Fibrillation Detection using Accelerometer and Gyroscope of a Smartphone

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ISSN No: 2456
International
Research
Fibrillation Detection using Accelerometer
and Gyroscope of a Smartphone
Arun Pranav K. R
Department of Computer Science and Engineering,
G.K.M. College of Engineering and Technology
Chennai, Tamil Nadu, India
ABSTRACT
Using the smartphone as an answer for the
identification of Atrial Fibrillation (AFib), which uses
the built-in accelerometer and gyroscope sensors
(Inertial Measurement Unit, IMU) of the smartphone
for detection? Contingent upon the patient's
circumstance, it is conceivable to utilize the created
cell phone application either routinely or at times for
making an estimation of the subject with no outer
sensors is required. From that point forward, the
application decides if the patient experiences AFib or
not.
Keywords: Atrial Fibrillation, gyroscope,
accelerometer
I. INTRODUCTION
We consider the accurate detection of a
fibrillation (AFib) with modern micro
electromechanical (MEMS) accelerometers and
gyroscopes embedded into modern smartphones.
Previously, smartphone based AFib detection has
been proposed through the camera of the smartphone
while illuminating the user’s finger with the camera
flash (PPG, photoplethysmography) [1].
Our approach is also based on smartphone, while the
acquisition of the heart signal is made in an
alternative way. The patient is advised to turn on the
application before keeping the smartphone in the
pocket of the patient. A measurement with similar
duration as in camera based measurement is taken
(approximately 2 minutes) with the already available
MEMS sensors within the smartphone. The precise
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 3 | Mar-Apr 2018
ISSN No: 2456 - 6470 | www.ijtsrd.com | Volume
International Journal of Trend in Scientific
Research and Development (IJTSRD)
International Open Access Journal
Fibrillation Detection using Accelerometer
Gyroscope of a Smartphone
G.K.M. College of Engineering and Technology,
India
Elavarasan C
G.K.M. College of Engineering and Technology
Chennai, Tamil Nadu
Using the smartphone as an answer for the
identification of Atrial Fibrillation (AFib), which uses
in accelerometer and gyroscope sensors
the smartphone
Contingent upon the patient's
circumstance, it is conceivable to utilize the created
cell phone application either routinely or at times for
making an estimation of the subject with no outer
point forward, the
application decides if the patient experiences AFib or
Atrial Fibrillation, gyroscope,
We consider the accurate detection of a trial
fibrillation (AFib) with modern micro
S) accelerometers and
gyroscopes embedded into modern smartphones.
Previously, smartphone based AFib detection has
been proposed through the camera of the smartphone
while illuminating the user’s finger with the camera
.
Our approach is also based on smartphone, while the
acquisition of the heart signal is made in an
alternative way. The patient is advised to turn on the
application before keeping the smartphone in the
pocket of the patient. A measurement with similar
duration as in camera based measurement is taken
(approximately 2 minutes) with the already available
The precise
stability of the partient can be detected using the
application.
II. RELATED WORK
In the field of medical, many research projects has
been developed for the health care of elderly person.
Here we use accelerometers and different sensors.
With the purpose to detect successfully typically falls
by applying threshold to accelerations position. It has
the following methods.
A. Wearable Detection Methods
Bagale.et al [4], presented an algorithms which is
based on accelerometer, of fall detection on fall
of real-world.
Cho, Nguyen and Lee [7], used an accelerometer
which Is worn on the waist, by applying thresholds to
the accretion, they detect accurate fall.
B. Audio, Video Surveillance Detection Methods
Lustrek and Kaluza [4], perform well and it tests and
can be found that more comfortable than
worn sensors body. However, the
worn on the body are most widely used
fall detection.
Diraco and others [5], separated movement area
from image by using bayesian segmentation
method based on the threshold of distance of
the 3 dimensional centroid human from the floor
plane and as they move furher found that the fa
Apr 2018 Page: 761
6470 | www.ijtsrd.com | Volume - 2 | Issue – 3
Scientific
(IJTSRD)
International Open Access Journal
Elavarasan C
G.K.M. College of Engineering and Technology,
, Tamil Nadu, India
stability of the partient can be detected using the
l, many research projects has
been developed for the health care of elderly person.
Here we use accelerometers and different sensors.
With the purpose to detect successfully typically falls
by applying threshold to accelerations position. It has
A. Wearable Detection Methods
Bagale.et al [4], presented an algorithms which is
based on accelerometer, of fall detection on fall
and Lee [7], used an accelerometer
which Is worn on the waist, by applying thresholds to
they detect accurate fall.
Surveillance Detection Methods
Lustrek and Kaluza [4], perform well and it tests and
that more comfortable than
However, the sensors which are
the body are most widely used method for
Diraco and others [5], separated movement area
from image by using bayesian segmentation
method based on the threshold of distance of
the 3 dimensional centroid human from the floor
plane and as they move furher found that the fall had

International Journal of Trend in Scientific Research and Development (IJTSRD) ISSN: 2456-6470
@ IJTSRD | Available Online @ www.ijtsrd.com | Volume – 2 | Issue – 3 | Mar-Apr 2018 Page: 762
happened when the human centroid is less than
the specified height and situation are maintained
more than 4s. The human threshold centroid value
of 0.4m, accuracy was 97.3% and the recognition
rate of 80.0%. Human body data in stooping,
standing, laying and sitting down analyzed them
they collected and concluded groups three of
threshold centroid and the recognition rate &
credibility.
C. Ambient Based Methods
Ambient based methods are usually depend on even
passive motion infrared sensors, acoustic sensors and
pressure sensors which is placed around
the caretaker’s houses.
Yan [7] proposed a perceived nature invasive method.
They developed such a system in which it is not
necessary that the user should waer the sensors,
although was able to detect the location of the
user which is based on interaction observation
with the network of sensor home-installed.
Winkley, Jiang and Jiang [1] proposed a verity of two
components, base station and direct monitoring
device. They find that the proposed classifier
outperforms the conventional classifiers with higher
distinguishing capability. In a particular system, they
measured real time skin/ambient temperatures.
The video detection methods are better than the
ambient based methods and wearable based methods
[7].These systems are often suffer from high risk of
privacy and also very high cost of implementing
cameras. So they used wearable sensor based
methods in such types of research. We successfully
detect fall with accuracy above 90% by applying
threshold to acceleration of accelerometer which
is worn on the body of the person. The
accelerometer also resulting in 100% accurate fall
detection using various accelerometers and sensors.
III. SYSTEM IMPLIMENTATION
The block diagram of proposed Fibrillation Detection
system shown in fig.1.It consist of the mobile
reading unit, accelearometer, gyroscopesecsor, global
positioning system, short message service and a
mobile communication network . Accelerometer
sensor is used to locate the exact movement of the
person in X and Y direction. The gyroscope sensor is
used to read the amount of inertia is exerted on the
subject. The readings from all these sensors is
given to the detection algorithm for an attempt of
afib detection.
A. Real –Time Detection
A detection algorithm suitable for real time, by using
the micro-controller of the accelerometer, various
sensor and various devices. The acceleration
component along with the three axes as X, Y and Z
respectively [4].
By using information from an body temperature and
pulse rate and monitor the health parameters as well
as fall detection using sensors such as accelerometer,
the impacts of these falls can successfully be
distinguished from activities of daily lives
reducing the false detection of falls. From the
experimentation results of 30 trials, it is found that the
proposed fall detection system achieved a high
accuracy of above90%, and specificity is 95%.
B. Accelerometers
We use 3-axialaccelerometer.The accelerometer
sensor find the movement of the person in X,Y, and Z
direction. Two methods of accelerometers are
threshold and orientation, both methods gives
accurate movement of the person during fall.
C. Gyroscope
To a successful detection, we use the gyroscope
(Inertial Measurement Unit, IMU).The gyroscope
sensor find the amount of inertia is exerted on the
subject for the purpose of avoiding a false alarm.
D. Global positioning system
In case of a detection event the Global positioning
system(GPS) is used to detect the location of the
subject to direct the emergency services to the
subjects location as fast as possible
IV. SOFTWARE REQUIREMENTS
In our Project we use Front End as Java (Eclipse) and
Back End as a SQLite.

A. Jdk 1.6
In our project we are using java to design the
application process. Java contains technologies such
as JEE (Servlet, Jsp) that is used to design the view
page easily. Since java is an open source and platform
independent this makes the application more flexible.
B. ADT(plugin)
Android Development Tools (ADT) is a plugin for the
Eclipse IDE that extends the capabilities of Eclipse to
let you quickly set up new Android projects, create an
application UI, add packages based on the Android
Framework API, debug your applications using the
Android SDK tools, and even export signed (or
unsigned) .apk files in order to distribute your
application.
C. SDK
The Android SDK separates tools, platforms, and
other components into packages you
can download using the SDK Manager. For example,
when the SDK Tools are updated or a new version of
the Android platform is released, you can use the
SDK Manager to quickly download them to your
environment.
D. Android XML
All user interface elements in an Android app are built
using View and ViewGroup objects. A View is an
object that draws something on the screen that the
user can interact with. A ViewGroup is an object that
holds other View (and ViewGroup) objects in order to
define the layout of the interface.
Android provides a collection of both View and
ViewGroup subclasses that offer you common input
controls (such as buttons and text fields) and various
layout models (such as a linear or relative layout).
E. SQLite
SQLite is an in-process library that implements a self-
contained, zero-configuration, serverless,
transactional SQL database engine. The source code
for SQLite exists in the public domain and is free for
both private and commercial purposes.
V. PROPOSED DETECTION ALGORITHM
The proposed detection algorithm is based on
placing an accelerometer sensor and the gyroscope
sensor on the user’s body. The algorithm is
programmed for the detection falls by the X, Y and Z
direction of acceleration sensor readings and the
amount of inertia inflicted on the subject by the
readings of the gyroscope The micro- controller
compares the readings and when these readings
cross threshold, then the fall is detected in X, Y
and Z directions and the amount of inertia exerted
The tracking section determine the exact position of
user by using GPS receiver which reads the latitude
and longitude co-ordinates to micro-controller in
every seconds and micro- controller will read
and display the co- ordinates on the LCD. Using
GSM modem sending these co- ordinates to base
station.
The algorithms following steps and the proposed
method [3] for fall detection is based on monitoring
the magnitudes of three axial X, Y and Z components
of the accelaertion, magnitudes in the three planes,
and the total magnitude. The fall detection is based on
the observation that a fall is associated In event of a
detection the exact location of the subject is sent to
the stored ICE(In Case Emergency) number and the
response to the emergency situation can be diverted as
fast as possible
CONCLUSION
In this paper, a detection system based on sensors
which is worn on the body of the person are
implemented that successfully detected afib. By
making the use of the information from an body
sensors, we can monitor the health parameters of the
elderly person as well as successfully detect fall
by using an accelerometer sensor and a gyroscope
sensor. Using GSM the concerned person will
receive the sms on his mobile phone, in case any of
the parameter goes beyond the normal range or if fall
is detected. Also by using GPS the concerned person
Start Section
start the motion
operation()
Find motion
find Axiz
Find Ratio()
Mobile
Vibration start
Intemation()
Use
Login
Registration
Exixt user()

know the exact position of the user. From the
experimentation of 30 trials, it is found that the
proposed detection system achieved a high
accuracy of above 90%, and the sensitivity and
specificity are 95%.
REFERENCES
1. Liyang Zhau, Zhen Liu, Jian Guo, “Asurveyoff all
detection algorithm for elderly health monitoring,”
in Proc.2015 fifth International IEEE Eng. pp,
270-274, 2015.
2. WangYe, Bai Xiangyu, “Research off all detection
and alarm applications for the elderly,” in
Proc.2013 International IEEE Eng. pp, 615-619,
2013.
3. Praveen Kumar, Prem C. Pandey, “A wearable
inertial sensing device for fall Detection and
motion tracking,” in Proc. 2013 annual
International IEEEEng.pp,978-1-4799-2275-
8,2013.
4. Lustrek and Kaluza, “Fall detection and activity
Recognition with machine learning,
”Informatica;2009;33:205-12.
5. Diraco G, Leone A, Siciliano P, “An active vision
system for fall detection and posture recognition
in elderly health care,”[C]//Proceeding of the
Conferenceon Design, Automation and Testin
Europe.[S.1.]:CDATU,2010:1536-1541.
6. C. Rougier, J. Meunier, A. S. Arnaud, and J.
Rousseau, “Robust video surveillance for fall
detection based on human shaped formation,”
IEEE Trans. Circ. Syst. for Video Tech., vol.21,
no.5, pp.611-622, May2011.
7. H.R.Yan, H.W.Huo, Y.Z.Xu, and M.Gidlund,
“Wireless sensor network based E-health system:
implementation and experimental results,” IEEE
Trans. Consumer Electron., vol.56, no.4, pp.2288-
2295, Nov.2010.
8. Chin-FengLai,Member,IEEE,Sung-
YenChang,HanChiehCha,“Detectionofcognitivein
juredbodyregion using multiple triaxial
accelerometers for elderly falling,” IEEE Sensor
Journal.,vol.11, no.3,pp.763-770,March 2011

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