Health, by definition, is the overall wellbeing of condition of the body or mind. Several citizens especially the top class citizens’ live sedentary lifestyles are at the risk of obesity and other health issues attached to it. Aerobic exercises of walking, jogging, or running has been suggested and proved significant in solving the problem mentioned above. It can be prescribed by medical experts to patients that need it. The traditional means of reporting the progress of this prescription to the medical expert suffer so many limitations. Hence, the need for a system to manage exercise schedules, exercise sessions and provide accurate reports to the medical expert. This paper reports the development of a software suite consisting of a mobile application (running on a mobile phone) that manages exercise schedules for the patient, gather required data (from GPS) in the course of an exercise session and provide the data upon request and a desktop application that manages exercise session records for the medical expert, generate graphs showing trends in the exercise session and store these information for future references.
Research has proved that routine exercise helps the body in so many different ways. Exercise is an activity that results in the contraction of skeletal muscle; it is a range of activities that promote physical fitness. Walking is a typical example of Aerobic exercises. As an aerobic exercise, it promotes blood circulation, and the respiratory system. According to ancient science Ayurveda, morning walk is considered to be the best form of exercise. It increases body metabolism and blood circulation and also lowers blood sugar level [2]
An exercise plan can be setup to achieve one or more of the following goals
• As a treatment/prescription for an ailment or disease (Correcting Health issues) • As a means of achieving a level of fitness (E.g. for Athletes) • As a means of simply staying in shape (Sustenance and wellness)
However, waking up from a sweet (sometimes short) sleep to change into a pair of shorts and trainers to go for a morning walk might not come palatable. For cases where exercises are done as a corrective measure, the process must be strictly monitored by health personnel. He will require progress report in the course of the exercise to determine how well the patient is responding to the exercise programme. The reports will then determine the level and kind of exercise required and provide a basis for deciding whether to stop, continue or modify the exercise program.
The progress report used by the health personnel must be accurate, detailed and specific as it is critical to decisions made concerning the exercise program. This report has traditionally been collected from the patient. This method or approach of data acquisition suffers significantly from the following limitations:
• Information supplied by the patient could be shallow because parameters that are key to the report might not be monitored by the patient because of his/her ignorance of the basic parameters of the exercise program.
• Progress report given by the patient could be indeterminate. Since no two persons can describe the same event in the same way, even a single person can give very different reports of the same event if reporting at different times. Hence, the physician might be basing his decisions on indeterminate results which could be fatal.
• Information given by a patient could sometimes be incorrect as the patient himself might be basing his/her reports on guesses. The activity of interest requires parameters such as time, distance covered, average speed, heartbeat rate and blood pressure for report generation and monitoring. Most persons will find it inconvenient to go out every morning (or whenever he wants to go for an exercise) with a tape rule to measure distance and might sometimes forget to check the time the activity started. Guessing out values will therefore be the next best thing to do for the patient which will provide a next
to worst report for the medical personnel to work with.
• Parameters like the heartbeat rate and blood pressure cannot be measured by the patient himself in the course of the exercise which makes the use of sensors coupled with monitoring computer programs expedient.
2. RELATED WORK • SportsTracker is an open source project hosted
at sourceforge [3]. • MedHelp's free Exercise [4]. • Endomondo. • Sports Tracker for Nokia S60 5.0 touch phones
This paper proposes an approach that is tightly coupled with a medical expert. The expert will be responsible for determining the right level of exercise for each individual and also for monitoring its progress, paying special attention to how the patient is reacting to the exercises.
3. PROBLEM STATEMENT
Tradition aerobic exercise prescriptions require significant level of discipline to administer in the characteristic busy lifestyle of the 21st century. A system that acts as a personal trainer that assists in the course of the exercise program would be beneficial. Such system would be required to perform the following tasks
• Data Collection: Collecting key parameters like duration of Exercise, distance covered et.c from sensors in the course of the exercise.
• Data Processing: Processing collecting data, converting to appropriate format for storage.
• Data Storage: Storing the Data to persistence storage for future reference.
• Data Analysis: Providing basic presentation and analysis of the stored data
Figure 1 presents a typical system model that applies to majority of health motivated exercise programs. However, cases of exercise programs that are created by users who think exercise is good for them can also fit into the model. The model shows the existing system and its approach. The labels in red are the problem areas addressed by our solution.
4. OUR SOLUTION
Figure 2 show our solution to the problems identified in the system model. The solutions provided to the identified problem areas (as shown in figure 1) are labeled in green.
The 21st century technology has given man endless opportunities to make life easier. The exponential development of information technology world features the invention of miniaturized devices of amazing computing power. Contemporary smart phones now come with amazing computing power for their size and portability.
Figure 1: System Model and Problem Description
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
The Global Positioning System (GPS) is a space-based global navigation satellite system (GNSS) that provides reliable location and time information in all weather and at all times and anywhere on or near the Earth when and where there is an unobstructed line of sight to four or more GPS satellites. It is maintained by the United States government and is freely accessible by anyone with a GPS receiver [5]. The GPS technology enables a fair estimation of the present location of a GPS receiver on about anywhere on the surface of the earth. It creates the possibility of measuring the distance and speed of a subject as it moves from place to place. A wide range of smart phones today have GPS receivers embedded that supports Navigation services provided by the manufacturers.
The computing power of these devices was exploited to create a solution. Our solution is in two phases. The first phase is the development of software that runs on smart phones called Mobile Health Assistant. This software will perform the following functions among others:
• Alarming: Reminding the user of scheduled exercise sessions
• Data Collection: Collecting necessary data (like: distance covered and speed, from inbuilt GPS receiver, time and duration of exercise and other body vitals via sensors worn by the user) in the course of the exercise.
• Presenting the summary of a just-concluded exercise session to the User.
• Store collected data for future transfer to the desktop computer at checkup.
• Presenting a Calendar-like summary of the overall exercise program showing exercises performed, missed, cancelled and scheduled.
The second phase is the development of software that runs on desktop computers called Desktop Health Assistant (DHA). This software will perform the following functions among others:
• Store detailed information of patients (Users). • Communicate with Mobile Health Assistant
(MHA) to set it up for a new patient (User). • Communicate with MHA to retrieve stored
exercise session data. • Perform analysis and processing of exercise
session data retrieved from MHA. • Generate detailed report upon request to the
medical expert. • Store retrieved exercise session data for further
references.
5. DESIGN ISSUES
Concepts - Exercise Session: An exercise session represents a specific exercise which may be in one of the states shown in figure 3 which shows the state diagram for exercise sessions. The first state is the scheduled state. The desired end state is shown in Green (Reported State), states that are inappropriate or undesired are shown in Red.
Figure 2: System Model and Proposed Solution
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
An exercise session is in a scheduled state if the time it should be performed is not yet come. An Exercise session is in a Started state if the exercise session has been allowed to start by the User. It is in a performed state if the exercise has been success performed with its data successfully gathered and stored for further analysis by the desktop Health Assistant, it is in a cancelled state if upon reminding the user of the exercise session, he/she decides to cancel it. It is in a failed state if the exercise session was started but due to one problem or the other which might be power failure (due to low battery or malfunction) or unforeseen exceptions, the session could not be completed. It is in a missed state if the application could not remind the user of the exercise session, a typical example is when the phone is dead throughout the day the exercise session should be performed. It is in a reported state if after it has been successfully
performed, the data acquired has been sent to the desktop health assistant for further processing and storage.
Exercise Program: An exercise program represents a specific prescription to perform morning walk, run or jogging that will span a range of time. As an example, a patient that requires exercise twice a week for two months will have an exercise program with a range of two months. It serves as a container for exercise sessions by grouping exercise sessions that are created as part of a single prescription
Exercise Session Record Data: An exercise session record data holds basic data about the progress of an exercise session when it is performed. Exercise Session Data like distance covered, Heartbeat rate, Systolic Blood Pressure and the Diastolic Blood Pressure are collected from various sensors and stored.
Figure 3: Exercise Session States
Figure 4: Basic Concepts
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
Figure 5 shows the general structure of the solution in block diagrams. It shows the two software and some of its internal components.
Use Cases
Figure 6 shows the use cases for the Desktop Health Assistant (DHA) while figure 6 shows the use cases for the Mobile Health Assistant (MHA). The human-like figure in the UML Use Case diagrams represents the actors or the Users of the software. The ovals represent actions. Dotted lines linking the ovals indicate dependency among the actions.
Figure 5: Structure
Figure 6: UML Use Case Diagram for DHA
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
Figure 8 shows the modules in the Desktop Health Assistant (DHA) and how these modules interact with required APIs and subsequently the hardware. The blocks in Orange are APIs provided by Microsoft (as part of the .NET framework) or third party developers, while the blocks in purple are modules in the DHA.
6.2 The Settings Provider Module
This module is responsible for providing application wide settings. It is responsible for persisting these settings to file in a way that makes it easy for external modification. These settings are loaded at program startup, and saved to file on program shutdown. Default values are assumed if an error was encountered while loading the settings.
6.3 The Data Access Layer Module
This module is responsible for providing persistence by interacting with the StartNow DataAccess Layer which in turn interacts with the Database Management Software for processing data requests. It interacts with the Settings Provider module to obtain the connection string to be used for connecting to the required DBMS. The Data Access Layer performs preliminary processing on data received from the database so as to present it in a more usable format to client modules.
6.4 The Bluetooth Data Link Layer Module
This module is responsible for communicating with the Mobile Health Assistant via Bluetooth. It interacts with 32feet.NET which in turn interacts with the Bluetooth stack installed on the deployment machine to send and receive data from the Mobile Health Assistant (MHA). It obtains the Bluetooth service address used for communication from the Settings Provider module. Upon invocation, it interacts with the User Interface Module to provide detailed progress reports of the current data transfer.
Figure 7: UML Use Case Diagram for MHA
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
This module is primarily based on the Windows Presentation Framework (WPF), a technology proprietary to Microsoft. It obtains data from the Data Access Layer and presents it to the user in an aestethic and interactive manner. It interacts with the user to obtain their selections and inputs.
6.6 The Webcam Control Module
This module is responsible for obtaining the passport of a User by interacting with appropriate webcams attached to the deployment computer. It receives raw data from the hardware, processes it and present it to the User Interface Module for display. It receives control is instructions from the User Interface Module.
6.7 The Graphing Module
This module is responsible for plotting the data Exercise Session Record Data recieved by the Bluetooth Data Link Layer on a graph, this graph is returned to the User Interface Module to display. It is responsible for analysing raw Exercise Session Data that might originate either from the Database or from the MHA and create a suitable graphical representation of the data using appropriate scales and dimensions
6.8 StartNow Data Access Layer module
This library provides an abstraction layer over the various database client libraries provided by existing Database Management Software Vendors like Microsoft
(Microsoft SQL Server range of DBMSs), Oracle (Oracle DBMA), MySQL, Postgre and others. It performs automatic SQL statement generation and provides access to database rows as declarative objects and lists. This allows one set of container classes to receive data from the wide range of available DBMSs. It is written in F#, harnessing the asynchronous workflows and parallel operations provided by the programming language.
6.9 32 Feet.Net
32feet.NET is a shared-source project managed by In The Hand Ltd. to make personal area networking technologies such as Bluetooth, Infrared (IrDA) and more, easily accessible from .NET code. It supports desktop, mobile or embedded systems[6].
6.10 Modules in the Mobile Health Assistant
Figure 9 shows the modular structure of the Mobile Health Assistant (MHA) and how these modules interact with required MIDP APIs and subsequently the mobile phone hardware. The blocks in Orange are APIs provided by mobile phone manufacturers or third party developers, while the blocks in purple are modules in the MHA.
6.11 The Data Access Layer
This module is responsible for providing persistence by interacting with the RecordStore API which in turn interact with record stores provided by the native implementation. It provides an abstraction layer to the record stores using container classes that are used to
Figure 8: Modules in the Desktop Health Assistant (DHA)
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
collect required data process and present it to client modules.
6.12 The Settings Provider
The Settings Provider module interacts with the Data Access Layer to provide persistence to user settings and preferences. The Settings Provider module store data such as the calibrations used by the Exercise Assistant Module, the Bluetooth service address used by the Bluetooth Data Link Layer.
6.13 The User Interface Manager
This module is responsible for constructing the user interface by combining basic user interface elements provided by the LWUIT API. It constructs an appropriate interface to collect user selections and to display results and notifications to the user. It interacts with the Settings Provider module to determine contextual data required to create some forms. It invokes the Action Dispatcher module to respond to user selections and events.
6.14 The Action Dispatcher Module
This module receives user selections and interactions from the User Interface Manager and invokes the appropriate module in response to the user requests. It obtains user preferences from the settings provider to respond appropriately so as to provide a response that makes the MHA look more personal.
6.15 The Exercise Assistant Engine
The Exercise Assistant Engine is the module responsible for monitoring exercise sessions. It interacts with the Location API to obtain location data from the embedded GPS hardware, these data are used to compute the distance moved by the subject in the course of the exercise. It interacts with the User Interface Module to display progress reports for the exercise session. The distance covered, distance remaining, start time, current time, and other data acquired is sent to the User Interface Module to be displayed to the User. At the end of the exercise session, it submits the acquired data to the Data Access Layer for storage.
Figure 9: Modules in the Mobile Health Assistant (MHA)
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
This module is responsible for scheduling exercise sessions and notifying the user if the time for an exercise session is up. It is responsible for rescheduling an exercise session if the user selects the snooze option. It updates the state of an exercise session to cancel if the user decides to cancel and exercise session. If the user decides to start an exercise session, it invokes the Exercise Assistant Engine and enters the paused state.
6.17 The Exercise Program Manager
This module is responsible for loading the exercise programs created for the current user from persistent storage. It achieves this by interacting with the Data Access Layer. It interacts with the User Interface Module to construct the Calendar showing Exercise Program Schedules, it obtains Exercise Session Data from the Data Access Layer and processes it to create Exercise Session report that the User Interface Module will display to the user.
6.18 The Bluetooth Data Link Layer
This module is responsible for communicating with the Desktop Health Assistant (DHA) form data synchronization. It obtains Exercise Programs, Exercise Sessions and Exercise Session Record Data from the Exercise Program Manager and transmits it by interacting with Java API for Bluetooth to the DHA during a checkup operation. It also obtains new user information from the DHA during initial setup or subsequent configurations.
6.19 LWUIT
The Lightweight UI Toolkit (LWUIT) is a lightweight widget library inspired by Swing but designed for constrained devices such as mobile phones and set-top boxes. Lightweight UI Toolkit supports
pluggable theme-ability, a component and container hierarchy, and abstraction of the underlying GUI toolkit [9]. It is a free library proprietary to Oracle Corporation.
6.20 The Location API for J2ME
The Location API for J2ME (JSR 179) specification defines an optional package, javax.microedition.location, that enables developers to write wireless location-based applications and services for resource-limited devices like mobile phones, and can be implemented with any common location method. The compact and generic J2ME location APIs provide mobile applications with information about the device's present physical location and orientation (compass direction), and support the creation and use of databases of known landmarks, stored in the device.
7. ANALYSIS AND RESULTS
7.1 GPS Seek Time VS GPS Idle Time
Immediately before the start of an Exercise Session, the Exercise Assistant Engine (EAE) seeks GPS signals and attempts to connect to these satellites. The seek-time (in seconds) is the time between the start of the GPS location provider and the time it locks on the GPS satellite signals. This (seek-time) time has been shown to be related to the idle-time of the GPS receiver. The idle time is the time in seconds between the last operation of the GPS system and the time it is requested to start operation.
Twelve (12) samples were taken to experiment this and the result is given in table 1. The table was plotted with the idle-time on the x-axis and the seek-time on the y-axis and the graph is shown in figure 11. These values were taken between 12:46AM and 8:47AM so as to ensure a clear, cloudless sky because weather conditions tend to also affect the seek-time.
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
It can be approximated from the graph that the GPS seek-time is directly proportional to the idle-time. However, under good weather conditions and a clear sky, the GPS signal seek-time should not exceed 4 minutes.
7.2 Precision of GPS Location Data
To test the precision of the location data received from the GPS system, a simple experiment was
carried out. 10 exercise sessions were performed covering the same distance. The Exercise Session data for the 10 sessions were collected and statistical analysis of the data is given in table 2. From the table, the average distance is 247.3268m, with an average deviation of 2.074404m (0.83873%) and a standard deviation of 2.409095m.
Figure 10: GPS Seek Time VS Idle Time Graph
VOL. 3, NO. 1, January 2012 ISSN 2079-8407 Journal of Emerging Trends in Computing and Information Sciences
Figure 11 is a screen shot of the graph generated by the Desktop Health Assistant from an Exercise Session Record data that it has received from the MHA. These data are collected when the patient goes for regular checkup. At checkup, the medical expert simply starts the DHA, puts on the Bluetooth device on the computer and on the user’s mobile phone and start a synchronization session where the MHA sends the data it has collected to the DHA via Bluetooth. Other parameters like blood pressure and heartbeat were collected in the course of the exercise session. These values however, were simulated by the MHA. Due to the challenges faced in acquiring sensors to read these body vitals, the values were randomly generated to shows how it will perform.
8. CONCLUSION
This project has proposed an exercise administration approach that is tightly coupled to a medical expert while not totally revoking the do-it-yourself privilege proposed by other approaches. This approach is important because the medical expert will provide professional medical guidance in the course of an exercise thereby increasing the efficiency and effectiveness of the exercise program.
9. FUTURE WORK
Future work can also be done to fully implement sensors that would read other body vitals like heartbeat rate, blood pressure body temperature et.c into the system. Further work is also required to fully implement the remote checkup functionality where a user is allowed to sent exercise session data from remote locations to a medical expert via existing communication framework.
REFERENCES
[1] MA Martinez-Gonzalez, J Alfredo Martinez, FB Hu, MJ Gibney and J Kearney, “Physical inactivity, sedentary lifestyle and obesity in the European Union” International Journal of Obesity pp1, 1999.
[2] “Morning walk – cool, calm and healthy”. http://www.ayushveda.com/magazine/morning-walk-cool-calm-and-healthy/
[3] “SportsTracker | Download SportsTracker software for free at SourceForge.net. (n.d.)”. http://sourceforge.net/projects/sportstracker/
[4] “Exercise Tracker - Free Exercise Tracker from MedHelp” http://www.medhelp.org/land/exercise-tracker
[5] “Global Positioning System – Wikipedia, the free encyclopedia” http://en.wikipedia.org/wiki/Global_Positioning_System
[6] “32feet.NET - In The Hand” http://inthehand.com/content/32feet.aspx
[7] “Java ME - the Most Ubiquitous Application Platform for Mobile Devices, 2010” Oracle - Sun Developer Network (SDN): http://java.sun.com/javame/index.jsp
[8] “W3Counter - Global Web Stats” W3Counter (2010, December): http://www.w3counter.com/globalstats.php
[9] “Lightweight UI Toolkit Developer's Guide” Oracle Coporation United States of America, 2010.
