Path Control: A Method for Patient-Cooperative Robot-Aided Gait Rehabilitation

1. Path Control: A Method forPatient-CooperativeRobot-Aided Gait Rehabilitation Duschau-Wicke, A; Zitzewitz, J; Caprez, A; Lünenburger, L; Riener, R

2. Gait Rehabilitation • Walking disabilities • stroke, • spinal cord injury, • traumaticbrain injury, • cerebral palsy, • multiple sclerosis. • Body weight supported treadmill training • Shown to be effective especially in stroke.

3. Rehabilitation Procedures • Originally, these devices moved along predefined, fixedtrajectories, and they did not adapt their movements tothe activity (or passivity) of the patient. • early phase of rehabilitation or who are severely affected • Patients can remain completely passive, which leads to reduced activity of muscles and metabolism

4. Patient-Cooperativity • To improve these shortcomings, patient-cooperative controlstrategies are being developed • Patient oriented movement with guidance and support • Let patients to influencetheir movements, while still providing sufficient guidanceand support to ensure successful walking. • Consistent feeling of success  Increased Motivation • Variations in muscle activation afferent feedback variations that retrain the neural networksin brain and spinal cord.

5. Assist as Needed • To stimulate a maximum of voluntary contribution, reduce robotic devices supportiveactions to a minimum. • Sufficientto ensure that patients can complete the desired taskin a physiologically correct way • adjusting the stiffness and damping constants of a closed-loop impedance control

6. Earlier Methods • First efforts towards patient-cooperativity concentrated onthe addition of compliance to the devices • Impedance controller • disadvantage of imposing a defined timingof movements on the patient • No freedom in timing without losing guidance in space • Built-in compliance in design • pneumatic PAM and POGO devices of University of California, Irvine • LOPES exoskeleton of Universiteit Twente

7. Moving Window – Virtual Tunnel • MIT-MANUS  The controller of the robot simulated a virtual tunnel in space • If not moved forward as desired, the moving “back wall” of this tunnel carried their hand along after a certain amount of time • Caiet al.  spinalized mice training with a virtual tunnel w/o “back wall” but with a “moving window” that allowed some freedom, but kept them synchronized to the treadmill

8. Here • A similar approach for the Lokomat, but on leg postures than on end-effector position  a torque field tunnel in joint space rather than a force field in Cartesian space • The iSCIsubjects could not cope with the freedom of timing (such as in virtual tunnel) • Superposed the “moving window” approach and the virtual tunnelto one control strategy • Moving window restricts the domain of possible leg postures to a region within the virtual tunnel

9. Here • The additional freedom (patient-cooperativecontrol) is combined with a training • patients have to autonomously control their legs • A visual display shows both the patients’movements and the reference movements, which the patients are supposed to track

10. Questions • Does the path control strategy allow subjects to influence the timing of their movements? • Does training with the path control strategy result inmore active participation of subjects? • Can we modulate howactively subjects participate in the training by adjusting the support? • Are iSCI subjects able to perform gait training with the path control strategy?

11. Device • Automate body weight supported treadmill training of patients • 2 actuated leg orthoses that are attached to the patient’s legs • one linear drive in thehip joint and one in the knee joint • Knee and hip joint torques from sensors • closed-loop controlled body weight support system

12. Path Control Algorithm • S : relative position in the gait cycle,which is normalized to the interval [0, 1). • Two subsequent heel strikes of the same foot define the beginning (S=0) and end of a step (S=1). • The reference trajectory has been recorded from healthysubjects and is used as set point for the impedance controller

13. Controller • ‘S’ is not calculated as afunction of the actual joint angles • A dynamic set point generation algorithm chooses ‘S’ s.t. • Min euclidean distance between qref and qact • An adjustable dead band of width wdba virtualtunnel around the reference trajectory

14. Virtual Tunnel • Allows more spatial variation during late swing and early stance phase to account for the large variability of knee flexion at heels strike • Subjects can move freely and with their own timing as long as they stay within the tunnel • Leg postures outside the tunnel arecorrected by the impedance controller

15. Supportive Torques • Adjustable supportive torques can be superimposed to the controller output • Direction of support is calculated by differentiating the reference trajectory qrefwith respect to the relative position in the gait cycle ‘S’ . • To avoid interferences between the τcor and the τsup, scaledthe support as a linear function of the distance to the center of the path. • ksup :scalar factor that determines the amount of support • ‘d’ : relative distance to the center of the path • Thus, supportive torques are only provided within the tunnel

16. TrainingTask • A visual display presents both task instructions and performance feedback to the patient • Patients are instructed to match the movements of their “mirror legs” with the movements of the “ghost legs.” The timing error: • ∆S can be used as a measure of the patient’s performance in the training task

17. ExperimentalDesign • First experiment with healthy subjects • the possibility to influence the timing of walking and the effects of different levels of support on muscle activity were assessed • Ten healthy young adults • Second experiments with iSCI subjects • Performed the training task to judge its feasibility • 15 patients

18. Healthy Subject Experiments • Prior to the experiment, surface EMG electrodes were attached to the subjects • gastrocnemiusmedialis (GM), • Tibialis anterior (TA) • vastusmedialis (VM) • rectus femoris (RF) • biceps femoris (BF) muscles • The electrodes were placed according to the SENIAM guidelines • Custom-built foot-switches were taped under the heel of the left foot of the subjects to determine heel strikes.

19. Healthy Subject Experiments • POS: position control with the Lokomatcontroller set to maximum stiffness • PATHLOW: path control with low support • PATHMEDIUM: path control with medium support • PATHHIGH: path control with high support • ZEROIMP: zero impedance control with gravity and friction torques of the Lokomat compensated. • Under all path control conditions, window was set to 20% of the gait cycle

20. Data was recorded for one minute after an acclimation phase of two minutes. • In addition to the EMG signals, joint angles from the left hip and knee joints were recorded by sensors at the joint axes.

21. Results - HSE • The spatial variability was significantly different between POS and PATHHIGH, between PATHLOW and ZEROIMP • The temporal variability under the conditions PATHLOW, PATHHIGH, and ZEROIMP was significantly increased compared to POS. • no other significant differences between other conditions • The interaction torques between robot and subject were • lowest under condition ZEROIMP, • highest during condition POS • intermediate values for conditions PATHLOW and PATHHIGH

22. More activity with path control

23. Results – EMG - HSE • In all muscles, PATHLOW activitywas significantly higher than the POS activity. • In RF and BF, PATHLOW activitywas additionally significantly higher than the ZEROIMP activity. • In GM, VM, RF, and BF, PATHHIGH activity was significantly reduced compared to the PATHLOW activity

24. Discussion - HSE • Aimed at validating the successful design implementation • Does the path control strategy allow subjects to influence the timing of their movements? • The POS condition  a benchmark for a completely predetermined type of robotic training • The ZEROIMP condition a benchmark for a robotic training that allowed as much influence as possible

25. Discussion - HSE • The temporal variability (path control mode) is close to temporal variability (zero impedance mode) • subjects experienced freedom of timing in path control mode (close to the experience of ZEROIMP) • The spatial variability, three distinct levels could be identified. • Only small spatial variability occurred under the POS condition. • Large spatial variability occurred under the unconstrained ZEROIMP condition. • Under both path control conditions, subjects showed medium spatial variability in their gait pattern • With path control strategy, subjects can influence the timing of their movements. • Spatial variability of movements is possible within a controllable range.

26. iSCIExperiments • position control • impedance control with the Lokomatcontroller set to 40% of the maximum stiffness • path control • Window set to 20% of the gait cycle • Support gain adjusted individually for each patient • The therapist adjust ksup to the minimal value that enabled the patient to walk in the path control mode • The order of the conditions was randomized. Subjects were unloaded with 30%–50% of their body weight

27. Assesment • Last60 s of the timing error ∆S during walking under the path control condition were analyzed. • performing well  median timing error lay closer to 0 (perfect performance) than to the window border (worst possible performance) • Performing with difficulties  median timing error lay closer to the window border than to 0

28. Results - iSCI • All iSCI subjects were able to understand the training task. • Nosubject showed any problems under the position control condition • Two subjects (P1 and P2) had minor difficulties to walkunder the impedance control condition • were not able to train successfully under the path control condition. • All other subjects managed to get synchronized with the“ghost legs” • both under theimpedance control and the path control condition.

29. Performingwithdifficulties Performingwell

30. Discussion - iSCI • iSCI subjects were able to move synchronously with a visual reference. • If managed to “perform well,” they were not pushed forward by the back wall of the moving window and autonomously controlling the timing of their leg movements • Moving window is big (20% of the gait cycle) • No “accidentially perform well” by means of passive fluctuations in the moving window

31. Discussion - iSCI • No relations between training performance and Asia Impairment Scale, SCIM III mobility score, or WISCI II score were observed. • The failed two subjects had very weak control over their extensor muscles. • Not able to induce sufficient knee extension at the end of swing phase to move along the desired path. • “local” weakness can’t be dealt with “global” support parameter • Automatic adaptation algorithm (individual deficits of a patient) as implemented for the upper extremity by Wolbrechtet al. can be used.

32. Discussion - iSCI • some iSCI subjects  slacking behavior (relying on tunnel wall to keep their legs extended during stance phase). ! • The reason may be : path control strategy is limited to the sagittal plane with independent controllers for each leg. • In future work, a path in higher dimensional space, involving both legs as well as additional degrees of freedom for movements of the pelvis • guide patients through the weight transfer from one leg to the other. • Or perturbations in support

33. Limitations • Constant treadmill speed • the temporal freedom of both the path control and the zero impedance mode were limited to swing phase. • Combine the path control strategy with subject intentionally adapted treadmill speed • The fixed walking pattern that defines the spatial movement path may not be ideal for every patient. • Can be adapted manually by the therapist • For hemi-paretic patients, derive a desired path for the affected leg from observing the unaffected leg • For iSCI patients, an adaptive reshaping of the path, similar to the approach by Jezerniket al. may improve the applicability of the path control strategy