New Method for Reduced-Number IMU Estimation in Observing Human Joint Motion

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.

Automated summary

Observation of human joint motion is important in various fields for obtaining information about musculoskeletal parameters. This study proposes a novel and low-cost method for monitoring human joint motion using a mathematical model and an Inertial Measurement Unit (IMU) device. The collected data based on signal features can reveal the conditions of multiple physical and mental health issues. The proposed method is verified and found to properly estimate joint motions with reduced-number IMUs using image-processing technology.