Observation of human joint motion plays an important role in many fields. The results of the human links can provide information about musculoskeletal parameters. Some devices can track real-time joint movement in the human body during essential daily activities, sports, and rehabilitation with memory for storing the information concerning the body. Based on the algorithm for signal features, the collected data can reveal the conditions of multiple physical and mental health issues. This study proposes a novel method for monitoring human joint motion at a low cost. We propose a mathematical model to analyze and simulate the joint motion of a human body. The model can be applied to an Inertial Measurement Unit (IMU) device for tracking dynamic joint motion of a human. Finally, the combination of image-processing technology was used to verify the results of model estimation. Moreover, the verification showed that the proposed method can estimate joint motions properly with reduced-number IMUs.

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.