Authors：Yeh-Liang Hsu, Yi-Fan Jiang, Ching-Hung Lin, Tsai-Ya
Lai, and Cheng-Li Chang (2015-12-01)；Recommend:
Yeh-Liang Hsu (2015-12-03).
Note: This paper is published
on International Journal of Automation and Smart Technology, vol. 5, no.4, pp.243-248,
Analysis of personal life patterns using accelerometer-based
commercially available wearable devices are equipped with sensors to measure motion
and physiological signals from the wearer. G-sensors are commonly used in such
wearable devices for counting steps, estimating energy expenditure and
detecting sleep duration. In this study, two features derived from G-sensor
motion signals, average cadence (step count divided by time) and ratio of high
G value (outside the range of 0.5g~1.5g), were used to classify physical
activities into four intensity levels (sedentary, light, moderate, hard).
Eighty physical activity samples were collected and trained by the Weka machine
learning software to form a classification model. G-sensor motion signals from
four participants were collected over two weeks and classified into four
activity intensity levels using the model. Physical activity levels (PAL) and
personal life patterns of the participants were then derived. This data can
then be used to tailor additional services for individual users of wearable
devices. A BLE (Bluetooth Low Energy) based system for older adults with
dementia, combining personal life pattern analysis with localization function,
is also proposed as an example application.
Keywords: Wearable device, motion
signal, activity intensity level, physical activity level
are becoming increasingly popular. Many commercially available wearable devices
are equipped with sensors to measure motion and physiological signals of the
users. G-sensors are commonly used in such wearable devices to measure motion
signals from the user. Such devices are less restricted in measuring positions
and do not require electrodes to touch the skin, providing increased
convenience and design flexibility and making them popular for use in wearable
devices for counting steps and detecting sleep duration.
devices often work with mobile device applications (Apps) for further data
processing and display. Algorithms based on step count, and the user’s height
and weight are used to estimate travel distance and energy expenditure.
However, estimations based on step count are relatively inaccurate. For
example, energy expenditure is assumed to be zero for activities with zero step
count, while walking and running are treated as having identical energy
expenditure for the same number of steps, though the intensities of the two
activities are very different. In other words, physical activity intensity (and
therefore energy expenditure) cannot be correctly classified based on step
activity intensity can be classified into four levels: sedentary, light,
moderate, and hard , and can be accurately measured by calculating the
calories burnt from the amount of oxygen uptake. However, this method can be
only be implemented through the use of a cardiopulmonary motion detection
system in a laboratory setting. On the other hand, the “heart rate reserve”
method is often used to estimate the level of physical activity intensity based
on the user’s heart rate while performing a given physical activity. The heart
rate reserve percentage is calculated by Eq. (1). This percentage can be used
to determine physical activity intensities from Table 1 , Heart rate reserve
where HR is the
user’s heart rate measured while performing a given physical activity,
HRmax=220-Age, and HRrest is the heart rate measured after the user rests for
five minutes. Note that age and individual differences are taken into account
in the heart rate reserve method through HRmax and HRrest.
1. Heart rate reserve to estimate activity intensity 
The purpose of
this study is to interpret users’ motion signals measured from G-sensors as
physical activity intensity. Two features derived from the G-sensor motion
signals, average cadence (step count divided by time) and ratio of high G value
(outside the range of 0.5g~1.5g), were used to classify the physical activities
into four intensity levels. Eighty physical activity samples were collected and
trained by Weka machine learning software  to construct a classification
model which can predict physical activity intensity from these two features.
From the physical activity intensity, total daily energy expenditure (TDEE),
physical activity levels (PAL) and personal life patterns can be derived.
Further services can then be tailored for the wearable device user based on the
data provided by wearable devices.
suffering from dementia were asked to wear GPS based wearable devices or RFID
tags, mainly for positioning or localization purposes. At the end of the paper,
a Bluetooth Low Energy (BLE) based system for older adults, combining personal
life pattern analysis with basic localization function, is proposed as an
Activity samples collection
earlier, after some preliminary trials, two features derived from the motion
signals sensed by G-sensors were selected to classify activity intensity:
average cadence (step count divided by time) and ratio of high G value (outside
the range of 0.5g~1.5g). In general, cadence and ratio of high G value measured
from sedentary activities are lower than those measured from light/moderate
activities. The ratio of high G value helps to distinguish a moderate activity
(e.g. fast walking) from a hard activity (e.g. running or jumping rope) which
have similar cadences, while certain hard activities (e.g. bicycling) have a
low ratio of high G value but a high cadence.
As shown at the
top of Fig. 1, activity samples were collected from 10 participants (six males
and four females, aged 22 to 27 years old, average 24.2 years old). Each
participant was asked to perform eight different physical activities at four
different levels of intensity, for a total of 80 samples. Sedentary level
activities included using a computer (c) and watching TV (t); light level
activities included walking (w) and housework (h); moderate level activities
included bicycling (b) and fast walking (fw); and hard level activities
included running (r) and jumping rope (j). When performing a physical activity,
the heart rate of the participant was monitored. Two-minute activity samples
were recorded only after confirming that the heart rate of the participant has
reached the required intensity level as defined by the heart rate reserve
method described earlier. The participant also carried a mobile phone with a
special App installed to record step count and G value (sampling rate 5 Hz).
These 80 activity samples (two minutes each) are plotted in Fig. 1 (top) using the
average cadence and percentage of high G value as the x and y axes. This figure
shows clear distinctions between the data points for sedentary level (yellow),
light level (green) and moderate level (blue) the data points for moderate
level and hard level (red) are less distinguishable.
Figure 1. Eighty
activity samples and the resulting visual display of machine learning models
learning with Weka
samples were then input into the Weka machine learning software . In
addition to the 80 activity samples, 20 "rest" samples were also
added. For the “rest” samples, the percentage of the higher G value and average
cadence were both considered as zero. Naive Bayes Classifier was selected for
machine learning, and 10-fold cross validation was used to assess the machine
learning result. The machine learning model’s predictive accuracy was 94%.
Certain errors occurred in the region between moderate and hard activities.
(bottom) shows a visual display of the machine learning model which can predict
the activity intensity from an activity data sample’s cadence and percentage of
high G value. Note that the data point in purple at the origin represents
“rest”. Only two features were used in this model, making it easily implemented
in wearable devices. Figure 1 (bottom) can be further updated if more activity
samples are collected.
Figure 2 shows a
real life example from a 27 year-old male office worker chosen from the 10
participants. The mobile App was used to collect motion signals over a 24-hour
period including the participant’s typical working day. The average cadence and
percentage of the high G value were recorded every 5 minutes. The data samples
were classified by the model in Fig. 1. As shown in Fig. 2, in the 24-hour
period, "rest" activity accounted for a total of 510 min (35.54% of
total activity), as opposed to 865 min (59.93%) for "sedentary"
activity, 45 min (3.14%) for "light" activity, 10 min (0.70%) each
for "moderate" and "hard" activity.
measurements with the participant’s real activity log show he woke up around
8:30 a.m., went to office and stayed sedentary for the whole day. He went at
jogging around 4:30 pm in the afternoon, had dinner with some friends and went
to bed around 12 midnight.
Figure 2. High G values
and cadence over a whole day
Estimation of PAL (Physical Activity Level)
PAL is often
used as a daily lifestyle index:
where TDEE is
Total Daily Energy Expenditure (in calories), and BMR means Basal Metabolic
Rate, defined as the minimal rate of energy expenditure per unit time when a
person is at rest. BMR is estimated by Harris-Benedict equations, which was
proposed in 1919 and amended in 1984 :
Where w is
weight (in kg), h is height (in cm), and a is age. For example, the participant
in Fig. 2 is a male, 173 cm, 60 kg, and 27 years old. His BMR is calculated as
Equivalent of Task) is a physiological measure of energy expenditure of
physical activities of different intensities. Table 2 compares the MET of
different physical activities and ages. The unit of MET is kcal/kg⋅hr.
2. MET of different physical activities and ages 
Table 2, the TDEE of the 27-year old participant in Fig. 2 can be estimated as
Finally, the PAL
of the participant for that particular day can be calculated:
PAL = TDEE / BMR
= 2294.64 / 1569.13 = 1.46
From the PAL, we
can conclude that the participant had a sedentary lifestyle (see Table 3).
Table 3. Lifestyle and level of activity 
analysis of four participants
participants were recruited for long-term analysis of personal lifestyles: a
college student (20 years old), two office workers (22 and 31 years old), and a
clothing store owner (58 years). All four participants are males.
participants were asked to carry a mobile phone with our G-sensor App installed
for 14 days. Average cadence and percentage of high G value were extracted from
the motion signals every 5 minutes. The data were then classified into four
physical activity intensities. TDEE and PAL were calculated, and finally the
lifestyle of that particular day was determined. The participants were also
asked to keep a daily activity log.
display some representative daily data. Lifestyle differences among the four
participants can be determined from the figures.
A: office worker, 22 years old, 180 cm, 78 kg, BMR = 1,872
was an ordinary office worker with regular working hours. On the particular day
shown in Fig. 3, he was involved in sedentary activity level during most of his
work time. He went to the gym in the evening and exercised for two hours. Thus
that particular day was classified as sedentary. The daily PAL was around 1.6
to 1.9, denoting a moderately active lifestyle.
B: college student, 20 years old, 174 cm, 65 kg, BMR = 1,681
was a college student. As shown in Fig. 4, he woke up later than the office
worker. He was engaged in moderate intensity level activity during lunch and
dinner, but sedentary / light intensity level during most of rest of the day.
The PAL on that particular day was 1.46, and the lifestyle was “sedentary”.
C: clothing store owner, 58 years old, 175 cm, 66 kg, BMR = 1,483
was 58 years old and his BMR value was lower than the other younger
participants. On the day displayed in Fig. 4, he woke up at around 6:00 am,
went for jogging for one hour at 7:00 am, and reached hard intensity for 38
minutes. He is a clothing store owner and was required to stand up and walk
around the store during most of his working time, frequently achieving light to
moderate activity intensity. He took an afternoon nap for about 1.5 hours, and
went to bed at around 23:00. His PAL on the day shown in Fig. 5 was 1.84, and
his lifestyle was “moderate”.
D: IC engineer, 31 years old, 172 cm, 64 kg, BMR = 1595
was a semiconductor plant engineer. On the day displayed in Fig. 6, he went to
bed at 1:00 am and woke up at 8:00 am, engaged in light to moderate intensity
activity while traveling to work (around 9:00), lunch time (12:00~13:00) and
returning home (around 20:00). He took an afternoon nap for about 0.5 hours. He
is required to stay standing during work time, frequently achieving light
intensity activity during working hours. He went jogging for one hour around
21:00. His PAL on the day shown in Fig. 6 was 1.79, and his lifestyle was
Figure 3. Activity
intensity graph of participant A on 12/9
4. Activity intensity graph of participant B on 12/12
5. Activity intensity graph of participant C on 12/21
Figure 6. Activity
intensity graph of participant D on 12/10
and future work
According to the
International Data Corporation (IDC) Worldwide Quarterly Wearable Device
Tracker published in June 2015 (http://www.idc.com/tracker/), vendors shipped a
total of 11.4 million wearable devices in 1Q15, a 200.0% increase from the 3.8
million wearables shipped in 1Q14. Fitbit (https://www.fitbit.com/) accounted
for 34.2% of total sales, followed by Miband (http://www.mi.com/sg/miband/) at
24.6%. Both Fitbit and Mi-Band provide functions for counting steps, estimating
energy expenditure and detecting sleep duration, based on G-sensor.
proposes how physical activity intensity levels and lifestyle patterns can be
derived from G-sensor motion signals. Further services can then be tailored for
the user to generate value for the data provided by wearable devices.
Currently, the types of activities used in the collected dataset are still
limited. More features from G-sensor motion signals should be explored to
account for a full range of daily activities.
performed in this study used a mobile App to collect motion signals from the
G-sensor. Many individuals suffering from dementia wear GPS-based wearable
devices or RFID tags to prevent them from getting lost. In addition to
positioning or localization purposes, physical activity intensity levels and
lifestyle patterns of the older adults can be monitored if existing G-sensor
based wearable devices are used (e.g., Mi-Band).
The Mi-band uses
Bluetooth low energy (BLE), a new class of wireless personal area network
technology. Compared to classic Bluetooth, BLE offers considerably reduced
power consumption and cost while maintaining a similar communication range.
Mi-Band uses BLE proximity sensing to transmit a universally unique identifier
which can be picked up by a compatible reader (app or operating system). The
identifier can then be looked up to determine the device's physical location.
proposes a BLE-based system combining personal life pattern analysis with a
localization function. This system consists of three parts: tag, reader, and
server. The tag is a BLE wearable device with a built-in G sensor (namely,
Mi-Band). Readers (such as Arduino Yun combined with Hm-10 BLE module as shown
in Fig. 7) are installed in the home or nursing facility. As a user wearing the
tag approaches a reader, the tag’s UUID, MAC address and RSSI are uploaded to
the cloud server, allowing for the location of the user to be determined. In
applications such as access control, the cloud server can also connect to the
Google Cloud Messaging (MCG) service to immediately send an alert to the
caregiver’s Android device. In the meantime, motion signals from the G sensor
are uploaded and converted to physical activity intensity levels and lifestyle
patterns as previously described, which greatly adds value to such a system.
Figure 7. BLE-based
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