A Real-Time Full-Chain Wearable Sensor-Based Musculoskeletal Simulation: An OpenSim-ROS Integration.

Musculoskeletal modeling and simulations enable accurate description and analysis of the movement of biological systems with applications such as rehabilitation assessment, prosthesis, and exoskeleton design. However, the widespread usage of these techniques is limited by costly sensors, laboratory-based setups, computationally demanding processes, and the use of diverse software tools that often lack seamless integration. In this work, we address these limitations by proposing an integrated, real-time framework for musculoskeletal modeling and simulations that leverages OpenSimRT, the robotics operating system (ROS), and wearable sensors. As a proof-of-concept, we demonstrate that this framework can reasonably well describe inverse kinematics of both lower and upper body using either inertial measurement units or fiducial markers. Additionally, when combined with pressure insoles, it effectively estimates inverse dynamics of the ankle joint and muscle activations of major lower limb muscles during daily activities, including walking, squatting and sit to stand, stand to sit. Based on the preliminary data, the proposed pipeline showed strong agreement (r>0.70) with motion capture measurements across tasks for joint angles and torques, except for knee torque during walking. RMSE values for joint angles were within 9° for all joints except the knee during squat. Overall, lower RMSE were observed for squat and sit-to-stand compared with walking, particularly at the knee and hip joints. We believe this work lays the groundwork for further studies with more complex real-time and wearable sensor-based human movement analysis systems and holds potential to advance technologies in rehabilitation, robotics and exoskeleton designs.