Machine Learning for Fatigue Detection using Fitbit Fitness Trackers

Erik Husom, Rustem Dautov, Adela-Aniela Nedisan, Adela-Aniela Nedisan, Fotis Gonidis, Spyridon Papatzelos, Nikolaos Malamas



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.


Paper Citation

in Harvard Style

Husom E., Dautov R., Nedisan A., Gonidis F., Papatzelos S. and Malamas N. (2022). Machine Learning for Fatigue Detection using Fitbit Fitness Trackers. In - icSPORTS, ISBN , pages 0-0. DOI: 10.5220/0011527500003321

in Bibtex Style

author={Erik Husom and Rustem Dautov and Adela-Aniela Nedisan and Fotis Gonidis and Spyridon Papatzelos and Nikolaos Malamas},
title={Machine Learning for Fatigue Detection using Fitbit Fitness Trackers},
booktitle={ - icSPORTS,},

in EndNote Style


JO - - icSPORTS,
TI - Machine Learning for Fatigue Detection using Fitbit Fitness Trackers
SN -
AU - Husom E.
AU - Dautov R.
AU - Nedisan A.
AU - Gonidis F.
AU - Papatzelos S.
AU - Malamas N.
PY - 2022
SP - 0
EP - 0
DO - 10.5220/0011527500003321