Sensor-Mesh-Based System with Application on Sleep Study

  • The process of restoring our body and brain from fatigue is directly depend-ing on the quality of sleep. It can be determined from the report of the sleep study results. Classification of sleep stages is the first step of this study and this includes the measurement of biovital data and its further processing. In this work, the sleep analysis system is based on a hardware sensor net, namely a grid of 24 pressure sensors, supporting sleep phase recognition. In comparison to the leading standard, which is polysomnography, the proposed approach is a non-invasive system. It recognises respiration and body move-ment with only one type of low-cost pressure sensors forming a mesh archi-tecture. The nodes implement as a series of pressure sensors connected to a low-power and performant microcontroller. All nodes are connected via a system wide bus with address arbitration. The embedded processor is the mesh network endpoint that enables network configuration, storing and pre-processing of the data, external data access and visualization. The system was tested by executing experiments recording the sleep of different healthy young subjects. The results obtained have indicated the po-tential to detect breathing rate and body movement. A major difference of this system in comparison to other approaches is the innovative way to place the sensors under the mattress. This characteristic facilitates the continuous using of the system without any influence on the common sleep process.

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Metadaten
Author:Maksym GaidukORCiD, Bruno Vunderl, Ralf SeepoldORCiDGND, Juan Antonio OrtegaORCiD, Thomas PenzelORCiDGND
DOI:https://doi.org/10.1007/978-3-319-78759-6_34
ISBN:978-3-319-78759-6
ISBN:978-3-319-78758-9
Parent Title (English):Bioinformatics and Biomedical Engineering, 6th International Work-Conference (IWBBIO 2018), 25th-27th April 2018, Granada, Spain - (Lecture Notes in Computer Science ; Vol. 10814)
Publisher:Springer
Place of publication:Cham
Document Type:Article
Language:English
Year of Publication:2018
Release Date:2019/01/16
Tag:FSR sensor; Movement detection; Respiration rate; Sleep study
First Page:371
Last Page:382
Institutes:Fakult├Ąt Informatik
Open Access?:Nein
Relevance:Keine peer reviewed Publikation (Wissenschaftlicher Artikel und Aufsatz, Proceeding, Artikel in Tagungsband)
Licence (English):License LogoLizenzbedingungen Springer