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Development of automatic positioning system for bicycle saddle based on lower limb's EMG signals during pedaling motion

机译:基于踏板运动下肢肌电信号的自行车鞍座自动定位系统的开发

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This paper proposes an automation system for cyclists, which provides the optimum setting of bicycle components such as saddle and handle against the physical properties of a cyclist. In order to complete our purpose, the evaluation criteria for saddle setting by using bio-signals of a cyclist who pedals a bicycle have to be established firstly. We focus on patterns of muscle activity of the leg muscles that activate in pedaling exercise, and assume that undesirable pattern of muscle activity would appear in the pedaling motion of inexperienced cyclists. We use principal component analysis (PCA) to extract the features of muscle activity from the leg muscles. Our previous work has already clarified that the PCA scores come close to zero in the pedaling motion performed by a skilled cyclist at the saddle height which is subjectively evaluated good. In this paper, we add other degrees of freedoms of saddle setting such as fore-and-back position and angle adjustment to the experimental device. Surface electromyogram (SEMG) of the leg muscles has also been used to measure bio-signals of a cyclist during pedaling exercise. With the use of the aforementioned experimental environment, the effects of setting height and fore-and-back position to the pattern of muscle activity of the leg muscles are investigated in this paper.
机译:本文提出了一种针对骑车人的自动化系统,该系统针对骑车人的物理属性提供了诸如鞍座和手柄之类的自行车组件的最佳设置。为了完成我们的目的,首先必须建立通过使用骑自行车的人的生物信号骑乘鞍座的评估标准。我们专注于在踏板运动中激活的腿部肌肉的肌肉活动模式,并假设在缺乏经验的骑自行车者的踏板运动中会出现不良的肌肉活动模式。我们使用主成分分析(PCA)从腿部肌肉中提取肌肉活动的特征。我们以前的工作已经阐明,熟练的自行车手在鞍座高度进行的踩踏板运动中,PCA得分接近于零,这在主观上被认为是良好的。在本文中,我们为实验装置增加了鞍座设置的其他自由度,例如前后位置和角度调整。腿部肌肉的表面肌电图(SEMG)也已用于测量骑车运动过程中骑车人的生物信号。利用上述实验环境,研究了设置高度和前后位置对腿部肌肉活动模式的影响。

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