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Adaptive control of an end-effector based electromechanical gait rehabilitation device

: Hussein, S.; Schmidt, H.; Krüger, J.


Institute of Electrical and Electronics Engineers -IEEE-:
IEEE 11th International Conference on Rehabilitation Robotics, ICORR 2009. Vol.1 : Kyoto, Japan, 23 - 26 June 2009
Piscataway/NJ: IEEE, 2009
ISBN: 978-1-4244-3788-7
ISBN: 978-1-4244-3789-4
International Conference on Rehabilitation Robotics (ICORR) <11, 2009, Kyoto>
Conference Paper
Fraunhofer IPK ()
adaptive control; End-Effector; gait analysis; medical robotic; patient rehabilitation; patient treatment; position control; shock absorber; vibration control electromechanical gait rehabilitation device; electromachanical gait trainer GT-I; end-effector adaptive control; footplate guidance characteristics; footplate trajectory; gait therapy; human behaviour models; human motor learning strategy; mechanical mass-damper system models; physical disability; stroke patient; window controller

In industrialized countries stroke is the major cause for physical disabilities in adults. In various clinical studies gait therapy with the help of the electromechanical Gait Trainer GT-I proved to enhance the rehabilitation outcome for subacute stroke patients. This paper presents control methods that were developed to enable variability during treatment in order to further improve gait therapy with this class of devices. The algorithms suitable for the Gait Trainer GT-I are analyzed in a simulation study. Therefore models which simulate the practicing subjects behaviour were developed. A purely mechanical mass-damper system models the passive subjects behaviour while motor learning models were adopted to simulate patient adaptation different types of footplate gui dance characteristics. Several adaptive approaches have been developed for other rehabilitation devices in the past. In this work two controllers were developed and evaluated. The first features a one dimensional control window along the footplate trajectory within which the patient is only slightly guided. Outside the window a force field draws the subject back to the window. The second algorithm extends the window controller with a human motor learning strategy for to adapt the window size and thereby the assistance provided to the subjects. They were tested in a simulation study with different human behaviour models, the results are presented in this paper.