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Problem

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Problem

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  1. Problem

  2. Problem Walking? Running? Jumping Jacks? Using a computer? …

  3. Problem – Why?

  4. Problem – Why?

  5. Problem – Why?

  6. Problem Walking? Running? Jumping Jacks? Using a computer? Texting? Talking? Eating? Sleeping? Snoring? Driving? Playing tennis? Playing hockey? Learning? Teaching? Playing an Instrument? Reading? Skating? Falling? Climbing? …

  7. Problem Bao, L. and Intille, S. Activity recognition from user-annotated acceleration data. Pervasive Computing, (2004), 1–17.

  8. Problem Feature Selection Mean Max Discrete Fourier ? Classifier Naïve Bayes kNN SVM ? Raw Data Accelerometer data Heart rate ?

  9. Feature Selection Mean Max Discrete Fourier ? Classifier Naïve Bayes kNN SVM ? Raw Data Accelerometer data Heart rate ? Problem – Raw Data • Most existing solutions use accelerometer data only • (Tapia et al, 2007) tried heart rate data, but it lags too much • How many sensors? 5 on-body used by Bao & Intille, one modern smartphone by Saponas et al

  10. Feature Selection Mean Max Discrete Fourier ? Classifier Naïve Bayes kNN SVM ? Raw Data Accelerometer data Heart rate ? Problem – Feature Selection • 50% overlapped windows for signals • Mean energy • Correlation between signals • Discrete Fourier outputs • DC component (Bao & Intille, 2004; Mannini & Sabatini, 2010)

  11. Feature Selection Mean Max Discrete Fourier ? Classifier Naïve Bayes kNN SVM ? Raw Data Accelerometer data Heart rate ? Problem – Classifier • C4.5 decision trees, Naïve Bayes & kNN popular (Bao & Intille 2004, Mannini & Sabatini 2010) • For very similar activities, accuracies as low as 40% reported • General-purpose vs user-specific • Mannini & Sabatini found some success with a sequential classifier, specifically HMM

  12. PAMAP2 http://archive.ics.uci.edu/ml/datasets/PAMAP2+Physical+Activity+Monitoring

  13. PAMAP2 - Sensors • 10 hours of sensor data: accelerometer, gyroscope, magnometer, temperature • Three sensor positions: Dominant wrist Chest Dominant ankle

  14. PAMAP2 - Activities

  15. PAMAP2 - Data • Some data missing • 9 participants, but not all did every action • “start up” and “tear down” sensor readings

  16. PAMAP2 - Features • Overlapping windows • Means • Correlations between signals • Discrete Fourier transforms • DC component

  17. Plan – Baseline All activities classifier accuracy: 89% (as low as 74% for some users) Used C4.5, Naïve Bayes and kNN Reiss & Stricker 2012

  18. Plan – Raw Data Feature Selection Mean Max Discrete Fourier ? Classifier Naïve Bayes kNN SVM ? Raw Data Accelerometer data Heart rate ? • Accelerometers • Gyroscopes • Magnometer • temperature • Will not use heart rate (Tapia et al. 2007) • Impute missing values

  19. Plan – Feature Selection Feature Selection Mean Max Discrete Fourier ? Classifier Naïve Bayes kNN SVM ? Raw Data Accelerometer data Heart rate ? • 50% overlapped windows for signals • Mean energy • Correlation between signals • Discrete Fourier outputs • DC component

  20. Plan – Feature Selection • Gyroscope signal, discrete Fourier output • Magnometer signal, discrete Fourier output • Temperature • Correlations across sensors Over 2000 features from frequency data alone! Principle Component Anlaysis (PCA)

  21. Plan – ML Technique Feature Selection Mean Max Discrete Fourier ? Classifier Naïve Bayes kNN SVM ? Raw Data Accelerometer data Heart rate ? • C4.5 decision trees, Naïve Bayes & kNN popular (Bao & Intille 2004, Mannini & Sabatini 2010) • General-purposevs user-specific • Mannini & Sabatini found some success with a sequentialclassifier, specifically HMM

  22. Plan – ML technique Take lessons from speech recognition: pe– ter – pipe – er- pick – ed – a – pe – ck – of – pick – eled – pep - pers …

  23. Plan – ML technique

  24. Plan – ML technique

  25. Plan – ML Technique HMM HMM HMM HMM HMM HMM HMM HMM HMM HMM HMM HMM HMM HMM HMM HMM

  26. Plan – ML Technique HMM HMM HMM

  27. References/Discussion Bao, L. and Intille, S. Activity recognition from user-annotated acceleration data. Pervasive Computing, (2004), 1–17. Krause, A., Siewiorek, D.P., Smailagic, A., and Farringdon, J. Unsupervised, dynamic identification of physiological and activity context in wearable computing. Proceedings of the 7th IEEE International Symposium on Wearable Computers, (2003), 88. Levinson, S.E., Rabiner, L.R., and Sondhi, M.M. An introduction to the application of the theory of probabilistic functions of a Markov process to automatic speech recognition. Bell Syst. Tech. J 62, 4 (1983), 1035–1074. Mannini, A. and Sabatini, A.M. Machine learning methods for classifying human physical activity from on-body accelerometers. Sensors 10, 2 (2010), 1154–1175. Reiss, A. and Stricker, D. Introducing a New Benchmarked Dataset for Activity Monitoring. 2012 16th International Symposium on Wearable Computers (ISWC), (2012), 108 –109. Saponas, T., Lester, J., Froehlich, J., Fogarty, J., and Landay, J.ilearn on the iphone: Real-time human activity classification on commodity mobile phones. University of Washington CSE Tech Report UW-CSE-08-04-02, (2008). Tapia, E.M., Intille, S.S., Haskell, W., et al. Real-time recognition of physical activities and their intensities using wireless accelerometers and a heart rate monitor. Wearable Computers, 2007 11th IEEE International Symposium on, (2007), 37–40.