Analyzing Physical Activity With the iPod

Accelerometer Leeann Mangoba

Prof. Sri Kurniawan

Graduate Student: Sonia Arteaga

ABSTRACT

Accelerometer-based mobile applications are on the rise and can be used as

a way to motivate youth to be more physically active. This project analyzed the

characteristics of the iPod Touch’s built-in accelerometer as a practical

measurement of physical activity. Four students were recruited to wear the iPod

inside their pants pocket while walking and running. The acceleration data was

collected on an application at specified sample rates. The data was loaded onto

excel and ran through a Matlab code which sorted, filtered, and took the average

magnitude of each session of ambulation. Though walking acceleration

measurements seemed to be accurate, the limitations of the iPod’s accelerometer are

apparent in the data from the running sessions. Future work will consist of collecting

and analyzing other physical activities and using these methods for physical activity

recognition on mobile applications.

INTRODUCTION

Accelerometers have been used as a way to measure acceleration in a variety

of applications such as automobile crash detection and airbag deployment, digital

camera image stabilization, and monitoring volcano activity. The iPod/iPhone’s

embedded accelerometer has been used for an assortment of different mobile

applications that require hand movement and tilting. These applications have shown

great popularity and demonstrate the accelerometer’s capability as a practical and

favorable motion sensor. The iPod/iPhone uses STMicroelectronic's LIS331 DL tri-

axial accelerometer that measures acceleration along the x, y, and z axis independent

of gravity. The accelerometer can measure up to ~±2.3 g (g = 9.8 m/s²) with a

resolution of 0.018g/digit [1].

Figure 1 – Accelerometer Configuration

The motivation of this project is to analyze the characteristics of the built-in

accelerometer as a measurement of physical activity in order to create a better

physical activity recognition program. This can be incorporated into mobile

applications targeted to teenagers that encourage physical activity. The

accelerometer was used to collect walking and running data at different sampling

rates. No other devices were connected to the iPod to record data as this may be

unfavorable to the user.

EXPERIMENT

Four UCSC students were recruited to collect data with the application

AcelDataCollection created by Sonia Arteaga. With this application, the user can

specify the activity and vary the sampling rate using the slider feature. Data was

collected with a range of 4-80 samples/sec. Once the data was imported via Xcode,

the session number, sample number, x, y, z values, physical activity type, timestamp,

and sample rate are shown.

Figure 2 – Screenshot of AcelDataCollection

Figure 3 – Acceleration Data Collected on iPod

The iPod Touch was placed in the subject’s pants pocket when collecting data. The

pocket was the preferred area of placement since it is close to the subject’s center of

mass [2] and more likely to enable recognition [3].

The data was then downloaded via Xcode, put into a text file, and imported into an

Excel spreadsheet for easy manipulation and reading using Matlab.

Figure 4 – Raw Walking Data

VARIOUS FILTER DESIGNS

Since the iPod accelerometer does not depend on gravity, a high pass filter

was applied to the raw data to remove any gravity offset. In order to find the best

filter to use, we compared the Chebychev and Butterworth filters with different

cutoff frequencies. To exclude gravity alone, the ideal cutoff frequency would range

from 0.1 to 0.5 Hz [2]. Frequencies around 0.25 Hz have shown to do this accurately

without losing too much signal.

The filter that ended up being using is a 4th order high pass Butterworth filter

with cutoff frequency around 0.25 Hz. This was the preferred type of filter because

of the absence of a ripple to get rid of the gravity offset. As shown in Figure 4, the

filtered data is now centered on the horizontal axis as desired.

Figure 5 – Filtered and Unfiltered Walking Data

RUNNING DATA & ACCELEROMETER LIMITATION

Running data was collected using the same methods as with walking. As

mentioned earlier, the accelerometer can measure up to ~±2.3 g. As seen from our

data, this limitation is demonstrated as the raw data seems to be truncated once it

reaches -2.3g.

Figure 6 – Filtered and Unfiltered Walking Data

CONCLUSION

From my project, it seems that the iPod’s accelerometer can provide good

measurements for walking. The filtered data can be used to create a physical activity

program in the future. This is desired since the mobile activities will be more

attractive if only the iPod was being used and not any other attachable sensor device.

Future work will consist of collecting and analyzing other types of physical activity

(i.e. jumping, bicycling) and studying the built-in gyroscope as well. With this

program, I gained a thorough understanding of the accelerometer and became

familiar with Xcode programming and Matlab. I also learned about signals and

filtering and how to perform an experiment with subjects.

MATLAB SAMPLE CODE % Leeann Mangoba % % This code imports the acceleration data from a spreadsheet and uses the % butterworth function to create a high pass filter to get rid of gravity. % clear figure(1); clf reset set(gcf,'position',[100,50,500,600]) set(gcf,'paperposition',[0.5,0.5,7.5,10.0]) % % specify spreadsheet location B = xlsread('w4.xls',1,'A1164:H1610') % assign values x0 = B(: ,3); y0 = B(: ,4); z0 = B(: ,5); % sampling frequency is 1/(sampling rate) fs = 1/(B(1,8)); % N=size(B,1); t=B(:,2); % % use 4rd order high-pass butterworth filter % with cutoff frequency 0.25hz fc = 0.25 [b,a] = butter(4,fc,'high'); % filter data x = filter(b,a,x0); y = filter(b,a,y0); z = filter(b,a,z0); % %plot subplot(3,1,1) plot(t,x0,'-','color',[0.5 0.5 0.5]) hold on plot(t,x,'b-','linewidth',1.5) set(gca,'fontsize',14) xlabel('index') ylabel('x') set(gca,'xtick',[100:50:625])

h1 = legend('raw data','filtered data') set(h1,'fontsize',12) mag = sqrt(x.^2 + y.^2 + z.^2); aver = mean(mag); title(['average magnitude = ',num2str(aver),' at ',num2str(fs),' Hz']); % subplot(3,1,2) plot(t,y0,'-','color',[0.5 0.5 0.5]) hold on plot(t,y,'r-','linewidth',1.5) set(gca,'fontsize',14) xlabel('index') ylabel('y') set(gca,'xtick',[100:50:625]) h2 = legend('raw data','filtered data') set(h2,'fontsize',12) % subplot(3,1,3) plot(t,z0,'-','color',[0.5 0.5 0.5]) hold on plot(t,z,' m-','linewidth',1.5) set(gca,'fontsize',14) % set(gca,'xtick',[100:50:625]) h3 = legend('raw data','filtered data') set(h3,'fontsize',12) set(gca,'ytick',[0:2:8]) xlabel('index') ylabel('z') %

REFERENCES [1] STMicrolectronics www.st.com/stonline/books/pdf/docs/14699.pdf [2] Yuichi Fujiki, iPhone As A Physical Activity Measurement Platform, CHI 2010: Student Research Competition, April 12-14, 2010, Atlanta, Georgia, USA [3] Bao, L., & Intille, S. (2004, April). Activity recognition from user-annotated acceleration data. In Proc. pervasive (p. 1-17). Vienna, Austria: Springer-Verlag Heidelberg: Lecture Notes in Computer Science.