CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data
CNN-Based Estimation of Sagittal Plane Walking and Running Biomechanics From Measured and Simulated Inertial Sensor Data
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DOI：
10.3389/fbioe.2020.00604
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发表时间：
2020-06-26
影响因子：
5.7
通讯作者：
Eskofier, Bjoern M.
中科院分区：
文献类型：
--
作者：
Dorschky, Eva;Nitschke, Marlies;Eskofier, Bjoern M.
机器学习是一种基于可穿戴传感器数据评估人体运动的有前途的方法。用于训练数据驱动模型的代表性数据集对于确保模型很好地推广到看不见的数据至关重要。然而，获取足够的数据是耗时的，而且往往不可行。我们提出了一种方法来创建现实的惯性传感器数据与相应的生物力学变量的2D步行和跑步模拟。我们用模拟数据增强了测量的惯性传感器数据集，用于训练卷积神经网络，以估计行走和跑步的矢状面关节角度、关节力矩和地面反作用力（GRF）。加入模拟数据后，髋关节、膝关节和踝关节角度的均方根误差（RMSE）分别下降了17%、27%和23%，膝关节和踝关节力矩的RMSE分别下降了6%和2%;前后向和垂直向GRF的RMSE分别下降了6%和2%。生物力学模型的不准确性限制了关节力矩和GRF的模拟辅助估计。改进基于物理的模型和领域自适应学习可以进一步增加模拟数据的益处。未来的工作可以利用生物力学模拟来连接不同的数据源，以创建具有代表性的人体运动数据集。总之，机器学习可以受益于生物力学模拟的现有领域知识，以补充繁琐的数据收集。
Machine learning is a promising approach to evaluate human movement based on wearable sensor data. A representative dataset for training data-driven models is crucial to ensure that the model generalizes well to unseen data. However, the acquisition of sufficient data is time-consuming and often infeasible. We present a method to create realistic inertial sensor data with corresponding biomechanical variables by 2D walking and running simulations. We augmented a measured inertial sensor dataset with simulated data for the training of convolutional neural networks to estimate sagittal plane joint angles, joint moments, and ground reaction forces (GRFs) of walking and running. When adding simulated data, the root mean square error (RMSE) of the test set of hip, knee, and ankle joint angles decreased up to 17%, 27% and 23%, the RMSE of knee and ankle joint moments up to 6% and the RMSE of anterior-posterior and vertical GRF up to 2 and 6%. Simulation-aided estimation of joint moments and GRFs was limited by inaccuracies of the biomechanical model. Improving the physics-based model and domain adaptation learning may further increase the benefit of simulated data. Future work can exploit biomechanical simulations to connect different data sources in order to create representative datasets of human movement. In conclusion, machine learning can benefit from available domain knowledge on biomechanical simulations to supplement cumbersome data collections.