A real-time full-chain wearable sensor-based musculoskeletal simulation: an OpenSim-ROS Integration

-- Musculoskeletal modeling and simulations enable the 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, we show that it can effectively estimate 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 when combined with pressure insoles. 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. CCURA TE description of human movement includes a comprehensive analysis of different components of the human body involved in performing physical actions, such as body postures, joint kinematics and kinetics, and muscle forces. Such analysis is not only fundamental for understanding the biomechanics of movement but also critical for enabling a wide range of applications. A comprehensive movement analysis is typically performed in specialized laboratories and limited to a small number of accessible participants. This work was supported in part by the Swedish Research Council under Grant 2022-03268, Digital Futures Research Pair and WASP-WISE joint project (corresponding author: Ruoli Wang). Frederico Belmonte Klein, Zhaoyuan Wan and Ruoli Wang are with KTH MoveAbility, Department of Engineering Mechanics, Royal Institute of T echnology, SE-100 44 Stockholm Sweden (e-mail: frekle@kth.se;