Integration Design of Motion Capture and Sensor Technology in Home Fitness

Background and Purpose: This study aims to design an integrated home fitness system combining motion capture (Mocap) and sensor technology (Sensing Tech). With remote work and home-based lifestyles becoming the norm, traditional home fitness methods face challenges in personalized guidance and real-time feedback. This system aims to provide personalized fitness advice and instant corrections by monitoring users' movement quality and physiological state in real time, thereby enhancing the safety, effectiveness, and overall health level of home fitness. Technical Overview: Mocap captures human motion data through devices such as cameras and sensors, generating precise three-dimensional data models, primarily divided into optical, inertial, and hybrid systems. Sensing Tech utilizes accelerometers, gyroscopes, heart rate sensors, etc., to detect and respond to physical, chemical, or biological changes in the environment, providing real-time data for monitoring health conditions. System Design: The system comprises four parts: architecture, data acquisition, data processing, and user interface. It adopts a hierarchical architecture including the data acquisition layer, data processing layer, and user interface layer. User actions and physiological data are collected using IMU sensors, heart rate monitors, and camera systems. Integrated signal processing, feature extraction, behavior recognition algorithms run on microprocessors or cloud servers. Real-time feedback and personalized training advice are provided through a graphical user interface (GUI). Technical Implementation and Simulation: Software such as MATLAB/Simulink is used for system modeling and simulation to validate feasibility. Machine learning algorithms are developed for action recognition and health monitoring, optimizing model parameters and conducting validation. Performance Evaluation: Model performance is evaluated using metrics like confusion matrices, accuracy, recall rate, and F1 scores. User feedback is collected through surveys and interviews to assess system usability and satisfaction. Conclusion: The system designed in this study provides personalized fitness guidance through real-time monitoring and analysis of user movement and physiological data. The hierarchical architecture and efficient data processing ensure real-time performance and accuracy. Through theoretical simulation and algorithm development, the system's performance has been optimized. In the future, this system is expected to be further refined and expanded into broader application areas, offering new possibilities for the development of health technology.