Menrva Research Group, Schools of Mechatronic Systems & Engineering Science, Simon Fraser University, Metro Vancouver, BC V5A 1S6, Canada.
Department of Physical Therapy, Faculty of Medicine, University of British Columbia, Vancouver, BC V5T 1Z3, Canada.
Sensors (Basel). 2020 Sep 29;20(19):5573. doi: 10.3390/s20195573.
Fatigue is a multifunctional and complex phenomenon that affects how individuals perform an activity. Fatigue during running causes changes in normal gait parameters and increases the risk of injury. To address this problem, wearable sensors have been proposed as an unobtrusive and portable system to measure changes in human movement as a result of fatigue. Recently, a category of wearable devices that has gained attention is flexible textile strain sensors because of their ability to be woven into garments to measure kinematics. This study uses flexible textile strain sensors to continuously monitor the kinematics during running and uses a machine learning approach to estimate the level of fatigue during running. Five female participants used the sensor-instrumented garment while running to a state of fatigue. In addition to the kinematic data from the flexible textile strain sensors, the perceived level of exertion was monitored for each participant as an indication of their actual fatigue level. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data. The machine learning algorithm obtained a root mean squared value of 0.06 and a coefficient of determination of 0.96 in participant-specific scenarios. This study highlights the potential of flexible textile strain sensors to objectively estimate the level of fatigue during running by detecting slight perturbations in lower extremity kinematics. Future iterations of this technology may lead to real-time biofeedback applications that could reduce the risk of running-related overuse injuries.
疲劳是一种多功能且复杂的现象，会影响个体执行活动的能力。跑步过程中的疲劳会导致正常步态参数发生变化，并增加受伤的风险。为了解决这个问题，可穿戴传感器已被提出作为一种非侵入式和便携式系统，以测量由于疲劳导致的人体运动变化。最近，一类备受关注的可穿戴设备是柔性纺织应变传感器，因为它们能够编织到服装中以测量运动学。本研究使用柔性纺织应变传感器在跑步过程中连续监测运动学，并使用机器学习方法来估计跑步过程中的疲劳程度。五名女性参与者在跑步时穿着带有传感器的衣服，直至达到疲劳状态。除了来自柔性纺织应变传感器的运动学数据外，还监测了每位参与者的感知用力程度，作为其实际疲劳水平的指示。使用堆叠随机森林机器学习模型从运动学数据估计感知用力水平。在参与者特定的场景中，机器学习算法获得了 0.06 的均方根值和 0.96 的确定系数。本研究强调了柔性纺织应变传感器通过检测下肢运动学的微小变化来客观估计跑步过程中疲劳程度的潜力。该技术的未来迭代可能会导致实时生物反馈应用，从而降低与跑步相关的过度使用损伤的风险。
