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Barnan's Profile Presentation Transcript

  • 1. Barnan Das Software Engineering Intern PC Client Group, Intel Manager: Narayan Biswal PhD Candidate Washington State University Advisor: Dr. Diane J. Cook
  • 2. Machine Learning Smart Environments Data Mining Research Interests Pervasive Computing Mobile Health 2
  • 3. Machine Learning Driven Caregiving for the Elderly
  • 4. 36 million Worldwide Dementia population 13.2m Actual and expected number of Americans >=65 year with Alzheimer’s 7.7m 5.1m 2010 2030 2050 $200 Payment for care in 2012 billion 15 Unpaid caregivers million 4 Source: World Health Organization and Alzheimer’s Association.
  • 5. 5
  • 6. Automated Prompting Help with Activities of Daily Living (ADLs) 6
  • 7. Architectural Overview 7 Published at ICOST 2011 and Journal of Personal and Ubiquitous Computing 2012.
  • 8. Experimental Setup Raw Data 8 daily activities 150 Sweeping Cooking Medication Watering Plants Etc. elderly participants Prompts issued when errors were committed Clean Data 1 activity step 17 1 data point engineered features 0/1 Binary class {prompt, no-prompt} Length of activity step Location in apartment # sensors involves # distribution of sensor events Etc. 8
  • 9. Class Distribution 149 Total number of data points 3980 3831 9
  • 10. Machine Learning Contribution Automated Prompting Imbalanced Class Distribution Overlapping Classes 10
  • 11. Machine Learning Contribution Automated Prompting Imbalanced Class Distribution Overlapping Classes 11
  • 12. Imbalanced Class Distribution 12
  • 13. Proposed Approach  Preprocessing technique to oversample minority class Approximate discrete probability distribution using Generate new minority class data points using Chow-Liu’s algorithm Gibbs sampling 13 Published at International Conference on Data Mining 2013 and IEEE Transaction on Knowledge & Data Engineering 2014
  • 14. (wrapper-based)RApidly COnverging Gibbs sampler: RACOG & wRACOG  Differ in generated sample selection RACOG wRACOG Runs for predefined number of iterations Stops when there is no further improvement of the learning model Effectiveness of new samples is not judged Judges effectiveness of new samples using a Boosting-like method Total number of new samples generated is more Total number of new samples generated is far less 14
  • 15. Experimental Setup Datasets • • • • • • Classifiers prompting abalone car nursery letter connect-4 • C4.5 decision tree • SVM • k-Nearest Neighbor • Logistic Regression Other Methods • SMOTE • SMOTEBoost • RUSBoost Implemented Gibbs sampling, SMOTEBoost, RUSBoost in MATLAB 15
  • 16. Results (RACOG & wRACOG) Geometric Mean (TP Rate, TN Rate) TP Rate 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 16
  • 17. Results (RACOG and wRACOG) ROC Curve 17
  • 18. Machine Learning Contribution Automated Prompting Imbalanced Class Distribution Overlapping Classes 18
  • 19. Overlapping Classes 19
  • 20. Overlapping Classes in Prompting Data 3D PCA Plot of prompting data 20
  • 21. Tomek Links 21
  • 22. Cluster-Based Under-Sampling(ClusBUS) Form clusters Under-sampling clusters 22 Published in IOS Press Book on Agent-Based Approaches to Ambient Intelligence, 2012 and ICDM Workshop 2013.
  • 23. Experimental Setup Dataset prompting Clustering Algorithm DBSCAN Minority class dominance Empirically determined threshold Classifiers C4.5 Decision Tree Naïve Bayes k-Nearest Neighbor SVM 23
  • 24. Results (ClusBus) SMOTE ClusBUS Original G-mean 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 C4.5 Naïve Bayes IBk SMO SMOTE ClusBUS 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 C4.5 Naïve Bayes Original AUC TP Rate Original SMOTE IBk SMO ClusBUS 1 0.9 0.8 0.7 0.6 0.5 0.4 0.3 0.2 0.1 0 C4.5 Naïve Bayes IBk SMO 24
  • 25. Personal and Pervasive Sensor Suite Computation Power 25
  • 26. Harnessing Pervasiveness of Mobile Devices Locomotive Activity Recognition Complex Daily Activity Recognition 26
  • 27. Harnessing Pervasiveness of Mobile Devices Locomotive Activity Recognition Complex Daily Activity Recognition 27
  • 28. Locomotive Activity Recognition 28
  • 29. • • • • • • • • Sitting Standing Walking Running Climbing stairs Lying Biking Driving Complex Simple Activities • • • • • • Cleaning Cooking Medication Sweeping Hand washing Watering plants 29
  • 30. Feature Generation Sensors Sampling Rate Participants Accelerometer, Rotation Vector Sensor 30 Hz 10 Feature Acceleration Rotation Vector Mean X, Y, Z x*sin(/2), y*sin(/2), z*sin(/2) Min X, Y, Z x*sin(/2), y*sin(/2), z*sin(/2) Max X, Y, Z x*sin(/2), y*sin(/2), z*sin(/2) Standard Deviation X, Y, Z x*sin(/2), y*sin(/2), z*sin(/2) Zero-Crossing Rate X, Y, Z Pair-wise Correlation X/Y, X/Z, Y/Z 30
  • 31. Results: Accuracy Performance of Different Classifiers 31 Published at International Conference on Intelligent Environments, 2012. [Most Commended Paper Award]
  • 32. Harnessing Pervasiveness of Mobile Devices Locomotive Activity Recognition Complex Daily Activity Recognition 32
  • 33. Complex Daily Activity Recognition Time of Day ? Location Magnetic field-based indoor location estimation Simple Activities Daily Activities Cooking Eating Sleeping Toileting Brushing Teeth Work at Home Watching TV Exercising 33
  • 34. Indoor Location Estimation  Magnetic field along X, Y, Z (T)  Sampling rate: 30Hz  50% overlap on sliding window Bedroom Bathroom Kitchen Dining table Living room Living room couch Home office Supervised Machine Learning Model Location Prediction >95% accuracy C4.5 Decision Tree 10-fold cross validation 34
  • 35. Performance on Complex Daily Activities 3 weeks participants 2 apartments daily 9 activities Time of day Accelerometer Rotation Vector Sensor Magnetometer  Location Machine Learning Model Daily Activity Recognition >90% accuracy C4.5 Decision Tree and kNN 10-fold cross validation 35
  • 36. Conclusion Algorithms Applications Imbalanced Class Distribution Automated Prompting Overlapping Classes Smart PhoneBased Activity Recognition 36
  • 37. Publications Book Chapters • • • Journal Articles • • • • Conferences • • • • • • Workshops • • • • B. Das, N.C. Krishnan, D.J. Cook, “Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset”, Springer Book on Data Mining for Services, 2012 B. Das, N.C. Krishnan, D.J. Cook, “Automated Activity Interventions to Assist with Activities of Daily Living”, IOS Press Book on AgentBased Approaches to Ambient Intelligence, 2012 B. Das, N. C. Krishnan, D. J. Cook, “RACOG and wRACOG: Two Gibbs Sampling-Based Oversampling Techniques”, Transaction of Knowledge and Data Engineering (TKDE), 2014 (Accepted) B. Das, D.J. Cook, M. Schmitter-Edgecombe, A.M. Seelye, “PUCK: An Automated Prompting System for Smart Environments”, Journal of Personal and Ubiquitous Computing, 2012 A.M. Seelye, M. Schmitter-Edgecombe, B. Das, D.J. Cook, “Application of Cognitive Rehabilitation Theory to the Development of Smart Prompting Technologies”, IEEE Reviews on Biomedical Engineering, 2012 B. Das, N. C. Krishnan, D. J. Cook, “wRACOG: A Gibbs Sampling-Based Oversampling Technique”, International Conference on Data Mining (ICDM), 2013 S. Dernbach, B. Das, N.C. Krishnan, B.L. Thomas, D.J. Cook, “Simple and Complex Acitivity Recognition Through Smart Phones”, International Conference on Intelligent Environments (IE), 2012 B. Das, C. Chen, A.M. Seelye, D.J. Cook, “An Automated Prompting System for Smart Environments”, International Conference on Smart Homes and Health Telematics (ICOST), 2011 E. Nazerfard, B. Das, D.J. Cook, L.B. Holder, “Conditional Random Fields for Activity Recognition in Smart Environments”, International Symposium on Human Informatics (SIGHIT), 2010 C. Chen, B. Das, D.J. Cook, “A Data Mining Framework for Activity Recognition in Smart Environments”, International Conference on Intelligent Environments (IE), 2010 B. Das, N. C. Krishnan, D. J. Cook, “Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset”, ICDM Workshop on Data Mining in Bioinformatics and Healthcare, 2013 B. Das, B.L. Thomas, A.M. Seelye, D.J. Cook, L.B. Holder, M. Schmitter-Edgecombe, “Context-Aware Prompting From Your Smart Phone”, Consumer Communication and Networking Conference Demonstration (CCNC), 2012 B. Das, A.M. Seelye, B.L. Thomas, D.J. Cook, L.B. Holder, M. Schmitter-Edgecombe, “Using Smart Phones for Context-Aware Prompting in Smart Environments”, CCNC Workshop on Consumer eHealth Platforms, Services and Applications (CeHPSA), 2012 B. Das, D.J. Cook, “Data Mining Challenges in Automated Prompting Systems”, IUI Workshop on Interaction with Smart Objects Workshop (InterSO), 2011 B. Das, C. Chen, N. Dasgupta, D.J. Cook, “Automated Prompting in a Smart Home Environment”, ICDM Workshop on Data Mining for Service, 2010 C. Chen, B. Das, D.J. Cook, “Energy Prediction Using Resident’s Activity”, KDD Workshop on Knowledge Discovery from Sensor Data (SensorKDD), 2010 C. Chen, B. Das, D.J. Cook, “Energy Prediction in Smart Environments”, IE Workshop on Artificial Intelligence Techniques for Ambient Intelligence (AITAmI), 2010. 37
  • 38. Barnan Das  (208) 596-1169  barnandas@gmail.com  www.barnandas.com 38