TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Gaiduk, Maksym
A1  - Seepold, Ralf
A1  - Martínez Madrid, Natividad
A1  - Penzel, Thomas
A1  - Weber, Lucas
A1  - Conti, Massimo
A1  - Orcioni, Simone
A1  - Ortega, Juan Antonio
ED  - Saponara, Sergio
ED  - De Gloria, Alessandro
T1  - Evaluating body movement and breathing signals for identification of sleep/wake states
T2  - Applications in electronics pervading industry, environment and society : APPLEPIES 2021. - (Lecture Notes in Electrical Engineering ; 866 )
N2  - 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.
Y1  - 2022
SN  - 978-3-030-95497-0
SB  - 978-3-030-95497-0
SN  - 978-3-030-95498-7
SB  - 978-3-030-95498-7
U6  - https://doi.org/10.1007/978-3-030-95498-7_29
DO  - https://doi.org/10.1007/978-3-030-95498-7_29
SP  - 206
EP  - 211
S1  - 6
PB  - Springer
CY  - Cham
ER  -