Human gait monitoring using wearable fabric-based strain sensors and deep supervised learning

Continuous lower body monitoring is an important step for real-time feedback training of runners and in-home rehabilitation assessment. Optical motion capture systems are the gold standards for gait analysis, but they are spatially limited to laboratories. Recently, wearable sensors have gained attention as unobtrusive methods to analyze gait metrics and health conditions. In this study, a wearable system capable of estimating lower body joint angles in sagittal, frontal, and transverse planes during gait was developed. 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 minutes of running at 5 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 squared error (RMSE) and normalized root mean squared 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.