DETECTION OF MENTAL FATIGUE IN CNC OPERATORS THROUGH MACHINE VISION

Abstract

CNC (Computer Numerical Control) operators are often put through long and sustained periods of focus inspection while working in high-precision manufacturing settings that lead to mental fatigue that compromises both safety and productivity. This study investigated mental fatigue detection with a non-intrusive machine vision system that observed for behavioral and psychovisual indicators i.e., blink rate, gaze stability, head pose, micro-expressions. Ultimately, based on cognitive fatigue and vigilance theory, the study utilized video feed in real time and tried to correlate visual and behavioural patterns to psychometric states of mental depletion. The results demonstrated psychovisual trends consistent with cognitive load and the study demonstrates that machine vision can indicate early signs of operator fatigue. This study results have considerable implications with industrial ergonomics and a first step towards developing adaptive work environments that promote mental health and safety. Ultimately, it provides an opportunity to combine psychological theory with machine vision monitoring, and provide insight to a new generation of smart and human-friendly manufacturing systems that considers human in a smart system design for mental health as well as process efficiency.