Lu Zhenghui, Sun Dong, Xu Datao, Li Xin, Baker Julien S, Gu Yaodong
Faculty of Sports Science, Ningbo University, Ningbo 315211, China.
Savaria Institute of Technology, Eötvös Loránd University, 9700 Szombathely, Hungary.
Biology (Basel). 2021 Oct 22;10(11):1083. doi: 10.3390/biology10111083.
Longtime standing may cause fatigue and discomfort in the lower extremities, leading to an increased risk of falls and related musculoskeletal diseases. Therefore, preventive interventions and fatigue detection are crucial. This study aims to explore whether anti-fatigue mats can improve gait parameters following long periods of standing and try to use machine learning algorithms to identify the fatigue states of standing workers objectively.
Eighteen healthy young subjects were recruited to stand on anti-fatigue mats and hard ground to work 4 h, including 10 min rest. The portable gait analyzer collected walking speed, stride length, gait frequency, single support time/double support time, swing work, and leg fall intensity. A Paired sample t-test was used to compare the difference of gait parameters without standing intervention and standing on two different hardness planes for 4 h. An independent sample t-test was used to analyze the difference between males and females. The K-nearest neighbor (KNN) classification algorithm was performed, the subject's gait characteristics were divided into non-fatigued and fatigue groups. The gait parameters selection and the error rate of fatigue detection were analyzed.
When gender differences were not considered, the intensity of leg falling after standing on the hard ground for 4 h was significantly lower than prior to the intervention ( < 0.05). When considering the gender, the stride length and leg falling strength of female subjects standing on the ground for 4 h were significantly lower than those before the intervention ( < 0.05), and the leg falling strength after standing on the mat for 4 h was significantly lower than that recorded before the standing intervention ( < 0.05). The leg falling strength of male subjects standing on the ground for 4 h was significantly lower than before the intervention ( < 0.05). After standing on the ground for 4 h, female subjects' walking speed and stride length were significantly lower than those of male subjects ( < 0.05). In addition, the accuracy of testing gait parameters to predict fatigue was medium (75%). After standing on the mat was divided into fatigue, the correct rate was 38.9%, and when it was divided into the non-intervention state, the correct rate was 44.4%.
The results show that the discomfort and fatigue caused by standing for 4 h could lead to the gait parameters variation, especially in females. The use of anti-fatigue mats may improve the negative influence caused by standing for a long period. The results of the KNN classification algorithm showed that gait parameters could be identified after fatigue, and the use of an anti-fatigue mat could improve the negative effect of standing for a long time. The accuracy of the prediction results in this study was moderate. For future studies, researchers need to optimize the algorithm and include more factors to improve the prediction accuracy.
长时间站立可能会导致下肢疲劳和不适，增加跌倒及相关肌肉骨骼疾病的风险。因此，预防性干预和疲劳检测至关重要。本研究旨在探讨抗疲劳垫是否能改善长时间站立后的步态参数，并尝试使用机器学习算法客观识别站立工作者的疲劳状态。
招募18名健康年轻受试者，分别站在抗疲劳垫和硬地面上工作4小时，包括10分钟休息时间。便携式步态分析仪收集步行速度、步长、步态频率、单支撑时间/双支撑时间、摆动功和腿部下落强度。采用配对样本t检验比较无站立干预以及在两种不同硬度平面上站立4小时后的步态参数差异。采用独立样本t检验分析男性和女性之间的差异。执行K近邻（KNN）分类算法，将受试者的步态特征分为非疲劳组和疲劳组。分析步态参数选择及疲劳检测的错误率。
不考虑性别差异时，在硬地面上站立4小时后的腿部下落强度显著低于干预前（P<0.05）。考虑性别因素时，女性受试者在硬地面上站立4小时后的步长和腿部下落强度显著低于干预前（P<0.05），在抗疲劳垫上站立4小时后的腿部下落强度显著低于站立干预前记录的值（P<0.05）。男性受试者在硬地面上站立4小时后的腿部下落强度显著低于干预前（P<0.05）。在硬地面上站立4小时后，女性受试者的步行速度和步长显著低于男性受试者（P<0.05）。此外，测试步态参数预测疲劳的准确率为中等（75%）。站立在抗疲劳垫上分为疲劳状态时，正确率为38.9%，分为非干预状态时，正确率为44.4%。
结果表明，站立4小时引起的不适和疲劳会导致步态参数变化，尤其是女性。使用抗疲劳垫可能会改善长时间站立所造成的负面影响。KNN分类算法的结果表明，疲劳后可以识别步态参数，使用抗疲劳垫可以改善长时间站立的负面影响。本研究预测结果的准确率为中等。对于未来的研究，研究人员需要优化算法并纳入更多因素以提高预测准确性。
