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A Novel Trunk Rehabilitation Robot Based Evaluation of Seated Balance Under Varying Seat Surface and Visual Conditions

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Abstract
Physical therapy involving the use of varying types of seating surface and visual input is recommended for individuals suffering from trunk instability. Some robots have been developed to assist in such therapy protocols, but none of them fully constrains the user's lower extremities to move with the seat, which is required to fully transfer the task of maintaining balance to the trunk. To fulfill this requirement, we have developed a robot that can provide a static, unstable or forced perturbation seating surface. The instability of seating surface is provided by having the robot follow movements in the user's center of pressure (COP) and forced perturbations are provided by moving the surface according to an operator's commands irrespective of the COP position. The system is also capable of providing visual feedback of the user's COP. This paper presents a study conducted using this novel robot aimed at evaluating the effect of the different seat modes on the balance of healthy subjects under different visual conditions (blindfold, eyes open and visual feedback). Various COP and trunk movement parameters were observed and the results indicate that the system can elicit similar responses in the unstable mode as the conventional devices, showing that it may be used as a controllable alternative to such devices for the training and objective evaluation of stroke survivors. The results under perturbation conditions showed deviations from the generally held notions about the use of visual feedback. Thus, revealing the need for further studies on the implications of using visual feedback under perturbation conditions. The observation of effects similar to conventional systems that may be beneficial for stroke survivors and the system's ability to help assess recovery progress show that the system holds promise for use as a trunk training and objective performance evaluation tool for stroke survivors.
Author(s)
Eizad, AmreLee, HosuPyo, SanghunAfzal, Muhammad RaheelLyu, Sung-KiYoon, Jungwon
Issued Date
2020-11
Type
Article
DOI
10.1109/ACCESS.2020.3036435
URI
https://scholar.gist.ac.kr/handle/local/11882
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Citation
IEEE ACCESS, v.8, pp.204902 - 204913
ISSN
2169-3536
Appears in Collections:
Department of AI Convergence > 1. Journal Articles
공개 및 라이선스
  • 공개 구분공개
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