1. Vikramaditya Jakkula Washington 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) 1VJ AI@WSU © 2007

2. 2 Smart Environments

3. MavHome: 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. VJ AI@WSU © 20073

4. MavHome: Core Technologies 1 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. VJ AI@WSU © 20074

5. MavHome: Core Technologies 2 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. VJ AI@WSU © 20075

6. Experimentation Environment 1 VJ AI@WSU © 20076

7. Experimentation Environment 2 VJ AI@WSU © 20077 MavHome Environment  MavLab  MavKitchen  MavPad

8. Experimentation Environment 3 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. 8VJ AI@WSU © 2007

9. Earlier Work Monitoring Health by Detecting Drifts and Outliers for a Smart Environment Inhabitant help us gain information about different types of drifts and outliers that are part of the inhabitant’s lifestyle anomalies in inhabitants health such as blood pressure, pulse and temperature values. Gives information about sudden changes observed in inhabitants health Identification of Lifestyle Behavior Patterns with Prediction of the Happiness of an Inhabitant in a Smart Home Identify correlations among everyday activity in smart home. we use the machine learning technique, the k-nearest neighbor algorithm, to predict the state of wellness the inhabitant will experience on the following day.we use the machine learning technique, the k-nearest neighbor algorithm, to predict the state of wellness the inhabitant will experience on the following day. simple sensor network in smart home can be used to detect lifestyle patternsShow that a simple sensor network in smart home can be used to detect lifestyle patterns 9VJ AI@WSU © 2007

10. Experimentation 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. 10VJ AI@WSU © 2007

11. Experiment 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. VJ AI@WSU © 200711

12. Experiment I Results VJ AI@WSU © 200712 Class SMO (Reg.) NN MLP Lazy LWL KNN Systolic3.34%18%27.86%47% Diastolic14%8.20%42%53% Pulse2%8.33%16.66%53.30% Average Accuracy 6%12%29%51%

13. Experiment II Goal: Learn to predict abnormal days. Challenges: Unexpected events [Emotional/Physical] Sudden health and environment changes Food consumption and sleep and so forth! VJ AI@WSU © 200713

14. Experiment II VJ AI@WSU © 200714 Run#Correctly Classified Instances Incorrectly Classified Instances Acc (%)Err. (%)MAE KNN6185.714.20.1788 10-Fold C.V.49492.57.540.0925  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.

15. Conclusions 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. 15VJ AI@WSU © 2007

16. Acknowledgements 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. VJ AI@WSU © 200716

17. Questions! 17VJ AI@WSU © 2007