Rousseau Thomas, Venture Gentiane, Hernandez Vincent
Faculty of Odontology, University of Reims Champagne-Ardenne, 51100 Reims, France.
Department of Mechanical Engineering, The University of Tokyo, Tokyo 113-8654, Japan.
Sensors (Basel). 2024 Dec 4;24(23):7775. doi: 10.3390/s24237775.
Fatigue plays a critical role in sports science, significantly affecting recovery, training effectiveness, and overall athletic performance. Understanding and predicting fatigue is essential to optimize training, prevent overtraining, and minimize the risk of injuries. The aim of this study is to leverage Human Activity Recognition (HAR) through deep learning methods for dimensionality reduction. The use of Adversarial AutoEncoders (AAEs) is explored to assess and visualize fatigue in a two-dimensional latent space, focusing on both semi-supervised and conditional approaches. By transforming complex time-series data into this latent space, the objective is to evaluate motor changes associated with fatigue within the participants' motor control by analyzing shifts in the distribution of data points and providing a visual representation of these effects. It is hypothesized that increased fatigue will cause significant changes in point distribution, which will be analyzed using clustering techniques to identify fatigue-related patterns. The data were collected using a Wii Balance Board and three Inertial Measurement Units, which were placed on the hip and both forearms (distal part, close to the wrist) to capture dynamic and kinematic information. The participants followed a fatigue-inducing protocol that involved repeating sets of 10 repetitions of four different exercises (Squat, Right Lunge, Left Lunge, and Plank Jump) until exhaustion. Our findings indicate that the AAE models are effective in reducing data dimensionality, allowing for the visualization of fatigue's impact within a 2D latent space. The latent space representation provides insights into motor control variations, revealing patterns that can be used to monitor fatigue levels and optimize training or rehabilitation programs.
疲劳在体育科学中起着关键作用，对恢复、训练效果和整体运动表现有重大影响。理解和预测疲劳对于优化训练、预防过度训练以及将受伤风险降至最低至关重要。本研究的目的是通过深度学习方法利用人类活动识别（HAR）进行降维。探索使用对抗自编码器（AAE）在二维潜在空间中评估和可视化疲劳，重点关注半监督和条件方法。通过将复杂的时间序列数据转换到这个潜在空间，目标是通过分析数据点分布的变化来评估参与者运动控制中与疲劳相关的运动变化，并提供这些影响的可视化表示。假设疲劳增加会导致点分布发生显著变化，将使用聚类技术进行分析以识别与疲劳相关的模式。数据使用Wii平衡板和三个惯性测量单元收集，这些单元分别放置在臀部和两个前臂（靠近手腕的远端部分）以捕获动态和运动学信息。参与者遵循诱导疲劳的方案，包括重复进行四组不同练习（深蹲、右弓步、左弓步和平板支撑跳跃），每组10次重复，直至筋疲力尽。我们的研究结果表明，AAE模型在降低数据维度方面是有效的，能够在二维潜在空间中可视化疲劳的影响。潜在空间表示为运动控制变化提供了见解，揭示了可用于监测疲劳水平以及优化训练或康复计划的模式。
