AN APPLYING OF ACCELEROMETER IN ANDROID PLATFORM FOR CONTROLLING WEIGHT

AN APPLYING OF ACCELEROMETER IN ANDROID PLATFORM FOR CONTROLLING WEIGHT Sasivimon Sukaphat Computer Science Program, Faculty of Science, Thailand sasivimo@swu.ac.th ABSTRACT This research intends to present a mobile calorie counting application which utilizes Android accelerometer to perform human movement recognition. The proposed application uses accelerometer on the Android platform for identifying the physical activity a user is performing. The acceleration generated by user s movement will be converted into speed and further be used in ACSM metabolic equations in order to find the number of calories burned. The proposed application also shows the statistics of calories burned per day and suggests the appropriate number of calories burned for each user which can help people to control their weight anytime and anywhere. Keyword: Accelerometer, Android, Activity Recognition, Calorie counting INTRODUCTION Health-care is one of people s major concerns, especially when obesity problem becomes fast growing. Several weight control techniques have been proposed for diminishing this health problem. In particular, foot pod gadgets such as running watch, pedometer and embedded foot pod shoe (Willson, 2010) were introduced for helping people to control weight. These gadgets calculate the amount of burned calories by counting the number of steps that the user walks or runs which will be used for measuring the distance that the user takes. However, the ability of these devices may not be quite accurate. The pedometer and related devices identify person s step by using a hair spring mechanism which tends to droop after a constant usage (Flaherty, 2005). In addition, the distance of each person gait varies, thus requiring an informal calibration which is quite inconvenient to perform. Moreover, people need to pay more for purchasing these gadgets. It would be better if we can use everyday life devices to perform this task. In order to solve the problems mentioned above, we try to present a new health-care paradigm which only uses a common device for helping people control their weight at ease. This research presents SWL (SWU Weight Loss), a mobile calorie counting application on the Android platform which utilizes an accelerometer for classifying continuous human motions. The Android platform was proved by many researches (Ayu, Mantoro, Matin, & Basamh, 2011), (Brezmes, Gorricho, & ISS 294

Cotrina, 2009), (Kwapisz, Weiss, & Moore, 2010) that it has an ability to recognize human activities. By using an accelerometer, the acceleration generated when a user is moving will be converted into speed which is used for classifying user s activities. Because the vertical motion acceleration such as stair climbing is still difficult to recognize by Android accelerometers (Kwapisz, Weiss, & Moore, 2010), the SWL application only focus on the acceleration from the x-axis for classifying planar motion. The speed will be further used in ACSM metabolic equations (ACMS, 2006) for calculating the number of calories used in that activity. The SWL application also shows the statistics of calorie burned per day and suggests the appropriate number of calories burned for each user which can help people who want to control weight anytime and anywhere. LITERATURE REVIEW 1.1.Embedded Accelerometer in Smartphone The SWL application works with Android accelerometer which is a built-in sensor that measures the motion and tilt of a mobile. By using interface SensorListener (SensorListener, 2013), we can measure the acceleration force in m/s 2 which is applied to a mobile on the x, y, and z axes, including the force of gravity. By using IBMEyes program (Ableson, 2009), we can demonstrate the example outputs from the accelerometer when a mobile was lying and when it was tilting shown in figure 1. Figure 1 Example outputs from Android accelerometer when mobile was lying (left) and when mobile was tilting (right). The acceleration from the accelerometer is converted into speed for using in ACSM metabolic equations described in the next section. ISS 295

1.2.ACSM metabolic equations In order to find the number of calories burned in each activity, we use two ACSM metabolic equations: maximal oxygen consumption equations and caloric expenditure equation for two purposes. Firstly, the maximal oxygen consumption equation is used for calculating the maximum oxygen consumption of a client's body (VO 2 Max) for a given exercise. Secondly, the caloric expenditure equation is used for calculating the number of calories burned from physical activity. In addition, the Basal Metabolic Rate (BMR) equation is also used for calculating the appropriate number of calories expended per day for each person. The components of each equation are described below: 2.1. Maximal Oxygen Consumption Equation VO 2 Max = H + V + R Where H, V and R are the amount of oxygen consumed in horizontal motion, vertical motion and resting (ml/kg/min). This research only focuses on two activities: walking and running. In the case of walking (the speed is not over 5.95 kilometer/hour or 99.167 meter/minute), the components of equation are: VO 2 Max = (0.1 x Speed) + (1.8 x Speed x Gradient) + 3.5 In the case of running (the speed is greater than 5.95 kilometer/hour or 99.167 meter/minute), the components of equation are: VO 2 Max = (0.2 x Speed) + (0.9 x Speed x Gradient) + 3.5 Where 0.1 is oxygen cost per meter of moving each kilogram (kg) of body weight while walking (horizontally). 0.2 oxygen cost per meter of moving each kg of body weight while running (horizontally). 1.8 is oxygen cost per meter of moving total body mass against gravity (vertically). 0.9 is oxygen cost per meter of moving total body mass against gravity (vertically). 2.2. Caloric Expenditure Equation Caloric expenditure = (VO 2 Max x Weight/1000) x 5 Where the unit of caloric expenditure is kilocalorie (kcal). ISS 296

2.3. BMR (Basal Metabolic Rate) Equation In order to suggest the appropriate number of calories expended per day for each person, we use the BMR (Basal Metabolic Rate) equation (Wikipedia, 2013) to find the number of calories the client body needs at rest for each day. The BMR equation for male and female consists of components described below: Male BMR = 66 + (13.7 x Weight) + (5 x Height) (6.8 x Age) Female BMR = 665 + (9.6 x Weight) + (1.8 x Height) (4.7 x Age) RELATED WORK There are various weight control applications on the Android platform which can be classified into three major groups: 1. GPS Tracking Application The GPS tracking application is used for measuring the distance of the client s exercise. An example of Android GPS tracking application is Runstar (Runstar, 2013), which can track distance and time of user s exercise. However, the flaw of this application is the GPS network that has limited range and the lack of abilities to pierce through barriers (Otsason, Varshavsky, LaMarca, & Lara, 2005). Thus, it does not work well indoor. 2. Pedometer Application The pedometer application generally mimics the functions of the pedometer device. Therefore, this application can count user steps, show the approximate distance, speed and the number of calories burned. Accupedo-Pro Pedometer (LLC, 2013) is an example of this kind of application. Because this application measures the approximate distance calculated from user s paces, it cannot measure user s speed accurately, thereby affecting the precision of calorie calculation. 3. Calorie Counting Application Android calorie counting application is an application that helps users to keep track of their meals, exercise and weight. An example of Android calorie counting application is Calorie Counter by FatSecret (FatSecret, 2013). This application works by providing necessary information such as nutrition facts on foods and number of calories burned in each exercise mode. The number of calories burned by user s activities will be counted and recorded in the application. Since the calorie counting process does not come from the real practice, the result may be incorrect. ISS 297

SYSTEM DESIGN AND IMPLEMENTATION The SWL application was developed as a calorie counting tool that helps people control their weight in anywhere and anytime. This application works by applying Android accelerometer to perform human activity recognition and task classification. The SWL application consists of four modules: interface module, motion recognition and classification module, calorie calculation module and SQLite module. 1. Interface Module Interface module deals with user input and display output. First of all, user has to register into SWL application by submitting personal information such as age, gender, height and weight (figure. 2) which will further be used in calorie calculation module. SWL application displays two types of outputs: 1.1. Calorie Per Activity This is a single result of each activity a user performs and is immediately shown after the user finishes his/her motion. Figure 4 (left) shows the output screen which consists of activity date, activity type, total time spent, average speed and the number of calorie burned. 1.2.Calorie Burned Statistic This is a summary result of all activities the user performs throughout the day, including with the BMR suggestion. Figure 4 (right) shows the output screen which consists of user s BMR in a specific date. The BMR result will be used to compare with the BMR standard for giving a suggestion about the appropriate metabolic rate to the user. Under BMR suggestion is the accumulated number of calories burned from all exercises that the user performs in one day, including the details of each exercise. 2. Motion Recognition and Classification Module In order to perform motion recognition and classification task, the Android APIs were used to receive the acceleration from the accelerometer. The interface SensorListener (SensorListener, 2013) was used for receiving notifications from the SensorManager class when sensor values have changed. By calling onaccuracychanged method and onsensorchanged method, the acceleration from sensor can be received and further be used in the calorie calculation module. Figure 3 (right) shows the acceleration from the x-axis while the user is moving. 3. Calorie Calculation Module This module consists of two tasks: ISS 298

3.1 Maximum Oxygen Consumption Calculation After the user finishes his/her exercise, the maximum oxygen consumption calculation task is performed by using the ACSM's maximal oxygen consumption equations to find VO 2 Max of the client's body for a given exercise. The VO 2 Max value will be used in the caloric expenditure equation for finding caloric expenditure of each activity that user performs. The result from this process will be sent to the interface module for showing calories per activity on screen. 3.2 BMR Calculation In case that user wants to know his/her statistics of calorie burned per day, the BMR calculation task will be performed. The BMR from this calculation process will be sent to the interface module for showing the statistics of calories burned on screen. 4. SQLite Module In order to compute the statistics of calories burned per day, the number of calories burned from each user s activity has to be kept in SQLite database. After specifying date on the calorie statistics screen, the number of calories from every activity that user performed in that day will be retrieved from SQLite database and further be accumulated. The result from this process will be sent to the calorie calculation module under the BMR calculation task. Figure 2 The input screen of SWL application. ISS 299

Figure 3 (Left) The acceleration starting screen, (right) the user speed acquired from accelerometer sensor Figure 4 (Left) The output screen of calories per activity, (right) the output screen of the statistics of calories burned per day. EXPERIMENT RESULT The experiment was conducted by calculating the number of calories burned in 2 activities: walking and running, of which a 10-minute continuous movement was performed 15 times per activity. The 3-axis accelerometer, Sumsung Galaxy S II, was used for installing the SWL application. In order to evaluate the accuracy of the SWL application, an accelerated standard device, Tech 4 O Accelerator Woman s Running ISS 300

Watch: the built-in accelerometer and calorie counting was used to compare the result to the SWL application. The accelerometer watch and mobile phone were both attached to the tester s body throughout the testing period. After testing 15 times in each activity, we found that the SWL application can reach a good accuracy rate of activity classification: the overall walking speeds are lower than 99.167 meter/minute (table 1) and the overall running speeds are greater than 99.167 meter/minute (table 2). Besides, the number of calories burned from the SWL application is closely to the one from the running watch: the average percent of discrepancies which are 28.57% and 26.27% in walking and running activity respectively. TABLE 1 THE RESULT OF WALK TESTING Running Watch SWL Application Calories Speed Calories Percent of (kcal/min) (m/min) (kcal/min) Discrepancy 6.67 84.96 5.88 +11.80 7.35 80.71 5.66 +22.96 7.35 60.01 4.66 +36.51 7.06 84.5 5.86 +16.92 6.79 58.9 4.6 +32.22 7.06 80.15 5.64 +20.50 5.66 42.65 3.8 +32.81 6.92 68.56 5.08 +26.56 5.66 57.43 4.52 +20.90 6.92 41.70 3.76 +45.63 4.90 39.48 3.20 +34.63 4.90 40.49 3.24 +33.82 5.28 44.43 3.42 +35.15 4.70 47.07 3.52 +25.50 5.30 46.94 3.52 +33.52 The average percent of discrepancy 28.57 ISS 301

TABLE 2 THE RESULT OF RUN TESTING Running Watch SWL Application Calories Speed Calories Percent of (kcal/min) (m/min) (kcal/min) Discrepancy 11.54 98.24 6.54 +43.32 12.00 117.92 13.28-10.67 12.00 106.38 12.14-1.17 12.50 145.84 16.00-28.00 13.04 95.35 6.38 +51.09 9.60 97.96 6.52 +32.08 12.00 97.96 6.52 +45.67 10.91 129.44 14.40-32.00 10.43 103.60 11.86-13.66 12.50 119.01 13.38-7.04 10.43 109.42 12.44-19.22 13.04 135.07 14.96-14.69 10.43 99.00 6.56 +37.13 10.43 115.45 13.02-24.78 10.91 131.13 14.56-33.47 The average percent of discrepancy 26.27 CONCLUSION This research aims to propose the SWL application: a new paradigm of weight controlling application which can be used anywhere and anytime. By utilizing an accelerometer on the Android platform, we can create a mobile application that users can use for counting the number of calories burned from their exercises including statistics of calorie burned. From the experiment, we found that the SWL application ISS 302

was well performed in activity classification task, precisely identifying user activities in horizontal movements. Moreover, this application also has an average percent of discrepancy from both waking and running activity less than 30% comparing to the accelerometer running watch. Therefore, we can conclude that the SWL application is accurate and reliable enough to use as a calorie counting device. However, user needs to calibrate the accelerometer sensor at the first time of use. Thus, the accelerometer calibration program installation is required for improving sensor performance by removing structural errors in the sensor outputs. REFERENCES SensorListener. (2013, June 21). Retrieved from Android Developer: http://developer.android.com/reference/android/hardware/sensorlistener.html Ableson, F. (2009, June 16). Tapping into Android's sensors. Retrieved from IBM: http://www.ibm.com/developerworks/library/os-android-sensor/ ACMS. (2006). Amercan college of Sports Medicine's Guidelines for Exercise Testing and Prescription. Lippinkott Williams & Wilkins. Ayu, M. A., Mantoro, T., Matin, A. F., & Basamh, S. S. (2011). Recognizing User Activity Based on. IEEE Symposium on Computers & Informatics, (pp. 617-621). Brezmes, T., Gorricho, J.-L., & Cotrina, J. (2009). Activity Recognition from Accelerometer Data on a Mobile Phone. IWANN 2009 (pp. 769-799). Springer-Verlag Berlin Heidelberg. FatSecret. (2013, January 26). Calorie Counter by FatSecret. Retrieved from Google-Play: https://play.google.com/store/apps/details?id=com.fatsecret.android&hl=en Flaherty, C. (2005, May 2). Hip-riding pedometers are popular, but how accurate are they? Retrieved from Montana State University: http://www.montana.edu/news/2410/hip-riding-pedometers-are-popular-but-ho w-accurate-are-they Kwapisz, J. R., Weiss, G. M., & Moore, S. A. (2010). Activity Recognition using Cell Phone Accelerometers. SensorKDD 10. Washington, DC.: ACM. LLC, C. (2013, May 12). Accupedo-Pro Pedometer. Retrieved from Google-Play: https://play.google.com/store/apps/details?id=com.corusen.accupedo.widget& hl=en Otsason, V., Varshavsky, A., LaMarca, A., & Lara, E. d. (2005). Accurate GSM Indoor Localization. UbiComp (pp. 141-158). Springer-Verlag Berlin Heidelberg. Runstar. (2013, Febuary 21). Runstar. Retrieved from Google-Play: https://play.google.com/store/apps/details?id=se.runstar.pro ISS 303

Wikipedia. (2013, April 12). Basal metabolic rate. Retrieved from http://en.wikipedia.org/wiki/basal_metabolic_rate Willson, S. (2010, September 20). Nike+ ipod vs Nike+ GPS Application Review. Retrieved from Learn Fitness: http://www.learnfitness.com/2010/09/nike-ipod-vs-nike-gps-application-revie w/ ISS 304