A Real-time Control System of Upper-limb Human Musculoskeletal Model with Environmental Integration

The intricate dynamics of the human musculoskeletal system require complex mathematical computations for accurate simulation, posing challenges in estimating muscle activity. Real-time processes and comprehensive analysis greatly influence effectiveness monitoring applications. objective our research was to enhance real-time activity predictions by incorporating environmental data into simulations, focusing on upper extremity. Our model, developed using MuJoCo software, consisted 50 Hill-type muscles integrated context. Information posture collected from single RGBD sensors positioned at 32 three-dimensional node locations. We used inverse kinematics convert this joint angle parameters simulation model. stretch reflex each regulated initiate movement target joints. Desired length derived mechanical interaction between bone structure muscle-tendon actuator connected it. model also allowed application artificial force simulate external load conditions. To validate we performed basic movements with extremity measured EMG sensors. results confirmed model’s ability accurately predict activation exerted muscle. Further experiments demonstrated its potential seamless integration dynamic conditions, thereby enhancing utility as a physical system.