预测感知体力疲劳等级的无损测量和评价

T. Kiryu, Y. Doi, K. Ohstuka
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引用次数: 3

摘要

有一种肌肉力量衰竭是由身体疲劳引起的。由于肌电活动的变化发生在衰竭之前,表面肌电图(SEMG)是有价值的。然而,表面肌电信号表现的准确性在时空上并不总是得到保证。我们首先引入了一个二维表面电极来搜索电极在皮肤上的位置,在那里可以高精度地完全检测到表面肌电信号的表现。此外,足够的精度对于解释生理活动非常重要。然后,我们采用肌肉协同的思想,确定运动相关的目标肌肉。也就是说,基于功能性活动源于一个分层的多时间尺度系统的事实,我们建立了一个感知身体疲劳(RPF)的评级模型,将心理表现(长期事件)与生理表现(短期事件)相结合。这将导致无损的测量和评估,以更快地预测物理疲劳引起的失效。最终目标是通过建立安全控制电力辅助系统的RPF来实现无处不在的风险规避策略。
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Lossless measurement and evaluation for predicting the rating of perceived physical fatigue
There is a muscle force failure caused by physical fatigue. Since the change in myoelectrical activity occurs in advance of the failure, surface EMG (SEMG) is valuable. However, the accuracy of SEMG manifestation is not always guaranteed spatiotemporally. We first introduced a 2D surface electrode to search the location of electrode on the skin where SEMG manifestations are fully detected with high accuracy. Furthermore, enough precision is important to interpret the physiological activities. Then we determine the motion related target muscles, adopting the idea of muscle synergy. That is, based on the fact that functional activities are stemmed from a hierarchical multi-timescale system, we model a rating of perceived physical fatigue (RPF) integrating the psychological manifestation (long-term event) by physiological one (short-term event). This will lead to lossless measurement and evaluation for a faster prediction of the physical fatigue induced failure. The ultimate goal is a ubiquitous strategy for risk avoidance by establishing the RPF for safely controlling the electrical power assist.
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