Smart Phone-Based Sensor Mining

1. Smart Phone-Based Sensor Mining Gary M. Weiss Fordham University gweiss@cis.fordham.edu

2. Background and Motivation • Smart phones are ubiquitous • As of 4th quarter 2010 outpaced PC sales • We carry them everywhere at almost all times • Smart phones are powerful • Increasing processing power and storage space • Filled with sensors • Smart phones include the following sensors: • Tri-Axial Accelerometer • Location sensor (GPS, cell tower, WiFi) • Audio sensor (microphone), Image sensor (camera) • Proximity, light, temperature, magnetic compass Gary M. Weiss Einstein

3. Data Mining and Sensor Mining • Data mining: application of computational methods to extract knowledge from data • Most data mining involves inferring predictive models, often for classification • Sensor mining: application of computational methods to extract knowledge from sensor data • Supervised machine learning • Obtain labeled time-series training data • Create examples described by generated features • Build model to predict example’s label Gary M. Weiss Einstein

4. The WISDM Project • Three years ago started what is now WISDM • Began with focus on activity recognition • Determine what a user is doing based on accelerometer • Moved to an Android-based smartphone platform • Expanded to other applications • Biometric identification • Identifying user characteristics (soft biometrics) • Mining GPS data (project starting with Bronx Zoo) • Current focus on Actitracker • Track user activities and present info to user via the web as a health app (NSF “Health and Well-Being Grant) Gary M. Weiss Einstein

5. The WISDM Platform • Based on Android Smartphones but could be extended to other mobile devices • Client/Server architecture • Smartphones are the client (they run our app) • We have a dedicated server • Right now raw data is sent to the server and processing occurs there • Data can be streamed or sent on demand • In future more responsibility moved to the phone Gary M. Weiss Einstein

6. WISDM Platform Continued • Web Interface • Users can access their data via a web interface • Accessible from smartphone or full-screen computer • Security • Secure logins and data encrypted • Resource Issues: Power • Power is an issue if collect GPS data and maybe if we collect data 24x7, but not for periodic data collection Gary M. Weiss Einstein

7. Smart Phone Accelerometer • Measures acceleration along 3 spatial axes • Detects/measures gravity (orientation matters) • Measurement range typically -2g to +2g • Okay for most activities but falling yields higher values • Range & sensitivity may be adjustable • Sampling rates ~20-50 Hz • Study found 20Hz required for activity recognition • WISDM project found could not reliably sample beyond 20Hz (50ms) and this may impact activity recognition Gary M. Weiss Einstein

8. Existing WISDM Applications • Activity Recognition • Identify the activity a user is performing (walking, jogging, sitting, etc.) • Biometric Identification • Identify a user based on prior accelerometer data collected from that user • Trait Identification • Identify characteristics about a user based (height, weight, age) Gary M. Weiss Einstein

9. Why is Activity Recognition Useful? • Context-sensitive applications • Handle phone calls differently depending on context • Play music to suit your activity • New & innovative apps to make phones smarter • Tracking & Health applications • Track overall activity levels & generate fitness profiles • Care of elderly • Detect dangerous situations like (falling) • Warn if some with Alzheimer’s wanders outside of area Gary M. Weiss Einstein

10. Accelerometer Data for Six Activites • Accelerometer data from Android phone • Walking • Jogging • Climbing Stairs • Lying Down • Sitting • Standing Gary M. Weiss Einstein

11. Accelerometer Data for “Walking” Gary M. Weiss Einstein

12. Accelerometer Data for “Jogging” Gary M. Weiss Einstein

13. Accelerometer Data for “Up Stairs” Gary M. Weiss Einstein

14. Accelerometer Data for “Lying Down” Gary M. Weiss Einstein

15. Accelerometer Data for “Sitting” Z axis Gary M. Weiss Einstein

16. Accelerometer Data for “Standing” Gary M. Weiss Einstein

17. WISDM Activity Recognition • Six activities: walking, jogging, stairs, sitting, standing, lying down • Labeled data collected from over 50 users • Data transformed via 10-second windows • Accelerometer data sampled (x,y,z) every 50ms • Features (per axis): • average, SD, ave diff from mean, ave resultant accel, binned distribution, time between peaks Gary M. Weiss Einstein

18. WISDM Activity Recognition • The 43 features used to build a classifier • WEKA data mining suite used, multiple techniques • Personal, universal, hybrid models built • Architecture (for now) uses “dumb” client • Basis of soon to be released actitracker service • Provides web based view of activities over time Gary M. Weiss Einstein

19. WISDM Results • WISDM Results are shown for various things • Personal, universal, and hybrid models • Most results aggregated over all users but a few per user to show how performance varies by user • Results for 6 activities (ones shown in the plots) Gary M. Weiss Einstein

20. WISDM Universal Model- IB3 Matrix Gary M. Weiss Einstein

21. WISDM Personal Model- IB3 Matrix Gary M. Weiss Einstein

22. WISDM Accuracy Results Gary M. Weiss Einstein

23. Biometric Identification Gary M. Weiss Einstein

24. Biometrics • Biometrics concerns unique identification based on physical or behavioral traits • Hard biometrics involves traits that are sufficient to uniquely identify a person • Fingerprints, DNA, iris, etc. • Soft biometric traits are not sufficiently distinctive, but may help • Physical traits: Sex, age, height, weight, etc. • Behavioral traits: gait, clothes, travel patterns, etc. Gary M. Weiss Einstein

25. Gait-Based Biometrics • Numerous accelerometer-based systems that use dedicated and/or multiple sensors • See related work section of Cell Phone-Based Biometric Identificationfor details • Possible uses: • Phone security (e.g., to automatically unlock phone) • Automatic device customization • To better track people for shared devices • Perhaps for secondary level of physical security Gary M. Weiss Einstein

26. WISDM Biometric System • Same setup as WISDM activity recognition • Same data collection, feature extraction, WEKA, … • Used for identification and authentication • Identification: predicting identity from pool of users • Authentication is binary class prediction problem • Evaluate single and mixed activities • Evaluate using 10 sec. and several min. of test data • Longer sample classify with “Most Frequent Prediction” • Results based on 36 users • But hold up on preliminary experiments with 200 users Gary M. Weiss Einstein

27. WISDM Biometric Prediction Results Based on 10 second test samples Based on most frequent prediction for 5-10 minutes of data Gary M. Weiss Einstein

28. WISDM Biometric Authentication Results • Authentication results: • Positive authentication of a user • 10 second sample: ~85% • Most frequent class over 5-10 min: 100% • Negative Authentication of a user (an imposter) • 10 second sample: ~96% • Most frequent class over 5-10 min: 100% Gary M. Weiss Einstein

29. Biometric Identification Summary Can do remarkably well with short amounts of accelerometer data (10s – 2 min) Since we can distinguish between ways different people walk may be able to distinguish between different gaits Gary M. Weiss Einstein

30. Trait Identification Gary M. Weiss Einstein

31. WISDM Trait Identification • Data collected from ~70 people (now over 200) • Accelerometer and survey data • Survey data includes anything we could think of that might somehow be predictable • Sex, height, weight, age, race, handedness, disability • Type of area grew up in {rural, suburban, urban} • Shoe size, footwear type, size of heels, type of clothing • # hours academic work , # hours exercise • Too few subjects investigate all factors • Many were not predictable (maybe with more data) Gary M. Weiss Einstein

32. WISDM Trait Identification Results Results for IB3 classifier. For height and weight middle categories removed. Gary M. Weiss Einstein

33. Trait Identification Summary • A wide open area for data mining research • A marketers dream • Clear privacy issues • Room for creativity & insight for finding traits • Probably many interesting commercial and research applications • Imagine diagnosing back problems via your mobile phone via gait analysis … Gary M. Weiss Einstein

34. Connections to Your Work • Can collect accelerometer data from patients • On demand or in the background • Data transmitted wirelessly or stored on the phone for periodic download • Can extend study beyond gait • Can monitor overall activity levels • Can monitor daily routine Gary M. Weiss Einstein

35. Connections to Your Work cont. • Facilitate quantitative analysis of gait • “Fourth, although experienced clinicians assessed gait, quantitative analysis of gait might be more reliable” (Verghese et al. 2002) • Accelerometer data can provide basis for gait classification • Can use data mining to learn a classifier for gait • Just need carefully selected training data • Yields consistent measure Gary M. Weiss Einstein

36. Connections to Your Work cont. Can look at other neurological diseases besides non-Alzheimer’s dementia Can try to track progression of Alzheimer’s Note can monitor daily routine, travel, etc. Smartphone can also administer surveys, record video, provide voice prompts, etc. Besides diagnosis, can assist people suffering from these diseases Gary M. Weiss Einstein

37. My Contact Information • Gary Weiss • Fordham University, Bronx NY 10458 • gweiss@cis.fordham.edu • http://storm.cis.fordham.edu/~gweiss/ • WISDM Information • http://www.cis.fordham.edu/wisdm/ • WISDM papers available: click “About” then “Publications” • By end of summer Actitracker will allow you to track your activities via our Android app (actitracker.com) Gary M. Weiss Einstein

38. WISDM Members • WISDM research group • Current Active Members • Linna AI*, Shaun Gallagher*, Andrew Grosner*, Margo Flynn, Jeff Lockhart*, Paul McHugh*, Tony Pulickal*, Greg Rivas*, Isaac Ronan*, Priscilla Twum, Bethany Wolff * Working full-time on the project at Fordham over the summer Gary M. Weiss Einstein

39. References Available from: http://www.cis.fordham.edu/wisdm/publications Kwapisz, J.R., Weiss, G.M., and Moore, S.A. 2010. Activity recognition using cell phone accelerometers, Proceedings of the Fourth International Workshop on Knowledge Discovery from Sensor Data, 10-18. Kwapisz, J.R., Weiss, G.M., and Moore, S.A. 2010. Cell phone-based biometric identification, Proceedings of the IEEE Fourth International Conference on Biometrics: Theory, Applications and Systems. Lockhart, J.W., Weiss, G.M., Xue, J.C., Gallagher, S.T., Grosner, A.B., and Pulickal, T.T. 2011. Design considerations for the WISDM smart phone-based sensor mining architecture, In Proceedings of the Fifth International Workshop on Knowledge Discovery from Sensor Data, San Diego, CA. Weiss, G.M., and Lockhart, J.W. 2011. Identifying user traits by mining smart phone accelerometer data, Proceedings of the 5th International Workshop on Knowledge Discovery from Sensor Data. Weiss, G.M., and Jeffrey W. Lockhart (2012). The Impact of Personalization on Smartphone-Based Activity Recognition, Proceedings of the AAAI-12 Workshop on Activity Context Representation: Techniques and Languages, Toronto, CA. Gary M. Weiss Einstein