An Oculometrics-based Biofeedback System to Impede Fatigue Development during Computer Work: a Proof-of-Concept Study

Abstract

A biofeedback system may objectively identify fatigue and provide an individualized timing plan for micro-breaks. We developed and implemented a biofeedback system based on oculometrics using continuous recordings of eye movements and pupil dilations to moderate fatigue development in its early stages. Twenty healthy young participants (10 males and females) performed a cyclic computer task for 31-35 min over two sessions: 1) self-triggered micro-breaks (manual sessions), and 2) biofeedback-triggered micro-breaks (automatic sessions). The sessions were held with one-week inter-session interval and in a counterbalanced order across participants. Each session involved 180 cycles of the computer task and after each 20 cycles (a segment), the task paused for 5-s to acquire perceived fatigue using Karolinska Sleepiness Scale (KSS). Following the pause, a 25-s micro-break involving seated exercises was carried out whether it was triggered by the biofeedback system if the fatigue state (KSS[≥]5) was detected in automatic sessions or by the participants in manual sessions. National Aeronautics and Space Administration Task Load Index (NASA-TLX) was administered after sessions. The functioning core of the biofeedback system was based on a Decision Tree Ensemble model for fatigue classification, which was developed using an oculometrics dataset previously collected during the same computer task. The biofeedback system identified fatigue states with a mean accuracy of approx. 70% and remained robust against circadian rhythms. Perceived workload obtained from NASA-TLX was significantly lower in the automatic sessions compared with the manual sessions, p=0.01 Cohens d=0.89. The results give support to the robustness and effectiveness of integrating oculometrics-based biofeedback in time planning of micro-breaks to impede fatigue development during computer work.