This study presents a pioneering effort to replicate human neuromechanical experiments within a virtual environment utilising a digital human model. By employing MyoSuite, a state-of-the-art human motion simulation platform enhanced by Reinforcement Learning (RL), multiple types of impedance identification experiments of human elbow were replicated on a musculoskeletal model. We compared the elbow movement controlled by an RL agent with the motion of an actual human elbow in terms of the impedance identified in torque-perturbation experiments. The findings reveal that the RL agent exhibits higher elbow impedance to stabilise the target elbow motion under perturbation than a human does, likely due to its shorter reaction time and superior sensory capabilities. This study serves as a preliminary exploration into the potential of virtual environment simulations for neuromechanical research, offering an initial yet promising alternative to conventional experimental approaches. An RL-controlled digital twin with complete musculoskeletal models of the human body is expected to be useful in designing experiments and validating rehabilitation theory before experiments on real human subjects.

翻译：本研究开创性地尝试在虚拟环境中利用数字人体模型复现人体神经力学实验。通过采用MyoSuite这一由强化学习增强的先进人体运动仿真平台，我们基于肌肉骨骼模型复现了人体肘关节的多类型阻抗辨识实验。在扭矩扰动实验中，我们对比了强化学习智能体控制的肘关节运动与真实人体肘关节在阻抗辨识结果上的差异。研究表明，相较于人体，强化学习智能体在扰动条件下会表现出更高的肘关节阻抗以稳定目标运动，这很可能归因于其更快的反应速度和更优越的感知能力。本研究初步探索了虚拟环境仿真在神经力学研究中的潜力，为传统实验方法提供了具有前景的替代方案。预计具备完整人体肌肉骨骼模型的强化学习控制数字孪生体，将在真实人体实验前的实验设计及康复理论验证中发挥重要作用。