Paper Info
Title
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 degrees 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 degrees 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.
Year
DOI
Venue
2019
10.3390/s19235325
SENSORS
Keywords
DocType
Volume
strain sensors,running,convolutional neural networks,gait analysis,kinematics
Journal
19
Issue
ISSN
Citations 
23
1424-8220
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
Mohsen Gholami100.34
Ahmad Rezaei200.34
Tyler J Cuthbert300.34
Christopher Napier400.34
Carlo Menon54321.20