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Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

Abstract

Fatigue can be a pre-cursor to many illnesses and injuries, and cause fatal work-related incidents. Fatigue detection has been traditionally performed in lab conditions with stationary medical-grade diagnostics equipment for electroencephalography making it impractical for many in-field scenarios. More recently, the ubiquitous use of wearable sensor-enabled technologies in sports, everyday life or fieldwork has enabled collecting large amounts of physiological information. According to recent studies, the collected biomarkers related to sleep, physical activity or heart rate have proven to be in correlation with fatigue, making it a natural fit for applying automated data analysis using Machine Learning. Accordingly, this paper presents our novel Machine Learning-driven approach to fatigue detection using biomarkers collected by general-purpose wearable fitness trackers. The developed method can successfully predict fatigue symptoms among target users, and the overall methodology can be further extended to other diagnostics scenarios which rely on collected wearable data.
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Category

Academic chapter

Language

English

Author(s)

Affiliation

  • SINTEF Digital / Sustainable Communication Technologies
  • Gnomon Informatics
  • University of Oslo

Year

2022

Publisher

SciTePress

Book

Proceedings of the 10th International Conference on Sport Sciences Research and Technology Support (icSPORTS 2022)

ISBN

9789897586101

Page(s)

41 - 52

View this publication at Norwegian Research Information Repository