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A Prosthesis Control System Based on the Combination of Speech and sEMG Signals and Its Performance Assessment

机译:基于语音和sEMG信号组合的假体控制系统及其性能评估

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Surface electromyographic (sEMG) signals from the residual limb muscles after amputation have been widely used for prosthesis control. However, for the amputees with high-level amputations, there usually exists a dilemma that the sEMG signal sources for prosthesis control are limited but more limb motions need to be recovered, which strongly limits the practicality of the current myoelectric prostheses. In order to operate prostheses with multiple degrees of freedom (DOF) of movements, several control protocols have been suggested in some previous studies to deal with this dilemma. In this paper, a prosthesis control system based on the combination of speech and sEMG signals (Strategy 1) was built up in laboratory conditions, where speech commands were applied for the prosthesis joint-mode switching and sEMG signals were applied to determine the motion-class and execute the target movement. The control performance of the developed system was evaluated and compared with that of the traditional control strategy based on the pattern recognition of sEMG signals (Strategy 2). The experimental results showed that the difference between Strategy 1 and Strategy 2 was insignificant for the control of a 2-DOF prosthesis, but Strategy I was much better in the control of a prosthesis with more DOFs in comparison to Strategy 2. In addition, the positive user experience also demonstrated the reliability and practicality of Strategy 1.
机译:截肢后残留肢体肌肉的表面肌电图(sEMG)信号已广泛用于假体控制。然而,对于截肢程度较高的截肢者,通常存在一个难题,即用于假体控制的sEMG信号源有限,但需要恢复更多的肢体运动,这严重限制了当前肌电假体的实用性。为了操作具有多个自由度(DOF)的假体,在先前的一些研究中已经提出了几种控制方案来解决这一难题。本文在实验室条件下建立了基于语音和sEMG信号组合的假体控制系统(策略1),其中语音命令应用于假体联合模式切换,而sEMG信号用于确定运动。进行分类并执行目标运动。对开发系统的控制性能进行了评估,并与基于sEMG信号的模式识别的传统控制策略进行了比较(策略2)。实验结果表明,策略1和策略2之间的差异对于2-DOF假体的控制微不足道,但是与策略2相比,策略I在具有更多DOF的假体控制中要好得多。积极的用户体验也证明了策略1的可靠性和实用性。

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