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Multisensor Monitoring System to
Establish Correlations Between Sleep
Performance and Environment Data
Celestino GONÇALVESa,b, Fábio SILVAb,c, Paulo NOVAISb and Cesar ANALIDEb
a Polytechnic Institute of Guarda, Guarda, Portugal
c School of Management and Technology, Polytechnic Institute of Porto, Portugal
b Algoritmi Centre, University of Minho, Braga, Portugal
celestin@ipg.pt, {fabiosilva, pjon, analide}@di.uminho.pt
Abstract. The importance of sleep in people's lives and concern about their quality
have both been gaining an increasing relevance and awareness. Nowadays, the
number of people who use mobile and wearable devices to monitor their sleep and
day activity is quite revealing. But while some of those recent devices may even
provide reliable measurements of some of the sleep parameters and sleep structure
itself, they do not provide yet a justification for what has led to that situation and to
those values. With the present study, we intend to verify and establish relationships
between some environmental factors, such as temperature, humidity, luminosity,
noise or air quality and between sleep performance and activity during the day.
Several time series machine learning models were used to predict sleep stages and
to estimate the level of day activity of a person.
Keywords. Sleep monitoring, sleep structure, monitoring system, machine learning,
sleep stages prediction, activity estimation
1. Introduction
Sleep represents a significant part of human life, since about one-third is, or should be,
spent sleeping, being an essential physiological function for the proper functioning of
the brain [1], and therefore for the general well-being and for a good quality of life [2].
On the other hand, it is a complex process [3] that has only recently been given due
attention by science and medicine, given the growing awareness of its importance [4]. It
is a complex process and it can be measured considering multiple objective and
subjective aspects and dimensions. For example, Buysse [4] proposes the following five
dimensions to characterize sleep health: satisfaction, alertness, timing, efficiency, and
duration. Examples of subjective methods for measuring sleep are the Pittsburgh Sleep
Quality Index (PSQI) and sleep diaries, while the most commonly used objective
methods are polysomnography (PSG) and actigraphy [2]. PSG is considered the ground-
truth for measuring sleep, as it combines multiple sensors to measure various parameters,
such as eye movement, brain activity, heart rate, muscle tone, and physical movement,
however it is an expensive and intrusive method, usually carried out in clinical or
laboratory environment, and is not normally used for data collection for very long periods.
The actigraphy consists of the use of a device like a wrist watch, which by means of an
accelerometer, measures movement as an indicator of wakefulness. It is a more
Intelligent Environments 2019
A. Muñoz et al. (Eds.)
© 2019 The authors and IOS Press.
This article is published online with Open Access by IOS Press and distributed under the terms
of the Creative Commons Attribution Non-Commercial License 4.0 (CC BY-NC 4.0).
doi:10.3233/AISE190019
26
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Titel
- Intelligent Environments 2019
- Untertitel
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Autoren
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-Cía
- Verlag
- IOS Press BV
- Datum
- 2019
- Sprache
- deutsch
- Lizenz
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Abmessungen
- 16.0 x 24.0 cm
- Seiten
- 416
- Kategorie
- Tagungsbände