Roessingh Research and Development, 7522 AH Enschede, The Netherlands.
Department of Biomedical Signals and Systems, Faculty of Electrical Engineering, Mathematics and Computer Science (EEMCS), University of Twente, 7522 NB Enschede, The Netherlands.
Sensors (Basel). 2022 Apr 14;22(8):3008. doi: 10.3390/s22083008.
Physical exercise (PE) is beneficial for both physical and psychological health aspects. However, excessive training can lead to physical fatigue and an increased risk of lower limb injuries. In order to tailor training loads and durations to the needs and capacities of an individual, physical fatigue must be estimated. Different measurement devices and techniques (i.e., ergospirometers, electromyography, and motion capture systems) can be used to identify physical fatigue. The field of biomechanics has succeeded in capturing changes in human movement with optical systems, as well as with accelerometers or inertial measurement units (IMUs), the latter being more user-friendly and adaptable to real-world scenarios due to its wearable nature. There is, however, still a lack of consensus regarding the possibility of using biomechanical parameters measured with accelerometers to identify physical fatigue states in PE. Nowadays, the field of biomechanics is beginning to open towards the possibility of identifying fatigue state using machine learning algorithms. Here, we selected and summarized accelerometer-based articles that either (a) performed analyses of biomechanical parameters that change due to fatigue in the lower limbs or (b) performed fatigue identification based on features including biomechanical parameters. We performed a systematic literature search and analysed 39 articles on running, jumping, walking, stair climbing, and other gym exercises. Peak tibial and sacral acceleration were the most common measured variables and were found to significantly increase with fatigue (respectively, in 6/13 running articles and 2/4 jumping articles). Fatigue classification was performed with an accuracy between 78% and 96% and Pearson's correlation with an RPE (rate of perceived exertion) between = 0.79 and = 0.95. We recommend future effort toward the standardization of fatigue protocols and methods across articles in order to generalize fatigue identification results and increase the use of accelerometers to quantify physical fatigue in PE.
体育锻炼（PE）对身心健康都有益处。然而，过度训练会导致身体疲劳和下肢受伤的风险增加。为了根据个人的需求和能力调整训练负荷和持续时间，必须估计身体疲劳。可以使用不同的测量设备和技术（即，测功计、肌电图和运动捕捉系统）来识别身体疲劳。生物力学领域已经成功地使用光学系统以及加速度计或惯性测量单元（IMU）来捕捉人体运动的变化，后者由于其可穿戴的性质，更加用户友好且适用于实际场景。然而，使用加速度计测量的生物力学参数来识别 PE 中的身体疲劳状态的可能性仍然存在争议。如今，生物力学领域开始朝着使用机器学习算法识别疲劳状态的可能性发展。在这里，我们选择并总结了基于加速度计的文章，这些文章要么（a）分析由于下肢疲劳而改变的生物力学参数，要么（b）基于包括生物力学参数在内的特征进行疲劳识别。我们进行了系统的文献检索，并分析了 39 篇关于跑步、跳跃、步行、爬楼梯和其他健身运动的文章。胫骨和骶骨峰值加速度是最常见的测量变量，并且随着疲劳的增加而显著增加（分别在 6/13 篇跑步文章和 2/4 篇跳跃文章中发现）。疲劳分类的准确率在 78%至 96%之间，与 RPE（感知用力率）的 Pearson 相关系数为 = 0.79 至 = 0.95。我们建议未来努力在文章之间标准化疲劳协议和方法，以推广疲劳识别结果，并增加使用加速度计来量化 PE 中的身体疲劳。
