Mechanical Engineering Department, Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain.
Institute for Automotive Vehicle Safety (ISVA), Universidad Carlos III de Madrid, Avda. de la Universidad 30, 28911 Leganés, Spain.
Sensors (Basel). 2019 Jul 20;19(14):3204. doi: 10.3390/s19143204.
Injuries caused by the overstraining of muscles could be prevented by means of a system which detects muscle fatigue. Most of the equipment used to detect this is usually expensive. The question then arises whether it is possible to use a low-cost surface electromyography (sEMG) system that is able to reliably detect muscle fatigue. With this main goal, the contribution of this work is the design of a low-cost sEMG system that allows assessing when fatigue appears in a muscle. To that aim, low-cost sEMG sensors, an Arduino board and a PC were used and afterwards their validity was checked by means of an experiment with 28 volunteers. This experiment collected information from volunteers, such as their level of physical activity, and invited them to perform an isometric contraction while an sEMG signal of their quadriceps was recorded by the low-cost equipment. After a wavelet filtering of the signal, root mean square (RMS), mean absolute value (MAV) and mean frequency (MNF) were chosen as representative features to evaluate fatigue. Results show how the behaviour of these parameters across time is shown in the literature coincides with past studies (RMS and MAV increase while MNF decreases when fatigue appears). Thus, this work proves the feasibility of a low-cost system to reliably detect muscle fatigue. This system could be implemented in several fields, such as sport, ergonomics, rehabilitation or human-computer interactions.
肌肉过度紧张导致的损伤可以通过检测肌肉疲劳的系统来预防。大多数用于检测肌肉疲劳的设备通常都很昂贵。那么问题来了，是否可以使用能够可靠检测肌肉疲劳的低成本表面肌电图（sEMG）系统呢？带着这个主要目标，这项工作的贡献在于设计了一种低成本的 sEMG 系统，该系统可以评估肌肉何时出现疲劳。为此，使用了低成本的 sEMG 传感器、Arduino 板和 PC，并通过 28 名志愿者的实验来验证其有效性。该实验收集了志愿者的信息，例如他们的身体活动水平，并邀请他们进行等长收缩，同时由低成本设备记录他们的股四头肌的 sEMG 信号。在对信号进行小波滤波后，选择均方根值（RMS）、平均绝对值（MAV）和平均频率（MNF）作为评估疲劳的代表性特征。结果表明，这些参数随时间的变化行为与文献中的研究结果一致（当出现疲劳时，RMS 和 MAV 增加，而 MNF 减少）。因此，这项工作证明了低成本系统可靠检测肌肉疲劳的可行性。该系统可以应用于运动、人体工程学、康复或人机交互等多个领域。
