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CSC8499 Individual Project: User Activity Analysis

Using Accelerometer and Cloud Computing

Marwan Ali Issa

MSc in Advanced Computer Science,

School of Computing Science, Newcastle University.

m.a.issa@ncl.ac.uk

Abstract. This dissertation focuses on analyzing the accelerometer data collect-

ed from built-in sensors in smartphones using the advanced technology of cloud

computing for processing and analyzing. The motivation behind this project is

on how to get the benefit of cloud computing technology as backend such as the

performance and storage using mobile platform as a client. In addition, the

availability of such mobile applications has been tempting many people to rec-

ord their daily activities such as walking, running and sleeping on their

smartphones. Although, there are many mobile applications in the market deal

with the daily exercise, but with new paradigms of cloud computing technolo-

gy, it can be used with smartphones to get benefit from this technology.

As a result, the dissertation shows the advantages and disadvantages of using

advanced technology of cloud computing. In addition, this project aims to study

the efficiency of using cloud computing platform instead of mobile platforms

by comparing the two environments. Even though, the dissertation does not at-

tain all the objectives of this project, but still there are significant accomplish-

ments which can be a scientific potential for other research and projects. Final-

ly, recommendations to improve the structure of cloud computing platform (e-

Science Central) regarding analyzing data were presented.

Declaration: I declare that this dissertation represents my own work except

where otherwise explicitly stated.

1 Introduction

1.1 Overview

Nowadays most people are using smartphones which have become very popular in

everyday life. Smartphones have revolutionized technology by reducing the gap be-

tween the mobile phone as voice communication and computer capabilities [25].

Smartphones have more powerful processes which deal with different applications

simultaneously. The most useful norm is having the smartphones useful due to the use

of sensors and accelerometer to deal with a variety of applications in smartphones

which use accelerometer. This has allowed growth of usage of a variety of applica-

tions within smartphones in real life [18].

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Cloud computing is another paradigm that can be used to replace a single physical

computer with a virtual computer over the internet. In this paradigm users can use the

same functionality and move over the internet with less time and less cost. Advanced

technology nowadays provides users with a platform that implements numerous ap-

plications in different localities [24]. Thus in this case, a computer as piece hardware

can be regarded as less important due to provision of a cloud. Advanced technology

has the potential of converting a smartphone into a mini computer with an inbuilt

sensor whereas a real computer can be converted into a virtual one functioning as a

cloud technology environment [24].

Based on the aforementioned technology, people can use smartphones to get dif-

ferent accelerometer data and send it to a cloud to process and analyze. Most re-

searchers are studying the smartphone using the mobile platform for implementation.

This project aims at analyzing both the advantages and disadvantages of applying

accelerometer techniques using a cloud computing environment, in order to devise its

efficiency.

1.2 Motivation

To understand the relationship between sleep, health and daily activity, Chinese

medicine as an example focuses on the quality of sleep to indicate people’s health

status, also it is a medicine to cure different physiological disease [7]. So the question

here is, can we indicate or determine the quality of the sleep using collected data

while the user is sleeping to generate feedback on the user’s life and health.

Moreover, with advanced technology people are motivated to know more about

their daily activity like walking and running especially for those who have weight

problems, high blood pressure or cholesterol. This type of application may increase

people’s health awareness; also they can monitor daily life activity with less effort

and cost [19].

Most of the applications which use smartphone sensors are only using the storage,

power and the processors of the mobile, but looking at advanced technology such as

cloud computing and its capabilities, it incentivize us to make a transformation from

mobile based applications to mobile-cloud based. Cloud computing is believed that

has benefit and potential to support and solve mobile applications through huge per-

formance and infinity of storage which means smartphones with cloud computing do

not need high performance or storage in the smartphone itself.

The project does not have a particular category of people that will be assessed but

instead is dependent on the availability of a smartphone and internet.

1.3 Aim

The aim of this project is to demonstrate the possibility of using the advanced

technology of cloud computing to support mobile applications that can collect data

through smartphones sensors from user activities such as walking, running and sleep-

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ing. In addition to the above point, the project also aims to demonstrate the efficiency

of data processing within a cloud environment against a smartphone platform.

1.4 Objectives

The objectives of this project are to develop software which contains two parts us-

ing accelerometers:

1.4.1 Build mobile applications based on cloud technology

The objective of this part is to build an application which will capture three di-

mensions of the accelerometer when the user does the exercise such as walking and

running or sleeping and send all this data to the cloud to process and analyze. In addi-

tion, building required blocks of codes in the cloud platform to determine the number

of steps and number of movements through pedometer and sleep algorithms. The aim

of this part is also to get some benefit from the accelerometer indoor and outdoor.

Moreover, the user can run this software and put the mobile in his pocket or in suita-

ble place; at the end of the day feedback will be displayed about all the activity he has

done. Otherwise, it can use for sleep monitoring by running the application and plac-

ing the mobile on the bed to get the quality of sleep for each night.

1.4.2 Cloud computing using e-Science Central

Cloud computing in this application has an important aspect which is using the

Platform as a service (PaaS) provided by e-Science Central1 to process and analyzes

the data with huge processing power and free storage. The objective of this part is to

build the required blocks and connect them to each other. The target is to receive

collected data from mobile application and starts to process it through specific algo-

rithm and analyze the data, and also to generate the required result that will be saved

within the cloud.

In addition to the above point, the second part of this objective is to build another

application only using the mobile platform to compare and contrast the efficiency of

cloud environment against mobile regarding processing time.

1.5 Dissertation structure

Chapter 1: Introduction, this chapter defines the project through overview

and motivation behind the project to get some knowledge about the technol-

ogy and purpose of the project. It also explains the aim and objectives to de-

scribe what this project intended to do and how.

Chapter 2: Related Work, this chapter enlightens the background research

covering and analyzing some topics which have been done to investigate and

propose different solutions.

1 e-Science Central: Software and Platform as a Service for scientific data provided by Newcastle University.

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Chapter 3: Requirement Analysis, this chapter clarifies the needs, require-

ments and the boundary of the project.

Chapter 4: System design, this chapter designates how the proposed project

designs through different levels and categories and the reason behind the

idea of the proposed design.

Chapter 5: Implementation, this chapter labels the programming languages,

tools, technologies, platforms and different algorithms to solve the problems

behind this project.

Chapter 6: Evaluation and testing: this chapter shows different evaluation

and testing for different parts of the project to prove the idea of the project.

Chapter 7: Conclusion, this chapter is about the conclusion of the project re-

sult and some future work which may be inspired by technologies behind

this project.

2 Related Work

2.1 Background

This section covers the summary of some terms such as accelerometer, cloud com-

puting and e-Science Central. The purpose is to establish knowledge and understand

the area of the project.

2.1.1 Accelerometer

It is an “Instrument that measures acceleration. Because it is difficult to measure

acceleration directly, the device measures the force exerted by restraints placed on a

reference mass to hold its position fixed in an accelerating body” [17]. Most

smartphones now have an accelerometer which is used for different purposes such as

rotating the screen, games and shaking the device.

This dissertation focuses on using the accelerometer for different human activity

such as detecting changes of the device motion when the user puts the mobile on the

bed during sleep time. Furthermore, when a person is walking, the steps are usually

very regular and observing the output of the accelerometer enables the detection of

steps [29].

2.1.2 Cloud computing

The National Institute of Standards and Technology (NIST) defines cloud compu-

ting as “a model for enabling universal, appropriate, on-demand network access to a

shared pool of configurable computing resources such as networks, servers, storage,

applications and services that can be quickly provisioned and released with minimal

management effort or service provider interaction” [16]. From this definition, the

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target of cloud computing is to move processing and computing data from personal

computers or small networks to large scale on-demand access to computing data

Cloud service has three characteristics which are: sold on demand, elastic, fully

managed by providers. In addition cloud computing has three main categories as fol-

lows:

A- Infrastructure as a Service (IaaS): is a provision model in which an organiza-

tion outsources the equipment used to support operations, including storage,

hardware, servers and networking components. The service provider owns

the equipment and is responsible for housing, running and maintaining it.

The client typically pays on a per-use basis [16, 8].

B- Platform as a Service (PaaS): is a way to rent hardware, operating systems,

storage and network capacity over the Internet. The service delivery model

allows the customer to rent virtualized servers and associated services for

running existing applications or developing and testing new ones [16,8]

C- Software as a Service (SaaS): is a software distribution model in which ap-

plications are hosted by a vendor or service provider and made available to

customers over a network, typically the Internet [16,8]

2.1.3 e-Science Central

e-Science Central is defined as “software and Platform as a Service for scientific

data management, analysis and collaboration which can be deployed on both public

and private cloud” [26]. By this definition we can understand that it can be catego-

rized as:

Science software as service: it is a website, so the scientists only need a web

browser to access their scientific data from anywhere and use their PC or mobile de-

vice from the website to share, upload and analyze their data [27].

Science platform as service: it gives the scientists the ability to build their applica-

tions which supports storage services, workflow services, social networking and secu-

rity [26,27].

Storage: The storage data on e-Science Central is versioned, signed and supports

multiple storage systems for wide deployment options. It uses virtualized storage

which has an interface supporting document and folder versioning, and the structure

of e-Science Central storage service is based on file system [27].

Workflow: Scientists can design workflows using generic and native blocks wizard

using drag and drop through a web application. The input and output for each block

are typed to avoid incompatibility. Moreover, scientists can develop and build their

own blocks which can be part of the workflow [27].

Figure 1 shows the structure of e-Science Central which contains all the software,

platform and infrastructure as a service.

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Figure 1.e-Science Central architecture.

2.2 Accelerometer based health care application and research

Related work addresses relevant work that has been conducted academically, from

industry or research groups considering the use of mobile applications capturing sleep

motions and exercise activities such as walking and running. In addition, it intends to

critically evaluate some applications which address to the same topic.

Previous work and research can be categorized by their results and their approach-

es of analysis. Most of this work is only tested on mobile devices, but they have dif-

ferent algorithms to analyze the data collected. The following are some applications

and related research which are closely related to the subject of this dissertation.

The article “Automatic Step Detection in the Accelerometer Signal” by Ying et al

[29] studies three algorithms (Pan-Tompkins method, template matching method and

peak detection based on combined dual-axial signals) and describes disadvantages

and advantages for each of them.

A- Pan-Tompkins method is easy to implement, but yields the positive and negative

as a good feature, that can result in false peak detecting

B- The great advantage of the template matching method is that the algorithm is

capable of detecting the steps self-adaptively and generating the representative

template according to the current step signal.

C- Peak detection based on combined dual-axial signals is the fastest and easiest of

the three algorithms. So far, it provides good performance in the first experiment.

Moreover, it can be easily written as a fixed-point algorithm, which is suitable for

integration into microprocessors of limited computing power.

This article explains in detail each algorithm which is used partly in this project and it

studies different ways of implementing algorithm process collected data using differ-

ent algorithm. Each of these algorithms has advantages and disadvantages in achiev-

ing high quality in detecting the number of steps.

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Observing the advantages and disadvantages for each algorithm from the report can

thus help this dissertation to decide which algorithm can be implemented. Although,

all the algorithms have similarity, but still the difference for each algorithm may pro-

duce different result. In this dissertation, mostly the first algorithm is followed with

some modifications.

The article “Step Counting Using Smartphone-Based Accelerometer” by Naqvi et

al [19] identifies the steps by the relation between frequency of the step and the mag-

nitude of the accelerometer signal. The algorithm of this paper follows these steps:

A- Device position and orientation: device fixed near center of the body

B- Dataset used for analysis: frequency of the data is 100Hz, collect data from

different user and different level of walking.

C- Understanding the accelerometer reading: ignoring the amount of data due of

no movement of the user is named as white noise

D- Detecting of starting point

E- Detecting of steps

This article takes different approach than this dissertation; it deals with frequency and

magnitude of the accelerometer data, while this dissertation focuses on peak detection

algorithm and determines the walking and running status according to the magnitude

of the accelerometer data. The result of the algorithm in this article is accurate with

0.67% error rate. Otherwise, this article only deals with normal walking and it is not

applicable with different style of walking such as on stairs.

The article “A simple method for reliable footstep detection on embedded sensor

platforms” by Libby [14] simply presents a method for step detection using sensors

which are embedded in different devices. He follows the algorithm which explained

before in the first article “Pan-Tompkins Method” from the same source as the first

article. By taking this algorithm he faced the same disadvantage as described in the

first article which is false peak detection. Same disadvantage are faced as described in

the first article which is false peak detection. The advantage of this article is that it

opens new opportunities to modify the mentioned algorithm to let the algorithm work

faster and more effectively.

The article “Analysis of Sleeping Patterns Using Smartphone Sensors” by Steven

Meliopoulos and Suhel Sheikh [15] investigates how to detect the optimal moment to

wake up the user in the light stage of the sleep, also to identify possible sleep prob-

lems. The aim of this article is to build an application using smartphone and built-in

accelerometer and adding a server to collect data and make comparison with other

users. In this article they use an algorithm which is derived from different articles

which can be summarized as:

Filtering: the first step of this algorithm is to take the derivation of the collected

data

Threshold: to eliminate the noise they applied a threshold to cut off all the values

which are less than the value of the threshold.

Counting the number of changes: it calculate the number of changes per second,

minute and hour.

Although, this algorithm reassures the user that the position of the device does not

have any influence on the result, but through this dissertation it appears that ignoring

the magnitude of the movements may direct the application to detect unimportant

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movements caused by the nature of the built-in sensor. This dissertation focuses on

the same algorithm plus the magnitude value of the threshold for each time window.

They tested this research on mobile phones, but they used a website to view the re-

sult of multi-user instead of the mobile screen.

In analyzing the data they only propose weekly statistics of the average sleep dura-

tion and average of event frequency. Having the collected data and good algorithm to

identify the frequency of movements allows improvement of the analysis base, such

as letting the user check the level or stage of sleep, using the result to wake up the

user in the light stage of sleep.

The article “Monitoring Body Positions and Movements during Sleep using

WISPs” by Hoque, Dickerson and Stankovic [13] researches how body movement

while sleeping may relate to some particular diseases. In this article they use WISP

(Wireless Identification and Sensing Platform) with active RFID (Radio Frequency

Identification) based sensor equipment. The results of this research identify that the

system can detect all the movement while sleeping, studying four positions of the

body as lying left, right, stomach and back. However, in this study they use a similar

algorithm to the previous article by looking at the collected data from sensors and

comparing it with that threshold and count the number of values which cross the

threshold. In addition they do not unify the three axis in one value, the reason behind

that to identify the position of the body by monitoring the change of the value for

each axis.

There are many applications that use the same algorithm to determine the number

of steps such as pedometer app [6] and runtastic pedometer [22] and also many appli-

cations are using the algorithm of sleep movement detection such as Sleep Time

(alarm clock) [5], which works on Android and iOS operating systems. All the articles

and applications are focusing on the mobile platform or using other devices to capture

accelerometer data. In this project, mobile platform was used with Android operating

system. Moreover, we built this project upon these researches and modified some part

of the algorithm according to the requirements of this project and looking at many

applications to get better experience of what people expected when using these appli-

cations

2.3 e-Science Central cloud technology related researches

This section focuses on using e-Science Central to process and analyze the data on

cloud technology. Newcastle University provides scientists with the computational

resources which they need for their designs, implementation and to maintain their

application.

The article “Developing Cloud Applications using the e-Science Central Platform”

by Hiden, et al [11] describes some problems of cloud computing for each level of

cloud computing such as Iaas, Paas and SaaS. However, it describes the purpose of

designing e-Science Central to contribute in some areas such as platform cloud ser-

vices which provide storages, services execution, security, workflow provenance and

e-Science Central API.

As described in this article, storage can provide versioning for allowing users to

work with different versions of the document, services and workflows. Moreover, e-

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Science Central provides security to protect their data unless users want to share the

document publicly. Furthermore, it provides social networks for more communication

between scientists for more collaboration in scientific research. In addition, scientists

can create blocks or use generic blocks to run their workflows. Each block has input

and output which are typed to avoid conflict which can be data wrapper, file wrapper

or object wrapper.

In addition, e-Science Central allows scientists to connect the platform through

their computer or using mobile application using e-Science Central’s REST API. For

better security, this API required client registration which needs URL, username and

password for each communication.

Articles such as “e-Science Central for CARMEN: science as a service” by Wat-

son, Hiden, Woodman [27] and “e-Science Central: e-Science on the Web, powered

by Clouds” by Watson et al [26] explain the functionality and capability provided by

e-Science Central for scientists to get the benefits of advanced technology such as

cloud computing.

This project is built upon the technology and service provided by e-Science Central

to demonstrate using mobile applications instead of PCs to connect and send collected

data from accelerometer for processing and analyzing using the storage and workflow

services of e-Science Central.

3 Requirement analysis

3.1 Project scope

This project provides the functionality to run mobile applications using built-in

sensors to collect the data from user activities such as sleeping, walking and running.

Following that, it provides the accessibility to execute different algorithms which are

related to the mentioned exercises using the advanced technology of cloud computing.

Moreover, the project provides the functionality of using the workflow and storage of

cloud computing (e-Science Central) for processing and analyzing the data.

3.2 Software requirements

3.2.1 Technical requirement

To compare the functionality of using cloud computing in this project with the

functionality of using only mobile applications, another application was built using

the process and battery power of the mobile to process and generate the same func-

tions and results. The results derived from this application are used to compare differ-

ent tasks to prove that using mobile application with cloud technology can be better

than using only mobile utilities. This application will be mentioned in the design and

implementation chapters for comparison.

3.2.2 Functional requirement

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To describe all the interactions that the user will have with the application, use case

scenarios and diagrams can explain in detail these interactions which define the func-

tions performed by the system.

Use case is a textual representation of the structure of functional requirements

within the environment of the business or the system processes. It can be represented

graphically in a diagram and in a textual document.

3.2.2.1 Use Case (process of sleep)

Use case name: Process of Sleep.

Scope: Sleep part of the application.

Level: user goal

Primary actor: user

Stakeholders and Interests: The user wants an easy to use application and gets the

result after waking up

Pre-condition: The user is on the bed and the smartphone beside user on the bed

Post-condition: The user starts the application to collect the accelerometer data by

pressing START button.

Main scenario: When the user wants to start the application first he should lie

down on the bed and put the smartphone beside him and press the START button. It is

preferable for the smartphone to be on charge. In the morning, before getting up, the

user should press the STOP button to stop the accelerometer recording data. To look

at the result, first the user should press the SYNCHRONIZE button to upload the data

to the e-Science Central, after processing the data the user can look at the result by

pressing the RESULT button to look at the latest result and the result for previous

tests

Extension point (alternative flow ): When the user wakes up in the middle of the

night and presses STOP button the accelerometer will stop and when he want to start

again pressing START button will add the new value of the accelerometer to the old

one. If the user presses SYNCHRONIZE button, its mean the test is finished and if he

press the start button it will start new test for sleeping.

It does not matter the position of the smartphone, because the value of each accel-

erometer data is the absolute square root for summation of square three axis

3.2.2.2 Use Case (process of user activity)

Use case name: Process of user activity

Scope: Exercise part of the application

Level: user goal

Primary actor: user

Stakeholders and Interests: The user wants an easy to use application and gets the

result after daily activity like walking and running

Pre-condition: The user holds the smartphone. The rotation of the smartphone be-

fore walking does not matter.

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Post-condition: The user starts the application to collect the accelerometer data by

pressing the START button and starting to walk or run.

Main scenario: When the user wants to start the application first he should press

the START button and start to walk or run, the application starts collecting the data

after two seconds. When the user wants to stop walking he should press the STOP

button at the same time as actually stopping. If the user wants to continue walking

pressing start button again will continue collecting the data and add to the previous

data. If the user wants to process the data on the cloud by pressing SYNCH button the

smartphone sends the CSV file to the cloud and e-Science Central finishes processing

and analyzing the data and automatically send the result to the smartphone so the user

can look at the result by pressing the RESULT button

Both of the use case descriptions are fully addressed which describe the success

scenario of the application and also describe alternative scenarios which can happen if

the user does not follow the instructions. In addition use case diagrams can be found

in the appendix C.

3.2.2.3 System Sequence diagram (SSD) and description

A sequence diagram is a kind of interaction diagram that shows how processes op-

erate with one another and in what order. In this section SSD only focuses on the

system as a black box, it only explains the sequence of interaction between actor and

the system as one part. Figure 2 describes both requesting and return messages to and

from the system respectively.

Figure 2. System Sequence Diagram

3.2.3 Non-functional requirements (Supplementary requirements)

Before starting the design of the project, there are some requirements which de-

pend on the structure of the project, because the project has two main parts, mobile

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and cloud computing. For the first part users should have smartphone with Android

operating system which has a built in sensor and for the cloud computing part users

should have an account for e-Science Central which is free and have joined both Stor-

ageServices API and WorkFlowServices API. To communicate between mobile ap-

plication and cloud the smartphone should have access to the internet and it does not

matter if it is wireless or 3G.

4 System design

This chapter describes the overall system design, architecture and defines the com-

ponents which contributed to this project. This project has two main parts, mobile and

cloud computing based tasks. This chapter discusses the reasons and criteria behind

the chosen design between different architectures. It also discusses the interaction

between both users and mobile application to explain the user needs and how the

project can satisfy these requirements. It discusses and explains the reason behind

software design and what will happen inside the black box of the system through both

class diagrams and interaction diagrams.

4.1 Human-Computer interaction (HCI)

By definition, “it is a discipline concerned with the design, evaluation and imple-

mentation of interactive computing systems for human use and with the study of major

phenomena surrounding them” [10] the key component of this definition is that (HCI)

deal with the interaction between the computer as the digital part and humans as the

user part.

4.1.1 User Interface (UI) design

This section describes how the user interface design of the mobile application will

be simple and efficient to satisfy the user needs. In this project the user only interacts

with the mobile interface to run the application and thereafter looks at the results. To

simplify the interface, the interface of this mobile application only has some buttons

with clear text to let the user to go through the application in an easy way. The aim of

using only buttons is to make the interface of the application easy to understand.

The idea behind this design is that the developer or the designer can change the in-

terface to a different design according to the user’s point of view but the functionality

of the application will be the same, because the mobile part only collects the data and

sends it to the e-Science Central cloud for processing and analyzing. The current user

interface is shown in Figure 3. It represents the tree view showing the pages for navi-

gation.

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Welcome Page

Sleep App page (Start, Stop) Exercise App page (Start, Stop)

Result history pageMulti user result page

Instance result

Result history pageCross population result

page

Instance result

Layer 1 Layer 2 Layer 3 Layer 4

Longitudinal analysis page

Figure 3. User Interface Design

4.2 Software design

4.2.1 Overview

This section studies the idea behind each part of the application and the reason for

choosing the type of the architecture, criteria for both mobile and cloud computing

also it goes one step further to explain the detail for each mobile software architecture

and e-Science Central architecture. In addition, it goes through both class and interac-

tion diagram for each specific part of the system.

4.2.2 Mobile and cloud computing criteria and discussion

In this section, the chosen design will depend on some criteria followed for each

part of the application.

4.2.2.1 Mobile application criteria

As mentioned in Section 2.2 most of the research and applications which focus on

the accelerometer use mobile application or other sensor devices. In this project mo-

bile application was selected, because most people already have their smartphone and

most of these smartphones have built-in sensors to capture the accelerometer data

[25]. In this project Android platform was used to build mobile application, Reasons

for selecting this platform include: it is written using Java language, it can be plugged

with eclipse which is a free tool, it provides free SDK for the developer to create dif-

ferent applications and publish them in the market for free.

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4.2.2.2 Cloud computing criteria

Cloud computing in this project is the back end which implements all the pro-

cessing and analyzes the data. The importance of cloud computing is increasing with

increasing the number of users which increase the amount of processes. The question

here is why we choose cloud computing technology instead of using a server as the

back end? The challenge of this project is to find the way to minimize the work on

mobile platform to save time, battery power and performance, otherwise transfer the

hard work to other technology to help users to use simple devices such as

smartphones to get better result.

Cloud computing provides huge storage which can be suitable when thousands of

users use this application and also provides a platform to process the raw data. More-

over, the challenge of this project is how to share different result for different people

using the same application which let the application to be more interactive.

Cloud computing provides database structure to collect all the result from different

users. It may use for social networks for exchanging the data between different users

who want to share their result. In this project e-Science Center platform has the avail-

ability of social networking structure which may use in this project.

The advantage of cloud computing is that when there are any changes in the algo-

rithm or any process in the future it will be easy to change it in the cloud technology

instead of upgrading the mobile application from the mobile device itself.

The cost and performance of the cloud computing open a door for the developers to

use the storage and use great performance to run multiple tests with less cost than

using servers. Although, developers can use servers as back end, but using cloud

computing will make the maintenance and IT team who care on the technical side is

not required with cloud computing.

In light of these challenges and reasons, cloud computing technology was chosen

as a solution for different cases in the future.

4.2.3 Design options

As mentioned in the previous section that the project will use both mobile and

cloud computing platforms. This section goes through different options to distribute

process steps between both environments. Collecting data and displaying results will

be on mobile platform because of easy accessibility of using mobile interface instead

of using website. The remained steps will be explained as following.

Total acceleration: in this project the main point of using mobile to get the acceler-

ation magnitude and frequency values, so it does not focus on the value of each axis

rather the total acceleration. Each time when the sensor generates 3 values of acceler-

ation, it unifies them to one value which is the total acceleration of the device move-

ments. Doing this step in mobile platform will let the mobile application to save more

memory and more battery to send collected data with three times size to the cloud

environment

Data filtering: it is well-known that the power of the process in the cloud computer

is much greater than the mobile, so if this step happens in the mobile platform it will

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generate same size of the file but with extra power and extra time to filter each data

and this amount of time and power can be saved if the process happens in the cloud.

Data processing and data analysis: both processing and analyzing of the data need

good performance power and the aim of this project is to minimize the time of the

performance. Also centralizing the process in one place is better than decentralizing,

because both of processing and analyzing of the data are case of change and upgrade

in the future. On other hand, analyzing data may needs the data from different users

who share their results, so the processes of analyzing will grow more and more by

increasing the number of users who share their results.

Using a mobile processer for analyzing the data from one user may not be a big prob-

lem but dealing with thousands of users is a big problem and transferring this step to

the cloud environment will save the application from potential crash.

Save result: this step only deals with storage and we know that the storage of the

mobile device is limited comparing with e-Science Central storage. Implementing this

step in the cloud will save a lot of memory of the mobile device that may be used for

a different purpose.

Table 1 below illustrates the above mentioned process steps within the design. Table 1. Design distribution structure

Process name Mobile platform Cloud platform

Collect accelerometer data Yes

Total acceleration Yes

Data filtering Yes

Data processing Yes

Data analyzing Yes

Store result Yes

Display Result Yes

4.2.4 Mobile design

In the light of the discussion from previous sections, Figure 4 shows only the pro-

cess that happens in the mobile platform. In this design, mobile application duty is

only to generate accelerometer data and send it as one value for each time as total

acceleration and save all the values inside one file and send it to the cloud when the

test is finished.

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Start Application

Register the sensors

Axis X Axis Y Axis Z

Esc Cloud

Result of the activity

Send the result to the mobile

End Application

Total acceleration

Figure 4. Mobile design

4.2.5 e-Science Central workflow design

Figure 5 shows the design of the e-Science Central workflow which is independent

for each test conducted by the user. When the user finishes uploading the data using

mobile application, e-Science Central workflow starts to import the CSV file from the

store and select the column which has the raw accelerometer data, following that, it

starts to filter the noises. Processing the data will start when the data is filtered to

generate some values according to the requested algorithm, in the end analyzing the

data will generate results to save them in the store to be available for the user to ac-

cess the result at any time.

Start Process from Esc cloud

Upload data from mobile

Filter the data

Process the algorithm

Analysis the data

Analysis and process result

Multiple results

End Application

Figure 5. e-Science Central workflow design

4.2.6 Multi-users design Options

Depending on the previous e-Science Central workflow design; two options of de-

signing analysis multi user data will be discussed to leverage the performance of the

application when the number of users increases to thousands. In real life the applica-

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tion should query the users for sharing agreement, but for study purposes we will not

deal with security and authorization case which are out of the scope of this project.

Moreover, this section focuses on the case of cross population between the users to

present this property. The implementation section discusses which option is more

reasonable for this application.

4.2.6.1 Option one

Figure 6 shows the first option of cross population design between different users

using the same application. This design takes an example of four users using this ap-

plication and they want to look at the other users’ results for comparison. They do not

need to use their specific account for e-Science Central; they will only call the admin

account to provide such a result. After authentications succeed, it will ask the cross

population analysis block to go and check all the results. The cross population block

will check each client data and calculate the number of steps, distance, speed and

calories burned and compare all the users’ result and send the result to the admin ac-

count to send again to every user who requests cross population analysis. The ad-

vantage of this design is to minimize the process on the mobile platform, minimize

the usage of the internet, and maximize the performance of the application when the

number of users increases. The disadvantage of this design is that e-Science Central

does not allow the creating block to manipulate the data from different users inside e-

Science Central platform; e-Science Central only allows creating blocks inside a per-

sonal account and manipulation of the data inside that account. Multi-User Architecture

Cross Population Design

ESC C

loud P

hase

Mobil

e Pha

se Client 1 Client 2 Client 3 Client 4

ESC Storage

Client 1-Test 1

Client2-Test 1

Client3-Test1

Client 4-Test1

Cross Population Analysis

Client 1-Test 2

Client2-Test 2

Client3-Test 2

Client 4-Test2

One Account On ESC

Request X-Population AnalysisResult X-Population Analysis

Figure 6. Cross population design Option 1

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4.2.6.2 Option two

Figure 7 shows the second option of the design for cross population between dif-

ferent users using the same application and agreeing to share their information. In this

design each user has his own account and asks the cross population analysis block

which is inside mobile platform to generate the result from different users’ data. After

that the cross population block should request multiple data from each user using their

authentication information. At the end of the process each user gets the results from

the cross population block inside mobile application. The disadvantages of this design

are: more time to generate cross population result, more load on the internet for ex-

ample, if the number of users who want to share their information is 1000 and each

user has 10 tests, for each user to get cross population results, this mobile application

should connect to the internet 10 * 1000 = 10000 times which may cause the applica-

tion to crash. In addition it takes more power from the mobile device to analyze all the

data from different users. Multi-User Architecture

Cross Population Design

ESC

Clou

d Ph

ase

Mob

ile P

hase

Client 1 Client 2 Client 3 Client 4

ESC Storage

Client 1

Test1Test2Test3Test4Test5

Client2

Test1Test2Test3Test4Test5

Client3

Test1Test2Test3Test4Test5

Client 4

Test1Test2Test3Test4Test5

User 4 Account

User1 Account

User 2Account

User3 Account

Cross Population Analysis

Cross Population Analysis

Cross Population Analysis

Cross Population Analysis

Figure 7. Cross population design Option 2

4.3 Algorithm design

Designing the algorithm depends on the number of functions taken to provide cor-

rect result in the implementation chapter. This section describes the initial architecture

of designing the algorithm in both exercise and sleep parts of the project. The general

algorithm in this project will be divided according to the discussion section between

mobile and cloud computing platform (e-Science Central in this project). Each of the

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steps in the diagram below will be explained in the implementation chapter by using

different tools and projects in different platforms.

Figure 8 shows that the algorithm has four main steps which are data collection,

data processing, data analysis and generation of results. Each of these steps has sub

data and sub functions and it should be available to complete the algorithm process.

Algorithm Design

Function

Clou

d Ph

ase

Mob

ile P

hase

Start process Data Collection

Data Processing

Data Analyzing

Generate results

End process

X axis

Y axis

Z axis

Total acceleration axis data

Steps detect

Distance

Speed

Calories burned

Number of the

movement while

sleeping

analysis

Last activity

Longitudinal analysis

Cross population

Figure 8. Algorithm Structure

4.4 Design class diagram

4.4.1 Mobile class diagram:

The architecture design of mobile applications depends on two different types of

classes. In mobile applications there are activities and layout, also there are classes.

Activity and layout are the way to represent the items and tools in the mobile interface

to the user, it is written in Java code and XML, more detail about that is discussed in

the implementation chapter. To draw a class diagram for mobile part of the whole

application will be the mixture between activity classes as interfaces and normal clas-

ses which can be found in the appendixes D and E.

4.4.2 Blocks class diagrams

The architecture of blocks is fully dependent on the classes. In this application

there are two blocks and the design for each block can be found in the appendixes F

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and G. both of blocks are running on the e-Science Central platform. The aim of each

block is receiving data from previous blocks or file and generating different files ac-

cording to the requirements, so each class represents one file. However MyService

class plays the role of main classes to generate files from other classes and send them

to the next blocks.

4.5 Interaction diagram (sequence diagram)

This section describes the sequence of the message between parts of the system.

The previous chapter explained the system sequence diagram which only deals with

actors and the whole system as one block (black box), but this section goes inside the

system to design the interaction between different parts to complete the requirement.

When the user asks the system to start specific activity, the mobile interface starts to

collect the accelerometer data until the user wants to stop the application. When the

user uploads the data to the second part of this system (cloud), the workflow inside

the cloud will process the data to generate the result and store it as a file. The third

step is to generate the analysis process which needs some information from the pro-

cess part to be ready for the user. The user can display analyzed results by sending the

request for analyzed information. All the request and return messages are shown in

the Figure 9.

Figure 9. Sequence Diagram

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5 Implementation

5.1 Overview

Based on the design architecture in the previous chapter this chapter reviews the

implementation of the suggested design. Basically this project is divided into two

main phases; the first, mobile phase is divided into two parts. In this phase the appli-

cation was written using Java programming language and Android SDK.

The second phase of the implementation is the e-Science Central cloud based phase

which has two workflows, each of the workflows has some blocks which are e-

Science Central built in blocks and one block for processing and analyzing the data

using Java Maven project and written with Java programming language.

To communicate between these two phases, e-Science Central provides new API

which is upgraded from the e-Science Central API to accept mobile application. Both

phases and e-Science Central mobile API will be explained in detail in the next sec-

tions of this chapter. Figure 10 shows both phases.

User Activity Analysis (Implementation Phases)

Fu

nct

ion

E-SC Cloud PhaseMobile Phase

Mobile Application

Sleep Application

Exercise ApplicationExercise

WorkFlow

Sleep WorkFlow

Import CSV file Block

Column Select Block

Exercise Block

Csv, File Export

Java Maven Project

Import CSV file Block

Column Select Block

sleep Block

Csv, File Export

Java Maven Project

Java Programming language

Android SDK

E-SC Mobile API To connect mobile to e-SC cloud

Figure 10. Implementation Detail

5.2 Languages and tools

1- Programming language: this project is written in Java code in both Android

and Maven projects.

2- Tools: this project has been built using eclipse with Android SDK.

3- e-Science Central workflows include built-in blocks and other blocks which

are created for this project.

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5.3 Mobile platform

Implementation of the mobile application will be briefly explained. The mobile ap-

plication structure divides to two files of codes “activities and layout”.

Activity is a file of Java code to control the behavior of any element in the layout

file. The style of the activity is the same as Java classes, but the purpose of this activi-

ty is only to display Android tools [2]. However, the developer can add methods to

the activity codes like normal class methods. Here are some examples of general

methods used in the activity classes:

- Protected void onCreate(Bundle savedInstanceState)

- Public void onCreateOptionsMenu(Menu menu)

- Public void onSensorChanged(SensorEvent event)

- Public void onAccuracyChanged(Sensor sensor, int accuracy)

Usually each activity has a layout file; also it can be transferred between different

activities using actions and intents. Each activity should be declared in the manifest

file in order to be available.

<manifest>

<application>

<activity android:name="com.ncl.MAIN " />

</application>

</manifest >

Layout is the visual representation for a user interface, the elements of this file

usually are written using XML tags but it can be instantiated at runtime. Writing lay-

out as XML file is to separate visual representation from the code which controls the

behavior of each element [2]. Most of the codes in the activity to display Android

tools are from training course in the Android website [3]. An example of using layout

to represent the main page of this application can be found in appendix A.

In addition to using Android SDK, another open source API has been used which

is achartengine 1.0.0 to display required chart on this application. Achartengine has

multiple chart styles, tools and utilities which are available for the developer to gener-

ate multiple charts using the Java programming language [1]. Appendix B shows

some screen capture of using achartengine API in this application.

5.4 e-Science Central Platform as a Service

As mentioned in the design chapter about e-Science Central workflow architecture,

there are some blocks which are e-Science Central built-in blocks, the developers can

use them in their workflows. Other blocks are built for specific job inside the applica-

tion to run on e-Science Central platform. These blocks are built using Maven project

with Java programming language. The next section explains the Maven project and

what kind of implementations happens in each block. Figure 11 shows the structure of

workflow inside e-Science Central.

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Figure 11. e-Science Central Workflow Structure

5.4.1 Maven:

Maven is “a software project management and comprehension tool. Based on the

concept of a project object model (POM), Maven can manage a project's build, re-

porting and documentation from a central piece of information.” [4]. from this defini-

tion, it appears that a POM file which is written in XML is the main file of the Maven

project to access all the dependency for a specific project.

5.4.2 Maven workflow blocks

This section does not talk about code for each class inside blocks, but focuses on

the implementation logic behind the design of the e-Science Central Maven workflow

block. Each block has an XML file named service.xml, this file contains the name of

the service, category and most importantly the types and names of input and output

files. Each input and output file has a different type such as data-wrapper, file-

wrapper and object-wrapper.

The other file in the block is MyService.java class, in this class all the input and

output files should be declared and the number of input and output should be the same

as service.xml. In this project other classes can be added but the generated result

should follow the structure of blocks inside e-Science Central. To connect created

blocks with built-in blocks, each block has defined both input and output which is the

way to connect between different blocks inside one workflow which is mentioned in

Figure 11.

5.5 e-Science Central REST API

5.5.1 Overview

Although the user can use e-Science Central software service through a website,

programmatic access is also provided through a REST API to enable developers to

Maven block

Built-in blocks

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build scalable, secure cloud applications. Security is managed by Java and .Net client

libraries which also give developers an object-oriented version of the API.

e-Science Central API divides into two main categories which are Storage API and

WorkFlow API. In the next section there will be details of system functionality for

each of them. All the codes in section 5.5 are taken from the same document [12].

5.5.1.1 The Storage API

The way of storing the data inside e-Science Central is file system, and each file in

the system represents a document object (EscDocument) inside a folder (EscFolder).

Each item has associated Metadata which can be represented as EscMetaDataItem and

this API provides a list of metadata for each item to display or to attach new metadata

to EscDocument.

In this API the developer can manipulate the folder and items inside the storage us-

ing some built in functions such as:

EscFolder homeFolder = client.homeFolder();

To list all the documents inside homeFolder:

EscDocument[]docs =

client.folderDocuments(homeFolder.getId());

Figure 12. e-Science Central Document Structure

To list all the folder inside homeFolder :

EscFolder[]folders =

client.listChildFolders(homeFolder.getId());

Figure 13. E-SC Folder Structure

To set new name to folder or to change existing name:

Folder.setName(“new name”);

To download and upload file to or from the e-Science Central:

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Upload file

File fileToUpload = new File(“file Direction”);

EscFolder folder = client.homeFolder();

EscDocumentVersion version =

client.upload(folder,fileToUpload);

Download file

EscDocument doc =client.getDocument(“document id”);

File localFile=new File(“file Direction”);

Client.download(doc,localFile);

5.5.1.2 The Workflow API

This API allows external programs to run and monitor e-Science Central work-

flows and use e-Science Central as the engine to calculate and analyze variety of data.

This API has different tasks and in this section mentions some of them as an example:

Running workflows

EscWorkflowInvocation invocation =

client.executeWorkflowOnDocument(“workflow

id”, “document id”)

Terminate workflow

EscWorkflowInvocation I =

client.executeWorkflow(“workflow

id”);

Client.terminateInvocation(i.getId());

5.5.2 e-Science Central REST API implementation

The functionality of e-Science Central REST API can be used to connect a mobile

application to a cloud. The scalability and security of the API ensure that the applica-

tion can be executed and store the result by using both storage service and workflow

service API’s of e-Science Central. For this project same functions are used to con-

nect, execute, upload and download the information.

To connect mobile application to cloud, the user should provide correct (URL,

username and password), because both sub API’s need these parameters to access the

cloud in secure manner.

storageService = new StorageClient

(“URL”,port,secure;”username”,”password”);

workflowService = new WorkflowClient

(“URL”,port,secure;”username”,”password”);

After connecting to the cloud, “workflow id” is required to upload files and exe-

cute the workflow to let the API distinguishes which workflow is the target to be exe-

cuted. As mentioned previously about uploading the file the same lines of code used

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in this project and all the documents are automatically versioned, in this case if the

API does not succeed in uploading the file the workflow execution will use the previ-

ous version of the file in the home folder and if there is no file in the home folder the

workflow will not get executed.

To execute the workflow, workflow id as well as all the files which have been up-

loaded and put in the “workflow parameter list” to prepare all the files for execution.

After that the API creates an object of EscWorkflowInvocation to execute the work-

flow with both (workflowid ,parameter list) parameters.

EscWorkflowInvocation invoFolder =

workflowService.executeWorkflowWithParameters

(workflowid,parameterList) ;

5.6 Data analysis implementation

Looking at the design chapter, there are two options for data analysis. Because of

the authorization and authentication problem, first option was not implemented when

multi-user access is needed, so second option will be selected. The implementation of

data analysis has been applied on the exercise part of the application. The implemen-

tation was not repeated for sleep part.

5.6.1 Last activity analysis Implementation

In this section the data from multi users is not required, so the first option is select-

ed from design. When the user test the application, it generate HTML file which con-

tains all the information required for that test and display a nice graph for last activity.

The application lets the user to navigate between all the tests that took place to com-

pare between different results for the same user.

Analyzing last activity happened in the cloud platform. The code to generate

HTML file is mixed of HTML and Java code. The design of the HTML page can be

improved by using different client side programming languages.

Moreover, this implementation produces a graph for each test which is saved in the

e-Science Central storage and also is embedded in the HTML file. In addition, it pro-

duces two CSV files which contain different information required for both (Longitu-

dinal analysis Implementation, Cross population analysis Implementation).

5.6.2 Longitudinal analysis Implementation

In this section, because we need the information from multi users, second option of

the design was applied of which all the implantations are happening on the mobile

platform. The application takes the example of three users’ different tests and all the

results for each user save in the storage in the user account. The idea behind this anal-

ysis is to compare the date and time for multi test for each user. In addition, the aim

also to give the user knowledge about who is testing this application and what the

time for each test in the current day.

To implement this section correctly, some changes are made on the workflow

which generates another CSV file containing the time of the test, number of steps,

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distance, speed and calories. By applying the second option of the data analysis de-

sign, mobile application downloads each file from the storage of the first user to the

mobile storage and takes the values required and loops this process until it finishes

from the first user and after that it checks the second and third with the same loop.

In this implementation, it will only display the tests for the current day with differ-

ent color for each user. The result will be a graph displaying the number of steps for

each user for more encouragement and competition.

5.6.3 Cross population analysis Implementation

To implement cross population analysis, second option of the data analysis design

is selected, because it needs the information from different users. However, it displays

a graph viewing the total number of steps for each user who has decided to share their

information; in this project, the example of three users is taken. In this analysis it

calculates the number of steps for each test to get the total value following the same

process of previous section. The result displays all the information inside the storage

of all the accounts who agree for sharing information.

The aim of this implementation is to foster a competitive spirit in the user, whereby

knowledge of other users’ scores may incentivize the users to increase their activities.

5.7 Algorithms

5.7.1 Related algorithms research

The algorithms in this project are derived from different researches on the same

fields which has been explained in Chapter Two. The implementation of both algo-

rithms used in this application is explained in the detail as follows:

5.7.2 Exercise activity algorithm

Peaks detect algorithm is simple as concept. This algorithm is processed on the

user by holding the smartphone and starts the application when the user starts walking

and stops the application when the user stops. The algorithm takes the action when the

user stops the application and synchronizes the data with the cloud part by following

the steps outlined below.

Collect data: In this application the algorithm does not depend on the orientation

of the device, so it is not sensitive to the orientation but only care about the frequency

and the magnitude of each datum. Data is collected from 3D acceleration with the

frequency of 5 Hz. Every time the accelerometer generates 3D acceleration data it is

unified in one value to get the total acceleration. All these values are saved as CSV

file in the directory of the smartphone storage to be available temporarily until the

next test, so the file is available for execution unless if the user want to test again.

Total acceleration can be calculated by following the formula [23]:

𝛼 = √𝑥2 + 𝑦2 + 𝑧2 (1)

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In this equation (a) is the total acceleration derived from the combination of three

acceleration data to eliminate negative values. Figure 14 and 15 show 3D and total

acceleration.

Figure 14. 3D Accelerometer Representation

Figure 15. Total Acceleration

Low pass filter: This step starts when the mobile application upload the CSV file

from temporary storage in the mobile directory to the home folder of e-Science Cen-

tral storage. Although, all total acceleration are collected in one file, but these data

still suffer from gravity and noise from the nature of the device. For example, if we

put the mobile device on the table without any movement still the value of the accel-

eration will be about 9.87 m/s2. Applying a low pass filter will remove the value of

the gravity from the data and reduce the amount of noise by passing the constant val-

ue of ALPHA by following the formula below [20].

ALPHA a=0.15 𝑜𝑢𝑡𝑝𝑢𝑡 = 𝑖𝑛𝑝𝑢𝑡 + 𝛼 ∗ (𝑖𝑛𝑝𝑢𝑡 − 𝑜𝑢𝑡𝑝𝑢𝑡) (2)

Figure 16 represents the accelerometer values after implementing the low pass fil-

ter.

-20

-15

-10

-5

0

5

10

15

1 5 9 13 17 21 25 29 33 37 41 45 49 53 57 61 65 69 73 77 81 85 89 93 97

101

105

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157

X Y Z

0

5

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25

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13

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25

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37

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49

55

61

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79

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1

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7

a

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Figure 16. Low Pass Filter

Threshold: Looking at the values of the accelerometer it is notable that every time

there is different maximum and minimum values. The average of these two values is

called the threshold, so finding the number of steps from the values of the accel-

erometer; we compare the values with the threshold value every two seconds. The

value of the threshold decides if the step has been taken or not. Figure 17 below is

generated by finding the dynamic value of the threshold with the formula below [29].

𝑡 = 𝑚𝑎𝑥 + 𝑚𝑖𝑛/2 (3)

Figure 17. Threshold Representation

Time window for every two seconds: The purpose of this step is to eliminate the

amount of noise and to ignore any value which may be detected as a step. Through

observation we see that the speed of the human when walking or running is about one

step (walking) and six steps (running) in two seconds. If the number of values is

greater than the threshold is more than six in each two seconds, the extra values will

be discard in that window [29]:

𝑤 = ∑ 𝑎(𝑖) > 𝑡

𝑖=0

𝑖=𝑛

(4)

Step detect: This is the final step to detect the number of steps by taking the condi-

tion of the previous steps. For each value which has been accepted as the value of one

of the peak should be more than the value of the threshold, the number of the values

which are more than threshold should be less than or equal to six in each two second

0

1

2

3

4

1 7

13

19

25

31

37

43

49

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a

t

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window also to be more specific the value of the peak should be greater than the value

next to it.

To calculate the other parameters such as distance speed and calories burned, we

just implement the following formulas which depend on each other.

𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 = 𝑆𝑡𝑒𝑝 𝑁𝑢𝑚𝑏𝑒𝑟 ∗ 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 𝑓𝑜𝑟 𝐸𝑎𝑐ℎ 𝑆𝑡𝑒𝑝 (5)

𝑆𝑝𝑒𝑒𝑑 = 𝐷𝑖𝑠𝑡𝑎𝑛𝑐𝑒 /𝑇𝑖𝑚𝑒 (6) 𝐶𝑎𝑙𝑜𝑟𝑖𝑒𝑠 = 1.25 ∗ 𝑆𝑝𝑒𝑒𝑑 (7)

5.7.3 Sleep monitoring algorithm

The following algorithm is to determine the number of movements for each minute

when the user is sleeping. To apply this algorithm, the user should put the smartphone

on the bed and run the application. In the morning, the user stops the application and

synchronizes the data with the cloud part to implement the following steps.

Collect data and low pass filter: The data collection and low pass filter steps in this

algorithm are the same as the previous algorithm so it is meaningless to repeat it.

Noise filter: The aim of this step is to remove all the noise. In this application we

test the application by putting the mobile on the table with no movement, but still

there are different values of the accelerometer which are between 1.44 and 1.48.

Without this step the algorithm will take all the values and find the number of changes

according to the value of the threshold and give different results which are wrong,

because if the smartphone is not moving the result should be zero changes.

To filter all of these values we implement the following formula:

𝑖𝑓(𝑜𝑢𝑡𝑝𝑢𝑡 > 1.44 𝐴𝑁𝐷 𝑜𝑢𝑡𝑝𝑢𝑡 < 1.48) 𝑜𝑢𝑡𝑝𝑢𝑡 = 1.46

(8)

Figure 18.Noise Filter

Standard deviation: The aim of the standard deviation process is to find the adja-

cent elements by implementing the following formula (14):

𝑆𝐷𝑉(𝑎) = 𝑎𝑏𝑠([𝑎(𝑟𝑜𝑤1) − 𝑎(𝑟𝑜𝑤0), 𝑎(𝑟𝑜𝑤2) − 𝑎(𝑟𝑜𝑤1) … 𝑎(𝑟𝑜𝑤 𝑛) − 𝑎(𝑟𝑜𝑤 𝑛 − 1)]) (9)

1.35

1.4

1.45

1.5

1.55

1

11

21

31

41

51

61

71

81

91

10

1

11

1

12

1

13

1

14

1

15

1

16

1

17

1

18

1

19

1

20

1

21

1

22

1

23

1

24

1

25

1

26

1

27

1

28

1

29

1f(a) f(a)

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Figure 19.Standard Deviation

Threshold and comparison with values for each minute: After implementing stand-

ard deviation on the values, all the values which are less than the threshold should be

eliminated. To find the threshold we will use the formula from previous algorithm

which is summation of maximum and minimum of one minute values and dividing by

two to get the value of the threshold for each minute.

Figure 20. Dynamic Threshold

Display each time movement and feedback: After extracting the number of changes

per minute different feedbacks can be prepared to the user to explain different move-

ment for different stages of sleeping and users can understand the nature of their

sleeping by looking at different feedback for different nights.

6 Testing and Evaluation

6.1 Testing

The mobile application in this project was tested according to the functional re-

quirements in the previous chapter. To test this application, it can be categorized into

four main sections as follows.

6.1.1 Test cases

As mentioned in the functional requirement section, this application was divided

into two main parts. The testing process will take each of these parts and test it ac-

cording to the scenario in the next section as shown by table 2.

0.00000

0.05000

0.10000

0.150001

11

21

31

41

51

61

71

81

91

10

1

11

1

12

1

13

1

14

1

15

1

16

1

17

1

18

1

19

1

20

1

21

1

22

1

23

1

24

1

25

1

26

1

27

1

28

1

29

1

SDV SDV

0.00000

0.02000

0.04000

0.06000

0.08000

0.10000

0.12000

1

14 27 40 53 66 79 92

105

118

131

144

157

170

183

196

209

222

235

248

261

274

287

300

313

326

339

352

365

378

391

404

417

430

443

456

469

482

495

508

521

534

547

560

573

586

599

SDV threshold

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Table 2. Test Cases

ID Test Case

Name

Subject Test Sequence Test Data UD

ID

1 Exercise

monitoring

All functions Following the

instruction in the

Use Case (process

of user activity)

Testing accelerometer

data collected from

moving the device in

the hand while walking

or running

1

2 Sleep

Monitoring

All Functions Following the

instruction in the

use case (process

Sleep)

Testing the accelerome-

ter data collected from

movement of the device

on the bed while sleep-

ing

2

3 Analyzing

process

Testing each of

(last activity,

longitudinal anal-

ysis, cross popula-

tion )

User clicks on the

button to display

analysis result and

wait for the app to

respond

Testing the data stored

in the storage in e-

Science Central using

the second option of

analysis design

1,2

6.1.2 Test Scenarios

The mobile application in this project was tested using different scenarios such as

the following:

1- Different versions: Test the mobile application in different Android mobile

devices with version between 2.3.3 to 4.2.2 to check the accessibility and usa-

bility in different interfaces. In this test, the mobile application was installed in

different devices with different versions to test the first test case. The test data

will be explained in the next section.

2- Different user account: Because this application deals with different users

with different credentials, it was tested to verify if it is working for multi-

users. This application deals with storage service and workflow service. Each

of these services needs user credentials to access the platform of e-Science

Central platform. The instruction of testing for multi-user was mentioned be-

fore in Section 3.2.3. The scenario of this test was done by running the public

workflow with admin credential parameters with different credential parame-

ters for each user to store the result on their storage, by testing both cases one

and two. The result of this test will be shown in the next section.

3- Usability: To check the usability of the mobile application, this application

and another open source application such as a pedometer were tested by dif-

ferent people using the same mobile devices. This application was running by

five students in the class with instruction for testing case one. The result will

be shown in the next section.

4- Performance: To test the performance of mobile applications in this project,

both cases one and two were tested for the time and amount of battery used in

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both mobile application with cloud and mobile application using only mobile

platform.

5- Testing Analysis Processing: for this application we follow the second option

of the analysis design which was mentioned in Section 4.2.6. The mobile ap-

plication has some buttons, each of them to display one type of analysis result.

In this test we used three different accounts as an example of this testing. To

test the performance, functionality and usability of this test, it calculates the

information stored in each account and displayed in the chart. This test was

done using test case one and three.

6.1.3 Test Data

After testing the first case through the first scenario, the data collected from three

tests for each of four versions of Android mobile is shown in Table 3. Table 3. Data of Test Scenario 1

Test case ID Test Scenario Version No. Average Step taken Step recorded

1 1 2.3.6 20 19

1 1 4.1.2 20 18.5

1 1 4.2.2 20 51

1 1 4.3 20 56

Looking at table 3, it appears that this application is working with Android ver-

sions between 4.1.2- 2.3.6 and the result from new versions of Android is more than

double of the expected result. In the new version of the android the frequency is about

15Hz, but in this application it takes the frequency 5Hz. To correct this error there

should be some modification of the algorithm implementation to accept high frequen-

cy rate.

The data collected from the first test case by running the second scenario is from

three user accounts and each of them tests the first case of the test for three times to

check the availability and accessibility for multi-user to the same workflow in the e-

Science Central platform. The data of the scenario is presented in Table 4. Table 4. Data of Test Scenario 2

Test case Test scenario Admin User Account Client user account Result

1,2 2 Admin (u, pw) Client 1(u, pw) Pass

1,2 2 Admin (u, pw) Client 2(u, pw) Pass

1,2 2 Admin (u, pw) Client 3(u, pw) Pass

The Data collected from test case 1 while running test scenario 3 is from five stu-

dents who tested this mobile application and an open source pedometer mobile appli-

cation and the data is shown in Table 5.

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Table 5. Data of Test Scenario 3

Test

case

Test

scenario

Step taken

(walking)

Steps recorded

by pedometer

app

Steps taken

(walking)

Steps recorded by

this application

1 3 20 24 20 18

1 3 20 23 20 22

1 3 20 22 20 21

1 3 20 22 20 21

1 3 20 23 20 22

Average=20 Average = 22.8 Average

(100/5=20)

Average

(104/5=20.8)

For test case 1 and 2, test scenario 4 was run on this mobile application and anoth-

er mobile application to investigate the advantage and drawbacks for each part of this

app.

Time: the data collected from separating the time of uploading and executing the

same function over the bandwidth of the internet of Newcastle University using an-

droid mobile version 4.1.2. Table 6 shows the result. Table 6. Time to test scenario 4

Test Case Scenario Test action Time Any drawback

1 4 Upload 1kb 889-688 ms Pass

2 4 Upload 250 Mb 3.9-5.2 seconds Pass

1 4 Execute 11-15 seconds Pass

2 4 Execute 80-200 seconds Failed (crash)

Testing mobile applications on the mobile platform does not need to upload the da-

ta to another place. In this case, it saves the time spent on uploading the data. In addi-

tion, the time to execute the process is far from comparing with mobile platform

which takes less than one second to process the same codes.

The time of processing the codes to run the algorithm and to generate one CSV file

using the process power of mobile device is about 200 ms, on the other hand, using

the same code for the cloud platform to generate five files takes less than 25 ms.

Amount of the battery used: to measure the power spent by both mobile applica-

tion on the mobile platform and mobile application on both mobile with cloud plat-

form, open source application named PowerTutor 1.4 is used and it gave us these

results: for mobile with cloud the result is 49% of the total power, the time of the

process is 30 seconds and the amount of energy spend using LCD, CPU and Wi-Fi is

10.1 Joule on the other hand using only the mobile application to test the process time

takes 63% of the total energy, the time of the process is about 30 seconds and the

amount of energy used on CPU is about 12.1 Joule by taking into account that 1%

fully charged battery≈32.84 Joules [30].

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The data collected for test case one and three, test scenario 5 depends on the num-

ber of the users who want to share their information for analysis also on the number

of tests for each user in the storage as illustrated by table 7. Table 7. Data from Analysis Test scenario 5

Test case Scenario Analysis type Time spend Drawbacks

1,3 5 Last activity 123-140 ms pass

1,3 5 Longitudinal analysis 10 seconds Pass

1,3 5 Cross population 10-11 seconds Pass

6.1.4 Test Result

As a summary of the entire test undertaken on this dissertation, the table 8 explains

in detail each test status. Table 8. Test Result

Test case ID Attempt Test Scenario Result

1 5 1 Pass

1,2 3 for each 2 Pass

1 5 for each app 3 Pass

1,2 More than 10 4.1 Pass

2 More than 10 4.1 Failed

1 More than 10 4.2 Pass

1,3 More than 10 5 Pass

6.2 Evaluation

The section discusses and critically evaluates the results achieved during this pro-

ject. As mentioned in Section 1.3, the aim of this project is to demonstrate the possi-

bility of developing mobile applications with a cloud environment. In addition, it also

aims to test the efficiency of data processing using cloud technology. To evaluate the

objectives of this project, each objective will be explained in detail as follows:

Evaluate (Build mobile application based on cloud technology): An Android appli-

cation was successfully developed and it uses the cloud technology as a platform to

run the process of all the data using the e-Science Central platform and software as

service. This application can collect the accelerometer data about mentioned exercises

and upload and execute automatically. Looking at the test result, testing mobile appli-

cation passed, but the time to process the data using cloud computer technology is

more than using mobile platform, because the e-Science Central in this project de-

pends on the file systems to transfer the data which takes more time and storage com-

pared to the mobile platform.

The process of uploading and executing is running sequentially. The time of both

processes in the mobile application takes more than required which sometime be-

comes the reason for crashing the application. To solve this problem, both processes

are separated. The user can only recognize the process when the file uploads to the

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cloud, but executing workflow took place in the backend. This problem happened

when the size of the raw data is too much such as 2.5 Mb for one night sleep.

Evaluate (Cloud computing using e-Science Central): To implement the first objec-

tive, the mobile application needs a backend technology to support the functionality

of the application. The second objective of this project is using the technology of e-

Science Central which provides software and platform as service for desktop, web and

mobile application. After implementing this objective it appears that there are some

drawbacks in the structure of the API such as it only accepts file systems which leads

to delay of uploading and downloading the data to and from the cloud. This problem

can be avoided using compression mechanism to reduce the size of the file. Further-

more, it does not allow the developer to manipulate the data shared from different

users’ accounts to run workflow inside e-Science Central.

According to the test result, the reason behind failing analysis part of this applica-

tion is that the time of processing the code inside the mobile platform is too much

which can be avoided, if the structure of e-Science Central allows such workflow.

Evaluate (Efficiency of cloud environment against mobile): The efficiency in this

objective is only deal with the time and usage of the battery spent on processing the

data. This objective was not achieved, because of the structure of e-Science Central.

The test result appears that the time of uploading and executing the test is more than

the test on mobile platform.

Although, the time of uploading and executing is too much, but the time of running

only the algorithm on the e-Science Central is less than mobile platform, also the

cloud platform saves all the battery powers which may spend if all the process hap-

pened on the mobile platform.

7 Conclusion

In this section, critical assessment about the project result will be presented and re-

viewed for all the objectives. This will be done to give a summary about the accom-

plishments and failures in this project and what is the reason behind each of them. In

addition, the section “lessons learned” will have the detail about all the personal expe-

riences and suggestions which were discovered through the implementation of this

project. In the end, “future work” will contain some future researches and opportuni-

ties which may take this project as a foundation.

7.1 Conclusion

First, observing the objectives of this project. The first objective to build mobile

application based on cloud computing was achieved. A mobile application which

contains two parts for user activity and sleep respectively was achieved. Developing

mobile application went through the requirement analysis, design, implementation,

testing and evaluation. This application connects to cloud side using e-Science Cen-

tral REST API to upload the data and execute the blocks using e-Science Central’s

platform service.

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The second objective is achieved. The aim of this objective is to run the process

and analyze data on the cloud. Running the process part was achieved successfully

but failed in some parts to achieve the analysis inside cloud platform. To develop this

objective, it had been researched and discussed through different options of the design

and implementation. The comparison was achieved to prove the efficiency of the

cloud computing platform. The result of the comparison is clear in the testing and

evaluation chapter.

Although, analyzing the information from multi-user was not successful when us-

ing e-Science Central platform, but it does not mean that it is impossible to improve

this application. Having the current structure of e-Science Central, other option was

implemented to use mobile platform instead of cloud platform to analyze the infor-

mation for multi-user. Shortcomings of this project relating to data analysis aspect

within the e-science platform, can be regarded as an opportunity that creates room for

further investigations and room for further research. This would thus improve the

provision of a suitable cloud computing platform regarding the accessibility and per-

formance.

In conclusion, it seems that cloud computing (e-Science Central) needs some im-

provements in the structure of the platform as a service to accept the challenge of

using mobile applications based on cloud computing. In addition this project may play

a role in identifying more challenges about using the platform of cloud computing in

the future.

7.2 Lesson learned (Personal Experience)

The lessons learned from this project are: developing mobile application using An-

droid SDK and Java programming language which was the beginning for me to deal

with this kind of the project. It was a good experience for me to use e-SC REST API

for communication and manipulation the storage and workflow inside the platform of

cloud computing (e-Science Central).

Another experience from doing this project was learning how to handle projects

when there are some obstacles that constrain achievement of project objectives. There

are other options to pass the problem and understand the current problem for more

improvements and developments.

On the other hand, there are other experiences while doing this project which can

be as suggestions to improve the structure of e-Science Central and to let scientists get

more involved. Improving the platform can entail the provision of more documenta-

tion as well as providing more help and User Guide to support users in creating

blocks. My other discovery within the project is that another software developer can

think beyond what I have done regarding transforming from mobile to cloud compu-

ting as platform and service.

7.3 Future work

For the future work, providing interface for authentication is suggested to allow the

user to provide log in and sign up before using this application. This implementation

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will help the developer to enhance and improve our understanding of using this appli-

cation for multiple users.

To establish the objectives of this project, there is another suggestion to improve

the structure of the e-Science Central platform by providing tools for the developer to

create blocks inside the platform to manipulate the data of multi-user. The first option

of the cross population design could provide a good start.

This application can have a good opportunity to access the data of multi-user to

create a good environment for social network, but first the previous suggestion should

be implemented. In this suggestion the user can check different users who agree to

share their information publically. In this project there are only three type of analysis

which may increase according to the user requirement.

Acknowledgment

I would like to thank all parties who helped me in conducting this project. Special

gratitude goes to:

Professor Paul Watson, the project supervisor who has guided me from the project

proposal preparation also for the great help and supervision he gave me throughout

the life of the project. Without his patience, this research could not have been at any

valuable stage that it claims it is.

I would also like to thank Dr. Simon Woodman and Dr. Hugo Hiden for the help

and discussion regard e-Science Central.

Special thanks to my teacher Hossein Hassani for the help about the dissertation

structure.

Special thanks to my friend Lesego Peter for editing my dissertation.

And last but not least, my family for their prayer and encouragement during my

life.

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Appendix

Appendix A: this appendix shows the layout file which written by XML to represent Android tools for user interface.

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Appendix B: this appendix shows some screen capture of chart used in this application using achartengine open source API

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Appendix C:

This appendix shows use case diagrams for both user activity and sleep process, both of them has similar cases but with different implementation. Both use cases have one actor who is the user

of the mobile application.

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Appendix D: this appendix shows the class diagram for exercise part of the mobile application. There is one class which work as main class (exercise main activity), class (ExConnect) to con-

nect and run the workflow in the cloud from mobile application, the other classes are for analyzing the information and show them as a chart. There are two classes (Prefs, Preferance activity) to

manipulate the sitting information to be available for the application when they are required.

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Appendix E: this appendix shows the class diagram for sleep part of the mobile application. There is one class which work as main class (sleep main activity), class (Connect) to connect and

run the workflow in the cloud from mobile application, the other classes are to show the result for each sleep activity. There are two classes (Prefs, Preferance activity) to manipulate the sitting

information to be available for the application when they are required.

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Appendix F: this Appendix shows the exercise block class which contains (MyService) as main class and (TextFileGenerator) to produce number of the files required for the processing, (De-

tailData) class to produce a file which contains information for analyzing the test result.

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Appendix G: this Appendix shows the sleep block class which contains (MyService) as main class and (SleepFileGenerator) to produce number of the files required for the processing, (ChartGen-

erator) class to display the result as graphic using chart, (SleepTimer) class to manipulate the time of sleeping by second, minute and hour.