barnan's profile
TRANSCRIPT
Barnan DasSoftware Engineering Intern
PC Client Group, IntelManager: Narayan Biswal
PhD CandidateWashington State University
Advisor: Dr. Diane J. Cook
2
Research Interests
Machine Learning
DataMining
Mobile Health
Pervasive Computing
Smart Environments
Machine Learning Driven Caregiving for the Elderly
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Worldwide Dementia population
Source: World Health Organization and Alzheimer’s Association.
Actual and expected number of Americans >=65 year with Alzheimer’s
Payment for care in 2012$200billion
Unpaid caregivers15million
36million
2010 2030 2050
5.1m
7.7m
13.2m
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Automated Prompting
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Help with Activities of Daily Living (ADLs)
Architectural Overview
7Published at ICOST 2011 and Journal of Personal and Ubiquitous Computing 2012.
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Experimental Setup
8 dailyactivities
150 elderlyparticipants
Prompts issued when errors were committed
Raw Data
1 activitystep
17 engineered features
Binary class{prompt, no-prompt}
Clean Data
1 datapoint
0/1
SweepingCookingMedicationWatering PlantsEtc.
Length of activity stepLocation in apartment
# sensors involves# distribution of sensor events
Etc.
Class Distribution
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Total number of data points
39803831
149
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Machine Learning Contribution
Automated Prompting
Imbalanced Class
Distribution
Overlapping Classes
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Machine Learning Contribution
Automated Prompting
Imbalanced Class
Distribution
Overlapping Classes
Imbalanced Class Distribution
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Proposed Approach
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Preprocessing technique to oversample minority class
Published at International Conference on Data Mining 2013 and IEEE Transaction on Knowledge & Data Engineering 2014
Approximate discrete probability distribution using
Generate new minority class data points using
Chow-Liu’s algorithm Gibbs sampling
(wrapper-based)RApidly COnverging Gibbs sampler: RACOG & wRACOG
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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
Experimental Setup
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Datasets
• prompting• abalone• car• nursery• letter• connect-4
Classifiers
• C4.5 decision tree
• SVM• k-Nearest
Neighbor• Logistic
Regression
Other Methods
• SMOTE• SMOTEBoost• RUSBoost
Implemented Gibbs sampling, SMOTEBoost, RUSBoost in MATLAB
Results (RACOG & wRACOG)
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TP RateGeometric Mean
(TP Rate, TN Rate)
Baseline SMOTE SMOTEBoost RUSBoost RACOG wRACOG0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Baseline SMOTE SMOTEBoost RUSBoost RACOG wRACOG0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Results (RACOG and wRACOG)
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ROC Curve
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Machine Learning Contribution
Automated Prompting
Imbalanced Class
Distribution
Overlapping Classes
Overlapping Classes
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Overlapping Classes in Prompting Data
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3D PCA Plot of prompting data
Tomek Links
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Cluster-Based Under-Sampling(ClusBUS)
22Published in IOS Press Book on Agent-Based Approaches to Ambient Intelligence, 2012 and ICDM Workshop 2013.
Form clusters Under-sampling clusters
Experimental Setup
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Dataset prompting
Clustering Algorithm DBSCAN
Minority class dominance Empirically determined threshold
Classifiers C4.5 Decision TreeNaïve Bayesk-Nearest NeighborSVM
Results (ClusBus)
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C4.5 Naïve Bayes IBk SMO0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Original SMOTE ClusBUSTP
Rat
e
C4.5 Naïve Bayes IBk SMO0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Original SMOTE ClusBUS
AUC
C4.5 Naïve Bayes IBk SMO0
0.1
0.2
0.3
0.4
0.5
0.6
0.7
0.8
0.9
1
Original SMOTE ClusBUS
G-m
ean
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Personal and Pervasive
Sensor Suite
ComputationPower
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Harnessing Pervasiveness of Mobile Devices
Locomotive Activity Recognition
Complex DailyActivity Recognition
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Harnessing Pervasiveness of Mobile Devices
Locomotive Activity Recognition
Complex DailyActivity Recognition
Locomotive Activity Recognition
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Activities
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Sim
ple • Sitting
• Standing• Walking• Running• Climbing stairs• Lying• Biking• Driving
Com
plex • Cleaning
• Cooking• Medication• Sweeping• Hand washing• Watering plants
Feature Generation
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Feature Acceleration Rotation VectorMean 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
Sensors Accelerometer, Rotation Vector SensorSampling Rate 30 HzParticipants 10
Results: Accuracy
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Performance of Different Classifiers
Published at International Conference on Intelligent Environments, 2012. [Most Commended Paper Award]
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Harnessing Pervasiveness of Mobile Devices
Locomotive Activity Recognition
Complex DailyActivity Recognition
?
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Time of Day
SimpleActivitiesLocation
DailyActivities
Magnetic field-based indoor location estimation
CookingEating
SleepingToiletingBrushing
TeethWork at HomeWatching TV
Exercising
Complex Daily Activity Recognition
Indoor Location Estimation
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BedroomBathroomKitchen
Living roomLiving room couch
Dining table
Home office
Magnetic field along X, Y, Z (T) Sampling rate: 30Hz 50% overlap on sliding window
Supervised Machine
Learning ModelLocation
Prediction
>95%C4.5 Decision Tree
10-fold cross validation
accuracy
Performance on Complex Daily Activities
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Time of dayAccelerometerRotation Vector SensorMagnetometer Location
Machine Learning
Model
>90%C4.5 Decision Tree and kNN
10-fold cross validation
accuracy
Daily Activity Recognition
3participantsapartments2weeks
9 daily activities
Conclusion
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Algorithms Applications
Smart Phone-Based
Activity Recognition
Automated Prompting
Overlapping Classes
Imbalanced Class
Distribution
Publications
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Book Chapters
• 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 Agent-Based Approaches to Ambient Intelligence, 2012
Journal Articles
• 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
Conferences
• 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
Workshops
• 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.
