Inductive Textile Sensor Design and Validation for a Wearable Monitoring Device.

Textile sensors have gained attention for wearable devices, in which the most popular are the resistive textile sensor. However, these sensors present high hysteresis and a drift when stretched for long periods of time. Inductive textile sensors have been commonly used as antennas and plethysmographs, and their applications have been extended to measure heartbeat, wireless data transmission, and motion and gesture capturing systems. Inductive textile sensors have shown high reliability, stable readings, low production cost, and an easy manufacturing process. This paper presents the design and validation of an inductive strain textile sensor. The anthropometric dimensions of a healthy participant were used to define the maximum dimensions of the inductive textile sensor. The design of the inductive sensor was studied through theoretical calculations and simulations. Parameters such as height, width, area, perimeter, and number of complete loops were considered to calculate and evaluate the inductance value.

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors.

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.