Gao Zixiang, Xiang Liangliang, Fekete Gusztáv, Baker Julien S, Mao Zhuqing, Gu Yaodong
Department of Radiology, Ningbo No. 2 Hospital, Ningbo 315010, China.
Faculty of Engineering, University of Pannonia, Veszprém H-8201, Hungary.
Appl Bionics Biomech. 2023 Sep 26;2023:7022513. doi: 10.1155/2023/7022513. eCollection 2023.
Detecting fatigue at the early stages of a run could aid training programs in making adjustments, thereby reducing the heightened risk of injuries from overuse. The study aimed to investigate the effects of running fatigue on plantar force distribution in the dominant and nondominant feet of amateur runners.
Thirty amateur runners were recruited for this study. Bilateral time-series plantar forces were employed to facilitate automatic fatigue gait recognition using convolutional neural network (CNN) and CNN-based long short-term memory network (ConvLSTM) models. Plantar force data collection was conducted both before and after a running-induced fatigue protocol using a FootScan force plate. The Keras library in Python 3.8.8 was used to train and tune deep learning models.
The results demonstrated that more mid-forefoot and heel force occurs during bilateral plantar and less midfoot fore force occurs in the dominant limb after fatigue ( < 0.001). The time of peak forces was significantly shortened at the midfoot and sum region of the nondominant foot, while it was delayed at the hallux region of the dominant foot ( < 0.001). In addition, the ConvLSTM model showed higher performance (Accuracy = 0.867, Sensitivity = 0.874, and Specificity = 0.859) in detecting fatigue gait than CNN (Accuracy = 0.800, Sensitivity = 0.874, and Specificity = 0.718).
The findings of this study could offer empirical data for evaluating risk factors linked to overuse injuries in a single limb, as well as facilitate early detection of fatigued gait.
在跑步初期检测疲劳有助于训练计划进行调整，从而降低因过度使用而导致受伤风险增加的可能性。本研究旨在调查跑步疲劳对业余跑步者优势脚和非优势脚足底力分布的影响。
招募了30名业余跑步者参与本研究。使用卷积神经网络（CNN）和基于CNN的长短期记忆网络（ConvLSTM）模型，采用双边时间序列足底力来促进自动疲劳步态识别。使用FootScan测力板在跑步诱导疲劳方案前后进行足底力数据收集。使用Python 3.8.8中的Keras库来训练和调整深度学习模型。
结果表明，疲劳后双侧足底时前脚掌中部和足跟的力更大，优势肢体中足前部的力更小（<0.001）。非优势脚中足和总和区域的峰值力时间显著缩短，而优势脚拇趾区域的峰值力时间延迟（<0.001）。此外，在检测疲劳步态方面，ConvLSTM模型（准确率=0.867，灵敏度=0.874，特异性=0.859）比CNN（准确率=0.800，灵敏度=0.874，特异性=0.718）表现更好。
本研究结果可为评估单肢过度使用损伤相关风险因素提供实证数据，并有助于早期检测疲劳步态。
