Vikramaditya JakkulaWashington State University

vikramaditya@wsu.edu

IEEE Workshop of Data Mining in Medicine 2007 (DMMed '07)

In conjunction with IEEE International Conference in Data Mining 2007 (ICDM '07)

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Smart EnvironmentsSmart Environments

MavHome: Smart Home ProjectMavHome: Smart Home Project

Project Unique◦Focus on entire home

House perceives and acts◦Sensors◦Controllers for devices◦Connections to the mobile user and Internet

Unified project incorporating varied AI techniques, cross disciplinary with mobile computing, databases, multimedia, and others.

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MavHome: Core TechnologiesMavHome: Core Technologies11

Minimal Sequential Patterns Using “ED”

Given an input stream S of event occurrences O, ED: Partitions S into Maximal Episodes, Pmax. Creates Itemsets, I, from the Maximal Episodes. Creates a Candidate Significant Episode, C, for each

Itemset I, and computes one or more Significance Values, V, for each Candidate.

Identifies Significant Episodes by evaluating the Significance Values of the candidates.

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MavHome: Core TechnologiesMavHome: Core Technologies22

Decision Making using ProPHeT

ProPHeT is the main controlling component of the system.

It uses data filtered through Episode Discovery (ED) to create a Hierarchical Hidden Markov Model (HHMM).

HHMM represents a user model that includes all of the

episodes (e.g., entering a room, watching TV, sitting in a chair and listening to music, and so forth) that a person performs in the environment.

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Experimentation EnvironmentExperimentation Environment11VJ AI@WSU © 2007 6

Experimentation EnvironmentExperimentation Environment22

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MavHome Environment

MavLabMavKitchenMavPad

Experimentation EnvironmentExperimentation Environment33

MavHome Smart Apartment

The evaluation environment is a student apartment with a deployed Argus and X-10 network

There are over 150 sensors deployed in the MavPad that include light, temperature, humidity, and switches.

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Earlier WorkEarlier Work

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Experimentation OverviewExperimentation Overview Basic overall goal is to build a forecasting system for

healthcare system in smart home.

Used 90 days data for training and 61 days data for testing.

Use Weka workbench for the experimentation process.

Experiment 1: Comparing different learning algorithms prediction accuracy on health vital datasets collected in a smart home.

Experiment 2: Learning to predict abnormal or unhealthy days in a smart home residents life.

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Experiment IExperiment I

Goal: Compare prediction accuracy of classifiers to choose the best classifier to predict the health vitals.

Challenges: Health Vital value prediction is dependent

on many factors and major factors including food intake, current health condition/history/previous illnesses and physical activity performed.

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Experiment I ResultsExperiment I Results

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ClassSMO(Reg.)

NNMLP

Lazy LWL

KNN

Systolic 3.34% 18%27.86

%47%

Diastolic 14% 8.20% 42% 53%

Pulse 2% 8.33%16.66

%53.30

%

Average Accuracy

6% 12% 29% 51%

Experiment IIExperiment IIGoal: Learn to predict abnormal days.

Challenges: Unexpected events [Emotional/Physical] Sudden health and environment changes Food consumption and sleep and so forth!

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Experiment IIExperiment II

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Run# Correctly Classified Instances

Incorrectly Classified Instances

Acc (%) Err. (%) MAE

KNN 6 1 85.7 14.2 0.178810-Fold C.V. 49 4 92.5 7.54 0.0925

Abnormality to be the any value greater than 137/84 mm Hg (Myers MG) and for pulse the normal range is 60 to 100 beats per minute (Wikipedia) combined with physical activity. Did not observe any significantly extreme values!

Future work includes observation on subjects from different age groups and different genders.

Conclusions and future workConclusions and future work K-NN outperforms other classifiers with an overall prediction

accuracy of 51% in experiment 1 and has an prediction accuracy of 86% in experiment 2.

Predicting time series data is still a difficult challenge.

We observe that the prediction models act as useful components to the health care system in smart homes.

Future work would include improving the prediction, collecting more data over time and experimenting larger datasets.

Anomaly detection based prediction for health care system and adaptive healthcare systems.

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AcknowledgementsAcknowledgements

I would like to thank my professor Dr. Diane J. Cook for her encouragement and support.

I would also like to thank the Human subject who participated in these trials.

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Questions!Questions!

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