Comprehensive Monitoring of Bad Head and Shoulder Postures by Wearable Magnetic Sensors and Deep Learning
Hyeong Kyu Jang, Hobeom Han, Sang Won Yoon
Abstract
In this work, a simultaneous monitoring method for bad posture, including the forward head posture (FHP), rounded shoulder (RS), and elevated shoulder (ES), is proposed. These postures and the resulting symptoms are becoming increasingly prevalent, and a comprehensive, simultaneous, and extensive analysis of such posture disorders is needed. The proposed method involves collecting posture data from a new combination of accelerometers and magnetometers paired with miniature magnets. The sensor locations are optimally chosen to reliably calculate neck and shoulder angles representing the craniovertebral angle (CVA) for FHP, forward shoulder angle (FSA) for RS, and symmetry angle (SA) for ES. Processing of the collected sensor data is achieved by deep neural network (DNN) and convolutional neural network (CNN) algorithms. Experimental results demonstrate successful bad posture classification with high accuracy (DNN: 88.1%, CNN: 88.7%) even with the simultaneous analysis of FHP, RS, and ES.