A Real-Time Control System of Upper-Limb Human Musculoskeletal Model With Environmental Integration

Abstract

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