Millions of people worldwide suffer from stroke each year. One way to assist patients cost-effectively during their rehabilitation process is using end-effector-based robot-assisted rehabilitation. Such systems allow patients to use their own movement strategies to perform a movement task, which encourages them to do self-motivated training but also allow compensation movements if they have problems executing the movement tasks. Therefore, a patient supervision system was developed on the basis of inertial measurement units and a patient-tailored movement interpretation system. Very light and small inertial measurement units were developed to record the patients’ movements during a teaching phase in which the desired movement is shown to the patient by a physiotherapist. During a following exercise phase, the patient is training the previously shown movement alone with the help of an end-effector-based robot-assisted rehabilitation system, and the patient’s movement is recorded again. The data from the teaching and exercise phases are compared with each other and evaluated by using fuzzy logic tailored to each patient. Experimental tests with one healthy subject and one stroke patient showed the capability of the system to supervise patient movements during the robot-assisted end-effector-based rehabilitation.
The authors gratefully acknowledge the support of the KUKA Laboratories, Augsburg, Germany.
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