Intelligent motion monitoring system based on multisource feature fusion

Incorrect motion state detection is a significant obstacle of motion monitoring systems in application of the real world. To address this issue, this paper proposes a multi-source feature fusion-based intelligent motion monitoring (IMM) system. First, an edge-end (EE) motion monitoring module is designed to recognize physical movements. The EE module adopts MoveNet model to detect pose keypoints in video frames, then the extracted keypoint coordinate information is classified to the EE motion features. Second, a Kalman fusion filtering (KFF) algorithm is designed for the to extract device-end (DE) module features. The KFF algorithm combines acceleration and angular velocity features to acquire the accurate inertial features of the device-end module, which effectively mitigating external interference caused by device movement. Based on these, the IMM system is built based on the wireless communication between the EE module and the DE module. Then, the IMM system integrates multi-source features from computer vision and inertial sensors, realizing the real-time monitoring of physical movements. The experimental results validate the IMM system for real-time movement monitoring in real-world scenarios.