Mechanical and Aerospace Department, Monash University, Melbourne, VIC 3800, Australia.
Department of Mechanical Systems Engineering, Tokyo University of Agriculture and Technology, Tokyo 184-0012, Japan.
Sensors (Basel). 2021 Feb 22;21(4):1499. doi: 10.3390/s21041499.
Fatigue increases the risk of injury during sports training and rehabilitation. Early detection of fatigue during exercises would help adapt the training in order to prevent over-training and injury. This study lays the foundation for a data-driven model to automatically predict the onset of fatigue and quantify consequent fatigue changes using a force plate (FP) or inertial measurement units (IMUs). The force plate and body-worn IMUs were used to capture movements associated with exercises (squats, high knee jacks, and corkscrew toe-touch) to estimate participant-specific fatigue levels in a continuous fashion using random forest (RF) regression and convolutional neural network (CNN) based regression models. Analysis of unseen data showed high correlation (up to 89%, 93%, and 94% for the squat, jack, and corkscrew exercises, respectively) between the predicted fatigue levels and self-reported fatigue levels. Predictions using force plate data achieved similar performance as those with IMU data; the best results in both cases were achieved with a convolutional neural network. The displacement of the center of pressure (COP) was found to be correlated with fatigue compared to other commonly used features of the force plate. Bland-Altman analysis also confirmed that the predicted fatigue levels were close to the true values. These results contribute to the field of human motion recognition by proposing a deep neural network model that can detect fairly small changes of motion data in a continuous process and quantify the movement. Based on the successful findings with three different exercises, the general nature of the methodology is potentially applicable to a variety of other forms of exercises, thereby contributing to the future adaptation of exercise programs and prevention of over-training and injury as a result of excessive fatigue.
疲劳会增加运动训练和康复过程中受伤的风险。在运动过程中尽早发现疲劳有助于调整训练，以防止过度训练和受伤。本研究为使用力板（FP）或惯性测量单元（IMU）自动预测疲劳发作并量化随之而来的疲劳变化奠定了基础。力板和佩戴在身上的 IMU 用于捕获与运动相关的动作（深蹲、高膝跳、螺旋脚趾触地），使用随机森林（RF）回归和基于卷积神经网络（CNN）的回归模型以连续方式估计参与者特定的疲劳水平。对未见数据的分析表明，预测的疲劳水平与自我报告的疲劳水平之间存在高度相关性（深蹲、高膝跳和螺旋脚趾触地的相关性分别高达 89%、93%和 94%）。使用力板数据进行预测的性能与使用 IMU 数据的性能相当；在这两种情况下，卷积神经网络的效果最佳。与力板的其他常用特征相比，压力中心（COP）的位移与疲劳相关。Bland-Altman 分析也证实，预测的疲劳水平接近真实值。这些结果通过提出一种可以在连续过程中检测运动数据相当小的变化并量化运动的深度神经网络模型，为人体运动识别领域做出了贡献。基于三种不同运动的成功发现，该方法的一般性可能适用于多种其他形式的运动，从而有助于未来调整运动计划，并防止因过度疲劳而导致过度训练和受伤。
