Iterative Learning Synchronization of Robotic Rehabilitation Tasks

Duschau-Wicke, Alexander; Zitzewitz, Joachim von; Banz, Raphael; Riener, Robert

doi:10.1109/icorr.2007.4428447

Cited by 15 publications

(14 citation statements)

References 7 publications

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“…This replay speed is synchronized with the treadmill speed, which is chosen by the therapist. This synchronization can either be done manually, or automatically by an iterative learning algorithm [27].…”

Section: B Nearest-neighbor Path Control With Gravity Cancellationmentioning

confidence: 99%

Generalized elasticities improve patient-cooperative control of rehabilitation robots

Vallery

Duschau-Wicke

Riener

2009

2009 IEEE International Conference on Rehabilitation Robotics

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Section: B Nearest-neighbor Path Control With Gravity Cancellationmentioning

confidence: 99%

Generalized elasticities improve patient-cooperative control of rehabilitation robots

Vallery

Duschau-Wicke

Riener

2009

2009 IEEE International Conference on Rehabilitation Robotics

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“…It can either be set manually by the therapist or automatically by an iterative learning algorithm [22].…”

Section: B Path Control Algorithmmentioning

confidence: 99%

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

Duschau-Wicke

Zitzewitz

Caprez

et al. 2010

IEEE Trans. Neural Syst. Rehabil. Eng.

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Path control: a method for patient-cooperative robot-aided gait rehabilitation Abstract-Gait rehabilitation robots are of increasing importance in neurorehabilitation. Conventional devices are often criticized because they are limited to reproducing predefined movement patterns. Research on patient-cooperative control strategies aims at improving robotic behavior. Robots should support patients only as much as needed and stimulate them to produce maximal voluntary efforts. This paper presents a patient-cooperative strategy that allows patients to influence the timing of their leg movements along a physiologically meaningful path. In this "path control" strategy, compliant virtual walls keep the patient's legs within a "tunnel" around the desired spatial path. Additional supportive torques enable patients to move along the path with reduced effort. Graphical feedback provides visual training instructions. The path control strategy was evaluated with 10 healthy subjects and 15 subjects with incomplete spinal cord injury. The spatio-temporal characteristics of recorded kinematic data showed that subjects walked with larger temporal variability with the new strategy. Electromyographic data indicated that subjects were training more actively. A majority of iSCI subjects was able to actively control their gait timing. Thus, the strategy allows patients to train walking while being helped rather than controlled by the robot.

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“…Therefore the model was reduced to a 1-dof model with the additional assumption that the foot of the subject does not slip on the treadmill and the foot sole stays flat on the ground. To fulfill this assumption, an automatic synchronization algorithm [19] kept Lokomat and treadmill perfectly synchronized, preventing foot slipping on the treadmill.…”

Section: B Model Of the Leg During Stance Phasementioning

confidence: 99%