Citation

Chen D, Asaeikheybari G, Chen H, Xu W, Huang MC. IEEE J. Biomed. Health Inform. 2020; ePub(ePub): ePub.

Copyright

(Copyright © 2020, Institute of Electrical and Electronics Engineers)

DOI

10.1109/JBHI.2020.3046701

PMID

33351772

Abstract

Falls are leading causes of nonfatal injuries in the workplace, which has a substantial injury and economic consequences. To help avoid fall injuries, asking safety managers to inspect working areas routinely is normally used. However, it is difficult for a limited number of safety managers to inspect instant fall hazards in time, especially in large workplaces. To address this problem, a novel fall hazard identification method was proposed in this paper, which makes it possible for all the workers in the workplace to report the potential hazards automatically. This method is based on the fact that people use different gaits to get across different floor surfaces. Through analyzing the gait patterns, potential fall hazards could be identified automatically. In this research, Smart Insole, an insole shaped wearable system for gait analysis, was applied to measure and analyze gait patterns for fall hazard identification. Since slips and trips are two main causes of falls in workplace, recognizing fall hazards that could lead to slips and trips was the focus of this study. Five effective gait features were extracted to train a Support Vector Machine (SVM) model for recognizing slip hazard, trip hazard, and safe floor surfaces. Experiment results showed that fall hazards could be recognized with high accuracy (98.1%).

Language: en