Sensors 2019, 19(6), 1288; https://doi.org/10.3390/s19061288
Robot-Assisted Eccentric Contraction Training of the Tibialis Anterior Muscle Based on Position and Force Sensing
1
Graduate School of Saitama Prefectural University, Graduate Course of Health and Social Services, 820 Sannomiya, Koshigaya-Shi, Saitama 343-8540, Japan
2
Graduate School of Science and Engineering, Saitama University, 225 Shimo-Okubo, Sakura-Ku, Saitama-Shi, Saitama 338-8570, Japan
*
Author to whom correspondence should be addressed.
Received: 11 January 2019 / Revised: 7 March 2019 / Accepted: 11 March 2019 / Published: 14 March 2019
(This article belongs to the Special Issue Sensor Fusion in Assistive and Rehabilitation Robotics)
Abstract
The purpose of this study was to determine the clinical effects of a training robot that induced eccentric tibialis anterior muscle contraction by controlling the strength and speed. The speed and the strength are controlled simultaneously by introducing robot training with two different feedbacks: velocity feedback in the robot controller and force bio-feedback based on force visualization. By performing quantitative eccentric contraction training, it is expected that the fall risk reduces owing to the improved muscle function. Evaluation of 11 elderly participants with months training period was conducted through a cross-over comparison test. The results of timed up and go (TUG) tests and 5 m walking tests were compared. The intergroup comparison was done using the Kruskal-Wallis test. The results of cross-over test indicated no significant difference between the 5-m walking time measured after the training and control phases. However, there was a trend toward improvement, and a significant difference was observed between the training and control phases in all subjects. View Full-TextKeywords:
position sensing; force sensing; rehabilitation; training robot
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Kubota, K.; Sekiya, M.; Tsuji, T. Robot-Assisted Eccentric Contraction Training of the Tibialis Anterior Muscle Based on Position and Force Sensing. Sensors 2019, 19, 1288.
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