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A computer vision approach to continuously monitor fatigue during resistance training

计算机科学 自感劳累评分 人工智能 蹲下 过度训练 感知器 人工神经网络 梯度升压 模拟 机器学习 随机森林 运动员 物理医学与康复 物理疗法 心率 血压 医学 放射科
作者
Justin Amadeus Albert,Bert Arnrich
出处
期刊:Biomedical Signal Processing and Control [Elsevier BV]
卷期号:89: 105701-105701 被引量:2
标识
DOI:10.1016/j.bspc.2023.105701
摘要

Monitoring fatigue during resistance training is essential to avoid injuries caused by overtraining. Fatigue can be comprehensively quantified by the external and internal load, where the external load is the work done by the athlete, and the internal load is the psychological and physiological response to the external load. This paper proposes a computer vision method to continuously monitor fatigue during resistance training by predicting external and internal parameters, namely the generated power and the rating of perceived exertion. We utilize the human pose estimation from two Microsoft Azure Kinect cameras to capture the movement of athletes while performing stationary exercises. Our method processes the obtained kinematic data, computes skeleton features to train traditional machine learning algorithms, and constructs feature maps to train convolutional neural network-based models to predict the load parameters. For evaluation, we recorded a dataset of 16 subjects who performed squat exercises on a Flywheel and rated their perceived exertion after each set. A measuring unit integrated into the Flywheel provided power readings for each repetition. The results show that our method achieves good results in predicting both parameters. Gradient Boosting Regression Trees best predicted perceived exertion with a mean absolute percentage error of 8.08% and a Spearman's ρ=0.74. Multi-layer Perceptron performed best in predicting power with a mean absolute error of 23.13 Watts and ρ=0.79. Our findings show that our approach delivers promising external and internal load quantifications for fatigue, with great potential to provide external feedback to coaches or athletes.
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