Quintessa Ltd., Newtown Road, Henley-on-Thames, Oxfordshire RG9 1HG, UK; Department of Applied Mathematics, Liverpool John Moores University, Liverpool, UK.
Research Institute for Sport and Exercise Sciences, Liverpool John Moores University, Liverpool, UK; School of Sport, Exercise and Rehabilitation Sciences, University of Birmingham, UK.
Med Eng Phys. 2020 Apr;78:82-89. doi: 10.1016/j.medengphy.2020.02.002. Epub 2020 Feb 27.
Prediction of ground reaction force (GRF) magnitudes during running-based sports has several important applications, including optimal load prescription and injury prevention in athletes. Existing methods typically require information from multiple body-worn sensors, limiting their ecological validity, or aim to estimate discrete force parameters, limiting their ability to assess overall biomechanical load. This paper presents a neural network method to predict GRF time series from a single, commonly used, trunk-mounted accelerometer. The presented method uses a principal component analysis and multilayer perceptron (MLP) to obtain predictions. Time-series r coefficients with test data averaged around 0.9 for each impact, comparing favourably with alternative approaches which require additional sensors. For the impact peak, r was 0.74 across activities, comparing favourably with correlation analysis approaches. Several modifications, such as subject-specific training of the MLP, may help to improve results further, but the presented method can accurately predict GRF from trunk accelerometry data without requiring additional information. Results demonstrate the scope of machine learning to exploit common wearable technologies to estimate GRF in sport-specific environments.
预测基于跑步的运动中的地面反作用力（GRF）大小具有几个重要的应用，包括运动员的最佳负荷处方和预防受伤。现有的方法通常需要来自多个佩戴在身体上的传感器的信息，这限制了它们的生态有效性，或者旨在估计离散的力参数，从而限制了它们评估整体生物力学负荷的能力。本文提出了一种使用单个常用躯干安装加速度计预测 GRF 时间序列的神经网络方法。提出的方法使用主成分分析和多层感知器（MLP）来获得预测。使用测试数据计算时间序列 r 系数，每个冲击的平均值约为 0.9，与需要额外传感器的替代方法相比具有优势。对于冲击峰值，跨活动的 r 值为 0.74，与相关分析方法相比具有优势。一些修改，例如 MLP 的特定于主题的训练，可能有助于进一步提高结果，但所提出的方法可以准确地从躯干加速度计数据预测 GRF，而无需额外的信息。结果表明，机器学习可以利用常见的可穿戴技术在特定于运动的环境中估计 GRF。
