基于肌电肌电信号和arduino的健身训练肌肉疲劳监测,采用包络和滑动窗口方法

S. S. Suprapto, Vicky Andria Kusuma, M. Farid, Muhammad Agung Nursyeha, Kharis Sugiarto, Aji Akbar Firdaus, Dimas Fajar Uman Putra
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引用次数: 0

摘要

肌肉是人体骨骼运动中进行体育活动的重要器官。运动过程中肌肉活动的测量可以用肌电图(EMG)来完成。本研究使用myware肌肉传感器(AT-04-001)集成Arduino Uno和Xbee,无线监测肱二头肌疲劳。客观上采用包络和滑动窗口法对疲劳数据进行处理,主观上采用被访者的口头描述。本研究发现,当肌电振幅增加,窗口大小为5 s时,该方法可以客观地测量肌肉疲劳。运动时右臂肱二头肌疲劳表现更强,平均测量时间从69.67 s增加到41.87 s;从98.90 s到98.80 s分别为53.53 s和76.87 s,其中左臂倾向于疲劳的比值从42.13 s到23.53 s;从51.60秒到23.53秒。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Gym training muscle fatigue monitoring using EMG myoware and arduino with envelope and sliding window methods
Muscles are an important organ in the movement of the body's skeleton to carry out sports activities. Measurement of muscle activity during the exercise process can be done using electromyography (EMG). This research uses Myoware muscle sensor (AT-04-001) which is integrated with Arduino Uno and Xbee to monitor biceps brachii muscle fatigue wirelessly. Fatigue data processing is carried out objectively using the envelope and sliding window method and subjectively verbally from the respondents. From this study, it was found that muscle fatigue can be measured using the method objectively when there is an increase in EMG amplitude with a window size of 5 s. The indication of biceps brachii muscle fatigue for the right arm is stronger to withstand the load during exercise with the average duration of the measurement of the right arm is 41.87 s from 69.67 s; 53.53 s from 98.90 s and 76.87 s from 98.80 s with the ratio of the left arm tending to fatigue more quickly is 23.53 s from 42.13 s; 41.87 s from 51.60 s and 23.53 s from 44.73 s.
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