4.7 Article Proceedings Paper

A Method of Estimating the Degree of Active Participation During Stepping in a Driven Gait Orthosis Based on Actuator Force Profile Matching

出版社

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TNSRE.2008.2008281

关键词

Biofeedback; gait training; rehabilitation robotics; signal processing

向作者/读者索取更多资源

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 hest. 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.

作者

我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。

评论

主要评分

4.7
评分不足

次要评分

新颖性
-
重要性
-
科学严谨性
-
评价这篇论文

推荐

暂无数据
暂无数据