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behavioural aspects, and the latter includes
meteorological, environmental, geographical, and
temporal factors. Human body has to levy upon
immune system and auto regulation mechanism to
adapt various regular and irregular stimulants
physically and mentally. Activities in daytime often
preserve memories for a certain period
physiologically and psychologically, and usually can
be reflected as a physiological response at night.
On the other hand, sleep insufficiency or disorder
may cause unpleasantness even illness. Good sleep
can help to not only secrete growth hormone and
recover human body’s physiological functions, but
also relieve mental aspect from stress and build up
immune system.
Instead of identifying every detail of daily
activities in daytime, we believe that it is possible to
monitor physiological condition during sleep at night
to reflect the lifestyle change in daytime indirectly.
Standard polysomnography method provides an
accurate approach to monitor multiple parameters
and perform comprehensive sleep analysis, but
requires professional intervention and is highly
expensive, therefore is unsuitable for daily
application at home.
We developed a convenient device for automatic
collecting PR data during sleep and an algorithm to
detect lifestyle change from these PR data. Various
specific events, such as alcohol drink, mental
depression and physical illness, and other commonly
non-routine epochs in daytime are confirmed often
bringing disturbance or disorder in sleep at night,
and are probably reflected on night-time PR profiles.
This study demonstrated availability to detect these
daily behavioural changes during waking hours by
the PR data collected during sleep.
This method is recognized feasible for a user
over one year test. However, more data from more
users in different age groups and longer period of
data collection are desirable in further validation of
the proposed method. More sensitive and robust
algorithms are also worth to be explored in depth.
5 CONCLUSIONS
In this study, we developed a convenient system to
measure PR and SpO
2
data during sleep, and a
DTW-based algorithm to detect lifestyle change
using daily PR profile. The proposed method was
examined by one-year data and confirmed sensitive
in detecting lifestyle change due to various
incentives. It suggests a promising method for daily
health management and chronic disease prevention.
ACKNOWLEDGEMENTS
The authors would like to thank the volunteer for his
endurance in daily data collection over a long period.
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