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Updated: May 23, 2025
Collecting Sleep, Circadian, Fatigue, and Performance Data in Complex Operational Environments
Published on: August 8, 2019
Evaluating the Effectiveness of Camera-Based Fatigue and Distraction Detection Technology in a Rural Truck Driver
Jennifer Cori1, Lauren A Booker, Tracey L Sletten
1From the Institute for Breathing and Sleep, Austin Health, Melbourne, Australia (J.C., L.A.B., M.E.H.); School of Psychology & Public Health, La Trobe University, Bendigo, Victoria, Australia (L.A.B.); Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, Victoria, Australia (J.C., T.L.S., S.M.W.R., M.E.H.); Utility Creative, Fitzroy, Victoria, Australia (D.S., L.F., K.M.); Seeing Machines, Fyshwick, ACT, Australia (K.M., M.M.); and Department of Medicine, the University of Melbourne, Melbourne, Victoria, Australia (M.E.H.).
Objectives:
This study aims to evaluate the effectiveness of fatigue detection technology (FDT) cabin alarms in reducing fatigue events in rural truck drivers, assess the accuracy in detecting fatigue events alarms, and examine whether drivers habituate to alarms over time.
Methods:
This is longitudinal naturalistic study of fatigue events before and after alarm activation in 12 rural commercial trucks.
Results:
The rate showed fatigue events were significantly higher when alarms were off (0.06), compared to when alarms were activated (0.03) (rate ratio = 0.5 [0.4, 0.7], P < 0.001). Fatigue events increased as the alarm phase continued, indicating habituation. The device classified fatigue events with 49% precision, 32% were false positives, and 18% reclassified as distraction when human verified.
Conclusions:
In-cabin fatigue, alarms significantly reduced the rate of fatigue events initially but increased again overtime.
