Activity Recognition Using Cell Phone Accelerometers

1. Activity Recognition Using Cell Phone Accelerometers Jennifer Kwapisz, Gary Weiss, Samuel Moore Department of Computer & Info. Science Fordham University SensorKDD 2010

2. We are Interested in WISDM • WISDM: WIreless Sensor Data Mining • Powerful portable wireless devices are becoming common and are filled with sensors • Smart phones: Android phones, iPhone • Music players: iPod Touch • Sensors on smart phones include: • Microphone, camera, light sensor, proximity sensor, temperature sensor, GPS, compass, accelerometer SensorKDD 2010

3. Accelerometer-Based Activity Recognition • The Problem: use accelerometer data to determine a user’s activity • Activities include: • Walking and jogging • Sitting and standing • Ascending and descending stairs • More activities to be added in future work SensorKDD 2010

4. Applications of Activity Recognition • Health Applications • Generate activity profile to monitor overall type and quantity of activity • Parents can use it to monitor their children • Can be used to monitor the elderly • Make the device context-sensitive • Cell phone sends all calls to voice mail when jogging • Adjust music based on the activity • Broadcast (Facebook) your every activity SensorKDD 2010

5. Our WISDM Platform • Platform based on Android cell phones • Android is Google’s open source mobile computing OS • Easy to program, free, will have a large market share • Unlike most other work on activity recognition: • No specialized equipment • Single device naturally placed on body (in pocket) SensorKDD 2010

6. Our WISDM Platform • Current research was conducted off-line • Data was collected and later analyzed off-line • In future our platform will operate in real-time • In June we released real-time sensor data collection app to Android marketplace • Currently collects accelerometer and GPS data SensorKDD 2010

7. Accelerometers • Included in most smart phones & other devices • All Android phones, iPhones, iPod Touches, etc. • Tri-axial accelerometers that measure 3 dimensions • Initially included for screen rotation and advanced game play SensorKDD 2010

8. Examples of Raw Data • Next few slides show data for one user over a few seconds for various activities • Cell phone is in user’s pocket • Earth’s gravity is registered as acceleration • Acceleration values relative to axes of the device, not Earth • In theory we can correct this given that we can determine orientation of the device SensorKDD 2010

9. Standing SensorKDD 2010

10. Sitting SensorKDD 2010

11. Walking SensorKDD 2010

12. Jogging SensorKDD 2010

13. Descending Stairs SensorKDD 2010

14. Ascending Stairs SensorKDD 2010

15. Data Collection Procedure • User’s move through a specific course • Perform various activities for specific times • Data collected using Android phones • Activities labeled using our Android app • Data collection procedure approved by Fordham Institutional Review Board (IRB) • Collected data from 29 users SensorKDD 2010

16. Data Preprocessing • Need to convert time series data into examples • Use a 10 second example duration (i.e., window) • 3 acceleration values every 50 ms (600 total values) • Generate 43 total features • Ave. acceleration each axis (3) • Standard deviation each axis (3) • Binned/histogram distribution for each axis (30) • Time between peaks (3) • Ave. resultant acceleration (1) SensorKDD 2010

17. Final Data Set SensorKDD 2010

18. Data Mining Step • Utilized three WEKA learning methods • Decision Tree (J48) • Logistic Regression • Neural Network • Results reported using 10-fold cross validation SensorKDD 2010

19. Summary Results SensorKDD 2010

20. J48 Confusion Matrix SensorKDD 2010

21. Conclusions • Able to identify activities with good accuracy • Hard to differentiate between ascending and descending stairs. To limited degree also looks like walking. • Can accomplish this with a cell phone placed naturally in pocket • Accomplished with simple features and standard data mining methods SensorKDD 2010

22. Related Work • At least a dozen papers on activity recognition using multiple sensors, mainly accelerometers • Typically studies only 10-20 users • Activity recognition also done via computer vision • Actigraphy uses devices to study movement • Used by psychologists to study sleep disorders, ADD • A few recent efforts use cell phones • Yang (2009) used Nokia N95 and 4 users • Brezmes (2009) used Nokia N95 with real-time recognition • One model per user (requires labeled data from each user) SensorKDD 2010

23. Future Work • Add more activities and users • Add more sophisticated features • Try time-series based learning methods • Generate results in real time • Deploy higher level applications: activity profiler SensorKDD 2010

24. Other WISDM Research • Cell Phone-Based Biometric identification1 • Same accelerometer data and same generated features but added 7 users (36 in total) • If we group all of the test examples from one cell phone and apply majority voting, achieve 100% accuracy • Can be used for security or automatic personalization • Interested in GPS spatio-temporal data mining 1Kwapisz, Weiss, and Moore, Cell-Phone Based Biometric Identification, Proceedings of the IEEE 4th International Conference on Biometrics: Theory, Applications, and Systems (BTAS-10), September 2010. SensorKDD 2010

25. Thank You Questions? SensorKDD 2010