1. Made by:
Surbhi Jain
Aishwarya Jain

3. The Android Framework
The Android platform is an open platform for mobile devices consisting of an operating system,
applications and middleware
Android gives users the opportunity to build and publish their own applications by providing an open
development environment. Android treats all applications (native and third-party) as equals.
Therefore, having such an open development environment requires security measures to be taken in
order to protect the integrity of the Android platform and the privacy of its users.

4. • Android is an open source mobile operating system with Linux
kernel.
• The Android SDK is installed in to Eclipse.
• Android treats both native and third party applications as the same.
So we can build and develop our own applications easily here.
• The android software development kit includes a set of
development tool such as a debugger, libraries, handset emulator,
documentation, sample code and tutorials.
• Android SDK has a java frame work and a powerful API for the
hardware embedded on smartphones.
Why Android ?

5. Android Architecture

6. Various Android Sensors
The Android has several sensors available:
• Accelerometer
• Orientation
• Ambient Light
• Proximity
• Magnetic Force
Use of these sensors does not require direct user
permission!

7. Accelerometer Usage(contd.)
Activity Recognition
Distance travelled

8. Activity Recognition
Desired Outputs:
- Physical Activities (e.g., Running, Walking)
- Approximate time spans
- Quick detection of change
Accelerometer Usage

9. Our Objective
To explore the Accelerometer as a
measure of context- aware based
applications for physical activity
recognition on the Android
framework.

10. Literature Survey
List Of Paper Studied:
• Paper 1 : Applications of Mobile Activity Recognition
Authors : Jeffrey W. Lockhart, Tony Pulickal AND Gary M. Weis
• Paper 2 : User, Device and Orientation Independent Human Activity
Recognition on
Mobile Phones: Challenges and a Proposal
Authors : Yunus Emre Ustey, Ozlem Durmaz Incel AND Cem Ersoy
• Paper 3 : Simple and Complex Activity Recognition Through
SmartPhones
Authors : Das, B., Krishnan, Narayanan C., Thomas, B.L. AND Cook, D.J
• Paper 4 : Fall Detection by Built-In Tri-Accelerometer of Smartphone
Authors : Yi He, Ye Li AND Shu-Oi Bao
• Paper 5 : Feature Selection Based On Mutual Information For Human
Activity Recognition
Authors : Khan, A., Chehade, N.H., Chieh Chien AND Pottie. G

11. • Paper 6 : Smartphone-based Monitoring System for Activities of Daily
Living for Elderly People and Their Relatives Etc.
Authors : Kazushige Ouchi AND Miwako Doi
• Paper 7 : Environment Feature Extraction and Classification for
Context Aware Physical Activity Monitoring
Authors : Troped, P.J., Evans,J.J. AND Pour,G.M
• Paper 8 : Fall Detection based on movement in Smartphone
Technology
Authors : Gueesang Lee AND Deokjai Cho
• Paper 9 : Activity logging using lightweight classification techniques in
mobile devices
Authors : Henar Martı´n ,Ana M. Bernardos ,Josue´ Iglesias • Jose´ R.Casar
• Paper 10 : Privacy control in smart phones using semantically rich
reasoning and context modeling
Authors : Dibyajyoti Ghosh, Joshi, A., Finin, T. AND Jagtap, P
contd..

12. • Paper 11 : Towards Successful Design of Context-Aware Application
Frameworks to Develop Mobile Patient Monitoring Systems Using
Wireless Sensors
Authors : Al-Bashayreh, M.G. Hashim, N.L. AND Khorma, O.T
• Paper 12 : ActivityMonitor: Assisted Life Using Mobile Phones
Authors : Matti Lyra AND Hamed Ketabdar

13. Comparison among the papers
Paper Parameters Used Algorithm Used/Proposed
Paper 1 Nil Neural networks and J48 decision trees
Autocorrelation, K-nearest neighbors (KNN),
Paper 2 Mean, Variance, Std. Dev, fast Fourier transform (FFT) coefficients
Zero Crossing Rate, Period
Mean, min, max, Std. Dev, Multi-layer Perceptron,
Paper 3 Zero Crossing Rate, correlation Naïve Bayes, Bayesian network, Decision
Table, Best-First
Tree, and K-star
Acceleration due to Signal Magnitude Vector, Signal Magnitude
Paper 4 body movement; 2) Area (SMA), Tilt Angle (T A)
gravitational acceleration,
median filter
Standard deviation, Mean, Tree-based, feature selection algorithm based
Paper 5 Absolute mean, Energy ratio, on mutual information, binary
Ratio of DC to sidelobe, First decision-tree with a naıve Bayes classifier
sidelobe location, Max value,
Short time energy, Correlation
Paper 6 Average, minimum, maximum Stochastic model, Neural Networks, SVM every
and variance, MFCC (Mel- 1 sec.
Frequency
Cepstral Coefficient), RMS
(Root Mean Square) and ZCR
(Zero-Crossing Rate)

14. Paper 7 Mean and sigma K-nearest neighbor
of the Gaussian function
Paper 8 Lower
NIL Threshold (LT) and Upper Threshold (UT).
Paper 9 Mean, Variance, Zero crossing Naıve Bayes,
rate ,75 percentile Decision Table and Decision Tree
Paper 10 NIL NIL
Paper 11 NIL NIL
Paper 12 Average magnitude value, Multi-Layer Perceptron (MLP)
average rate of change,
weighted sum

15. Current Problems
• First and Foremost is the use of Body Worn sensors. Today, In most of
the apps we have found that external sensors are used to detect the
physical movements of a person. Practically it is not possible to carry an
external device with you Sometimes people forget to wear the device.
• In most of the apps Positioning of the device is the concerned for the
success of application i.e. Most of the apps build are position specific of
device. If the device is kept in hands then values will be different from the
values generated when the device is kept in pocket.
• Use of multiple Sensors to achieve the same goal which makes the
application bulky leading to slower processing of the data and also affects
its cost.

16. Restating the Problem
We primarily focused on the Activity Recognition project
Inputs:
-X acceleration
-Y acceleration
-Z acceleration
Desired Outputs:
- Physical Activities (e.g., Running,
Walking)
- Approximate time spans
- Quick detection of change

17. The Activity Recognition Process

18. Data Collection Process
The first step to the project was to collect raw accelerometer data and transform it
into features that WEKA, the machine-learning tool that we implemented, used to
train a classifier. To accomplish this, we first took in sensor samples made up of
acceleration readings in the x, y, and z directions and computed their magnitudes.
Labeled all the data manually in terms of running, walking, standing, sitting. To
make the data more accurate data of more than 20 minutes have been taken.
Data gathering was done by performing experiments on four subjects. Each of the
four subjects were asked to collect the data activity one by one by placing
smartphone at the positions mentioned above. Each subject performed the set of 6
activities one by one for the duration of two minutes and the respective data was
recorded in a .csv file in the external storage of the smartphone.
Contd...

19. Data Collection Process

20. Feature Extraction
Feature Extraction is the process of extracting key “features” from a
signal. Features will be extracted from every sample window of 512
samples. The following features we Will be using in our project:
1. The Fundamental Frequencies: The average of the three
dominant frequencies of the signal over the sample window.
This was found via a Discrete Fourier Transformation.
2. Average Acceleration: The arithmetic mean of the
acceleration magnitudes over the sample window.
3. Max Amplitude: The maximum acceleration value of the
signal in the sample window.
4. Min Amplitude: The minimum acceleration value of the signal
in the sample window.

21. Classification is the process of labeling unknown patterns based on the knowledge of
known patterns of data. Four different classifiers were used:
K-Nearest Neighbor: Based on the shortest euclidean distance between the
unknown and known data’s feature vector
Naïve Bayes: Assumes the absence of one feature does not disqualify a candidate
(e.g., an object which is red and round is an apple, even if is not known to be a fruit)
J48(Decision Tree): J48 builds decision trees from a set of labeled training data
using the concept of information entropy. It uses the fact that each attribute of the
data can be used to make a decision by splitting the data into smaller subsets.
Random Forest: An ensemble classifier using many decision tree models. It can be
used for classification or regression.
Classification

22. Use Case Diagram

23. The application will be divided in several modules which will be
implemented time to time.
Module-1 Activity Recognition
Physical Activites like cycling, running,walking, standing etc performed
by the user will be recognized in this module. User will click on the app
icon or start button in the app which make him able to run sensors and
thus his motion wil be detected.
Module-2 Location Based Activity Recognition
In addition with Activity Recognition GPS sensor will be used to find the
location of user also what activity he is performing at that location. This
will help in Fall Detection Module discussed later.
Overall Description
Contd…

24. Module-3 Fall Recognition
In addition with physical Movement Recognition, Fall Recognition will be
there.. Whenever Fall detection will have positive result then alarm will be
raised instatntly and then app will monitor physical activities and if motion
is not detected it will send an emergency message to the guardian
informing about this accident.
Module-4 Physical Activity Chart
The User will be able to see his/her daily physical activity chart i.e. how
much he has done workouts today and what type of physical activity
he/she performed during the day.
Module-5 Calorie Burnt
The user will have the ability to see the calories burnt by him within a day
and how much calories he/she should be burnt to be physically fit.
Contd…

25. Module-6 Medical Reminder
The user can set the medical reminder if he wants. For this he/she have
to feed the prescription in the phone with the timings and the made
reminder active.
Module-7 Distance Travelled
The user have the ability to see the distance he travelled by running or
by cycling or by walking

26. Data Flow Diagram

27. Algorithm Flow Diagram

28. IMPLEMENTATION

30. Risk and Mitigation
Risk Description Risk Area Prob Impa RE (P* Risk Mitigati
Id of Risk ( Identify abilit c I) Select on Plan
Risk y t (I) ed if 8 is ‘Y’
Areas for (P) for
your Mitiga
project) ti
on
(Y/N)
Position of mobile Sensor
1. i.e. Whether the readings High High High Taking data by
phone is taken in variation Y considering all the
hand or kept in possible locations.
chest pocket or in
pant’s pocket
2. Battery drainage Hardware High Medi Medium N NIL
um
3. Sending each sms Security Medi Low Medium N NIL
to a hidden 3rd um
Party address
Device
4. Computational limitations Hardware Medi Low Medium Y On cloud storage service.
um

31. Sources
International Journal of Distributed Sensor Networks provided by
Hindawi.com
IEEE Sensors Journal provided by http://www.ieee-
sensors.org/journals.
IJCA Proceedings on International Conference on Recent Trends in
Information Technology and Computer Science 2012
Acoustics, Speech and Signal Processing (ICASSP), 2012 IEEE
International Conference
Sensors Applications Symposium (SAS), 2013 IEEE
2012 IEEE RIVF International Conference
IEEE Symposium on Security and Privacy Workshops@2012
ACM Transactions on Knowledge Discovery from Data (TKDD)
ACM Journal of Data and Information Quality

32. The End