Intelligent Eye Tracker Integrated with Cylindrical Capacitive Sensors for Chronic Fatigue Assessment

Abstract Fatigue negatively impacts health, safety, and productivity, yet current monitoring methods are often subjective, labor‐intensive, and inaccurate. To address these challenges, this study presents a capacitive sensor‐based eye tracker (ET) leveraging cylindrical carbon nanotube‐paper composite (CCPC) sensors for chronic fatigue (CF) assessment. Fabricated by novel wet‐fracture and paper‐rolling methods, CCPC sensors demonstrate superior proximity sensitivity with a small form factor. These 1D sensors are seamlessly integrated into an eyeglass frame for noncontact monitoring of blink rates and eye closures. Fifteen‐minute testing protocol, combining cognitive tasks and noise exposure, is designed to induce acute fatigue and identify CF. By analyzing changes in the digital markers against established fatigue indicators, CF is assessed with the aid of machine learning models for the evaluation of accuracy, sensitivity, and specificity. This real‐time, wearable monitoring platform provides an objective, effortless, and noncontact approach to fatigue assessment. With further testing and optimization, it holds the potential for user‐friendly evaluation of acute fatigue or fatigue‐associated diseases, such as myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS).