ICAP: An IoT based Wearable For Real-Time Accidental Fall Detection and Health Monitoring of Remote Maintenance Workers

Abstract

Falls are the second largest cause of workplace injuries and unintentional deaths across the globe. In many cases, timely medical attention can reduce the severity of injuries and prevent death. The existing solutions provide means for detection of falls, but are susceptible to false positives and do not provide a way to enforce strict safety regulations. To solve the problems mentioned above, this research aims to implement an IoT-based smart wearable helmet (iCAP) that actively monitors workers for falls and health abnormalities in order to ensure timely provision of medical attention in case of health and safety hazards. The sensor node continuously monitors the height, acceleration and vitals of the workers and uploads this data to the cloud and a local web server. False positives are filtered out by monitoring the duration of fall and change in altitude during the fall. The data received is further processed by the local server in real-time to monitor the vitals, track attendance, monitor safety practices and calculate the duration of work. Attendance tracking and calculation of duration of work is only done for workers wearing the helmet, which allows the employer to enforce safety practices. The proposed iCAP solution can by used by workers of any industry due to it’s robustness for fall and health monitoring.