Faculty of Sports Science, Ningbo University, Ningbo 315211, China.
Department of International Office, Ningbo University, Ningbo 315211, China.
J Healthc Eng. 2023 Feb 21;2023:7461729. doi: 10.1155/2023/7461729. eCollection 2023.
The treadmill is widely used in running fatigue experiments, and the variation of plantar mechanical parameters caused by fatigue and gender, as well as the prediction of fatigue curves by a machine learning algorithm, play an important role in providing different training programs. This experiment aimed to compare changes in peak pressure (PP), peak force (PF), plantar impulse (PI), and gender differences of novice runners after they were fatigued by running. A support vector machine (SVM) was used to predict the fatigue curve according to the changes in PP, PF, and PI before and after fatigue. 15 healthy males and 15 healthy females completed two runs at a speed of 3.3 m/s ± 5% on a footscan pressure plate before and after fatigue. After fatigue, PP, PF, and PI decreased at hallux (1) and second-fifth toes (2-5), while heel medial (HM) and heel lateral (HL) increased. In addition, PP and PI also increased at the first metatarsal (1). PP, PF, and PI at 1 and 2-5 were significantly higher in females than in males, and metatarsal 3-5 (3-5) were significantly lower in females than in males. The SVM classification algorithm results showed the accuracy was above average level using the 1 PP/HL PF (train accuracy: 65%; test accuracy: 75%), 1 PF/HL PF (train accuracy: 67.5%; test accuracy: 65%), and HL PF/1 PI (train accuracy: 67.5%; test accuracy: 70%). These values could provide information about running and gender-related injuries, such as metatarsal stress fractures and hallux valgus. Application of the SVM to the identification of plantar mechanical features before and after fatigue. The features of the plantar zones after fatigue can be identified and the learned algorithm of plantar zone combinations with above-average accuracy (1 PP/HL PF, 1 PF/HL PF, and HL PF/1 PI) can be used to predict running fatigue and supervise training. It provided an important idea for the detection of fatigue after running.
跑步机在跑步疲劳实验中被广泛应用，足底力学参数的变化（包括疲劳和性别引起的变化），以及机器学习算法对疲劳曲线的预测，在提供不同的训练方案方面发挥着重要作用。本实验旨在比较跑步疲劳前后新手跑者的峰值压力（PP）、峰值力（PF）、足底冲量（PI）的变化以及不同性别之间的差异，并使用支持向量机（SVM）根据疲劳前后 PP、PF 和 PI 的变化来预测疲劳曲线。15 名健康男性和 15 名健康女性在跑步机上以 3.3m/s±5%的速度进行两次跑步，在疲劳前后分别在足底压力板上进行测试。疲劳后，大脚趾（1）和第二到第五脚趾（2-5）的 PP、PF 和 PI 降低，而跟骨内侧（HM）和跟骨外侧（HL）升高。此外，第一跖骨（1）的 PP 和 PI 也增加。女性的 1 和 2-5 的 PP、PF 和 PI 明显高于男性，而女性的 3-5 跖骨（3-5）明显低于男性。SVM 分类算法的结果显示，使用 1 PP/HL PF（训练准确率：65%；测试准确率：75%）、1 PF/HL PF（训练准确率：67.5%；测试准确率：65%）和 HL PF/1 PI（训练准确率：67.5%；测试准确率：70%）的分类算法准确率处于中等偏上水平。这些值可以提供有关跑步和性别相关损伤的信息，例如跖骨应力性骨折和拇外翻。SVM 在识别足底力学特征方面的应用。可以识别足底区域的特征，并且可以使用具有中等偏上准确率（1 PP/HL PF、1 PF/HL PF 和 HL PF/1 PI）的足底区域组合的学习算法来预测跑步疲劳并监督训练。这为跑步后疲劳的检测提供了一个重要的思路。
