Evaluating body movement and breathing signals for identification of sleep/wake states
- Recognition of sleep and wake states is one of the relevant parts of sleep analysis. Performing this measurement in a contactless way increases comfort for the users. We present an approach evaluating only movement and respiratory signals to achieve recognition, which can be measured non-obtrusively. The algorithm is based on multinomial logistic regression and analyses features extracted out of mentioned above signals. These features were identified and developed after performing fundamental research on characteristics of vital signals during sleep. The achieved accuracy of 87% with the Cohen’s kappa of 0.40 demonstrates the appropriateness of a chosen method and encourages continuing research on this topic.
Author of HS Reutlingen | Martínez Madrid, Natividad |
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DOI: | https://doi.org/10.1007/978-3-030-95498-7_29 |
ISBN: | 978-3-030-95497-0 |
ISBN: | 978-3-030-95498-7 |
Erschienen in: | Applications in electronics pervading industry, environment and society : APPLEPIES 2021. - (Lecture Notes in Electrical Engineering ; 866 ) |
Publisher: | Springer |
Place of publication: | Cham |
Editor: | Sergio Saponara, Alessandro De Gloria |
Document Type: | Conference proceeding |
Language: | English |
Publication year: | 2022 |
Page Number: | 6 |
First Page: | 206 |
Last Page: | 211 |
PPN: | Im Katalog der Hochschule Reutlingen ansehen |
DDC classes: | 610 Medizin, Gesundheit |
Open access?: | Nein |
Licence (German): | In Copyright - Urheberrechtlich geschützt |