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economical, non-intrusive alternative, which can be used in the domestic environment
and for long periods of time, however it is not as accurate as PSG [5].
This recent interest in sleep and concern for its measurement coupled with the recent
development of technologies and growth in the consumer market for wearable health
devices has allowed the emergence of multiple, non-intrusive, affordable sleep
monitoring solutions that users can use at home, in some cases with very acceptable
levels of precision [6] [7] [8]. So we can say that nowadays, with all this technology and
quantity of devices available, it is relatively simple to obtain sleep data at home,
unobtrusively and for long periods of time. However, what these solutions are not yet
able to provide is a justification for the values obtained for the various sleep parameters,
or what may have contributed to those particular outcomes. In fact, sleep is multi-
dimensional and involves not only objective but also subjective parameters [4] [9], so it
has not been easy to obtain a strict and concise definition of sleep quality [2]. On the
other hand, sleep can also be affected by a particular set of contextual factors [3], such
as environmental factors like temperature, humidity, luminosity, noise and poor air
quality [10] [11]. Thus, it would be interesting and useful to be able to establish
relationships between the obtained sleep data and relevant environmental factors for
these results, thereby to help people to be aware and to properly interpret their sleep
architecture and performance. This new opportunity for research led us to devise a
multisensor system for monitoring the sleep environment to try to understand the effect
that these factors have on the structure and quality of sleep. This issues were explored
through an experimental study over a period of one week.
The rest of the paper is organized as follows. Next section presents a brief
characterization of human sleep and its main parameters, followed by a discussion of
sleep monitoring systems that take into account the effect of environmental factors. We
than continue describing the developed multisensor sleep monitoring system and the
experimental study. Next section elaborates on the developed machine learning models
and experimental results and finally, last section presents the conclusions of our findings
and future work.
2. Related Work
This section starts with a brief characterization of human sleep, followed by a discussion
of some examples of sleep monitoring systems that take into account the effect of
environmental factors.
2.1. Sleep Architecture
Human sleep is characterized by two distinct phases, NREM (non-rapid eye movements)
and REM (rapid eye movements), which alternate cyclically, as illustrated in Figure 1,
making four to five sleep cycles in one night, lasting about 90 to 120 min each [3]. The
NREM sleep phase, or slow sleep, is usually subdivided into three states or depth levels:
N1 (onset of sleep), N2 (light sleep), and N3 (deep sleep) [12]. Often, the N3 state is also
referred to as slow wave sleep (SWS), presenting regular breathing and heart rate and
low muscle tone. The REM sleep phase presents irregular breathing and heart rate, null
muscle tone and, of course, rapid eye movements. The N3 phase occurs essentially in the
first third of sleep, whereas REM sleep occurs predominantly in the last third.
C.Gonçalvesetal. /MultisensorMonitoringSystem toEstablishCorrelations 27
Intelligent Environments 2019
Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Title
- Intelligent Environments 2019
- Subtitle
- Workshop Proceedings of the 15th International Conference on Intelligent Environments
- Authors
- Andrés Muñoz
- Sofia Ouhbi
- Wolfgang Minker
- Loubna Echabbi
- Miguel Navarro-CĂa
- Publisher
- IOS Press BV
- Date
- 2019
- Language
- German
- License
- CC BY-NC 4.0
- ISBN
- 978-1-61499-983-6
- Size
- 16.0 x 24.0 cm
- Pages
- 416
- Category
- Tagungsbände