Exercise Condition Sensing in Smart Leg Extension Machine

• Published in: Sensors (Basel, Switzerland) Vol. 22; no. 17; p. 6336
• Main Authors: Shiao, Yaojung, Hoang, Thang
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 23-08-2022; MDPI
• Subjects: Electroencephalography; Electromyography; Evaluation; Exercise equipment; Fatigue; fitness exercise; Fourier transforms; Inertial platforms; leg extension; Machinery; Magneto-electric machines; Measuring instruments; Metabolism; Muscle fatigue; Muscles; Muscular fatigue; Personal health; Physical fitness; Rehabilitation; rehabilitation exercise; Signal processing; User behavior; Wavelet transforms
• ISSN: 1424-8220; 1424-8220
• 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.