Design of Fitness Movement Detection and Counting System Based on MediaPipe

Abstract

With the rapid advancements in artificial intelligence and computer vision technology, the field of visual-based human pose detection has emerged as a highly sought-after research area in recent years. The identification of human poses has practical applications in diverse domains, ranging from motion-sensing games for human-computer interaction to activity prediction and medical rehabilitation. The present study is focused on the utilization of human pose detection for fitness movement counting. The ultimate aim of the system design is to accurately detect the skeletal key points of each body part in the image and subsequently connect them to form a human pose skeleton, which serves as a vital representation of the characteristics of human motion, particularly in the context of video data, where multiple human poses can be linked to form a certain movement trajectory. By judging the trajectory and angle changes, the system can determine whether people's fitness movements are correct and help them improve their fitness effectiveness. Hence, an increasing number of researchers are investing time and effort in this field. One common approach for human pose detection is OpenPose, but this model has a large and complex structure and low detection accuracy. Therefore, this fitness movement detection and counting system uses a lightweight MediaPipe model and improves it to enhance the algorithm's accuracy and recognition speed. The specific work in this paper includes three main points: (1) a suitable network structure to detect human skeletal points; (2) the appropriate skeletal structure for fitness movements through experiments to obtain accurate results; and (3) a Qt interface for human-computer interaction.