Department of Vehicle Engineering, National Taipei University of Technology, Taipei City 106344, Taiwan.
Center of Electrical Engineering, Duy Tan University, Danang 550000, Vietnam.
Sensors (Basel). 2022 Aug 23;22(17):6336. doi: 10.3390/s22176336.
Skeletal muscles require fitness and rehsabilitation exercises to develop. This paper presents a method to observe and evaluate the conditions of muscle extension. Based on theories about the muscles and factors that affect them during leg contraction, an electromyography (EMG) sensor was used to capture EMG signals. The signals were applied by signal processing with the wavelet packet entropy method. Not only did the experiment follow fitness rules to obtain correct EMG signal of leg extension, but the combination of inertial measurement unit (IMU) sensor also verified the muscle state to distinguish the muscle between non-fatigue and fatigue. The results show the EMG changing in the non-fatigue, fatigue, and calf muscle conditions. Additionally, we created algorithms that can successfully sense a user's muscle conditions during exercise in a leg extension machine, and an evaluation of condition sensing was also conducted. This study provides proof of concept that EMG signals for the sensing of muscle fatigue. Therefore, muscle conditions can be further monitored in exercise or rehabilitation exercise. With these results and experiences, the sensing methods can be extended to other smart exercise machines in the future.
骨骼肌需要健身和康复运动来发展。本文提出了一种观察和评估肌肉伸展状况的方法。基于肌肉的理论和影响它们在腿部收缩期间的因素，使用肌电图（EMG）传感器来捕获 EMG 信号。通过信号处理与小波包熵方法对信号进行处理。实验不仅遵循健身规则以获得正确的腿部伸展 EMG 信号，而且惯性测量单元（IMU）传感器的组合也验证了肌肉状态，以区分非疲劳和疲劳肌肉。结果显示了非疲劳、疲劳和小腿肌肉状态下的 EMG 变化。此外，我们创建了算法，可以在腿部伸展机上成功感知用户运动时的肌肉状况，并对状况感知进行了评估。本研究为肌电信号感知肌肉疲劳提供了概念证明。因此，可以在运动或康复运动中进一步监测肌肉状况。有了这些结果和经验，未来可以将传感方法扩展到其他智能运动机器。
