A novel helmet design and implementation for drowsiness and fall detection of workers on-site using EEG and Random-Forest Classifier

Abstract

Abstract This paper proposes a low-cost novel EEG based BCI prototype to detect if an on-site worker is sleep-deprived or not elegantly. The worker is required to wear a modified safety helmet with an innocuously placed signal acquisition device and it’s paraphernalia that does not hinder the worker’s activities. A few time and frequency domain features have been derived from the collected data to recognize sleep deprivation of workers. The smart helmet communicates with a local server within radio range. The server runs a random forest classifier algorithm to classify if the worker is sleep deprived or not and alerts the supervisor if necessary. A single Inertial Measurement Unit (IMU) sensor is utilized to detect if the worker has fallen down. The entire setup is supported by an android application that keeps the supervisor up-to-date on the statuses of the workers. A classification accuracy as high as 98% for the helmet based EEG setup was obtained through in-house live experiments upon sleep-deprived subjects.