TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Gaiduk, Maksym
A1  - Seepold, Ralf
A1  - Martínez Madrid, Natividad
A1  - Orcioni, Simone
A1  - Conti, Massimo
T1  - Recognizing breathing rate and movement while sleeping in home environment
T2  - Applications in Electronics Pervading Industry, Environment and Society : APPLEPIES 2019, 11. - 13. September 2019, Pisa, Italy, (Lecture Notes in Electrical Engineering ; Vol. 627)
N2  - The recovery of our body and brain from fatigue directly depends on the quality of sleep, which can be determined from the results of a sleep study. The classification of sleep stages is the first step of this study and includes the measurement of vital data and their further processing. The non-invasive sleep analysis system is based on a hardware sensor network of 24 pressure sensors providing sleep phase detection. The pressure sensors are connected to an energy-efficient microcontroller via a system-wide bus. A significant difference between this system and other approaches is the innovative way in which the sensors are placed under the mattress. This feature facilitates the continuous use of the system without any noticeable influence on the sleeping person. The system was tested by conducting experiments that recorded the sleep of various healthy young people. Results indicate the potential to capture respiratory rate and body movement.
KW  - Sleep study
KW  - Breathing rate
KW  - Pressure sensors
Y1  - 2020
SN  - 978-3-030-37276-7
SB  - 978-3-030-37276-7
SN  - 978-3-030-37277-4
SB  - 978-3-030-37277-4
U6  - https://doi.org/10.1007/978-3-030-37277-4_38
DO  - https://doi.org/10.1007/978-3-030-37277-4_38
SP  - 333
EP  - 339
PB  - Springer
CY  - Cham
ER  -