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Visual biofeedback with information about the patients' degree of activity is a valuable adjunct to robot-assisted gait training as means of increasing the motivation and participation of the patients during highly repetitive training sessions. In the driven gait orthosis (DGO) Lokomat, an estimation of the patient's activity level was based on man-machine interaction forces as measured at the hip and knee actuators of the exoskeletal device. In an early approach, theoretical assumptions about the expected man-machine interaction forces, due to the varying behavior of the patients, were formulated for the calculation of quantitative biofeedback. In contrast to this theory-based approach, we have developed a novel method where the biofeedback calculations were based on measured reference man-machine interaction force profiles of healthy subjects when walking with different degrees of activity. To account for intrasubject and intersubject variability, reference force profiles were processed in a model to generate multiple force profiles describing each activity state. To estimate the activity of a subject walking in the DGO, the man-machine interaction force profile was measured, matched to each of the generated force profiles, and the best fitting profile of the different activity states was identified by the smallest Euclidian distance, respectively. By calculating the difference between these Euclidian distances, a quantitative estimate of the patient's degree of activity was obtained. The novel method was evaluated and compared to the conventional approach in a study with 18 healthy subjects. This comparison showed that the novel method was more reliable in detecting different activity states and is, therefore, a promising approach for future biofeedback systems.
Neural Systems and Rehabilitation Engineering, IEEE Transactions on (Volume:17 , Issue: 1 )
Date of Publication: Feb. 2009