Lower Body Kinematics Monitoring in Running Using Fabric-Based Wearable Sensors and Deep Convolutional Neural Networks

Abstract

Continuous kinematic monitoring of runners is crucial to inform runners of inappropriate running habits. Motion capture systems are the gold standard for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as an unobtrusive method to analyze performance metrics and the health conditions of runners. In this study, we developed a system capable of estimating joint angles in sagittal, frontal, and transverse planes during running. A prototype with fiber strain sensors was fabricated. The positions of the sensors on the pelvis were optimized using a genetic algorithm. A cohort of ten people completed 15 min of running at five different speeds for gait analysis by our prototype device. The joint angles were estimated by a deep convolutional neural network in inter- and intra-participant scenarios. In intra-participant tests, root mean square error (RMSE) and normalized root mean square error (NRMSE) of less than 2.2° and 5.3%, respectively, were obtained for hip, knee, and ankle joints in sagittal, frontal, and transverse planes. The RMSE and NRMSE in inter-participant tests were less than 6.4° and 10%, respectively, in the sagittal plane. The accuracy of this device and methodology could yield potential applications as a soft wearable device for gait monitoring of runners.

Textile‐Based Inductive Soft Strain Sensors for Fast Frequency Movement and Their Application in Wearable Devices Measuring Multiaxial Hip Joint Angles during Running

“…We utilized a previous approach developed in our group to maximize accuracy with four sensors in specific placements to monitor multiaxes hip kinematics . Each sensor was placed in different orientations (horizontal/vertical/orthogonal, Figure A–C) which allowed the system to learn as many unique relationships between strain of each sensor and hip movement in different planes (sagittal, frontal, and transverse).…”

“…Sensor placement and orientation and the number of sensors can play a major role in improving the accuracy of the multiaxes body motion tracking systems . In this study, sensor locations were used from our previous work that identified optimal sensor orientations for multiaxes hip angle tracking with piezoresistive sensors . The sensors were connected to an LDC1614 chip (see Supporting Information for details, Figure ).…”

Fatigue Monitoring in Running Using Flexible Textile Wearable Sensors

“…The sensor placement was optimized in a previous study [ 38 ] and four positions were selected by genetic algorithm for a multi-axis hip joint. The positions of the sensors on the knee and ankle were selected according to the joint axes.…”

“…To our knowledge, the use of flexible textile (soft) strain sensors for continuous monitoring of fatigue has not yet been studied. Flexible textile strain sensors are a category of wearable sensors that have recently been used for human gait analysis [ 38 , 39 , 40 ], trunk motion monitoring [ 41 , 42 ], and hand gesture recognition [ 43 ]. These sensors work by measuring changes in the resistance or capacitance when they are elongated [ 44 ].…”

“…The advantages of flexible textile strain sensors are their easy integration into clothing and convenient use compared to rigid IMUs. In a previous study, we optimized a wearable flexible textile strain sensor system for three dimensional lower extremity joint angle measurement and subsequently validated this system for the measurement of lower extremity running kinematics [ 38 ]. In this study, we extend our previous research to use flexible textile strain sensors for the estimation of fatigue level during running.…”