Instrumentation & Measurement Magazine 24-2 - 32

where k is the filtering constant, x(t) is the raw beat to beat
signal value, and y(t) is the filtered beat to beat value. This
filtering method was used in our previous work [9] and employed again in the prototyped IoT sleep tracking platform
that is presented in this paper as a demonstration example.
Other researchers have used different filtering methods
such as adaptive filtering, which depends on the specific device sensors used for the analysis [7]. Cardiac features can
model changes in sleep states with heart rate variability (HRV)
from BCG being an important property because it can be related to the autonomic nervous system (ANS) that affects the
body's activity or rest response [17]. In addition, it can help
with sleep stage estimation and the transition between sleep
stages by observing whether the HRV properties change over
the sleep cycle. HRV is commonly separated into time-domain measures, frequency domain measures, and non-linear
domain measures [18]. Common time-domain measures are
standard deviation of beat to beat (B2B) intervals (SDNN), percentage of successive B2B intervals that differ by more than 50
ms (pNN50), and the root mean square of successive B2B interval differences (RMSSD) [18].
Frequency-domain measures are split into low frequency
(LF) and high (HF) frequency (0.04-0.15 Hz and 0.15-0.4 Hz,
respectively) components and the ratio of LF/HF. The ratio of LF/HF generally reflects the ratio of the sympathetic
and parasympathetic nervous systems and tells which one
is more dominant. A low ratio would be a parasympathetic
dominance with a more restful state, and a high ratio would
be sympathetic dominance with an active state [17]. The frequency domain parameters were calculated through a Fast
Fourier Transform of the B2B for 5-minute intervals and divided into each of the time and frequency categories because
longer segments are required to properly model the cardiovascular responses to a wide range of environmental stimuli
[17]. Incorporating BCG as a non-contact sleep sensor is a convenient method to analyze sleep quality as the device would
always be stationed in the bed and serve a dual purpose as a
bed occupancy sensor that allows it to automate the detection
of sleep onset and outset.

dark. In addition, people turn off any lights in their bedroom
and use curtains to block natural light from outside to maintain
a dark environment while they sleep. However, light exposure
can shift one's chronotype which could result in sleep deprivation due to not being able to fall asleep right away [11]. Light
plays a major role in the body's circadian rhythm by setting the
biological clock of when the body should be active or should
rest [11]. Using electronic devices with bright screens before
bed could especially prevent one from falling asleep right
away. Light and sound exposure generally would not affect
one during sleep because of the ability to control the room environment, as normally people sleep in a quiet and dark room.
Temperature and humidity can also affect the sleep as the
body's reaction to thermoregulation is linked to its sleep regulation [13]. Changing weather temperatures such as hot
summer and cold winters make it difficult to directly control
the ambient temperature in the bedroom. In addition, a humid environment also results in an uncomfortable sleep as it
prevents sweat from evaporating and the body from cooling itself effectively [13]. Heat exposure during the night is known
to increase wakefulness and reduce deeper sleep, while cold
exposure directly affects cardiac autonomic activity due to the
body's attempt to warm itself up [13]. Cold exposure does not
seem to affect changes in sleep stages, but the changes in cardiac
parameters could affect blood pressure and have consequences
for people with heart disease sleeping in a colder environment.
Fig. 2 shows an example of the changes between a temperature
probe inside the bedding microclimate and the corresponding
HRV. Each parameter was observed with a Z-score to standardize both signals on the same scale for visual clarity. The
temperature and HRV tend to be related as increased temperatures appear to increase the HRV response. The increased HRV
appears to signify the participant awakening briefly to adjust
the bed covers as the microclimate was becoming too warm,
which is evidenced by the following temperature decrease and
a more relaxed HRV response (> 850 HRV epoch count in Fig.

Environmental Sensors
The environment can directly affect sleep quality and cause
disturbances that would prevent one from getting an appropriate amount of restful sleep. Abrupt sounds have shown to
reduce the number of nightly arousals in a quieter room [12].
In addition, noise can impact the transition into the deep sleep
phase which would result in less restful sleep for the user.
Therefore, reducing the amount of ambient noise should increase the amount of restful sleep one would get and allow
more productivity during their daily life [12]. Sleep in one's
own domestic home is usually the most familiar and comfortable as they can remove most noise or use ear plugs during
their sleep and only external noise disruptions can occur such
as outdoor traffic [12].
Light is similar to sound noise as it can be controlled by
the user and sleep occurs during the night when it is naturally
32	

Fig. 2. Z-scores of temperature probe and heart rate variability. Large peaks
indicate warming up and valleys indicate cooling down. The increased HRV
epochs towards the end shows the participant waking up.

IEEE Instrumentation & Measurement Magazine	

April 2021



Instrumentation & Measurement Magazine 24-2

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