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MyoSim: Fast and physiologically realistic MuJoCo models for musculoskeletal and exoskeletal studies

Huawei Wang, Vittorio Caggiano, Guillaume Durandau, Massimo Sartori, Vikash Kumar

Year
2022
Citations
5
Access
Open access

Abstract

Owing to the restrictions of live experimentation, musculoskeletal simulation models play a key role in biological motor control studies and investigations. Successful results of which are then tried on live subjects to develop treatments as well as robot aided rehabilitation procedures for addressing neuromusculoskeletal anomalies ranging from limb loss, to tendinitis, from sarcopenia to brain and spinal injuries. Despite its significance, current musculoskeletal models are computationally expensive, and provide limited support for contact-rich interactions which are essential for studying motor behaviors in activities of daily living, during rehabilitation treatments, or in assistive robotic devices. To bridge this gap, this work proposes an automatic pipeline to generate physiologically accurate musculoskeletal, as well as hybrid musculoskeletal-exoskeletal models. Leveraging this pipeline we present \textit{MyoSim} -- a set of computationally efficient (over 2 orders of magnitude faster than state of the art) musculoskeletal models that support fully interactive contact rich simulation. We further extend MyoSim to support additional features that help simulate various real-life changes/diseases, such as muscle fatigue, and sarcopenia. To demonstrate the potential applications, several use cases, including interactive rehabilitation movements, tendon-reaffirmation, and the co-simulation with an exoskeleton, were developed and investigated for physiological correctness. Webpage: \href{https://sites.google.com/view/myosuite}{\color{blue}https://sites.google.com/view/myosuite}

Keywords

Computer science

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