Vikramaditya Jakkula Washington State University First International Workshop on Smart Homes for Tele-Health.

Slides:

Advertisements

Similar presentations

Decision Tree Approach in Data Mining

Advertisements

Vikramaditya Jakkula. MavPad Argus Sensor Network  around 100 Sensors.  include Motion, Devices, Light, Pressure, Humidity and more.

Vikramaditya R. Jakkula, Diane J. Cook & Aaron S. Crandall Washington State University Presented by Aaron S. Crandall.

Civil and Environmental Engineering Carnegie Mellon University Sensors & Knowledge Discovery (a.k.a. Data Mining) H. Scott Matthews April 14, 2003.

Vikramaditya R. Jakkula & Diane J. Cook Washington State University.

BA 555 Practical Business Analysis

Chapter 12 - Forecasting Forecasting is important in the business decision-making process in which a current choice or decision has future implications:

Distributed Microsystems Laboratory: Developing Microsystems that Make Sense Sensor Validation Techniques Sponsoring Agency: Center for Process Analytical.

Week 9 Data Mining System (Knowledge Data Discovery)

Spatial Outlier Detection and implementation in Weka Implemented by: Shan Huang Jisu Oh CSCI8715 Class Project, April Presented by Jisu.

June 27-28, 2006 Vikramaditya Jakkula Monitoring Health by Detecting Drifts and Outliers for a Smart Environment Inhabitant Gaurav Jain, Diane J. Cook,

1 Ensembles of Nearest Neighbor Forecasts Dragomir Yankov, Eamonn Keogh Dept. of Computer Science & Eng. University of California Riverside Dennis DeCoste.

Computer Science and Engineering Department The University of Texas at Arlington MavHome: An Intelligent Home Environment.

Monitoring and Measurement

Enterprise systems infrastructure and architecture DT211 4

Microarray Gene Expression Data Analysis A.Venkatesh CBBL Functional Genomics Chapter: 07.

Intrusion and Anomaly Detection in Network Traffic Streams: Checking and Machine Learning Approaches ONR MURI area: High Confidence Real-Time Misuse and.

1 © Goharian & Grossman 2003 Introduction to Data Mining (CS 422) Fall 2010.

Data Mining Techniques

MAKING THE BUSINESS BETTER Presented By Mohammed Dwikat DATA MINING Presented to Faculty of IT MIS Department An Najah National University.

Data Mining for Intrusion Detection: A Critical Review Klaus Julisch From: Applications of data Mining in Computer Security (Eds. D. Barabara and S. Jajodia)

SoundSense: Scalable Sound Sensing for People-Centric Application on Mobile Phones Hon Lu, Wei Pan, Nocholas D. lane, Tanzeem Choudhury and Andrew T. Campbell.

Data Mining Chun-Hung Chou

Slide Image Retrieval: A Preliminary Study Guo Min Liew and Min-Yen Kan National University of Singapore Web IR / NLP Group (WING)

Chapter 8 Prediction Algorithms for Smart Environments

Enhancement of IPTV using a Wireless Sensor Network Sandeep Kakumanu,Sriram Lakshmanan, and Raghupathy Sivakumar GNAN Research Group Georgia Institute.

1 / 12 PSLC Summer School, June 21, 2007 Identifying Students’ Gradual Understanding of Physics Concepts Using TagHelper Tools Nava L.

Vikramaditya Jakkula Washington State University IEEE Workshop of Data Mining in Medicine 2007 (DMMed '07) In conjunction with IEEE.

Introduction to machine learning and data mining 1 iCSC2014, Juan López González, University of Oviedo Introduction to machine learning Juan López González.

1 SUPPORT VECTOR MACHINES İsmail GÜNEŞ. 2 What is SVM? A new generation learning system. A new generation learning system. Based on recent advances in.

Vikramaditya Jakkula & Diane J. Cook Artificial Intelligence Lab Washington State University 2 nd International Conference on Technology and Aging (ICTA)

Forecasting to account for seasonality Regularly repeating movements that can be tied to recurring events (e.g. winter) in a time series that varies around.

 Fundamentally, data mining is about processing data and identifying patterns and trends in that information so that you can decide or judge.  Data.

Data Mining: Software Helping Business Run

Learning from observations

Acknowledgements Contact Information Anthony Wong, MTech 1, Senthil K. Nachimuthu, MD 1, Peter J. Haug, MD 1,2 Patterns and Rules  Vital signs medoids.

Ping Zhu, AHC5 234, Office Hours: M/W/F 10AM - 12 PM, or by appointment M/W/F,

Intelligent Environments1 Conclusions and Future Directions.

REU 2004 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Distributed Rational.

Introduction  Use Allen’s Temporal Relations [3] to identify temporal relations among Activities in Daily Life of the resident.  Allen’s relations form.

Time Series Analysis and Forecasting. Introduction to Time Series Analysis A time-series is a set of observations on a quantitative variable collected.

Vikramaditya R. Jakkula, G. Michael Youngblood and Diane J. Cook AAAI ’06 Workshop on Computational Aesthetics July 16, 2006.

REU 2007 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Information Processing.

Identifying “Best Bet” Web Search Results by Mining Past User Behavior Author: Eugene Agichtein, Zijian Zheng (Microsoft Research) Source: KDD2006 Reporter:

Data Mining By Farzana Forhad CS 157B. Agenda Decision Tree and ID3 Rough Set Theory Clustering.

DEPARTMENT OF MECHANICAL ENGINEERING VII-SEMESTER PRODUCTION TECHNOLOGY-II 1 CHAPTER NO.4 FORECASTING.

WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.

Computer Science and Engineering Department The University of Texas at Arlington MavHome: An Intelligent Home Environment.

WHAT IS DATA MINING?  The process of automatically extracting useful information from large amounts of data.  Uses traditional data analysis techniques.

REU 2009 Computer Science and Engineering Department The University of Texas at Arlington Research Experiences for Undergraduates in Information Processing.

Data Mining for Hierarchical Model Creation G. Michael Youngblood and Diane J. Cook IEEE Transactions on Systems, Man, and Cybernetics, Part C, 37(4): ,

1 Creating Situational Awareness with Data Trending and Monitoring Zhenping Li, J.P. Douglas, and Ken. Mitchell Arctic Slope Technical Services.

Computer Science and Engineering Department The University of Texas at Arlington MavHome: An Intelligent Home Environment.

Forecasting. Model with indicator variables The choice of a forecasting technique depends on the components identified in the time series. The techniques.

DATA MINING and VISUALIZATION Instructor: Dr. Matthew Iklé, Adams State University Remote Instructor: Dr. Hong Liu, Embry-Riddle Aeronautical University.

Data Mining, Machine Learning, Data Analysis, etc. scikit-learn

Applying Deep Neural Network to Enhance EMPI Searching

Predicting Interface Failures For Better Traffic Management.

Meteorological Instrumentation and Observations

Data Mining 101 with Scikit-Learn

DEFECT PREDICTION : USING MACHINE LEARNING

An Enhanced Support Vector Machine Model for Intrusion Detection

Damiano Bolzoni, Sandro Etalle, Pieter H. Hartel

An Inteligent System to Diabetes Prediction

Data Mining: Introduction

Xin Qi, Matthew Keally, Gang Zhou, Yantao Li, Zhen Ren

Data Mining, Machine Learning, Data Analysis, etc. scikit-learn

Data Mining, Machine Learning, Data Analysis, etc. scikit-learn

Presentation transcript:

Vikramaditya Jakkula Washington State University First International Workshop on Smart Homes for Tele-Health

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 ©

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 ©

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 ©

6

MavHome Environment  MavLab  MavKitchen  MavPad 7 VJ © 2007

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.

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

Basic overall goal is to build a forecasting system for healthcare system in smart home. Used 40 days data for training and 20 days data for testing. Experiment 1: Use support vector machine algorithm found in Weka to predict whether health trends are increasing, decreasing or constant. Experiment 2: Use automated forecasting tools, such as Phicast, which enables us to use Box-Jenkins method for forecasting the range of health data values.

We are looking to prediction an increasing, decreasing or constant trend over time. Data values range is very small and close to each other. (Look at the figure). In a 2-D plot let us try to classify the trend. Which technique do you think is better to use here? SVM! Oh yes!

Inst No.ActualPredictedError 12:decrease No 21:increase No 31:increase No 42:decrease No 52:decrease No 62:decrease No 71:increase No 81:increase No 91:increase No 102:decrease No 112:decrease No 121:increase No 132:decrease1:increaseYes 141:increase No 152:decrease No 162:decrease No 172:decrease No 182:decrease No 191:increase2:decreaseYes 202:decrease No Predicted systolic trend using test set DatasetCorrectly classified Instances Incorrectly Classified Instances Mean Absolute Error Test Set Cross Validation Plot of Predicted systolic trend [test set]

InstNo.ActualPredictedError 12:decrease1:increaseYes 21:increase No 31:increase No 42:decrease1:increaseYes 52:decrease1:increaseYes 63:constant1:increaseYes 71:increase No 81:increase No 91:increase No 102:decrease1:increaseYes 112:decrease1:increaseYes 123:constant1:increaseYes 131:increase2:decreaseYes 141:increase No 152:decrease1:increaseYes 161:increase No 172:decrease1:increaseYes 182:decrease1:increaseYes 192:decrease1:increaseYes 201:increase No Predicted diastolic trend using test set Plot of Predicted diastolic trend [test set] DatasetCorrectly classified Instances Incorrectly Classified Instances Mean Absolute Error Test Set Cross Validation

Instance No ActualPredictedError 12:decrease No 21:increase No 31:increase No 42:decrease No 52:decrease No 62:decrease No 71:increase No 81:increase No 91:increase No 102:decrease No 112:decrease No 121:increase No 131:increase2:decreaseYes 141:increase No 152:decrease No 162:decrease No 172:decrease No 182:decrease No 191:increase No 202:decrease No Predicted pulse trend using test set Plot of Predicted pulse trend [test set] DatasetCorrectly classified Instances Incorrectly Classified Instances Mean Absolute Error Test Set Cross Validation

Box-Jenkins forecasting models are based on statistical concepts and principles. Box-Jenkins forecasting models regarded to be the efficient forecasting technique. Box-Jenkins forecasting provides some of the most accurate short-term forecasts. Other tools such as SAS, SPSS, Demeter needed more data for running similar experiment, where as Phicast had a successful run.

Instance # Actual Recorded Systolic Lower Predicte d Value Upper Predicte d Value Error [Yes/No] No No No No Yes Yes Yes Yes No No No No No Yes Yes No No No Yes No Yes No of Correctly Classified No of Incorrectly ClassifiedPercent Accuracy Test Set13862%

Instance # Actual Recorded Diastolic Lower Predicte d Value Upper Predicte d Value Error [Yes/No] Yes No Yes No No No No No No Yes No No No Yes Yes Yes Yes No No No Yes No of Correctly Classified No of Incorrectly ClassifiedPercent Accuracy Test Set13862%

Instanc e # Actual Recorde d Pulse Lower Predicte d Value Upper Predicte d Value Error [Yes/No ] No No No No No No No No No Yes No No No No Yes Yes No No No Yes Yes No of Correctly Classified No of Incorrectly ClassifiedPercent Accuracy Test Set16576%

Predicting time series data is difficult Continue performance on more larger datasets. 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. Anomaly detection based prediction for health care system and activities associated with healthcare.

Thank You