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A method of estimating the degree of active participation during stepping in a driven gait orthosis based on actuator force profile matching - Zurich Open Repository and Archive


Banz, R; Bolliger, M; Muller, S; Santelli, C; Riener, R (2009). A method of estimating the degree of active participation during stepping in a driven gait orthosis based on actuator force profile matching. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 17(1):15-22.

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Abstract

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.

Abstract

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.

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Additional indexing

Item Type:Journal Article, refereed, original work
Communities & Collections:04 Faculty of Medicine > Balgrist University Hospital, Swiss Spinal Cord Injury Center
Dewey Decimal Classification:610 Medicine & health
Language:English
Date:February 2009
Deposited On:22 Jul 2009 09:12
Last Modified:05 Apr 2016 13:18
Publisher:IEEE
ISSN:1534-4320
Additional Information:© 2009 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE
Publisher DOI:https://doi.org/10.1109/TNSRE.2008.2008281
PubMed ID:19211319

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