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.
Highlights
Running kinematics are important biomechanical parameters that are associated with injury risk and running economy [1,2]
The accuracy of the feature subset found by genetic algorithm (GA) did not significantly improve after the position subset size of four, and this enabled us to use the least number of sensors to reduce power consumption and decrease the device production time
The accuracy of the feature subset found by GA did not significantly improve after the position subset size of four, and this enabled us to use the least number of sensors to reduce power consumption and decrease the device production6 of time
Summary
Running kinematics are important biomechanical parameters that are associated with injury risk and running economy [1,2]. The range of motion of the hip joint in the frontal plane during running has been correlated with a risk of injuries of the pelvis, hip, and knee [2]. Foot and shank angles at the initial ground contact, along with knee and hip range of motion during the stance phase are related to running performance [1]. The ankle joint angle at initial contact has been related to several kinetic risk factors for running-related injuries [4]. Continuous multi-axis kinematic monitoring of lower extremities is an important consideration for the prevention of running injuries and performance improvement. Sophisticated gait analysis, conducted in clinics, provides the most accurate results, but this solution is not practical for long-term monitoring of runners during daily training. An alternative solution to this problem has been the development of wearable sensors to measure running kinematics
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