1 HealthSense : Classification of Health-related Sensor Data through User-Assisted Machine Learning Presenter: Mi Zhang Feb. 23 rd, 2009 From Prof. Gregory D. Abowd

2 Outline Part I: Background Part II: HealthSense Platform Part III: Experiments Part IV: Future Work

3 Part I: Background Problem Overview Research Methodology System Features & Contributions

4 Problem Overview How to automatically detect Directly Undetectable health-related conditions? –What is Directly Undetectable condition? Can NOT be detected by direct sensing technology Example: Pain, Depression, Itching

5 Research Methodology Assumption: –Assume the occurrence of Directly Undetectable Conditions is correlated with events that can be observed based on patient feedback. Adopt techniques from Activity & Gesture Recognition Model Design Methodology –Build up an initial model by a preliminary supervised learning procedure –Use user-query feedback and machine learning techniques to continuously improve the model

6 System Features & Contributions System Features –Update the system with Online Supervised Learning –Use patient inputs/feedback to assist with classification –Both techniques contribute to classification accuracy Contributions –A seminal work on detecting directly undetectable health- related events from on-body sensor streams

7 Part II: HealthSense Platform Platform Architecture Feature Extraction & Classification Strategy User Query Process

8 Platform Architecture Classic 3-tier Architecture: Tier 1: Sensor Tier –Witilt 3-Axis Accelerometer: Bluetooth interface –Communicate with Tier 2 via Bluetooth Tier 2: Mobile Device –Nokia N800 PDA: Bluetooth, , running Linux –Communicate with Tier 3 via Tier 3: Back-end Server –Web Server: Apache Tomcat –Database: Apache Derby –Machine Learning Engine: Weka –GUI: Berkeley PtPlot

9 Feature Extraction & Classification Strategy Extracted features –Frequency-Domain Energy –Product- Moment Correlation Coefficient –Standard Deviation –Root Mean Square (RMS) Classification Strategy –Each Event Window is a classification unit –Two Categories: Occurrence, Non-occurrence

10 User Query Process Strategy: –Server queries the user ONLY when positive classification occurs –Does NOT handle negative classification How: 1.Server detects a positive classification 2.Sends a SMS to Mobile hub 3.GUI of Yes/No questions 4.Mobile hub sends a SMS back to server

11 Part III: Experiments Case Overview: What & Why? Step 1: Choosing the right sensors Step 2: Choosing the right features Step 3: Choosing the right window size Step 4: Choosing the right classifier 4 Experiments & Results Analysis

12 Case Study: Detecting An Itch What: –Detect a scratching –Differentiate normal daily scratching from medically relevant scratching Why: –Detecting pain or depression is infeasible at this stage –Itch is also Directly Undetectable –Critique: NOT a direct proxy for Pain & Depression

13 Step 1 ~ 4 Step 1: Choosing the right sensors –2 wrist-mounted 3-Axis Accelerometers Step 2: Choosing the right features –Frequency-Domain Energy, Product- Moment Correlation Coefficient, Standard Deviation, Root Mean Square (RMS) –May NOT be the most appropriate features for this case Step 3: Choosing the right window size –In this case: Hz (Case by Case) –Tradeoff: Accuracy vs. Decision Period Step 4: Choosing the right classifier –Neural Network, Decision Tree, K-Nearest Neighbors, Bayesian Network (Naive Bayes) –In this case: Decision Tree performs the BEST

14 Experiment Results Test 1: No feedback –Accuracy: 63% ~ 73% –Accuracy remains fairly constant throughout the test Test 2: No scratching, Has user’s Feedback –Accuracy: 62% ~ 93% –Feedback helps a lot Test 3: Has scratching, Has user’s Feedback –Accuracy: 62% ~ 93% –Feedback helps a lot Test 4: With Feedback, differentiate normal daily scratching from medically relevant scratching –Require a priori knowledge of the locations of medically relevant scratches –Accuracy: 81% ~ 100% –Feedback helps a lot

15 Part IV: Future Work Expand feature pool & Indentify important features –Goal: Improve Classification Accuracy Add more sensors (Different types, numbers, places) –Goal: Improve Classification Accuracy Improve their model: Handle false negatives properly –Goal: Improve Classification Accuracy –Method I: Allow patients to voluntarily notify the system when system fails to detect scratch (Not good: timing issue) –Method II: Manually validate of both positive and negative classification (Not good: Annoy and obtrusive)

16 Questions Any Questions ? No……

17 Thank You Very Much !