Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

Abstract

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.

Towards Machine Learning-Based Detection of Running-Induced Fatigue in Real-World Scenarios: Evaluation of IMU Sensor Configurations to Reduce Intrusiveness

“…This indicates that knee and ankle joints are more suited than hips to detect fatigue in outdoor runs using IMUs. Gholami et al obtained opposite results using textile wearable sensors to detect fatigue in running, with the hip being the most reliable sensor location and the knee and ankle being less reliable [ 18 ]. However, wearable sensors used in the study measured biomechanical parameters only in the sagittal plane.…”

“…Yet, few studies have focused on the detection of a fatigue condition in sports and running, especially in out-of-the-lab environments. Gholami et al used machine learning techniques to detect the perceived exertion of runners on a treadmill using textile wearable sensors and assessed the importance of each sensor location, with the hip contributing more than the knee and the ankle to the final coefficient of determination of 0.96 [ 18 ]. Buckley et al located IMUs at the shanks and lumbar spine and compared three different locations and various machine learning classifiers to detect fatigue in outdoor running, obtaining a 75% accuracy with a single IMU placed at the lumbar spine [ 19 ].…”

The Role of Unobtrusive Home-Based Continuous Sensing in the Management of Postacute Sequelae of SARS CoV-2

“…In lieu of using EEG measurements, activity type and level can easily be measured to quantify fatigue symptoms, and while not specific for fatigue, they can be used by knowledgeable care providers in combination with other clinical measures to better understand a patient’s presenting signs. Such activity measurements can be made using either wearable or contactless solutions as presented in the previous sections [ 62 , 63 ].…”

Closing the Wearable Gap–Part IX: Validation of an Improved Ankle Motion Capture Wearable

“…The sensor and CNN could not distinguish these minor transverse and frontal angle changes from the larger sagittal plane. Gholami et al [17] further highlight the potential of flexible strain sensors, mounted over running tights on the hip, knee, and ankle positions, to objectively estimate the level of fatigue during running by detecting slight perturbations in the lower extremity kinematics. A stacked random forest machine learning model was used to estimate the perceived exertion levels from the kinematic data.…”