Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data

Martin Baumgartner; Manuel Grössl; Raphaela Haumer; Katharina Poimer; Flora Prantl; Katharina Weick; Markus Falgenhauer; Stefan Beyer; Andreas Ziegl; Aaron Lauschensky; Fabian Wiesmüller; Karl Kreiner; Dieter Hayn; Günter Schreier

doi:10.3233/SHTI240041

Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data

Martin Baumgartner, Manuel Grössl, Raphaela Haumer, Katharina Poimer, Flora Prantl, Katharina Weick, Markus Falgenhauer, Stefan Beyer, Andreas Ziegl, Aaron Lauschensky, Fabian Wiesmüller, Karl Kreiner, Dieter Hayn, Günter Schreier

Digital Health Information Systems

Publikation: Beitrag in Buch oder Tagungsband › Beitrag in Tagungsband mit Posterpräsentation › Begutachtung

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Abstract

BACKGROUND: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality.

OBJECTIVES: The primary objective was to correlate wearable device data with subjective sleep quality perceptions.

METHODS: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier.

RESULTS: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%.

DISCUSSION: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior.

Originalsprache	Englisch
Titel	dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health
Redakteure/-innen	Dieter Hayn, Bernhard Pfeifer, Günter Schreier, Martin Baumgartner
Herausgeber (Verlag)	IOS Press BV
Seiten	221-227
Seitenumfang	7
Band	313
ISBN (elektronisch)	978-1-64368-517-5
ISBN (Print)	978-1-64368-516-8
DOIs	https://doi.org/10.3233/SHTI240041
Publikationsstatus	Veröffentlicht - 26 Apr. 2024
Veranstaltung	dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health - Apothekertrakt of Schloss Schönbrunn, Vienna, Österreich Dauer: 7 Mai 2024 → 8 Mai 2024 Konferenznummer: 18 http://dhealth.at

Publikationsreihe

Name	Studies in Health Technology and Informatics
Herausgeber (Verlag)	IOS Press BV
ISSN (Print)	0926-9630

Konferenz

Konferenz	dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health
Kurztitel	dHealth 2024
Land/Gebiet	Österreich
Stadt	Vienna
Zeitraum	7/05/24 → 8/05/24
Internetadresse	http://dhealth.at

Research Field

Exploration of Digital Health

Zugriff auf Dokument

10.3233/SHTI240041

SHTI-313-SHTI240041-5Endgültige, publizierte Fassung, 430 KBLizenz: CC BY-NC

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Baumgartner, M., Grössl, M., Haumer, R., Poimer, K., Prantl, F., Weick, K., Falgenhauer, M., Beyer, S., Ziegl, A., Lauschensky, A., Wiesmüller, F., Kreiner, K., Hayn, D., & Schreier, G. (2024). Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data. in D. Hayn, B. Pfeifer, G. Schreier, & M. Baumgartner (Hrsg.), dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health (Band 313, S. 221-227). (Studies in Health Technology and Informatics). IOS Press BV. https://doi.org/10.3233/SHTI240041

Baumgartner, Martin ; Grössl, Manuel ; Haumer, Raphaela et al. / Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data. dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health. Hrsg. / Dieter Hayn ; Bernhard Pfeifer ; Günter Schreier ; Martin Baumgartner. Band 313 IOS Press BV, 2024. S. 221-227 (Studies in Health Technology and Informatics).

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abstract = "BACKGROUND: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality.OBJECTIVES: The primary objective was to correlate wearable device data with subjective sleep quality perceptions.METHODS: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier.RESULTS: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%.DISCUSSION: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior.",

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Baumgartner, M, Grössl, M, Haumer, R, Poimer, K, Prantl, F, Weick, K, Falgenhauer, M , Beyer, S, Ziegl, A, Lauschensky, A , Wiesmüller, F , Kreiner, K , Hayn, D & Schreier, G 2024, Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data. in D Hayn, B Pfeifer, G Schreier & M Baumgartner (Hrsg.), dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health. Bd. 313, Studies in Health Technology and Informatics, IOS Press BV, S. 221-227, dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health, Vienna, Österreich, 7/05/24. https://doi.org/10.3233/SHTI240041

Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data. / Baumgartner, Martin; Grössl, Manuel; Haumer, Raphaela et al.
dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health. Hrsg. / Dieter Hayn; Bernhard Pfeifer; Günter Schreier; Martin Baumgartner. Band 313 IOS Press BV, 2024. S. 221-227 (Studies in Health Technology and Informatics).

Publikation: Beitrag in Buch oder Tagungsband › Beitrag in Tagungsband mit Posterpräsentation › Begutachtung

TY - CHAP

T1 - Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data

AU - Baumgartner, Martin

AU - Grössl, Manuel

AU - Haumer, Raphaela

AU - Poimer, Katharina

AU - Prantl, Flora

AU - Weick, Katharina

AU - Falgenhauer, Markus

AU - Beyer, Stefan

AU - Ziegl, Andreas

AU - Lauschensky, Aaron

AU - Wiesmüller, Fabian

AU - Kreiner, Karl

AU - Hayn, Dieter

AU - Schreier, Günter

N1 - Conference code: 18

PY - 2024/4/26

Y1 - 2024/4/26

N2 - BACKGROUND: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality.OBJECTIVES: The primary objective was to correlate wearable device data with subjective sleep quality perceptions.METHODS: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier.RESULTS: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%.DISCUSSION: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior.

AB - BACKGROUND: This study focuses on the development of a neural network model to predict perceived sleep quality using data from wearable devices. We collected various physiological metrics from 18 participants over four weeks, including heart rate, physical activity, and both device-measured and self-reported sleep quality.OBJECTIVES: The primary objective was to correlate wearable device data with subjective sleep quality perceptions.METHODS: Our approach used data processing, feature engineering, and optimizing a Multi-Layer Perceptron classifier.RESULTS: Despite comprehensive data analysis and model experimentation, the predictive accuracy for perceived sleep quality was moderate (59%), highlighting the complexities in accurately quantifying subjective sleep experiences through wearable data. Applying a tolerance of 1 grade (on a scale from 1-5), increased accuracy to 92%.DISCUSSION: More in-depth analysis is required to fully comprehend how wearables and artificial intelligence might assist in understanding sleep behavior.

KW - Sleep Quality

KW - Wearable Electronic Devices

KW - Neural Network

KW - Multi-Layer Perceptrons

KW - Electronic Data Processing

U2 - 10.3233/SHTI240041

DO - 10.3233/SHTI240041

M3 - Conference Proceedings with Poster Presentation

C2 - 38682534

SN - 978-1-64368-516-8

VL - 313

T3 - Studies in Health Technology and Informatics

SP - 221

EP - 227

BT - dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health

A2 - Hayn, Dieter

A2 - Pfeifer, Bernhard

A2 - Schreier, Günter

A2 - Baumgartner, Martin

PB - IOS Press BV

T2 - dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health

Y2 - 7 May 2024 through 8 May 2024

ER -

Baumgartner M, Grössl M, Haumer R, Poimer K, Prantl F, Weick K et al. Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data. in Hayn D, Pfeifer B, Schreier G, Baumgartner M, Hrsg., dHealth 2024 - 18th Annual Conference on Health Informatics meets Digital Health. Band 313. IOS Press BV. 2024. S. 221-227. (Studies in Health Technology and Informatics). doi: 10.3233/SHTI240041

Neural Network-Based Prediction of Perceived Sleep Quality Through Wearable Device Data

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