Is this project an undergraduate, graduate, or faculty project?
Undergraduate
Project Type
group
Campus
Daytona Beach
Authors' Class Standing
Amanda Dhanpaul, Graduate Student Hannah Lyons, Senior Dominic Sandell, Sophomore Brianna Magdaleno, Junior Savannah Burke, Sophomore Inga Agustsdottir, Senior Alice Eriksson, Senior Rowan Knauf, Freshman Eva Zaragoza, Senior John French, Faculty Mentor
Lead Presenter's Name
Amanda Dhanpaul
Lead Presenter's College
DB College of Arts and Sciences
Faculty Mentor Name
John French
Abstract
Human performance can be critically impacted by fatigue resulting from inadequate sleep. In a technological society, the consequences of fatigue related inattention or impaired response time are greater than ever. Industries such as aviation, rail, trucking, and medicine are mandated to have a Fatigue Management System (FMS) in place to ensure their personnel are ready for duty. We are testing the idea that a commercial, off the shelf (COTS) smart watch application using a new biomathematical model can provide continuous, obvious, real-time predictions about the wearer’s level of fatigue impairment. Our evidence shows that the correlation between a clinically approved measure with that of our smart watch application is strong enough to warrant applied trials with industry. We will also report on the correlation between subjective fatigue measures and the model’s fatigue predictions of subjective fatigue score throughout the waking hours. The conclusion of this study may show that continuous prediction of fatigue is possible hours before the point of endangerment, particularly for those industries requiring FMS to ensure public safety.
Did this research project receive funding support (Spark, SURF, Research Abroad, Student Internal Grants, Collaborative, Climbing, or Ignite Grants) from the Office of Undergraduate Research?
No
Predicting Fatigue Impairment for Industry
Human performance can be critically impacted by fatigue resulting from inadequate sleep. In a technological society, the consequences of fatigue related inattention or impaired response time are greater than ever. Industries such as aviation, rail, trucking, and medicine are mandated to have a Fatigue Management System (FMS) in place to ensure their personnel are ready for duty. We are testing the idea that a commercial, off the shelf (COTS) smart watch application using a new biomathematical model can provide continuous, obvious, real-time predictions about the wearer’s level of fatigue impairment. Our evidence shows that the correlation between a clinically approved measure with that of our smart watch application is strong enough to warrant applied trials with industry. We will also report on the correlation between subjective fatigue measures and the model’s fatigue predictions of subjective fatigue score throughout the waking hours. The conclusion of this study may show that continuous prediction of fatigue is possible hours before the point of endangerment, particularly for those industries requiring FMS to ensure public safety.