Annu Int Conf IEEE Eng Med Biol Soc. 2021 Nov;2021:4546-4549. doi: 10.1109/EMBC46164.2021.9630948.
Fatigue is often associated with increased injury risk. Many studies have focused on fatigue in the lower extremity muscles brought on by running, yet few have examined the relationship between fatigue of the core musculature and associated changes in running gait. To investigate the relationship between trunk fatigue and running dynamics, this study had two goals: (1) to use machine learning to determine which gait parameters are most associated with trunk fatigue; and (2) to develop a machine learning algorithm that uses those parameters to classify individuals with trunk fatigue. We hypothesized that we could effectively differentiate between the non-fatigued and fatigued states using machine learning models derived from running gait parameters.
Seventy-two individuals performed a trunk fatigue protocol. Lower extremity running biomechanics were collected pre- and post- the trunk fatigue protocol using an instrumented treadmill and 10-camera motion capture system.The fatiguing protocol involved executing a series of trunk fatiguing exercises until established fatigue criteria were reached. Gait variables extracted from the non-fatigued and fatigued states served as model inputs to aid in the development of the machine learning model that would distinguish between non-fatigued and fatigued running.
The machine learning protocol determined three variables - stance time, maximum propulsive GRF and maximum braking GRF - to be the best discriminators between non-fatigued and fatigued running. The SVM with Bagging was the best performing model that discriminated between non-fatigued and fatigued running with an accuracy of 82%, precision of 77%, recall of 90%, and area under the receiver operating curve of 0.91.
The machine learning model was effective in classifying between non-fatigued and fatigued running using three gait parameters extracted from GRF waveforms. The ability to classify fatigue using these easy to measure GRF derived parameters enhances the ability for the model to be integrated into wearable technology and the clinical setting to aid in the detection of fatigue and potentially injury, as fatigue is often a precursor to injury.Clinical Relevance- This model has the potential to be implemented in a clinical setting to determine the onset of trunk fatigue through basic gait analysis, involving only the ground reaction forces. This model would be aimed toward injury prevention since fatigue is linked to increased risk of injury.
疲劳通常与受伤风险增加有关。许多研究都集中在跑步引起的下肢肌肉疲劳上，但很少有研究探讨核心肌肉疲劳与相关跑步步态变化之间的关系。为了研究躯干疲劳与跑步动力之间的关系，本研究有两个目标：（1）使用机器学习确定与躯干疲劳最相关的步态参数；（2）开发一种使用这些参数对躯干疲劳个体进行分类的机器学习算法。我们假设，我们可以使用源自跑步步态参数的机器学习模型有效地区分非疲劳和疲劳状态。
72 名个体进行了躯干疲劳方案。使用带仪器的跑步机和 10 个摄像机运动捕捉系统，在躯干疲劳方案前后采集下肢跑步生物力学。疲劳方案包括执行一系列躯干疲劳运动，直到达到既定的疲劳标准。从非疲劳和疲劳状态中提取的步态变量作为模型输入，以帮助开发能够区分非疲劳和疲劳跑步的机器学习模型。
机器学习方案确定了三个变量-站立时间、最大推进 GRF 和最大制动 GRF-是区分非疲劳和疲劳跑步的最佳判别器。具有 Bagging 的 SVM 是区分非疲劳和疲劳跑步的表现最佳的模型，其准确性为 82%，精度为 77%，召回率为 90%，接收器操作曲线下面积为 0.91。
使用从 GRF 波形中提取的三个步态参数，机器学习模型能够有效地对非疲劳和疲劳跑步进行分类。使用这些易于测量的 GRF 衍生参数来分类疲劳的能力增强了模型集成到可穿戴技术和临床环境中的能力，以帮助检测疲劳和潜在的伤害，因为疲劳通常是伤害的前兆。临床意义-该模型有可能在临床环境中实施，通过基本的步态分析确定躯干疲劳的发生，仅涉及地面反作用力。该模型旨在预防伤害，因为疲劳与受伤风险增加有关。
