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In order to quantify sleep performance, the following sleep parameters are usually
considered and measured [9] [8]: Sleep Onset Latency (SOL), Wake After Sleep Onset
(WASO), Total Sleep Time (TST), Sleep Efficiency (SE), Number of Awakenings
(NAWK). Table 1 presents their definitions.
Figure 1. Main sleep stages transitions.
Table 1. Sleep parameters
Sleep parameter Description
Sleep Onset Latency (SOL) Time needed to start sleeping
Wake After Sleep Onset (WASO) Time awake after sleep onset
Total Sleep Time (TST) Time asleep less awake time
Sleep Efficiency (SE) Percentage of time in bed spent in sleep
Number of Awakenings (NAWK) Number of awakenings during sleep time
2.2. Sleep Monitoring Systems and Environmental Data
Our search of related work considering sleep monitoring systems that take into account
the effect of environmental factors has resulted in the following three cases. The first
example is Lullaby [10], a capture and access system for understanding the sleep
environment. The authors of this system use temperature, light, and motion sensors,
audio and photos, and an off-the-shelf sleep sensor to acquire a complete recording of a
person’s sleep. The system visualizes graphs and parameter recordings, allowing users
to find trends and potential causes of sleep disruptions relatively to environmental factors,
helping them understand their sleep environment.
SleepExplorer [11] is a web-based visualization tool to make sense of correlations
between personal sleep data and contextual factors. It imports data from commercial
sleep trackers and online diaries and helps users to better understand their sleep and to
discover novel relationships between sleep data and contextual factors. The contextual
factors collected during the field study were organized under four main categories:
physiological factors (weight, body temperature and menstrual cycles), phychological
factors (mood, stress, tiredness and dream), behavioral factors (steps, minutes very active,
minutes fairly active, minutes lightly active, calories in, calories out, activity calories,
coffee, coffee time, alcohol, electronic devices usage, evening light, nap time, nap
duration, social activities, exercise time and dinner time) and environmental factors
(ambient temperature and ambient humidity).
Borazio and Laerhoven [13] present a long-term sleep monitoring system combining
wearable and environmental data to automatically detect the user’s sleep at home. The
system uses inertial, ambient light, and time data tracked from a wrist-worn sensor,
synchronized with night vision camera footage for easy visual inspection.
C.Gonçalvesetal. /MultisensorMonitoringSystem
toEstablishCorrelations28
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