Related Experiment Video
Updated: Jun 17, 2026

Investigating the Deployment of Visual Attention Before Accurate and Averaging Saccades via Eye Tracking and Assessment of Visual Sensitivity
Published on: March 18, 2019
Improving the saccade peak velocity measurement for detecting fatigue
Kati Hirvonen1, Sampsa Puttonen, Kristian Gould
1Brain Work Research Centre, Finnish Institute of Occupational Health, Topeliuksenkatu 41 a A, 00250 Helsinki, Finland. Kati.Hirvonen@ttl.fi
Abstract:
The aim of the study was to compare saccadic peak velocity (SPV) values measured with video based Fitness Impairment Tester (FIT) and electro-oculography (EOG) during prolonged wakefulness. We tested different numbers of saccades and two saccade paradigms to improve the EOG measurements for detecting fatigue. The SPVs were measured from 11 fast patrol boat navigators with FIT and EOG every sixth hour until 54 h. Subjective sleepiness was assessed with the Karolinska Sleepiness Scale. EOG was measured using an overlap and a gap paradigm and the data was divided into sequential five 20-saccade blocks and cumulative blocks of 20, 40, 60, 80, and 100 saccades. Compared to the gap paradigm, the overlap paradigm produced a higher number of analyzable saccades for a given measurement time. The shorter measurements (20-40 saccades) appeared to be more sensitive for fatigue, whereas the longer measurements (60-100 saccades) were more sensitive to time spent on the task. Thus, the optimal number of saccades varies also depending on the research question. The EOG method was more sensitive to fatigue than FIT. The FIT values measured after 30 and 36 h of wakefulness did not differ significantly from the baseline values, while subjective sleepiness and the EOG values showed that the participants were significantly less alert at these time points. The EOG measurements can be improved for detecting fatigue by using the overlap saccade paradigm. The SPV values measured with the EOG method appear to be somewhat more sensitive in detecting fatigue than the FIT method.
