Development of a Novel Classification and
Calculation Algorithm for Physical Activity
Monitoring and Its Application
J. Arnin, D. Anopas, P. Triponyuwasin, T. Yamsa-ard, and Y. Wongsawat
Department of Biomedical Engineering, Faculty of Engineering, Mahidol University, Thailand
E-mail: jetsada.arn@mahidol.ac.th, preechapawan.tri@gmail.com, traisak.y@gmail.com,
a.dollaporn@gmail.com, and yodchanan.won@mahidol.ac.th
Abstract— Exercise is a good alternative approach to be
healthy. However, it can cause a body in negative outcome for
people who over workout without proper manner. Therefore,
the objective of this project is to develop an activity tracker
called “Feelfit” that has a high accuracy to measure levels of
activity (with 5 intensities of exercises). Besides, challenging
and motivating the exercise via feedback of detailed
information such as burned calorie, activity percentage, and so
on are proposed. An accelerometer, high accuracy tri-axial
accelerometer (MMA8452Q), sends a value of acceleration in 3
axes acquired from body movement and then be processed and
calculated physical activity behavior on a low-power
microcontroller. This project has proposed a novel algorithm of
physical activity classification and calories burned calculation.
The algorithms were examined the accurateness by 10 healthy
subjects (5 males and 5 females) aged 15-25 years old. The
proposed algorithms were also compared with a commercial
activity monitoring device; the accuracy of calories burned
calculation is more than 80% and more than 90% for activity
classification.
INTRODUCTION
An exercise is one kind of procedures to become healthy
and energetic. The intensity of exercise is generally classified
into 3 levels: light-intensity activities, moderate-intensity
activities and vigorous-intensity activities [1]. The
advantages of the exercise are: to increase the strength of a
heart, a muscle, and a vascular; to protect many diseases such
as diabetes, cancer, and hypertension; and to promote a good
excretory system. These benefits would be accomplished
once getting a suitable level of exercise [2]. On the other
hand, inappropriate exercise can result in a negative outcome
instead of positive, for example, getting stressed, feeling
irritability, being sleepless, getting anorexia, decreasing in
sexual desire, muscle pain, and so on [3-4].
Levels of exercise intensities consist of light-intensity
activities, moderate-intensity activities and vigorous-intensity
activities. A percentage of maximum heart rate (% MHR) of
light intensity is approximately 40-54% MHR and a heart
rate is 68-92 beats per minute (BPM). The examples of the
light-intensity are gardening, slow walking and reading a
book. Moderate-intensity activities have 55-69% MHR and
the heart rate is 93-118 BPM. Basketball, volleyball and
quick walking are kinds of the moderate-intensity. Vigorous-
978-616-361-823-8 © 2014 APSIPA
intensity activities are equal to or more than 70% MHR with
larger than 119 BPM of heart rate [7].
There are many ways to indicate an index for evaluating
an exercise such as a step of walk, an acceleration of body
movements, etc. A prevalent device, pedometer, is used to
assess exercise. However, the pedometer shows only steps of
walk in a distance (kilometers or meters). This device cannot
express levels of intensity of physical activity [5-6]. Hence,
activity tracker is developed for more features to monitor the
exercise. An accelerometer is a chosen sensor for this device
because of measuring acceleration in 3 axes of body
movements. These 3 input data have much benefit to analyze
exercise behavior.
Currently, there are some commercial products tracking
an activity such as fitbit, Actigraph’s Uniaxial GT1M and
Actigraph’s Triaxial GT3X. The result of comparing between
uniaxial and triaxial of Actigraph tracker indicates that triaxial and uniaxial accelerometer is somewhat the same
accuracy in case of walking and running [8]. However, more
activity classification is still required.
This project has proposed an activity tracker for
monitoring intensities of the exercises developed on an
algorithm in tri-axial accelerometer that can classify in vary
activities more than running and walking. Many input data is
added via setting up mode to increase some features and
enhance an algorithm for further analysis.
MATERIALS AND METHODS
A. System Overview
Feelfit is a calories burnt evaluating device by measuring
the human moving acceleration from three axes and
presenting in Cal unit (kcal). Feelfit also calculates the
exercise intensity of body movements via intensity of users’
acceleration and shows percentage of five levels of activities.
This project composes of three sections: software algorithm
for physical activity analysis including two operation modeschallenge mode and free activity mode, hardware for exercise
monitoring, and data analysis. In challenge mode, users can
limit time for exercise that aims to challenge the users. A free
activity mode is designed for the research, no limitation on
time. Therefore, researchers can use Feelfit to analyze
physical activity and further examine exercise behavior. The
APSIPA 2014
flowchart of overall system embedded on processing unit
including sensor data, signal filtering, signal conditioning,
calories burned calculation, and activity classification was
shown in Fig. 1.
C. Calculation of Work-kinetic Energy in 3 Dimensions
According to one-dimensional motion, the kinematic
energy of motion is calculated by
∆ ,
(1)
where
is a multiplication of force ( ) and displacement
∆ in the same direction of force, with unit of Joule.
From Newton's 2nd laws of motion [9], the force occurring in
X-axis from point A to point B is calculated by
,
(2)
where
is a force which is the multiplication of mass
( ) in kilogram and acceleration from point A to B in the
same direction of force. Then, the acceleration is a derivative
of velocity by time as follow:
(2.1)
(2.2)
Set kinetic energy ( ) of the first point of motion to zero
because the velocity is equal to zero at the starting point
represented in (2.3) and
in each motion is equal to (2.4).
,
,
(2.3)
(2.4)
is energy consumption of moving in one direction
where
from point A to B represented in Joule unit. For three
dimensions, simplifying to Y-axis and Z-axis was shown in
(2.5) and (2.6), respectively.
(2.5)
The overall of physical activity analysis algorithm
B. Three-axis Acceleration Sensor
User’s acceleration is acquired by 3-axis acceleration
sensor with low power consumption from Freescale
Semiconductor (MMA8452Q). The constraint of this sensor
is that the maximum acceleration in each axis is limited to
±4G (G is an acceleration which is from gravity). The
resolution was set to 8 bits with 25 Hz of sampling
frequency.
(2.6)
Then 3-dimensional work-kinetic energy is
. (2.7)
To decrease data analysis in processor unit, (2.1) can be
revised into
(2.8)
VERTICAL DIRECTIO N
(YAW AXIS)
FO RW ARD DIRECTIO N
(RO LL AXIS)
where ac is the magnitude of acceleration in 3 axes and ds is
the difference of displacement from A to B that is
represented in (3) and (4), respectively.
,
SIDE DIRECTION
(PITCH AXIS)
The direction in each axis.
.
(3)
(4)
a physical activity into 5 trends of exercise intensity. The
used customized display is Static Liquid Crystal Display
(LCD) sizing 30x42.5 mm. There arre 3 buttons for operation:
utton.
(5) power button, OK button and shift bu
Consequently, the final equation used to calculate the
calories usage (
) from movements is
,
where, k is a constant variable for adjustingg parameter from
float to integer for faster calculation in processor unit.
D. Physical Activity Classification
Activity classification was analyzed froom the magnitude
of acceleration in 3 axes shown in (3). T
The maximum of
acceleration was set to 1.70G for female andd 1.705G for male
which is the possible acceleration that hum
man can generate
from movement. Step counts were also annalyzed based on
“more intensity of acceleration should bee more in steps”
assumption. Hence, activity classification w
was classified by 2
parameters which were equivalent in weightt for both of them.
F. Data Analysis
d 5 females with 62.4 kg
Participants include 5 males and
and 56.8 kg of average weight, resp
pectively. There were 2
tasks of the experiment. The first task is to evaluate physical
activity by comparing between Feelfit
F
and commercial
product that Omron Jog style model
m
HJA-300, activity
monitor, was employed. The resullt from both Feelfit and
Omron were compared with mathem
matical model of calories
expenditure to compute accuracy. The second task is to
examine accuracy of classificatio
on approach of activity
intensity representing the quality and quantity of users’
workout.
The information from sensor was filteredd by means of the
Finite Impulse Response (FIR) filter. The fiilter was designed
by two poles with equivalent in weight oof each parameter
called Summing Moving Average (SMA
A) that a transfer
function is presented in (6) in Z-domain.
1
(6)
To get the actual acceleration from movvements, the yaw
axis or acceleration from the earth graavity has to be
eliminated before calculating a magnitude. Since we cannot
essentially calculate the quantity of graavity acceleration
projecting onto each axis, subtraction of a present filtered
data with a previous filtered data from all aaxis are our tools
for gravity acceleration removal. Beforee calculating the
magnitude of acceleration, raw filtered dataa has to convert a
unit from sensor (MMA8452) to unit in mm
m/s2. It should be
2
noted that SI unit (m/s ) is not suitable too use because the
processor would calculate faster in case of innteger variable.
The intensity of activity level in eeach bound was
classified by levels of metabolism equivallent table (MET)
[11]. The MET of vigorous activity was set to 12 for the
maximum bound of classification. The interrval of lower level
was calculated from interval of MET in eeach activity. The
MET table was shown in Table I.
According to the sample frequency set too 40 milliseconds,
the system would update the activity level every 5 seconds.
The intensity level of activity would be sselected from the
average of the accumulated activity levels.
The icon shows 5 levels of exercise inteensity.
E. Hardware Design
The activity tracker is designed to be worn on the hip
position. The accelerometer developed inn this project is
MMA8452Q. It provides 3-axis acceleeration and the
maximum resolution is 12 bit. The used processor unit is
ATMEGA329PA with 2 Kbyte of RAM
M and 1kbyte of
EEPROM. The processor processes the algoorithms to classify
(a)
(b)
(a) Front panel of Feelfit and (b) Back
B
panel of Feelfit.
EXPERIMENTS AND RESULTS
A. Participant Preparation
y considering as follow:
The participant was chosen by
getting enough sleep and having no exercise
e
before testing 24
hours, taking sufficient food with
hout caffeine or alcohol
before investigating about 2 hourrs. Prior to conduct the
experiment, participants have to inp
put parameters containing
gender, weight, mode of testing, and
d time (only in challenge
mode). Also, the participants have to
t place the device at hip
position.
B. Physical Activity Testing
S
II.F, walking and
According to the first task in Section
running are selected for physical activity
a
testing by using
Integrity Series 97T Treadmill to compare a calories
spending with commercial devicee. For walking activity,
participants walked with constant veelocity at 0.9 m/s which is
3 times of basal metabolic equivalen
nt [12]. While at 1.67 m/s
for running activity is equal to 7 times
t
of basal metabolic
equivalent. Mathematical model representing the relationship
between levels of metabolic equivalent
e
and calories
expenditure was shown in (7) [13].
(7)
Besides, in the second task, participants
p
exercised by
following testing programs includin
ng walking at 0.9 m/s of
average velocity, aerobic exercise,, running at 2.5 m/s of
average velocity [14], and squad for sport [4]. According to
the previous study on physical perfo
ormance testing protocols
[15], following these protocols: 10 min for reading, 45 min
for walking, 24 min for aerobic, 16 min for running, and 12
min for sport. The participants repeated a similar process for
three times. Fig. 5 displayed the participant during testing.
classification, there is a direct and significant relationship
between the magnitude of acceleration and intensity of
activity; a light intensity has higher accuracy than modulate
and vigorous intensity.
C. Results of Physical Testing
The results indicated an accuracy of calories expenditure
of Feelfit compared with commercial device (Omron) and
mathematical model for walking and running in 10 minutes
shown in Table I. According to examined accuracy on
distribution of activity intensity, the scores of classification
were represented in Table II that is the average and the
standard deviation from 10 participations.
CONCLUSION
Feelfit is an activity tracker that is used to observe a
calories burnt, step of walking and physical activity
classification in 5 trends. The proposed algorithms
processing many parameters have a high accuracy
comparing with a commercial activity monitor. However,
this activity tracker cannot be used in swimming activity. A
limitation of Feelfit is that users have to wear in the hip
position only. For the future work, we will test a volume of
oxygen consumption during an exercise and develop Feelfit
to wirelessly synchronize with a mobile application that can
motivate the users to challenge or race with their friends.
ACKNOWLEDGMENT
The participants during testing physical activity analysis.
RESULTS OF THE ACCURACY IN WALKING AND RUNNING
Walking
Activity
Running
Male
Female
Male
Female
Calories burnt from Feelfit
37.0
32.2
75.8
78.2
Calories burnt from Omron
23.0
15.0
50.0
42.0
Mathematical model
31.2
28.4
72.8
66.3
Accuracy (%)
81.4
86.0
95.8
82.1
*Calories burned in kcal unit.
RESULTS OF THE ACCURACY IN ACTIVITY CLASSIFICATION
Activity
Accuracy (%)
Reading
100.0 ± 0.0
Walking
99.0 ± 0.6
Aerobic
90.5 ± 2.7
Runnning
92.6 ± 3.6
Sport
87.0 ± 4.1
DISCUSSION
The experiments observe an algorithm via a relationship
by comparing of total calories from activity tracker, calories
from commercial device (Omron) and mathematical model.
The results have indicated that the accuracy of proposed
activity tracker is more than 80% on estimating the number
of calories burned. Also, the device can accurately classify
the activities with more than 90% and achieve high
repeatability in measurement. According to activity
This project is supported by Thai Health Promotion
Foundation. The testing device is supported by College of
Sports Science and Technology (CSST), Mahidol
University. Finally, our thankfulness is extended to Team
Precision Public Company Limited for an appreciated
suggestion on conceptual design for manufacturing.
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