Biomed Tech 2006; 51:314–319 � 2006 by Walter de Gruyter • Berlin • New York. DOI 10.1515/BMT.2006.063

2006/51566029

Article in press - uncorrected proof

Application of EMG signals for controllingexoskeleton robots

Christian Fleischer*, Andreas Wege, KonstantinKondak and Gunter Hommel

Technical University of Berlin, Institute for ComputerEngineering and Microelectronics, Berlin, Germany

Abstract

Exoskeleton robots are mechanical constructionsattached to human body parts, containing actuators forinfluencing human motion. One important applicationarea for exoskeletons is human motion support, forexample, for disabled people, including rehabilitationtraining, and for force enhancement in healthy subjects.This paper surveys two exoskeleton systems developedin our laboratory. The first system is a lower-extremityexoskeleton with one actuated degree of freedom in theknee joint. This system was designed for motion supportin disabled people. The second system is an exoskeletonfor a human hand with 16 actuated joints, four for eachfinger. This hand exoskeleton will be used in rehabilitationtraining after hand surgeries. The application of EMG sig-nals for motion control is presented. An overview of thedesign and control methods, and first experimentalresults for the leg exoskeleton are reported.

Keywords: human body model; human-machine inter-action; powered orthosis.

Introduction

One of the key issues for successful application of exo-skeletons is reliable motion control. The challenge is toachieve cooperation between the human and the robotwith permanent direct contact. Depending on the appli-cation, different approaches can be applied to realise thiscooperation. One possible approach is to suppress thehuman influence on the exoskeleton, as demonstrated inpioneer work carried out by the Vukobratovic group atthe end of the 1970s using a full-body exoskeleton robotin walking support of a fully paralysed patient w1, 2x. Theexoskeleton was moved along precomputed trajectories,which were slightly modified by a motion controller tomaintain balance. However, most practical applicationsof exoskeleton robots need to account for humanmotion, and reaction to the imposed motion or evendetection of the human intention to perform some tasks.

The common approach to control exoskeleton robotsis based on using force sensors w3, 4x. In this approach,

*Corresponding author: Christian Fleischer, Technical Universityof Berlin, Institute for Computer Engineering andMicroelectronics, Sekr. EN 10, Einsteinufer 17,10587 Berlin, GermanyPhone: q49-30-31473114Fax: q49-30-31421116E-mail: fleischer@cs.tu-berlin.de

force sensors are placed between human body parts andthe mechanical construction or between different partsof the mechanical construction. The force signal servesas a measure of the discrepancy between motion desiredby the subject and the real motion of the exoskeleton.The advantage of this method is the simplicity of theforce signal measurement: once calibrated, the forcesensors provide reliable signals. However, there are pro-blems in using this approach. The first problem is relatedto natural delay in the control loop, which is caused byneuronal signal propagation from the brain to the mus-cles, as well as by biomechanical characteristics of themuscle contractions and limb motion. This delay leadsto problems in using large gains in the motion controlloop (desired for better system performance).

The second problem occurs if the mechanical con-struction is in contact with the environment: It is difficultto separate the measured force signal from the subjectand from the environment. Unfortunately, these situationsoccur regularly in practice, e.g., during foot contact withthe floor.

In our opinion, the use of EMG signals in control hasthe potential to improve the performance of exoskeletondevices. Measured EMG signals are related only to thesubject’s intention (for healthy individuals), so that theabove-mentioned ambiguity in force sensor signal duringcontact with the environment is avoided. Theoretically,proper interpretation of EMG signals should allow com-putation of the desired human motion in advance –before the muscles contract and reaction with the me-chanical construction takes place. Therefore, the delaydescribed in the control loop can at least be reduced.

In this work, two different approaches for applicationof EMG signals for motion control of exoskeleton robotsare presented. In the first approach, called a dynamichuman body model (DHBM), the intended human motionis calculated using EMG signal processing and an elab-orate biomechanical model of the human body. The pre-dicted human motion (positions and velocities) is usedas input for actuator controllers. In the second approach,called direct force control (DFC), the EMG signals areconverted to forces acting on limbs and compared tocurrent forces measured in actuators attached to the cor-responding limbs. The difference between these forcesis used as input for the actuator controller.

In both approaches the EMG signals are directly incor-porated in the control loop, in contrast to the HAL systemw7x, where the controller feedback signal is based on jointangle measurements, and EMG signals are used to cal-culate a correction term for system input.

One of the main issues of the approaches presentedis conversion of the measured EMG signals into musclesforces (often referred to as a myoprocessor in the liter-ature). The EMG signals depend very much on the sub-ject, on skin properties such as skin moisture, as well ason muscle fatigue. The main contribution of this paper is

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Figure 1 Leg exoskeleton composed of the orthosis with anactuator and sensors.

Figure 2 Hand exoskeleton with the mechanics for 16 degreesof freedom (motor unit not shown).

the adaptive algorithm presented for the myoprocessorand integration of the calibration procedure necessaryinto the system. In contrast to other work w5x, we use asimplified model for the EMG signal-muscle force rela-tionship with only a few parameters. EMG-driven motioncontrol is then demonstrated on a leg exoskeleton,whereas Rosen et al. used an arm exoskeleton w5x witha method similar to the DFC approach.

Both approaches presented in this work are based onphysical models with some simplifications and parameteradaptations. Other groups have reported interestingresults using neuro-fuzzy techniques for control of exo-skeletons by means of EMG signals w6x.

The paper is organised as follows. First, the leg exo-skeleton is described (Figure 1). The DHBM and DFCapproaches are explained and compared, and experi-mental results are presented and discussed. Second, thehand exoskeleton (Figure 2), and algorithms for its controlare described. Finally, conclusions are drawn.

Exoskeleton for the leg

The current system is composed of an orthosis with anactuator, a microcontroller connected to a PC, and EMG,force, and Hall sensors. The linear actuator is attachedbetween the thigh and shank with a force sensor in-lineto generate the supporting torque in the knee (Figure 1).

Data acquisition and processing

Three Delsys 2.3 differential electrodes (Delsys, Inc.,Boston, MA, USA) are placed on the rectus femoris, vas-tus lateralis, and semitendinosus muscles in the thigh.These muscles were selected because of their large con-tribution to knee flexion and extension torque and forease of recording.

The electrodes have an inbuilt amplifier with a gain of1000 V/V and a bandpass filter between 20 and 450 Hz.The EMG and force signals are sampled with a 12-bitA/D converter. The knee angle is measured using a Hallsensor and is also digitised.

Recorded EMG signals are offset-corrected, rectified,and low-pass filtered with a second-order Butterworthfilter with a cutoff frequency of 2 Hz, leading to the post-processed signal sn(t), where n is the index of the elec-trode and muscle.

The force measurements are also low-pass filtered at2 Hz during calibration to avoid misalignment of data.During normal operation mode, unfiltered force valuesare used.

Muscle and actuator models

Both control loops that are presented in the Control me-thods section need estimations of the muscle forces thatthe operator produces to derive the necessary support.The post-processed EMG signals are converted intomuscle forces by the EMG-to-force function (based onw8x):

-1ØA s Øsn n n,maxe -1F s s Øf ,Ž .n n n,maxAne -1

where n is the muscle index, An defines the shape of thecurve, and fn,max is the maximum force output for themaximum EMG signal sn,max. The parameters An and fn,max

have to be calibrated for each muscle. The resulting kneetorque is:

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Figure 3 Orthosis and the dynamic body model system.

B E™Z ™ ZZ ZN I -O© n nZ ZT s I = ØF s ,Ž .EMG n n n8C FZ Z™™

sn 1 Z ZZ ZI -OD Gn n

where N is the number of muscles measured. The kneejoint lies at the origin of the coordinate system. On and Inare the origin and insertion of muscle j in the thigh andshank, and were chosen by analogy to human anatomy.No other properties of the human body are taken intoaccount. The torque produced by the actuator is calcu-lated as follows. The values of the force sensor attachedto the shaft of the actuator are converted into the kneetorque contribution Tactuator through the moment arm ofthe actuator.

Control methods

Two approaches were investigated by our group: the firstutilises a dynamic human body model, and the secondDFC of the actuator.

In both systems, a hierarchy of two control loopsexists. The high-level control loop evaluates EMG signalsand the current state of the human body and the ortho-sis. The output of this loop is the desired motion express-ed, as either the desired knee angle or torque. The low-level loop controls the actuator with a PID controller.

Dynamic human body model

The general idea is to simulate a simplified dynamic mod-el of the human body that is affected by three musclesspanning the supported knee and external reaction forc-es applied to both feet and hip. As shown in Figure 3,the recorded EMG signals are converted into muscleforces and used as an input to the body model togetherwith the joint angles and floor contact information. Themodel outputs the desired knee angle of the operator,which is passed to the low level control loop as the targetvalue. The model computation is performed by solvingthe system of equations

˙M q usf q,u qg q TŽ . Ž . Ž .

for u (forward dynamics) and T (inverse dynamics), where˙q is the vector of generalised coordinates for the jointangles and coordinates of the reference point (sagittalplane only), u is the vector of generalised velocities qsu,˙M is the mass distribution, f represents inertial forces andgravity, T takes into account all joint torques and externalreaction forces at the feet, and g(q) is a non-linear func-tion representing the current system configuration andgeometry.

To be able to calculate the accelerations u, the external˙forces and joint torques T that affect the model have tobe calculated. The torque in the supported knee joint iscalculated by evaluating the EMG signals. All other jointtorques and external forces are approximated by com-puting the inverse dynamics with the current kinematicdata for the system. The resulting torques and forces areassumed to be constant for a small time-step and usedas the needed values for T during forward dynamicscomputation (very rough approximation). The body mod-el is simulated for a small time-step of Dts 20 ms, andthe resulting knee angle is passed to the low-level loopand converted into encoder ticks of the actuator to allowposition control. Owing to the amount of computation,the high-level loop runs at 100 Hz, whereas the low-levelloop runs at 1 kHz.

If a greater number of actuated joints are added, theoverall model complexity is similar: inverse dynamicscomputation for this joint is no longer needed, but theEMG signals from additional muscles have to be evalu-ated and the corresponding elements of u have to be˙computed and integrated for the controller.

Direct force control

The DFC method does not use a dynamic model. As inthe previous approach, the resulting knee torque from themuscle activation Tknee is calculated in the high-level con-trol loop from the EMG signals. This can be interpretedas the torque contribution of the operator to the motion.The supporting torque Tsupp is calculated by multiplyingTknee by the amplification factor Sampl, which can be cho-sen according to the support required (the upper bound-ary of Sampl has to be determined experimentally). Tsupp

forms the target value for the low-level loop (Figure 4).

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Figure 4 Orthosis with the operator and the direct force controlsystem.

Figure 5 Knee angle and torques during the experiment with a support of 0.0 (top) and 1.0 (bottom).

The error input of the P controller is calculated accord-ing to:

EsT -T .supp actuator

Both loops run at a frequency of 1 kHz.The complexity increases linearly with the integration

of additional joints.

EMG parameter calibration

In both approaches, the parameters An and fn,max of Fn(t)need to be determined. Unfortunately, the relationshipbetween muscle activation and the EMG signal dependson many different factors, such as electrode placement,muscle size, moisture on skin, etc. These parametershave to be calibrated at the beginning of every experi-mental session. The calibration is performed using iso-metric contractions of the knee flexor and extensormuscles without floor contact for the rectus femoris andsemitendinosus muscles. Currently, the vastus lateralismuscle is not optimised automatically.

The idea of the calibration algorithm is to store theEMG and force values in tables. Each muscle has an

associated table and every entry of the table contains theactivations of all recorded muscles and the force valuefrom the same point in time. The table entry where thevalues are stored is selected by a linear function thatmaps the activation of the muscle to which the tablebelongs to an entry index. This ensures that for the fol-lowing optimisation process data with many different lev-els of activation are used without letting the amount growunreasonably high or weighting certain activationsstronger because of longer durations. The error functionof the optimisation process calculates the sum of thesquared differences between Tknee and Tactuator for allentries. The optimisation yields values for An and fn,max

(the parameters are bounded, and sub-space searchingis possible).

Experimental results

The experiment presented here is a typical example froma series and consists of climbing a single step with twodifferent levels of support in which the supported leg ini-tiates the motion. The experiment was performed with ahealthy person and the DFC method. Figure 5 shows theknee angle, together with the torque computed from theEMG signals and the torque produced by the actuator.The leg is straight at an angle of 08, and negative anglesindicate knee flexion. The upper diagram shows themotion without support (the supporting torque is almostzero during the motion, the orthosis is ‘‘evading’’ the leg),and the lower diagram shows motion with support of 1.0:the actuator torque contribution follows the torque pro-duced by the muscles. As expected, the knee torquederived from the EMG signals is significantly lower com-pared to the trial without support. At ts3.8 s, an over-shoot can be observed: the operator was activating hismuscles as usual, but when the feedback of the actuatorsupport was recognised, muscle activation was imme-diately reduced. This results in a dent in the angle trajec-tory after ts4.1 s. At ts4.3 s, the operator increasesmuscle activation again to finish the motion with a sec-ond small overshoot.

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The actuator support is documented by the force thatis added to the system, resulting in decreased muscleactivation over the whole motion compared to the unsup-ported motion, as can be seen in Figure 5.

Discussion

Both of the above-mentioned algorithms can be used tocontrol an exoskeleton, but there are differences in appli-cation, as well as in the ease of use.

The DHBM approach is more complex in terms of thecalculations necessary. To obtain a solution to the inversedynamic calculation that represents muscle activation orinfluences by external contact forces, all sensor datahave to be accurate and consistent. Only during experi-ments in which the supported leg had no contact withthe environment was this possible. During experimentsinvolving climbing a step, inconsistent sensor readingslead to partially incorrect target motions. On the otherhand, this algorithm calculates the trajectory of the hu-man body, and allows integration of algorithms to main-tain stability based on solving dynamic equations withmotion constraints. It is also possible to predict theintended motion as if the orthosis is not attached to thesubject by disregarding it during forward dynamicscomputation.

The DFC method is more robust because the influenceof all properties such as inertia, joint velocity, and unmea-sured muscle activations, as well as all external contactforces, are implicitly taken into account by the force sen-sor, regardless of where they act on the operator or theorthosis. The force sensor acts as a substitution for thebody model. The computation is not performed by sim-ulation, but by measuring the resulting behaviour of thebody parts in reality. No data between sensors can beinconsistent. Integration of algorithms solving posturalstability issues is not possible, since no constraint equa-tions can be solved without introducing a dynamic modelagain.

Interestingly, although calibration of the muscle para-meters and the muscle model is very simple, support ofthe motion is possible.

Although the exact amplification factor of the opera-tor’s muscle force is unknown (because the calibrationuses only a few representative muscles from differentgroups), significant support can be achieved. The abso-lute torque support can be read from the force sensor.An important aspect is that the trajectory of the kneeangle was similar in both trials, showing that the operatorwas able to adapt to the support. Currently we have noobjective criterion for evaluating the quality of thesupport.

The main focus now is on improving the DFC methodin terms of the interaction between the operator and theorthosis to smooth the executed motion and minimiseundesired overshoots.

Exoskeleton for the hand

The hand exoskeleton was designed to support rehabil-itation and diagnostics after hand surgery or stroke. Thefollowing motions are supported in each finger: flexionand extension of the metacarpophalangeal (MCP), prox-

imal interphalangeal (PIP) and distal interphalangeal (DIP)muscles, and abduction/adduction in MCP joints. Thethumb is currently not actuated. The palm is free ofmechanical elements to allow interaction with the envi-ronment. The fingers are moved by a construction of lev-ers actuated through pull cables guided by flexiblesheaths (Figure 2). Pulleys at the levers allow bidirectionalmovement.

Finger joint angles are measured by Hall sensors inte-grated into the mechanical construction. Angles of theaxes at the motor units measured by optical encoderscorrespond to the angles measured by the Hall sensors.Because of varying tension in the connecting cables,both values for joint angles deviate. Force sensors inte-grated between the levers and finger attachments meas-ure forces during flexion and extension at the fingerjoints. Surface EMG electrodes (Delsys 2.3) measuremuscle activity at eight points on the forearm.

Control methods

Currently the hand exoskeleton supports two controlmodes based on joint angle and force sensor values.EMG sensors can be used for diagnosis, but are not usedfor control yet.

The first control mode allows trajectories determinedby the supervisor (e.g., physiotherapist or physician) tobe followed. This allows reliable repetition of exerciseswith high accuracy. Force and EMG sensor readingsallow the progress of rehabilitation to be assessed.

The second control mode uses force sensors to deter-mine the motion of the exoskeleton. The force sensorreadings are used to calculate a motion using an open-loop admittance control scheme. The exoskeleton canthus follow the motions of the hand. This control modeis needed to teach new motions and is useful for diag-nostic purposes.

However, as mentioned earlier, the force sensors can-not distinguish between internal and external contactforces. Therefore, they are insufficient to measure thesubject’s motion intention during contact with the envi-ronment (e.g., grasping), which is common during reha-bilitation. Integrating a greater number of force sensorsto distinguish between internal and external forces is notpractical for the hand exoskeleton owing to the constrict-ed space.

EMG for control of hand motions

Similar to the leg exoskeleton, EMG sensor data can beused to control the hand exoskeleton without measuringall contact forces. However, there are several difficultiesin application of the algorithms.

The first problem is that not all muscles responsible forhand motion can be measured by surface EMG sensors.Thus, only a subset of the muscles responsible for fingerand hand movement is sampled by surface electrodes.Therefore, it is not possible to use EMG signals alone tocontrol arbitrary motions in all supported degrees offreedom.

The second problem is that, owing to the high densityof different muscles in the forearm, EMG signal separa-tion is particularly relevant. For later application, the sig-nals have to be processed to recover the underlying

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original signals. As described by Farina et al. w9x, blindsource separation can be used to solve this problem.After separation, the signals can be used for control pur-poses. Physicians can also use the recovered musclesignals in concert with the motion and force data fordiagnosis.

Another idea is to use EMG signals to recognise theuser’s intention for a specific gesture (e.g., grasping anobject, pointing at something). EMG-controlled prosthe-ses that do this are commercially available. However,these devices often evaluate muscles that are not natur-ally used for controlled movement, as the original mus-cles are no longer fully functional. These devices requiresome training of the patients. Zecca et al. described thecontrol of multifunctional prosthetic hands by EMG sig-nals w10x.

However, the exoskeleton is intended for use duringrehabilitation of patients where muscle signals may beavailable. Therefore, muscles that are responsible forcontrolled movements will be used to control the handexoskeleton. Several groups used this approach to con-trol other devices, such as hand prostheses and robotichands w11x. Benjuya and Kenney reported on a simplehand orthosis with one degree of freedom controlled byEMG signals w12x.

The hand exoskeleton presented here will be used toextend existing work on EMG motion control to morecomplex movements. For application of the direct-forcecontrol method presented in the Control methods sec-tion, several modifications are necessary. The first stepis blind source separation of the measured muscle sig-nals. Second, force contributions of muscles that are notmeasured by EMG sensors have to be estimated. Onemethod is to derive these from measured muscle signalsby assuming a specific gesture that can be identified bypattern recognition. After these steps, the DFC or DHBMmethod can be used to control the resulting motion. Fur-ther research needs to identify which of the two methodsis superior for the hand exoskeleton because of multi-articular muscles.

Conclusion

The application of EMG signals for controlling exoskele-ton robots was demonstrated for the leg exoskeleton.

The application of EMG signals can improve controlschemes for exoskeleton robots based on force sensormeasurements. This was shown with experimentalresults presented for the leg exoskeleton. For this sys-tem, different control schemes were described. An inter-esting fact is that even simple control schemes based onEMG signals without complicated dynamic models pro-vide remarkable performance. In addition to the advan-tages for motion control, integration of EMG sensors intoexoskeleton robots opens interesting new applications inrehabilitation and diagnostics.

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