1. Gary M. Weiss Fordham University gweiss@cis.fordham.edu

2.  Smart phones are ubiquitous  As of 4 th 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 5/17/2012Gary M. Weiss Einstein2

3.  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 5/17/2012Gary M. Weiss Einstein3

4.  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) 5/17/2012Gary M. Weiss Einstein4

5.  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 5/17/2012Gary M. Weiss Einstein5

6.  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 5/17/2012Gary M. Weiss Einstein6

7.  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 5/17/2012Gary M. Weiss Einstein7

8.  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) 5/17/2012Gary M. Weiss Einstein8

9.  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 5/17/2012Gary M. Weiss Einstein9

10.  Accelerometer data from Android phone  Walking  Jogging  Climbing Stairs  Lying Down  Sitting  Standing 5/17/2012Gary M. Weiss Einstein10

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17.  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 5/17/2012Gary M. Weiss Einstein17

18.  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 5/17/2012Gary M. Weiss Einstein18

19.  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) 5/17/2012Gary M. Weiss Einstein19

20. 5/17/2012Gary M. Weiss Einstein 72.4% Accuracy Predicted Class WalkingJoggingStairsSittingStanding Lying Down Actual Class Walking 220946789240 Jogging 451656148100 Stairs 41254869310 Sitting 1004755330241 Standing 805764483 Lying Down 51730113131 20

21. 5/17/2012Gary M. Weiss Einstein 98.4% accuracy Predicted Class WalkingJoggingStairsSitting Standing Lying Down Actual Class Walking 30331240 00 Jogging 4178840 00 Stairs 42412921 00 Sitting 004870 26 Standing 50111 5090 Lying Down 40870442 21

22. 5/17/2012Gary M. Weiss Einstein % of Records Correctly Classified PersonalUniversal Straw Man IB3J48NNIB3J48NN Walking 99.297.599.172.477.360.637.7 Jogging 99.698.999.989.589.789.922.8 Stairs 96.591.798.064.956.767.616.5 Sitting 98.697.697.762.878.067.610.9 Standing 96.896.497.385.892.093.66.4 Lying Down 95.995.096.928.626.260.75.7 Overall 98.496.698.772.474.971.237.7 22

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24.  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. 5/17/2012Gary M. Weiss Einstein24

25.  Numerous accelerometer-based systems that use dedicated and/or multiple sensors  See related work section of Cell Phone-Based Biometric Identification for 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 5/17/2012Gary M. Weiss Einstein25

26.  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 5/17/2012Gary M. Weiss Einstein26

27. AggregateWalkJogUpDown Aggregate (Oracle) J4872.284.083.065.861.076.1 Neural Net69.590.992.263.354.578.6 Straw Man4.34.25.06.54.74.3 5/17/2012Gary M. Weiss Einstein AggregateWalkJogUpDownAggregate (Oracle) J4836/36 31/3231/3128/3136/36 Neural Net36/36 32/3228.5/3125/3136/36 Based on 10 second test samples Based on most frequent prediction for 5-10 minutes of data 27

28.  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% 5/17/2012Gary M. Weiss Einstein28

29.  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 5/17/2012Gary M. Weiss Einstein29

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31.  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) 5/17/2012Gary M. Weiss Einstein31

32. Accuracy 71.2% MaleFemale Male317 Female1216 5/17/2012Gary M. Weiss Einstein Accuracy 83.3% ShortTall Short155 Tall220 Accuracy 78.9% LightHeavy Light137 Heavy217 Results for IB3 classifier. For height and weight middle categories removed. 32

33.  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 … 5/17/2012Gary M. Weiss Einstein33

34.  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 5/17/2012Gary M. Weiss Einstein34

35.  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 5/17/2012Gary M. Weiss Einstein35

36.  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 5/17/2012Gary M. Weiss Einstein36

37.  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) 5/17/2012Gary M. Weiss Einstein37

38.  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 5/17/2012Gary M. Weiss Einstein38

39. 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 5 th 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. 5/17/2012Gary M. Weiss Einstein39