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Machine Learning Challenges For Automated Prompting In Smart Homes
The document discusses the use of machine learning algorithms to automate prompting in smart homes to assist older adults, particularly those with Alzheimer's. It focuses on the challenges of class imbalance in sensor data, and proposes techniques such as oversampling and one-class classification to improve the accuracy of activity error detection. The findings support the development of real-time prompting systems that emulate caregiver intervention, aimed at enhancing daily living activities for seniors.
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Machine Learning Challenges For Automated Prompting In Smart Homes
- 1. Machine Learning Challenges forAutomated Prompting in Smart Homes Barnan Das May 22, 2014
- 2. 2 2009 2030 Older adult(65+) population in US 72mn 40mn
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- 5. 5 Machine learning algorithms trainedon smart home sensor data can predict when an individual faces difficulty while performing everyday activities.
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- 7. 7 Smart Home Studies Study1 Study 2 Participants 400 180 Activities 8 6 Activity Errors Naturalistic Naturalistic
- 8. 8 Automated Prompting Emulating Caregiver PromptTiming Detecting Activity Errors in Real Time Imbalanced Class Distribution Class Overlap One-Class Classification Overview Study 1 Study 1, 2
- 9. 9 Emulating Caregiver PromptTiming 8Daily Activities Study 1 Prompts issued when errors were committed Raw Data 1Activity Step 17 Engineered Features Used by Algorithms 0/1 1Training Exampl e Binary class {prompt, no-prompt}
- 10. 10 Total # training examples 3980 3.94% ClassDistribution prompt class
- 11. 11 Automated Prompting Emulating Caregiver PromptTiming Detecting Activity Errors in Real Time Imbalanced Class Distribution Class Overlap One-Class Classification Overview
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- 13. 13 Preprocessing Sampling • Over-sampling theminority class • Under-sampling the majority class Oversampling • Spatial location of training examples in Euclidean space Existing Solutions
- 14. 14 Preprocessing technique tooversample minority class Approximate discrete probability distribution using Generate new minority class data points using Chow-Liu’s algorithm Gibbs sampling Proposed Approach
- 15. 17 Minority Class Samples Majority Class Samples MarkovChains Gibbs Sampling
- 16. 18 (wrapper-based) RApidly COnvergingGibbs Sampler RACOG wRACOG Sample selection Pre-defined lag on Markov chain Highest probability of misclassification by wrapper classifier Stopping criteria Pre-defined number of iterations No improvement of a performance measure RACOG & wRACOG
- 17. 19 Experimental Setup Datasets ApproachesClassifiers Study 1 (Prompting) Baseline Classifier C4.5 Decision Tree 9 UCI Datasets SMOTE SVM SMOTEBoost K-Nearest Neighbor RUSBoost Logistic Regression Baseline Prompting RACOG wRACOG
- 18. 20 Results (True PositiveRate) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1
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- 20. 22 Automated Prompting Emulating Caregiver PromptTiming Detecting Activity Errors in Real Time Imbalanced Class Distribution Class Overlap One-Class Classification Overview
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- 22. 24 Class Overlap inPrompting Data 3-dimensional PCA plot of prompting data
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- 24. 26 Form clusters Under-sampling clusters Cluster-BasedUnder-Sampling
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- 26. 28 Experimental Setup Dataset ApproachesClassifiers Study 1 (Prompting) Baseline C4.5 Decision Tree SMOTE Naive Bayes Clustering Algorithm ClusBUS K-Nearest Neighbor DBSCAN ClusBUS Ensemble SVM
- 27. 29 Result (True PositiveRate) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 C4.5 Naïve Bayes IBk SMO Baseline SMOTE ClusBUS ClusBUS Ensemble
- 28. 30 Result (G-mean) 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 C4.5 NaïveBayes IBk SMO Baseline SMOTE ClusBUS ClusBUS Ensemble
- 29. 31 Automated Prompting Emulating Caregiver PromptTiming Detecting Activity Errors in Real Time Imbalanced Class Distribution Class Overlap One-Class Classification Overview
- 30. 32 Detecting Activity Errorsin Real Time Sensor events labeled with activity steps Availability of information on activity errors
- 31. 33 Basic Idea Participants withno reported errors One-Class Classifier Participants who committed errors Normal Activity Data Train Test Activity Data with Errors Activity Data
- 32. 34 6Daily Activities Participants Annotated for error starttimes Raw Data 1Sensor Event >70 Engineered Features 1 1Training Exampl e One-class {normal} Used by Algorithms 580 DERT Data
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- 35. 37 Activity Error Classification WHY?To characterize change in daily activities of older adults HOW? Sensor data Error Types Accuracy* Study 1 4 73% Study 2 9 54% *Using C4.5 decision tree and 10-fold CV
- 36. 41 Activity Error Models One-ClassMulti-Class
- 37. 42 Ensembles One-Class SVM Test Sample ErrorModel One-Class Multi-Class Logical AND Normal/Error
- 38. 43 Experimental Setup Datasets Approaches Study1 (400 participants) Baseline Study 2 (180) participants OCSVM OCSVM + OCEM OCSVM + MCEM
- 39. 44 Results: Study 1 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1 Sweeping andDusting Taking Medication Watering Plants Cooking 0 0.02 0.04 0.06 0.08 0.1 0.12 0.14 0.16 Sweeping and Dusting Taking Medication Watering Plants Cooking Recall Precision Baseline OCSVM OCSVM+OCEM OCSVM+MCEM
- 40. 45 Results: Study 2 RecallPrecision Baseline OCSVM OCSVM+OCEM OCSVM+MCEM 0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 Sweeping and Dusting Cleaning Countertops Taking Medication Watering Plants Washing Hands Cooking 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 Sweeping and Dusting Cleaning Countertops Taking Medication Watering Plants Washing Hands Cooking
- 41. 46 Clinical Evaluation 18% Continuation of Previouserror Actually True Positives 33% • Evaluation of algorithm-predicted false positives • Psychology clinician looked at participant’s videos
- 42. • Emulate caregiver intervention. •Class imbalance and overlap. • Detect activity errors in real-time. 47 Conclusion • Validated primary hypothesis. • Foundation of a real-world prompting system. • RACOG and wRACOG for continuous values. • ClusBUS in other domains. • Precise annotation for activity errors. Summary Significance Future Work
- 43. 48 Publications Book Chapter Journal B.Das, N.C. Krishnan, D.J. Cook, “Handling Imbalanced and Overlapping Classes in Smart Environments Prompting Dataset”, Spinger book on Big Data, 2014. B. Das, N.C. Krishnan, D.J. Cook, “Real-Time Activity Error Prediction to Assist Older Adults in Smart Homes: An Outlier Detection-Based Approach”, AI in Medicine, 2014. (Submitted) B. Das, N.C. Krishnan, D.J. Cook, “Automated Activity Intervention to Assist with Activities of Daily Living”, IOS Press book on Agent-Based Approaches to Ambient Intelligence, 2012. B. Das, N.C. Krishnan, D.J. Cook, “RACOG and wRACOG: Two Probabilistic Oversampling Techniques”, IEEE Transaction of Knowledge and Data Engineering, 2014. 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 in Biomedical Engineering, 2012. B. Das, D.J. Cook, M. Schmitter-Edgecombe, A.M. Seelye, “PUCK: An Automated Prompting System for Smart Environments”, Journal on Personal and Ubiquitous Computing, 2012.
- 44. 49 Publications Conference Workshop B. Das,N.C. Krishnan, D.J. Cook, “wRACOG: A Gibbs Sampling-Based Oversampling Technique”, International Conference on Data Mining, 2013. B. Das, N.C. Krishnan, D.J. Cook, “Handling Imbalanced and Overlapping Classes in Smart Environments, ICDM Workshop in Data Mining in Bioinformatics and Healthcare, 2013. S. Dernbach, B. Das, N.C. Krishnan, B.L. Thomas, D.J. Cook, “Simple and Complex Activity Recognition Through Smart Phones”, International Conference on Intelligence Environments, 2012. B. Das, A.M. Seelye, B.L. Thomas, D.J. Cook, L.B. Holder, “Using Smart Phones for Context-Aware Prompting in Smart Environments”, International Workshop on Consumer eHealth Platforms, Services and Applications, 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, 2011. B. Das, D.J. Cook, “Data Mining Challenges in Automated Prompting Systems”, Interactions with Smart Objects Workshop, 2011. E. Nazerfard, B. Das, L.B. Holder, D.J. Cook, “Conditional Random Fields for Activity Recognition in Smart Environments”, ACM Symposium on Human Informatics, 2010. B. Das, C. Chen, N. Dasgupta, D.J. Cook, “Automated Prompting in Smart Home Environment”, ICDM Workshop on Data Mining Services, 2010. C. Chen, B. Das, D.J. Cook, “A Data Mining Framework for Activity Recognition in Smart Environments”, International Conference on Intelligent Environments, 2010. C. Chen, B. Das, D.J. Cook, “Energy Prediction Using Resident’s Activity”, International Workshop on Knowledge Discovery from Sensor Data, 2010.
- 45. 50 Acknowledgement Dr. Diane CookPrafulla Dawadi Adri Seelye Dr. Larry Holder Dr. Ehsan Nazerfard Carolyn Parsey Dr. Narayanan C. Krishnan (CK) Dr. Kyle Feuz Christa Simon Dr. Maureen Schmitter-Edgecombe Brian Thomas Alyssa Weakley Dr. Behrooz Shirazi Chris Cain Jennifer Williams Dr. Alex Mihailidis Shirin Shahsavand Dr. Aaron Crandall Dr. Hassan Ghasemzadeh And, all previous colleagues, collaborators and friends…
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Editor's Notes
- #3 As more individuals cross higher life expectancy thresholds, a large section of the older adult population is becoming susceptible to cognitive impairments such as Alzheimer’s disease and dementia.
- #5 One key service that caregivers provide is prompting individuals with memory limitations to initiate and complete daily activities. Therefore, there is a growing need for developing assistive living technologies to help older adults with their daily activities and thus reducing the burden on the caregivers.