A Smart Safety Helmet using IMU and EEG sensors for analysis of worker’s fatigue

Abstract

It is known that head gesture and mental states can reflect some human behaviors related to a risk of accident when using machine-tools. The research works presented in this paper aim to reduce the number of injury and thus increase worker safety. Instead using camera, this paper presents a Smart Safety Helmet (SSH) in order to track head gestures and mental states of worker able to recognize anomalous behavior. Information extracted from SSH is used for computing risk level of accident (a safety level) for preventing and reducing injury or accidents. The SSH system is an inexpensive, non-intrusive, non-invasive, and non-vision-based system, which consists of 9DOF Inertial Measurement Unit (IMU) and dry EEG electrodes. A haptic device, such as vibrotactile motor, is integrated to the helmet in order to alert the operator when computed risk level (fatigue, high stress or error) reach a threshold. Once the risk level of accident breaks the threshold, a signal will be sent wirelessly to stop the relevant machine tool or process