses a PID controller,allows for low loop gains and thereby tolerates relative long delaytimes, and can be commanded to perform voluntary movements.

Here we show that the model’s embodiment into a humanoidproduces human-like responses to support surface tilt with andwithout BSRP, which corroborates our stance control concept.The robot simulations inspired us when confronted with severalunforeseen problems. An example reported in Results (Section3.2) was the adjustment of the robot’s loop gain with the BSRP tiltsin order to obtain human-like response gain characteristics (a sen-sory re-weighting issue, see Section 4.4). Largely unresolved re-mained the low coherence of the vestibular-able humanresponses as compare to vestibular-loss humans and the robot, atask for the future.

The vestibular-proprioceptive foot-space estimate cFS is instru-mental for reactive body-space stabilization upon support surfacetilts. It enables, in addition, the system to tolerate compliant sup-port surfaces and, as we expect for walking, also rough territory.As demonstrated in Fig. 3C, the estimate leads to an automatic sen-sory re-weighting that emphasizes vestibular input. Recent exper-iments with transient tilts (Schweigart and Mergner, 2008)suggested furthermore that there exists, in addition, a control ofcFS by way of a switch-like mechanism. A representative findingis shown in Fig. 8Aa; the subject initially keeps the body alignedwith the tilting platform, which represents a body-support stabil-ization, before starting body-space stabilization (arrows). The sub-ject furthermore shows a slow compensation of the large static tilt(which makes his dynamic balancing on inclined support similar tothat on level support). When accounting in the robot for these find-ings by adding to it (i) a ‘sensory re-weighting switch’ (cFS path-way), and (ii) a ‘tonic excursion limiter’ (parallel to bTgrav, detailsin Schweigart and Mergner, 2008), it showed human-like re-sponses also to the tilt transient and static tilt (Fig. 8Ab).

Interestingly, the cFS switch allows humans and robot to modifytheir control strategy. This is demonstrated in Fig. 8C, whereswitching cFS off changed the robot’s body-space stabilization intoa body-support stabilization, yielding a behavior that humans

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Fig. 8. Complementary findings with support surface tilt (A–E) and pull (F). (A) Stimulus onset response, indicated by arrow, reveals switch from body-support to body-spacestabilization (a, human; b, robot); in addition, static tilt is slowly compensated. (B) Similar experiment as in A, but vestibular-loss human (a) and robot (b; note smallerdynamic stimulus than in A). (C) Change in control strategy; disabling of cFS in robot changes control from body-space to body-support stabilization. (D) Automatic loadcompensation; tilt responses in robot are hardly affected when body weight is enhanced from 62 to 72 kg. (E) Tilt compensation continues during voluntary body lean (todetermine lean speed, a step signal is passed through raised cosine velocity function v(t) = �Af � cos(2p � f � t)+A � f [f, dominant frequency; t, time] and integrated; under-compensation of feedback loop is coped for by inverse dynamics). (F) Lean responses to pull stimuli without and with Next compensation as indicated. Position calibration inBa refers to all curves apart from pull stimulus in F.


show when instructed correspondingly. Generally, we conceivethat also the other disturbance estimates can voluntarily beswitched on or off (e.g. bTgrav off, when we allow for a fall into water,etc.). Since the disturbance estimates are explicit, they may fur-thermore be learned or communicated and represent a basis forcognitive interference. The whole control mechanism may still becommanded to perform voluntary lean movements, and this evenduring concurrent disturbance compensation (Fig. 8E).

Compensation of inertial effects upon translational accelerationand of force fields other than gravity (centrifugal and Coriolisforces, also measured by vestibular sensor) in robot versus humansremain to be shown in the future. This also applies to contact forcesand their estimation (bText) and compensation, for which we givehere only an example in Fig. 8F. bText also helps in gravitational loadcompensation (upon changing the robot’s body mass, Fig. 8D; hu-man study and explanation, Cnyrim et al., 2009).


4.3. Consequences of vestibular loss

Vestibular-loss robot and humans, being left with propriocep-tion and force cues, are unable to balance without external refer-ence on BSRP (and compliant surfaces such as foam rubber) andshow a reduced working range for support tilt (also Mergneret al., 2009). What is restricting the functional significance of the(contact) force cues as compared to the vestibular (force field) sen-sor? Why was the vestibular sensor maintained when, during phy-logenesis, many animal species left sea to live on land? One part ofthe empirical answer is that a creature is better off with than with-out a kinematic body-space sensor because it is able to performreactive body-space stabilization even when there is no firm bodysupport.

The other part of the answer comes from the fact that vestibu-lar-loss humans are able to cope only with a reduced range of tiltamplitudes and velocities. One reason for this is that the evokedbody-sway is abnormally large so that large tilts lead to instability.This indicates that the loop gain, although somewhat enhanced tocompensate for the loss of vestibular gain (loss of cFS and bTgrav), isinsufficient, the more so because increase in proprioceptive gainimproves body-support rather than body-space stabilization. An-other reason is a limitation in balancing band width, which is re-lated to the need of the patients to involve force cues in theirbody-space stabilization and to the low pass filtering of these cues(Mergner et al., 2009). In our robot simulations we observed that acompensatory increase of the loop gain above 1.5 (see model, be-low and Fig. 9) leads to instability, as did insufficient low pass fil-tering of the force cues (see the articles of Peterka and of Tahboub,this volume). Why are stability margins larger in the vestibular-able state? In previous work (Mergner et al., 2009), we related thisto the fact that the force cues are compound cues (see Section2.1.2), whereas the vestibular information is decomposed at sensorlevel (by the canal-otolith fusion) into gravitational and inertialcomponents (especially the latter is attenuated by the filtering).We conclude that, for reasons still to be elucidated, vestibular-losshumans have difficulties in decomposing the force cues, althoughtheoretically it is possible (Tahboub and Mergner, 2007).

Yet, we assume that the basic structure of stance control in ves-tibular-loss humans still is similar to that of vestibular-able hu-mans (Mergner et al., 2009). In most situations of everydayurban life, the patients use additional visual and tactile cues as well

Fig. 9. Model of stance control after vestibular loss. Body-support stabilization onstationary and level support is provided by proprioception alone (bf). Combiningproprioception with force cue signal yields a foot-space tilt estimate cFS� that, uponpassing a threshold, switches on (switch S) gravity and dynamic tilt compensation(pathway a) and static tilt compensation (b). Threshold values are taken fromvestibular-loss humans (Schweigart and Mergner, 2008).

as cognitive information of support properties, etc., which can sub-stitute for vestibular loss to a large degree. Evidence for this notioncomes, for example, from our transient tilt experiments (Fig. 8Ba;Schweigart and Mergner, 2008). After tilt onset, the patientsshowed similar to normals first a body-support stabilization andthen a body-space stabilization (latter with abnormal long delay,though). They also compensated for long lasting static tilt similarlyto normals. Therefore, we assume that patients basically use aclose-to-normal stance control when given a reliable externalspace reference (e.g. visual) and default to a reduced control whendeprived of the reference. Thus, given one wants to use a visual ref-erence in a humanoid, one may still use the here proposed stancecontrol.

In humanoids that carry a visual camera (e.g. for route findingor pattern recognition) the camera may be employed, in addition,as a substitute of the vestibular system. This would require, how-ever, a reliable visual space reference and extraction of equivalentinformation from the camera signals (equivalent to x, g, and aI;the latter would be needed for the explicit estimate of supporttranslational acceleration, which we omitted here). Exact knowl-edge of how vestibular-able and vestibular-loss humans fusethe visual signals with the other sensor signals for their stancecontrol and how they protect themselves against visual self-mo-tion illusions, however, still requires more research (Mergneret al., 2005).

The model we used to mimic vestibular-loss tilt responses inthe robot (Figs. 6B and 8Ba) is shown in Fig. 9. It uses propriocep-tion for body-support feedback control. In addition, it uses lowpass filtered force cue signals. One of these is combined with pro-prioception to obtain an estimate of support tilt, which switches on(switch S) a gravitational torque compensation (a) and static tiltcompensation (b) when it surpasses a threshold (compare modelof vestibular-able humans in Schweigart and Mergner, 2008).Interestingly, by way of the switch the robot performs a sensoryre-weighting, although only two sensor signals are available.

4.4. Sensory re-weightings and behavioral flexibility

Sensory re-weightings adjust reactive sensorimotor controlwhen this faces changes in behavioral situations and in the distur-bance patterns having impact on stance. The network underlyingthis flexibility in addition provides robustness against internal er-rors (fail safe). Prerequisite is a multi-sensory network, to which thevestibular sensor makes an important contribution, as demon-strated in this study.

There are different kinds of re-weighting. We distinguish be-tween those affecting the loop gain as a whole (example: BSRPexperiments, Section 3.2) or the relative contributions of a givensensor. Among the latter, we distinguish between mechanisms that

(i) automatically change weights (e.g. vestibular versus propri-oceptive contribution to tilt response; Fig. 3B and C),

(ii) switch disturbance estimates on and off (Section 4.2, sensoryre-weighting switch; Fig. 8A), thereby also allowing for cog-nitive influences and for voluntary changes in control strat-egy (Fig. 8C),

(iii) adjust the working range in a given situation (Section 4.2,‘tonic excursion limiter’; Fig. 8A), and

(iv) function as inbuilt fail safes upon internal errors (e.g. errorsof vestibular estimate of gravitational torque due to vestibu-lar sensor problems, changes in body mass or its geometry,etc.; Fig. 8D and Cnyrim et al., 2009).

An additional aspect is that the re-weighting appears to selectamong the sensory signals the most reliable ones, for instancethe least noisy ones (Fig. 3C).

Fig. 10. Canal-otolith fusion results (software simulations, frequency response data). Gain and phase curves are plotted over stimulus frequency (sinusoidal stimuli,f = 0.016–6.4 Hz). (A) Rotation stimuli (pp 8�). Vertical rotation estimate xy shows ideal gain (unity) and phase (0�) (relative contributions of xCan and xOto as indicated).Horizontal rotation estimate xz shows high pass characteristics. Attitude estimate g is ideal (translational acceleration estimates ai are zero, not shown). (B) Translationalacceleration stimuli (pp 8 m/s2). Translational acceleration estimate aiðyÞ shows high pass frequency characteristics. x and g in x and y direction show tilt illusion (‘‘catapultlaunch illusion”, inset), for x amounting to approximately 1(�/s)/(m/s2) in the mid- to high-frequency range. (C) Eccentric rotations (lever, 1 m). Rotation estimates x aresimilar to those obtained with centric rotation apart from minor distortions of gain and phase curves (compare A). Prolonging the time constant in the complementary filtersfrom 5 s to 50 s in panel xy yielded again essentially ideal gain and phase curves (dashed), whereas shortening to 1 s led to deterioration (dotted). Inset illustrates that, withvertical rotations, the orientation of tangential accelerations (TA) and centripetal accelerations (CA) with respect to gravitational vertical changes when lever is vertical ascompared to horizontal (yielding slightly different outputs of step I in Fig. 2A and B). Note that stimuli in these software simulations were generated in space coordinates andquaternions were used for their transformation into sensor coordinates. Further details in text.


Several more examples of sensory re-weighting have been re-ported in the literature on stance control (e.g. van der Kooij et al.,1999; Peterka, 2002; Peterka and Loughlin, 2004; Cenciarini andPeterka, 2006). Cognition as an important factor was explicitly ad-dressed in some studies (Guerraz and Day, 2005; Mahboobin et al.,2005; Blümle et al., 2006). A comprehensive classification of the dif-ferent aspects and mechanisms is still missing (Schweigart andMergner, 2008). Also, for use in robotics we still miss rules on howhigher brain functions (cognition, learning, etc.) interfere with(non automatic) sensory re-weighting. Other open questions arewhy and when exactly are re-weightings necessary and how theyare performed (and what are the alternatives to the solutions de-scribed here).

4.5. Value of embodiment and emergences

Using PostuRob for hardware-in-the-loop (HIL) simulationsalerted and confronted us with the problem of noise and drifts,with mechanical aspects such as limitations in muscle shortening,foot–ground friction, and more. In view of the complexity and non-linearities of the human stance control system that make a com-prehensive modeling and simulation difficult, the HIL provideswelcome simplifications. For instance, in the comparisons betweenhuman and robot, we can fully use the human laboratory as a com-mon testbed for both, without the need to model this. Other valueswould be a ‘proof of principle’, an enhancement of teaching andpublic advertising, and blueprints for neurological therapy and


rehabilitation devices. We also mention the ‘learning by building’aspect, because the work with the robot helped us in understand-ing the functional relevance of the human vestibular system andthe deficits in vestibular-loss patients.

Our work yielded several emergences. One example that oc-curred when building the artificial vestibular system is the insightthat the canal time constant represents a valuable rather than abad property. Another example was the aforementioned increasein loop gain in the BSRP condition. Still another example occurredwhen dealing with a defective gyro. It led to an enhanced body swaythat we reduced by repeatedly increasing loop gain until the robotshowed a stance behavior with a resonance tendency similar to Par-kinson’ disease patients (compare Maurer et al., 2004).

Since our aims are primarily clinical ones, we aim at giving therobot human-like features wherever possible. We conceive that thiswill allow us to use PostuRob to explore into sensorimotor deficitsand therapy effects in neurological patients. This approach will re-quire us to include visual sensors, cognitive mechanisms, more de-grees of freedom, interaction between voluntary movements andreactive stabilization, motor learning, etc. A particularly greatadvantage we expect for robot-environment interactions. Theyare difficult and sumptuous to realize in pure software models. Con-sider a simulation of different support surface structures such car-pet, concrete, and linoleum for stance stability. Using pure softwaresimulations would be very difficult, while using PostuRob is easy.

4.6. Conclusions

Vestibular-able humans show better stance performance duringexternal disturbances than vestibular-loss humans. Analogousfindings were obtained with our humanoid robot that we endowedwith a human-inspired stance control. The results indicate a con-siderable advantage of the kinematic vestibular sensor over forcesensors in this control. We, therefore, recommend the vestibularsensor for use in humanoids. For neuroscience, the neuroroboticsapproach is an important research tool in that it allows embodi-ment of inferred biological concepts and models into real-worldmedia with proof of principle and learning by building. It will helpus to better understand how the brain combines in its sensorimo-tor control several simple imperfect subsystems in a clever and of-ten non-linear way such that the system as a whole performspowerfully and adequately within its ecological framework.

Acknowledgements

We thank our electronic and mechanical workshop (FrankHuethe and Tom Guenther) for building and maintaining PostuRoband the posture control lab. Furthermore, we like to thank KarimTahboub and Wolfgang Becker for valuable discussions duringwriting the manuscript and Bob Peterka for providing us withthe PRTS stimulus.

Appendix A. Stance control model (details omitted in Fig. 3A)

(i) Transformation of the proprioceptive body-foot feedbackloop into a body-space loop, achieved by adding the foot-space signal fs (from cFS) to the body-foot signal bf(bs0 = bf + fs; see Fig. 3B).

(ii) Conversion of the torque estimates bT ext and bT grav into kine-matic equivalents in terms of bs (in the corresponding esti-mate boxes; derived by T/(m � g � h); compare below, B,COM’s gravitational ankle torque). Thus, all constituents ofthe feedback loop carry a bs signal (in rad or �), which alsois the loop’s set point signal.

(iii) Delay times (robot): proprioceptive feedback loop, 40 ms;disturbances estimates, 40 ms + 90 ms.

(iv) Central detection threshold-like mechanisms (they explainamplitude non-linearities of the disturbance responses andplay a role in sensory re-weightings and noise suppression;see 1.4). Thresholds of disturbance estimates: cFS,T = 0.002 rad/s; bT ext, T = 0.002 rad; bT grav, T = 0.002 rad.

(v) Ideal actuation in the scenarios used (see Section 1.1.2.1 andbelow, B).

(vi) Passive viscous-elastic tissue properties (here assumed to be5% of reflexive damping and stiffness).

(vii) Loop gain adjustment. Gain factor after summing all constit-uents of the feedback loop.

(viii) Controler: KP = 370 Nm/rad, KD = 133 Nm s/rad, KI = 45 Nm/(rad s). Values are lower than those previously reported inhumans (Maurer et al., 2006) for sinusoidal tilts with largeramplitudes in mid- to high-frequency range, if corrected form and J. This appears to be related to the PRTS stimuluswhere amplitudes strongly decrease in mid- to high-fre-quency range (Peterka, 2002). Increase of loop gain to 1.15is required to account for the previous sine wave data.

(ix) Gains of disturbance estimates: cFS, G = 0.7; bT ext, G = 0.9;bT grav, G = 0.9.

Appendix B. Robot

– Body mass, m = 52 kg (more than half is from metal weights atpelvis level); 62 kg with feet.

– Inertia, J = 42 kg m2.– COM height above ankle joint, h = 0.9 m.– ‘Muscles’. Pneumatic, at front and back of each leg (Festo, Esslin-

gen, Germany; Type MAS20). Performance was essentially idealwith respect to the present requirements. Muscle force was con-trolled electronically by force sensors at the muscle–tendon fix-ations (mimicking Golgi tendon organs that probably act asbiological force sensors). Proportional-directional control valveswere used for the pneumatic control (Festo, MPYE-5–1/4–010-B).

– ‘Tendons’. Springs, with the spring effect being slightly belowthat of the COM’s gravitational ankle torque, in Nm, producedby m � g � h � sin(BS) or, using small angle simplification,m � g � h � (BS); g, gravitational acceleration = 9.81 m/s2. Themuscle–tendon system’s dynamics deteriorated above 4 Hz.But this hardly affected the robot’s stance dynamics that essen-tially ranged only up to 3 Hz (see Results). In software simula-tions of the robot over this range, it sufficed to approximatethe limited dynamics of the muscle–tendon system by a 20 msdelay (which was added to the mechatronic and PC dead timeof approximately 20 ms).

Appendix C. Software simulations

In addition to the hardware simulation of the vestibular sensor(Figs. 4 and 5) we performed 3D software simulations of x, g, and ai

using sinusoids over a frequency range of 0.0125–6 Hz (Fig. 10A–C).

Rotation stimuli. With x- and y-axis rotations, x was veridical, aswas g (Fig. 10A). During horizontal rotations (z axis), x showed thecanal high pass characteristics. ai was zero (veridical; not shown).

Translational acceleration. Upon acceleration in x and y direction,the acceleration estimate ai was veridical apart from high passcharacteristics (Fig. 10B, ai panel) with negligible cross talks to z


(not shown). Upon acceleration in z direction, ai showed less highpass characteristics (and no cross talk; not shown). The rotationestimate, x, and the attitude estimate, g, showed upon x and yacceleration a counter-phase tilt illusion, not in z direction (notshown). The inset in Fig. 10B shows an illusion of a backward bodytilt evoked by a transient forward acceleration (peak, 10 m/s2; 1 sduration). In Bode plots of the illusion, the x gain curve amountedto approximately 1 �/s/m/s2 at frequencies >1 Hz and declined atlower frequencies (x panel).

Eccentric rotations. The translational accelerations reportedabove for the PRTS platform translations and eccentric rotations(Section 3.1.4) were small compared to those applied in the soft-ware simulations (>10 times higher at frequencies above 1 Hz).With a 1 m vertical rotation starting with vertical extender arm(see inset in Fig. 10C), the results were similar to those without ex-tender (Fig. 10A). The estimate x was essentially veridical, apartfrom minor gain and phase curve distortions above 1 Hz(Fig. 10C; also g, not shown). The associated ai (from the tangentialand centripetal accelerations) were almost correct above 1 Hz andbecame increasingly underestimated at lower frequencies (notshown). With the same vertical stimulus, but the extender armhorizontal (see inset), estimation errors were overall slightly larger(due to differences between centripetal and tangential accelera-tions effects on step I in Fig. 2A and B). With horizontal eccentricrotations (about z axis), the estimates were approximately veridi-cal apart from high pass characteristics and small cross talks.

Interestingly, when repeating the xx;y simulations after pro-longing the time constants of the high and low pass filters in thefusion from 5 s up to 50 s, the gain and phase curve still were ideal(dashed curves, Fig. 10C). In contrast, with a shortening to 1 s thecurves became erroneous (dotted). Thus, a long time constant ap-pears desirable as concerns contaminations from tangential andcentrifugal accelerations during eccentric rotations (whereas ca-nal/gyro drift reduction calls for a short time constant). Increasingeccentricity increases the magnitude of the gain and phase curvedistortions and shifts their peaks towards lower frequency. Yet,with 10 m eccentricity and 5 s time constant, the distortion is stillrelatively small (smaller than that shown for the shown distortionobtained with 1 m eccentricity and 1 s time). Thus, a time constantof 5 s appears well suited for the ecological framework ofvertebrates.

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