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

2025-07-26

AI Summary

Existing musculoskeletal modeling and simulation approaches are hindered by reliance on expensive sensors, laboratory settings, high computational overhead, and poor tool integration. To address these limitations, this work introduces the first wearable, real-time musculoskeletal simulation framework that deeply integrates OpenSimRT with ROS, enabling online estimation of inverse kinematics, dynamics, and muscle activations. The system leverages low-cost wearable sensors—including IMUs, RFID tags, and pressure-sensing insoles—and employs ROS for low-latency, multi-source data communication and end-to-end real-time closed-loop analysis. Validation across daily activities—walking, squatting, and sit-to-stand/stand-to-sit transitions—demonstrates accurate estimation of ankle joint dynamics and key lower-limb muscle activations, confirming its efficacy in unconstrained, real-world environments. This framework overcomes environmental constraints, establishing a lightweight, deployable paradigm for real-time musculoskeletal analysis—advancing applications in rehabilitation assessment, prosthetic control, and exoskeleton design.

Technology Category

• Intelligent Robots: State Estimation
• Cognitive Modeling & Cognitive Systems: Simulating Human Behavior
• Machine Learning: Imitation Learning & Inverse Reinforcement Learning

Application Category

• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems
• Systems and Infrastructure for Web, Mobile and WoT: Experiences and lessons learnt from Web-based algorithms and system deployments
• Web Mining and Content Analysis: Web data generation and simulation

Abstract

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.

Problem

• Real-time musculoskeletal modeling with wearable sensors
• Integration of OpenSim and ROS for seamless simulation
• Accurate movement analysis for rehabilitation and robotics

Innovation

• Real-time OpenSim-ROS integration for simulations
• Wearable sensors enable portable movement analysis
• Combines IMUs and pressure insoles for dynamics