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Barnan Das
Software Engineering Intern
PC Client Group, Intel
Manager: Narayan Biswal
PhD Candidate

Washington State Univ...
Machine
Learning

Smart
Environments

Data
Mining

Research
Interests

Pervasive
Computing

Mobile
Health

2
Machine Learning Driven
Caregiving for the Elderly
36
million

Worldwide Dementia
population

13.2m

Actual and expected
number of Americans >=65
year with Alzheimer’s

7.7m...
5
Automated
Prompting
Help with Activities of Daily Living (ADLs)

6
Architectural Overview

7
Published at ICOST 2011 and Journal of Personal and Ubiquitous Computing 2012.
Experimental Setup
Raw Data

8

daily
activities

150

Sweeping
Cooking
Medication
Watering Plants
Etc.

elderly
participa...
Class Distribution

149

Total number
of data points

3980
3831

9
Machine Learning
Contribution

Automated
Prompting

Imbalanced
Class
Distribution
Overlapping
Classes

10
Machine Learning
Contribution

Automated
Prompting

Imbalanced
Class
Distribution
Overlapping
Classes

11
Imbalanced Class
Distribution

12
Proposed Approach
 Preprocessing technique to oversample minority class

Approximate discrete
probability distribution us...
(wrapper-based)RApidly COnverging
Gibbs sampler: RACOG & wRACOG
 Differ in generated sample selection
RACOG

wRACOG

Runs...
Experimental Setup

Datasets
•
•
•
•
•
•

Classifiers

prompting
abalone
car
nursery
letter
connect-4

• C4.5 decision
tre...
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...
Results (RACOG and wRACOG)
ROC Curve

17
Machine Learning
Contribution

Automated
Prompting

Imbalanced
Class
Distribution
Overlapping
Classes

18
Overlapping
Classes

19
Overlapping Classes in Prompting Data

3D PCA Plot of prompting data
20
Tomek Links

21
Cluster-Based Under-Sampling(ClusBUS)

Form clusters

Under-sampling
clusters
22

Published in IOS Press Book on Agent-Bas...
Experimental Setup
Dataset

prompting

Clustering Algorithm

DBSCAN

Minority class dominance

Empirically determined
thre...
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

S...
Personal and
Pervasive

Sensor
Suite

Computation
Power

25
Harnessing Pervasiveness of
Mobile Devices

Locomotive
Activity Recognition

Complex Daily
Activity Recognition

26
Harnessing Pervasiveness of
Mobile Devices

Locomotive
Activity Recognition

Complex Daily
Activity Recognition

27
Locomotive Activity Recognition

28
•
•
•
•
•
•
•
•

Sitting
Standing
Walking
Running
Climbing stairs
Lying
Biking
Driving

Complex

Simple

Activities

•
•
•...
Feature Generation
Sensors
Sampling Rate
Participants

Accelerometer, Rotation Vector Sensor
30 Hz
10

Feature

Accelerati...
Results: Accuracy

Performance of Different Classifiers
31
Published at International Conference on Intelligent Environmen...
Harnessing Pervasiveness of
Mobile Devices

Locomotive
Activity Recognition

Complex Daily
Activity Recognition

32
Complex Daily Activity Recognition

Time of
Day

?

Location

Magnetic field-based indoor
location estimation

Simple
Acti...
Indoor Location Estimation
 Magnetic field along X, Y, Z (T)
 Sampling rate: 30Hz
 50% overlap on sliding window

Bedr...
Performance on Complex Daily Activities

3 weeks
participants
2 apartments
daily
9 activities

Time of day
Accelerometer
R...
Conclusion
Algorithms

Applications

Imbalanced
Class
Distribution

Automated
Prompting

Overlapping
Classes

Smart PhoneB...
Publications
Book
Chapters

•
•
•

Journal
Articles

•
•
•
•

Conferences

•
•
•
•
•
•

Workshops

•
•
•
•

B. Das, N.C. K...
Barnan Das
 (208) 596-1169
 barnandas@gmail.com
 www.barnandas.com

38
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  1. 1. Barnan Das Software Engineering Intern PC Client Group, Intel Manager: Narayan Biswal PhD Candidate Washington State University Advisor: Dr. Diane J. Cook
  2. 2. Machine Learning Smart Environments Data Mining Research Interests Pervasive Computing Mobile Health 2
  3. 3. Machine Learning Driven Caregiving for the Elderly
  4. 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. 5
  6. 6. Automated Prompting Help with Activities of Daily Living (ADLs) 6
  7. 7. Architectural Overview 7 Published at ICOST 2011 and Journal of Personal and Ubiquitous Computing 2012.
  8. 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. 9. Class Distribution 149 Total number of data points 3980 3831 9
  10. 10. Machine Learning Contribution Automated Prompting Imbalanced Class Distribution Overlapping Classes 10
  11. 11. Machine Learning Contribution Automated Prompting Imbalanced Class Distribution Overlapping Classes 11
  12. 12. Imbalanced Class Distribution 12
  13. 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. 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. 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. 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. 17. Results (RACOG and wRACOG) ROC Curve 17
  18. 18. Machine Learning Contribution Automated Prompting Imbalanced Class Distribution Overlapping Classes 18
  19. 19. Overlapping Classes 19
  20. 20. Overlapping Classes in Prompting Data 3D PCA Plot of prompting data 20
  21. 21. Tomek Links 21
  22. 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. 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. 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. 25. Personal and Pervasive Sensor Suite Computation Power 25
  26. 26. Harnessing Pervasiveness of Mobile Devices Locomotive Activity Recognition Complex Daily Activity Recognition 26
  27. 27. Harnessing Pervasiveness of Mobile Devices Locomotive Activity Recognition Complex Daily Activity Recognition 27
  28. 28. Locomotive Activity Recognition 28
  29. 29. • • • • • • • • Sitting Standing Walking Running Climbing stairs Lying Biking Driving Complex Simple Activities • • • • • • Cleaning Cooking Medication Sweeping Hand washing Watering plants 29
  30. 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. 31. Results: Accuracy Performance of Different Classifiers 31 Published at International Conference on Intelligent Environments, 2012. [Most Commended Paper Award]
  32. 32. Harnessing Pervasiveness of Mobile Devices Locomotive Activity Recognition Complex Daily Activity Recognition 32
  33. 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. 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. 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. 36. Conclusion Algorithms Applications Imbalanced Class Distribution Automated Prompting Overlapping Classes Smart PhoneBased Activity Recognition 36
  37. 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. 38. Barnan Das  (208) 596-1169  barnandas@gmail.com  www.barnandas.com 38

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