Research Article
Preliminary Assessment of a Smart Mattress for Position and Breathing Sensing
@INPROCEEDINGS{10.1007/978-3-030-49289-2_19, author={Lucia Arcarisi and Carlotta Marinai and Massimo Teppati Los\'{e} and Marco Laurino and Nicola Carbonaro and Alessandro Tognetti}, title={Preliminary Assessment of a Smart Mattress for Position and Breathing Sensing}, proceedings={Wireless Mobile Communication and Healthcare. 8th EAI International Conference, MobiHealth 2019, Dublin, Ireland, November 14-15, 2019, Proceedings}, proceedings_a={MOBIHEALTH}, year={2020}, month={6}, keywords={Sleep analysis Sensing mattress Smart textile Breathing monitoring}, doi={10.1007/978-3-030-49289-2_19} }- Lucia Arcarisi
Carlotta Marinai
Massimo Teppati Losè
Marco Laurino
Nicola Carbonaro
Alessandro Tognetti
Year: 2020
Preliminary Assessment of a Smart Mattress for Position and Breathing Sensing
MOBIHEALTH
Springer
DOI: 10.1007/978-3-030-49289-2_19
Abstract
Sleep is a one of the most important activity for maintaining the health and well-being of each subject. In order to monitor continuously the quality of sleep of the general population in non-invasively way, we developed an innovative sensorized “smart” mattress (SmartBed). SmartBed is equipped with sensors to detect environmental and subject-related information. In particular, SmartBed is equipped with accelerometers and a sensing textile matrix able to detect the distribution of pressures of a subject laying on the mattress. The purpose of this work is to demonstrate how the sensing textile matrix is not only able to detect how the subject is positioned on the mattress over time, but also it allows to detect other physiological parameters and in particular the subject’s respiratory activity. In this work, we show that: (i) the sensing textile matrix allows a precise position detection; (ii) it is possible to extract accurately the respiratory frequency from the sensing textile matrix by using a specifically tailored algorithm. In conclusion, the sensors integrated in SmartBed make possible to detect important information (position and respiratory activity) to determine the quality of a subject’s sleep in a robust, accurate and non-invasive way.