Comparison between Accelerometer and Gyroscope in Predicting Level-Ground Running Kinematics by Treadmill Running Kinematics Using a Single Wearable Sensor

Sensors

Adrian Wai Yin Yeung¹  Peter Tin Wah Tse¹  Daniel Hung Kay Chow¹  Wilson Ho Wu Cheng¹  Chor Yin Lam²  Luc Tremblay³ 

[1] Department of Health & Physical Education, The Education University of Hong Kong, Hong Kong, China;Department of Orthopaedics & Traumatology, Li Ka Shing Faculty of Medicine, The University of Hong Kong, Hong Kong, China;Faculty of Kinesiology & Physical Education, University of Toronto, Toronto, ON M5S 2W6, Canada;

关键词: deep learning;    convolutional neural network;    running;    kinematics;    wearable sensor;    running kinematics analysis;   

DOI  :  10.3390/s21144633

来源: DOAJ

【 摘 要 】

Wearable sensors facilitate running kinematics analysis of joint kinematics in real running environments. The use of a few sensors or, ideally, a single inertial measurement unit (IMU) is preferable for accurate gait analysis. This study aimed to use a convolutional neural network (CNN) to predict level-ground running kinematics (measured by four IMUs on the lower extremities) by using treadmill running kinematics training data measured using a single IMU on the anteromedial side of the right tibia and to compare the performance of level-ground running kinematics predictions between raw accelerometer and gyroscope data. The CNN model performed regression for intraparticipant and interparticipant scenarios and predicted running kinematics. Ten recreational runners were recruited. Accelerometer and gyroscope data were collected. Intraparticipant and interparticipant R² values of actual and predicted running kinematics ranged from 0.85 to 0.96 and from 0.7 to 0.92, respectively. Normalized root mean squared error values of actual and predicted running kinematics ranged from 3.6% to 10.8% and from 7.4% to 10.8% in intraparticipant and interparticipant tests, respectively. Kinematics predictions in the sagittal plane were found to be better for the knee joint than for the hip joint, and predictions using the gyroscope as the regressor were demonstrated to be significantly better than those using the accelerometer as the regressor.

【 授权许可】

Unknown