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Towards Automated Fatigue Assessment using Wearable Sensing and Mixed-Effects Models

Authors:

Wan‑Fai NgAuthors Info & Claims

ISWC '21: Proceedings of the 2021 ACM International Symposium on Wearable Computers

Pages 129 - 131

https://doi.org/10.1145/3460421.3480429

Published: 21 September 2021 Publication History

Abstract

Fatigue is a broad, multifactorial concept that includes the subjective perception of reduced physical and mental energy levels. It is also one of the key factors that strongly affect patients’ health-related quality of life. To date, most fatigue assessment methods were based on self-reporting, which may suffer from many factors such as recall bias. To address this issue, in this work, we recorded multi-modal physiological data (including ECG, accelerometer, skin temperature and respiratory rate, as well as demographic information such as age, BMI) in free-living environments, and developed automated fatigue assessment models. Specifically, we extracted features from each modality, and employed the random forest-based mixed-effects models, which can take advantage of the demographic information for improved performance. We conducted experiments on our collected dataset, and very promising preliminary results were achieved. Our results suggested ECG played an important role in the fatigue assessment tasks.

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Cited By

Lin YLi NHuang WEcsedy KFeinberg MTeti DCarroll J(2024)"Ultimately We're Together": Understanding New Parents' Experiences of Co-parentingProceedings of the ACM on Human-Computer Interaction10.1145/36870188:CSCW2(1-25)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687018
Yan XBaroudi LBei RBoudalia LCain SBarton KShorter KNewman M(2024)Proposing a Context-informed Layer-based Framework: Incorporating Context into Designing mHealth Technology for Fatigue ManagementProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661615(571-583)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661615
Gashi SOldrati PMoebus MHilty MBarrios LOzdemir FKana VLutterotti ARätsch GHolz C(2024)Modeling multiple sclerosis using mobile and wearable sensor datanpj Digital Medicine10.1038/s41746-024-01025-87:1Online publication date: 11-Mar-2024
https://doi.org/10.1038/s41746-024-01025-8
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cover image ACM Conferences

ISWC '21: Proceedings of the 2021 ACM International Symposium on Wearable Computers

September 2021

220 pages

ISBN:9781450384629

DOI:10.1145/3460421

Copyright © 2021 Owner/Author.

Permission to make digital or hard copies of part or all of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for third-party components of this work must be honored. For all other uses, contact the Owner/Author.

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Published: 21 September 2021

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UbiComp '21: The 2021 ACM International Joint Conference on Pervasive and Ubiquitous Computing

September 21 - 26, 2021

Virtual, USA

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Cited By

Lin YLi NHuang WEcsedy KFeinberg MTeti DCarroll J(2024)"Ultimately We're Together": Understanding New Parents' Experiences of Co-parentingProceedings of the ACM on Human-Computer Interaction10.1145/36870188:CSCW2(1-25)Online publication date: 8-Nov-2024
https://dl.acm.org/doi/10.1145/3687018
Yan XBaroudi LBei RBoudalia LCain SBarton KShorter KNewman M(2024)Proposing a Context-informed Layer-based Framework: Incorporating Context into Designing mHealth Technology for Fatigue ManagementProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661615(571-583)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661615
Gashi SOldrati PMoebus MHilty MBarrios LOzdemir FKana VLutterotti ARätsch GHolz C(2024)Modeling multiple sclerosis using mobile and wearable sensor datanpj Digital Medicine10.1038/s41746-024-01025-87:1Online publication date: 11-Mar-2024
https://doi.org/10.1038/s41746-024-01025-8
Mu SLiao STao KShen Y(2024)Intelligent fatigue detection based on hierarchical multi-scale ECG representations and HRV measuresBiomedical Signal Processing and Control10.1016/j.bspc.2024.10612792(106127)Online publication date: Jun-2024
https://doi.org/10.1016/j.bspc.2024.106127

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