IMU- and EMG-driven simulation of muscle contraction during gait

Continuous monitoring of human movement is necessary to adapt personalized interventions, evaluate intervention efficacy, and facilitate research in cumulative-load dependent phenomena (e.g., muscle hypertrophy, osteoarthritis). Wearable sensors provide the hardware solution, but a minimal sensor set is required for practical deployment. This presents an analytical hurdle for use of physics-based simulation to calculate the biomechanical variables of interest; a minimal sensor set provides insufficient information. Machine learning techniques have been proposed as a potential solution but at the expense of generalizability and interpretability. Thus, we developed a hybrid approach that utilizes the best of both worlds: machine learning is used only to provide the missing information necessary to drive a physics-based simulation.We developed an algorithm for simulating muscle contraction during gait using only wearable sensors. To facilitate practical deployment, our method uses a reduced sensor array: two IMUs (one each on the thigh and the shank) and three surface electrodes to measure surface electromyograms of the lateral and medial gastrocnemius and vastus medialis. The musculoskeletal kinematics are computed using the IMU data and optimal state estimation. Machine learning is used only to estimate the excitation of the non-instrumented muscles. Muscle contraction is then simulated using EMG-driven techniques.Our validation study (https://ieeexplore.ieee.org/document/9507535) demonstrated that our algorithm performed similarly to state-of-art techniques (both physics- and data-driven approaches) in characterizing muscle and joint dynamics in walking gait.Code and an example dataset is publicly available and maintained at this GitHub repo: https://github.com/gurchiek/nms-dyn <br/><br/>This project includes the following software/data packages: <br/> <ul> <li> <a href="https://simtk.org/frs?group_id=2173#pack_2296">nms-dyn </a> : Refer to this GitHub repo for step-by-step instructions and dependencies: https://github.com/gurchiek/nms-dyn </li> </ul>

持续监测人体运动对于个性化干预措施的实施、干预效果的评估以及累积负荷相关现象（例如肌肉肥大、骨关节炎）的研究进展至关重要。可穿戴传感器提供了硬件解决方案，但实际部署中所需的传感器集合量最小。这为基于物理学的模拟计算感兴趣的生物力学变量构成了一道分析难题；最小传感器集合提供的信息不足。已提出机器学习技术作为潜在的解决方案，但以牺牲通用性和可解释性为代价。因此，我们开发了一种融合两者优势的混合方法：仅使用机器学习来提供驱动基于物理学的模拟所需的缺失信息。我们开发了一种仅使用可穿戴传感器模拟行走过程中肌肉收缩的算法。为了促进实际部署，我们的方法使用了一个简化的传感器阵列：包括大腿和胫骨上的各一个惯性测量单元（IMU）以及三个表面电极，用于测量腓肠肌外侧和内侧以及股内侧肌的表面肌电图。通过IMU数据和最佳状态估计计算肌肉骨骼运动学。仅使用机器学习来估计非仪器化肌肉的兴奋度。随后，利用肌电图驱动技术模拟肌肉收缩。我们的验证研究（https://ieeexplore.ieee.org/document/9507535）表明，我们的算法在表征行走步态中的肌肉和关节动力学方面与最先进的技术（无论是基于物理学的还是基于数据的方法）表现相似。代码和示例数据集公开可用，并由GitHub仓库维护：https://github.com/gurchiek/nms-dyn。本项目包括以下软件/数据包：
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<li><a href="https://simtk.org/frs?group_id=2173#pack_2296">nms-dyn</a> : 请参考此GitHub仓库以获取逐步说明和依赖项：https://github.com/gurchiek/nms-dyn</li>
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