Rehabilitation Robot Cell for MultimodalStanding-Up Motion Augmentation∗

Roman Kamnik and Tadej BajdUniversity of Ljubljana

Faculty of Electrical EngineeringTrzaska 25, 1000 Ljubljana, Slovenia

{roman.kamnik, tadej.bajd}@robo.fe.uni-lj.si

John Williamson1 and Roderick Murray-Smith1,2

1Department of Computing ScienceUniversity of Glasgow, Glasgow, Scotland

2 Hamilton Institute, NUI Maynooth, Ireland{jhw,rod}@dcs.gla.ac.uk

Abstract— The paper presents a robot cell for multimodalstanding-up motion augmentation. The robot cell is aimedat augmenting the standing-up capabilities of impaired orparaplegic subjects. The setup incorporates the rehabilitationrobot device, functional electrical stimulation system, mea-surement instrumentation and cognitive feedback system. Forcontrolling the standing-up process a novel approach wasdeveloped integrating the voluntary activity of a person inthe control scheme of the rehabilitation robot. The simulationresults demonstrate the possibility of “patient-driven” robot-assisted standing-up training. Moreover, to extend the systemcapabilities, the audio cognitive feedback is aimed to guidethe subject throughout rising. For the feedback generationa granular synthesis method is utilized displaying high-dimensional, dynamic data. The principle of operation andexample sonification in standing-up are presented. In thismanner, by integrating the cognitive feedback and “patient-driven” actuation systems, an effective motion augmentationsystem is proposed in which the motion coordination is underthe voluntary control of the user.

Index Terms— Rehabilitation robotics, standing-up, volun-tary control, audio cognitive feedback, granular synthesis.

I. INTRODUCTION

Rising from a chair is a common, however demanding,activity of daily living. Impaired persons and the elderlyoften have difficulty when rising to a standing position.To compensate for the lack of lifting forces produced bymuscles, a handicapped person usually practices an adaptedapproach to the standing–up manoeuvre. Normally, theupper extremities take over the body weight lifting role.This requires a fit upper body. Additionally, in patientswith lesions of the CNS, the standing–up exercise can befacilitated with the help of functional electrical stimulation(FES) evoking muscle contractions in the paralyzed ex-tremities [1]. In clinical praxis, the knee extensors (quadri-ceps muscle groups) are stimulated to elicit moments inthe knee joints [2], [3].

When training an impaired subject to adapt to a newstanding–up approach, the trainee needs to be, in numerousrepetitions, restrained in a position trajectory and ade-quately supported to maintain postural stability. Further-more, investigating new FES control approaches requiresfeedback to evaluate the effects of improvements. Train-ing of standing–up is usually performed by the manualsupport of physiotherapists using also different mechanical

∗This work is partially supported by Slovenian research pro-gram #P2-0228/2.06 to R. Kamnik and T. Bajd, and EPSRC grantGR/M76379/01CNSF, SFI grants to J. Williamson and R. Murray-Smith

aids. The back sled and seesaw construction are typicalexamples [4], [5]. Mechanical aids are usually constructedas counterweight-based passive devices intended to aidthe subject and assure his stability. None of the devicesprovides feedback information about the rising process orthe capability of motion trajectory programming.

For these reasons we have developed the standing–uprobot assistive device [6]. The device is designed as a 3DOF mechanism driven by an electrohydraulic servosystemwith a standard bike seat mounted at the end-effector. Sit-ting at the seat the impaired person is supported under thebuttocks. The motion trajectory of the seat is constrained tothe subject’s sagittal plane. The robot mechanical config-uration allows the subject to actively participate in rising.Moreover, to integrate the subject’s voluntary activity inthe control of robot device, a special control algorithmis proposed [7]. The algorithm allows to the subject tovoluntarily control the robot support by his own activity.

Actuationsystem

Sensorysystem

Cognitivefeedback

Evaluation

Processing

Patient

Fig. 1. Multimodal standing-up motion augmentation

In this paper, we are proposing the integration of thestanding-up robot device with the FES, measurement andcognitive feedback systems. The objective of the integrationis to build a robot cell for multimodal standing-up motionaugmentation. The concept is presented in Figure 1. Inthe robot cell, the subject is supposed to be assisted bythe actuation system providing the supportive forces. Asan actuation, the standing-up robot is used to generate theexternal support to the human body. In the case of para-plegia, the external support can be additionally combinedwith FES. FES is used to evoke the internal body forces byemploying the subject’s paralyzed muscles. In addition tothe actuation, the cognitive feedback is employed to informthe subject about the quality of standing-up pattern andanomalies. The sensory system implemented in the robotcell performs assessment of the kinematics and dynamics of

standing-up maneuver. The data acquired are processed andfed to the cognitive and actuation systems or used for theoff-line evaluation. The innovative segment of the proposedrobot cell is that it provides the training regime whichis under the voluntary control of the subject. Utilizingthe robot cell, the subject is in position to exercise thestanding-up in a preferable manner and speed and to adapthis motion pattern with regards to the cognitive feedbackinformation.

The paper is organized as follows. In the second section,the mechanical design of the standing-up robot device andits control system are described. The third section presentsthe robot cell configuration. The control approach proposedfor the patient-driven robot-supported standing-up trainingis described in the fourth section, while the fifth sectionoutlines the approach to cognitive audio feedback synthesis.In the conclusions, the advancements of the proposedtechnology are discussed.

II. STANDING–UP ROBOT ASSISTIVE DEVICE

In the standing–up manoeuvre, the upper body can beconsidered as restricted to three degrees of freedom ofmotion. It moves vertically and horizontally in the sagittalplane, while changing its orientation in the antero-posteriordirection. Thus, it can be reasonably assumed that themajority of subjects who are unable to stand-up (elderly,people with paraplegia or even some tetraplegic patients)will be able to control their upper body orientation bymeans of arm support. In this respect, an active mechanicalsystem supporting the rising subject under the buttocks, andin this way imposing the subject’s hip trajectory, meetsthe requirements for robot-assisted standing–up. The novelrobot device, developed according to the above criteria, ispresented in Figure 2. The robot assistive device is a 3 DOF

SEAT

FORCE SENSOR

ROBOT END EFFECTOR

SERVO VALVES PRESSURE SENSORS

ROTATIONAL DOF

HYDRAULIC ACTUATOR

TRANSLATIONAL DOF

HYDRAULIC ACTUATOR

SEAT ORIENTATION

BILATERAL SERVO

SLAVE CYLINDER

SEAT ORIENTATION

BILATERAL SERVO

MASTER CYLINDER

Fig. 2. Standing–up robot assistive device

mechanism which, in its way of supporting the subject,resembles half of a seesaw. The impaired subject sits on astandard bike seat mounted at the robot end-effector. Posi-tioning of the end-effector is accomplished by positioningof two robot segments. The first segment is rotating aroundits axis on the robot base, while the second translationalsegment is moving longitudinally along the first segment.Both segments are driven by linear hydraulic actuators.At the robot end-effector the orientational mechanism ismounted, assuring horizontal seat orientation in any robot

position. Constant seat orientation is maintained by a pas-sive hydraulic bilateral mechanism. The hydraulic bilateralsystem consists of two cylinders, master and slave, with themaster piston coupled to the driving first robot segment.Under the seat mechanism the six axis JR3 force/torquesensor (JR3 Inc., Woodland, USA) is mounted in order toassess the contact force between the robot end-point and therising subject. In this manner, subject/machine interaction,and hence the robot assistance to the standing-up process,can be assessed and controlled on-line.

The robot mechanism is driven by the electrohydraulicservosystem. The system is powered by a hydraulic pumpproviding a pressure of 50 bars and hydraulic current of1 l/s. The pump performance allows a maximal speed ofthe robot end-effector up to 2 m/s. The Moog servovalves(Moog Inc., New York, USA) are used to control thepressure difference applied to the linear hydraulic cylindersdriving the rotating and translating link. In this way, twooperational modes are provided. In the position controlmode, the system accomplishes the desired motion trajec-tory regardless of the interaction between the subject andthe robot, while in the force control mode, explicit controlof interaction force is possible.

III. CONFIGURATION OF THE ROBOT CELL FORSTANDING-UP MOTION AUGMENTATION

In Figure 3 the configuration of proposed robot cell forstanding-up motion augmentation is illustrated. The cell in-corporates the standing-up robot device, the audio feedbacksystem and the measurement instrumentation. Human bodysymmetry during standing-up task is presumed. Hence,measurements are accomplished only for the patient’s rightside. The robot, foot and arm reactions are assessed bymultidimensional force sensors. The force plate mountedin the floor acquires the foot supportive forces. The armsupportive forces are assessed by the robot force wristimplemented in the arm supportive frame, while the robotsupport is measured by the similar sensor mounted underthe seat. The assessment of motion kinematics is performedby an optical system or combination of simpler sensors.For example, lower extremity joint angles can be estimatedfrom information about the robot end point and footpositions, while HAT acceleration and angular velocity canbe acquired using accelerometers and a gyroscope attachedto the trunk.

The operation of sensory, actuation and feedback sys-tems is controlled by a computer system built on a 1 GHzPC Pentium III platform. On the platform, the RTLinux realtime operating system is running at a constant samplingrate of 2 KHz. Two PCI interface boards are employed toacquire analog and encoder signals. Via another PCI D/Aconverter board the hydraulic servosystem is controlled.Besides, a computer sound card and a laudspeaker are usedto generate the audio feedback signal.

The incorporation of FES actuation system is optionalfor paraplegic subjects. The surface stimulation of theM.quadriceps muscle group is common in praxis. Theknee extensors can be stimulated with intensity level that

ARM REACTIONS

FEET REACTIONS

ROBOTSUPPORT

STIMULATIONCONTROL

ROBOTCONTROL

FES

MOTIONASSESSMENT

REACTION FORCESASSESSMENT

F_arm, M_arm

F_foot

F_seat

AUDIO FEEDBACKCONTROL

TRUNK ACCELERATIONAND ANGULAR RATE

ROBOT POSITION

Sensory system Actuation andcognitive feedbacksystems

STIMULATOR

Fig. 3. Configuration of the robot cell for standing-up motion augmen-tation

corresponds to the desired knee joint torque profile oraccording to the phase of the rising process. The stimulatoris built as a galvanically separated unit and it is controlledvia a serial communication port.

IV. HUMAN VOLUNTARY ACTIVITYINTEGRATION IN THE CONTROL OF A

STANDING-UP ROBOT - PDRAMA APPROACH

As an alternative to position or impedance control [8],[9], we have developed a control approach integrating hu-man voluntary activity into the robot control scheme. In theapproach, the robot is supposed to operate in a force controlmode, while the force reference is determined accordingto the rising subject’s activity. In this way, the artificialrobot controller is integrated into the control actions ofthe intact neuromuscular system of the subject. Similarto approaches proposed in [5] and [10], the hand andfoot support forces are used to characterize the subject’svolition and are thus used as feedback to the controller.We named the approach ”Patient-Driven Robot-AssistedMotion Augmentation” (PDRAMA).

The basic idea behind the calculation of the referenceforce is to quantify the deficit in the force and momentequilibrium of the trunk. Namely, if we simplify thesituation and consider the subject’s head, arms and trunk(HAT segment) as a rigid body, the balance equations forthe forces and moments acting on this body segment can bedefined. During motion, the HAT segment is supported bythe lower and upper extremities in the hip and shoulderjoints. The contributions of shoulder joint force (Fsh),shoulder joint moment (Msh), hip joint force (Fhip), hipjoint moment (Mhip) and the inertial contributions due totranslational and angular accelerations are illustrated inFigure 4. Assuming human body symmetry during rising,the HAT motion can be considered as planar, constrainedto the subject’s sagittal plane. If the joint reactions andthe HAT motion are known, the HAT balance can bedetermined and thus postural stability assured by applyingadditional external force to the HAT segment. Additionalforce (Frobo) is, in robot assisted standing-up, contributedby the robot device supporting the HAT segment near thehip joints. Following the Newton-Euler approach to ana-lyzing rigid body motion dynamics, the force and moment

y0

z0

yi

zi

Mhip

FhipFrobo

rhip Fg

Fsh

Msh

rshIi ω i mhat Aω i × (Ii ω i)

Fig. 4. Forces and moments in robot-assisted standing-up acting to theHAT segment

balance equations for the HAT segment can be defined as:

Fhip + Fsh + Fg + Frobo = m A (1)

Mhip + Msh +Fhip × rhip++Fsh × rsh +Frobo × rhip = d(I0 ω0)/dt (2)

In (1) and (2) the notation of forces and momentscorresponds to notation in Figure 4, while the vectors rhipand rsh describe the position of the hip and shoulder jointswith respect to the HAT center of mass. Fg stands forgravitational force. The parameters mhat , I0 and ω0 denotethe HAT segment mass, inertia and angular velocity, re-spectively. All three parameters are expressed with respectto the inertial coordinate system. Normally, it is moreconvenient to describe the moment balance with respectto the local coordinate system of the HAT segment. Theresulting equation is then:

RiT0 Mhip + RiT

0 Msh − (RiT0 Fhip)× ri,hip − (RiT

0 Fsh)× ri,sh−−(RiT

0 Frobo)× ri,hip = Ii ω i +ω i × (Ii ω i) (3)

In (3), the inertial tensor Ii and the vectors ri,hip, ri,shrepresent parameters expressed in the HAT coordinatesystem and are thus constant. The relation between theinertial and the local coordinate system is described bythe homogenous rotational matrix Ri

0.To assure postural stability, equations (1) - (3) must be

satisfied in each time instant. Formulating vector equationsalong horizontal and vertical directions, and expressing thecomponents of the robot support, we get the algorithmfor determining the desired robot supportive force. Allvariables and parameters must be known, and the equationsmust be decoupled. Parameters of the body segments(masses, center-of-mass positions) can be estimated usinganthropometric data [11], while the shoulder and hip forcesand moments need to be assessed via the inverse dynamicapproach utilizing the force reactions and kinematic mea-surements.

A. Extended Kalman Filter algorithm

For decoupling (1) and (3), an Extended Kalman Filter(EKF) algorithm was employed. Kalman filtering is acommon approach in multisignal integration tasks relyingon an approximate analytical model of the system [12]. Inthe EKF, the model is represented by a nonlinear state space

description incorporating state and measurement differenceequations:

xk+1 = f (xk,uk,wk) (4)

zk = h(xk,vk) (5)

In (4) the nonlinear function f relates the state vector xand the input vector u at time step k to the state at stepk + 1. The measurement vector h in (5) relates the stateto the measurements zk. Vectors wk and vk denote thesuperimposed process and measurement noise, respectively.

We defined the state vector as xk =[φ φ φ Ay Az fy,robo fz,robo]T , where φ stands for thetrunk inclination angle, Ay and Az for the vertical andhorizontal accelerations, and fy,robo and fz,robo for thetrunk vertical and horizontal robot supportive force. Theparticular functions of the state equation were derivedfrom (1) and (3).

Measurement vector zk = [φ ay az fy,sh fz,sh fy,hip fz,hip

mx,sh mx,hip]T incorporates all the measurement values.Trunk inclination rate φ and accelerations ay,az are sup-posed to be measured by a gyroscope and accelerometersattached to the trunk. The shoulder and hip reactions( fy,sh, fz,sh, fy,hip, fz,hip,mx,sh,mx,hip) are assessable by an in-verse dynamics calculation for the lower and upper extrem-ities. The particular functions of the nonlinear measurementequation were expressed from (1) and (3) also.

The discrete-time EKF algorithm is implemented as in[13]. The complete set of equations is shown below. TheEKF measurement update equations are:

Kk = P−k HT

k (HkP−k HT

k +Rk)−1, (6)

xk = x−k +K(zk −h(x−k ,0)) , (7)

Pk = (I−KkHk)P−k (8)

while the EKF time update equations are as follows:

P−k+1 = AkPkAT

k +Qk (9)

x−k+1 = f (xk,uk,0) (10)

The EKF propagates the state and error covariance esti-mates (10) and (9) by computing the filter gain matrixKk (6), and by updating the state and covariance estimatesbased on the measurement residual (7) and (8). Matrices Ak

and Hk are the Jacobian matrices of partial derivatives off () and h() with respect to vector xk. Matrices Qk and Rk

represent the process and measurement noise covariances.

B. Evaluation of the PDRAMA control approach

The PDRAMA control approach proposal was evaluatedby a simulation study. For this purpose, the standing-upmanoeuvre of a paraplegic subject was modelled in a sim-ulation environment. In the simulation model, the modelsof voluntary activity, robot support and dynamics of humanbody motion were incorporated. The simulation resultswere compared with the measurement data acquired in theactual robot-supported standing-up trials of a paraplegicsubject. In the experiments, a person with paraplegia wasinvolved (subject MT, female, 30 years, 171 cm, 75 kg, in-jury level T 4-5). In the experimental trials the robot devicewas operating in the position control mode. No interaction

control was involved in this regime. In the experiments, therobot accomplished motion along the reference trajectorywhile the subject was trying to adapt to the imposed pelvisposition. In Figure 5 the experimental setup is shownincorporating the robot assistive device, the arm supportiveframe and the measurement instrumentation.

FEET REACTIONFORCE PLA TES

SEA T REACTIONFORCE SENSOR

ARM REACTIONFORCE SENSOR

INFRARED MARKERS

Fig. 5. Paraplegic subject in the experimental setup

In the simulation model, the motion of the human bodyin the standing–up process was simulated in the Matlab–Simulink software environment. The dynamics model ofthe human body included three rigid body segments -shanks, thighs and HAT - constrained to sagittal planemotion. In the model, the human body anthropometric pa-rameters [11], the visco–elastic properties of the joints [14]and the human voluntary control were incorporated. Aswith paraplegic subjects, no active moments were appliedin the lower extremities. Human volition was modelledvia three PD controllers that generated the shoulder forcesand moment, regarding the difference between the currentand the desired trunk position and orientation. The desiredtrajectory had been assessed in the actual standing-up of aparaplegic patient. In each integration step of the simulationtrack, the robot supportive force was determined by theKalman filter algorithm and applied as an external forcevector at the hip joint mimicking the robot support.

In the evaluation results, two approaches to the robotcontrol are compared. The actual standing-up facilitatedby the position controlled robot device is compared to thesimulated standing-up. In simulated standing-up, the modelof the subject is supported by the force determined by thePDRAMA algorithm.

In Figure 6 the contributions to the body weight supportare shown for two examples of the robot-assisted standing–up. The upper graph presents the vertical component ofthe shoulder joint force. Below the graph, the body motionkinematics is presented by dark and white stick figuresillustrating the simulated and actual standing-up examples.The arrows describe the amplitude and direction of theforce acting in the shoulder joint. The line style of thearrows corresponds to the line style in the graph. Thelower graph represents the vertical robot interaction forceacting near hip joints. In this way, Figure 6 represents the

voluntary activity of the subject and his robot assistanceduring rising.

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5-200

-100

0

100

200

300

400

500

600

700

t [s]

PDRAMA ALGORITHM FORCE+

POSITION CONTROLLED ROBOT

CONTROLLED ROBOT (SIMULATION)

(ACTUAL STANDING-UP)

SHOULDER FORCE (VERTICAL COMPONENT)

Start rise Standingfz,sh [N]

t = 0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2 4.5 4.8 s

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5200

300

400

500

600

700

800

t [s]

ROBOT SUPPORTIVE FORCE (VERTICAL COMPONENT)

Start rise Standingfz,robo [N]

PDRAMA ALGORITHM FORCE+

POSITION CONTROLLED ROBOT

CONTROLLED ROBOT (SIMULATION)

(ACTUAL STANDING-UP)

t = 0.0 0.3 0.6 0.9 1.2 1.5 1.8 2.1 2.4 2.7 3.0 3.3 3.6 3.9 4.2 4.5 4.8 s

Fig. 6. Voluntary activity and robot support in robot-assisted standing-up: measured results (robot in position control mode) and simulationresults (robot in force control mode, supportive force determined by thePDRAMA algorithm)

The evaluation results demonstrate that a similar motionpattern is demonstrated in actual and simulated standing-up examples. However, from Figure 6 a fundamental dif-ference between the control approaches can be observed.In actual standing-up facilitated by the position-controlledrobot device, a high peak in the subject/robot interactionforce is noticed at the beginning of rising. The highinteraction implies that the robot device acts as a masterdevice which imposes the motion to the subject. As aconsequence, the low voluntary activity of the subject ispresent in body weight lifting. On the other side, in thePDRAMA simulation example, it is evident that the subjectinitiated and guided the motion by upper body activity.The robot provided needed support only. In this way,voluntary control over the motion manoeuvre is assured tothe subject. Moreover, since the measured reaction forcescan be scaled before being fed to the controller, the amountof the body weight support between the voluntary andthe robot contributions can be varied. This feature opensthe possibility for altering the training conditions in robot-assisted standing–up.

V. GRANULAR SYNTHESIS FOR COGNITIVEAUDIO FEEDBACK IN STANDING-UP

A simple model for enhancing user performance withtraining system is to augment the users’s proprioceptionwith audio. This involves sonifiying the aspects of themotion maneuver state space corresponding to physicalmotion; e.g. position, velocity or acceleration. This canhelp the user intuitively understand how movements heis making are perceived by the system and potentiallyaccommodate the movement in the relevant dimension.

Here we describe a general framework for producingformative audio feedback based on granular synthesis [15],[16]. Granular synthesis is a probabilistic sound generationmethod, based on drawing short (10-500 ms) packets ofsound, called “grains” or “granules”, from source wave-forms. A large number of such packets are continuouslydrawn from various sources, shaped so as to avoid discon-tinuities and summed. Figure 7 shows the basic process.

Fig. 7. Simple granular synthesis process. A much greater number ofgrains would be used in practice for a smoother waveform. When a newgrain is created, a section of the source waveform is copied, the positionof which being determined by the distribution over waveform time. Thissection is then enveloped. All of the currently active grains are summedto produce the final output.

Even in situations where other synthesis techniquescould be used, granular synthesis gives strong, pleasingtextures which are easily manipulated in an elegant andintuitive manner. It also has the advantage that a distribu-tion can be defined over time inside the source waveform,defining the probability of a grain being drawn from aspecific time index in the wave. This allows for effectiveprobabilistic time-stretching, which is a powerful tool ininteractions where progress towards some goal is of im-portance, as it is in standing up.

A verbal description of the desired standing-up be-haviour might be an informal specification such as “Atthe beginning of standing-up, the position of COM is themost important, especially horizontal component COMy.The position COMy is related to the trunk inclination,thus φtrunk is important too. During the standing-up itis important for the subject to turn the motion from thehorizontal to the vertical. For successful redirection thetracking of COM velocity is important, especially trackingthe change in velocity direction. At the end of rising, again

COMy and vertical trunk inclination demonstrate that themaneuver is over.” This sort of suggestion needs to beconverted into an audio display which the subject can useto guide their behaviour. Desired positions of variables suchas the initial and final positions of COM are relativelyeasy to specify, via probability distributions around thereference values. Turning points such as the change inCOM velocity are landmarks which need to be highlightedto the user. Predictive techniques which can emphasizeexpected deviation from the reference trajectory in time forthe user to take corrective action can be used to ‘quicken’the audio display. Another simple but effective trainingtechnique to reduce jerky motions is to link a splashingnoise to events in the higher (e.g. 3rd) derivatives of theposition signal, explaining to the subject that they should’avoid making ripples while moving’. This is feedbackwhich makes them aware of the jerk, and can help themsmooth their motions in future attempts.

An example audio output in standing-up trial gen-erated with granular synthesis is presented for demon-stration purposes. The state vector was described asx(t) = [φtrunk φtrunk COMy COMz ˙COMy ˙COMz] encompass-ing trunk inclination angle and angular velocity, and trunkcenter-of-mass (COM) position and velocity. The desiredtrajectories were acquired in real standing-up, while theactual in simulated track. For clarity, in Figure 8, wepresent a caricature of the simplest sonification case. Herethe error in the subject’s state, compared to the referencesignals is converted to an audio signal.

Fig. 8. Schematic example mapping standing (dotted lines) comparedwith reference (solid) to audio output (lowest plot).

VI. CONCLUSIONS

This paper proposes a rehabilitation robot cell intendedfor augmenting the human capabilities in the standing-upmanoeuvre. The robot cell is based on the rehabilitationrobot device, FES system, measurement instrumentationand cognitive feedback system. It enables the multimodaltraining regime that encourages users for their own activity.For the robot control, the PDRAMA algorithm is presentedwhich incorporates human voluntary activity into the robotcontrol scheme. The algorithm determines the interactionforce between the subject and the robot, thus generatingthe reference to the robot explicit force controller. Thealgorithm controls the robot motion without the need forspecific reference defined. It accounts for the contributionsfrom trunk inertia in dynamic motion and the contributions

from the lower and upper extremities to body weight liftingforces. In this way, a unique approach in rehabilitationrobotics has been developed: a “patient-driven” control ofrobot-assisted training. Moreover, with altering of bodyweight bearing portions between the robot and the subjectthe standing-up training regime can be varied, dependingon the subject’s etiology and progress.

The multimodality of the robot cell is enhanced bythe cognitive feedback system. Providing the continuousfeedback during voluntary controlled motion allows thesubject to respond to the system in real-time and in this wayaccommodate the motion pattern. Audio is used to presenthigh-dimensional, dynamic data in a continuous feedback.For these purposes, the granular synthesis is employed. Asimple example of feedback synthesis in standing-up isdemonstrated.

The robot cell described presents an efficient tool forstanding-up training and evaluation. The remaining issuesin the development are the implementation of the proposedalgorithms to the robot cell controller and the evaluationof the therapeutic benefits of robot supported standing-uptraining.

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