1. Speaker: Sun Peng Identifying Drug (Cocaine) Intake Events from Acute Physiological Response in the Presence of Free-living Physical Activity

2. Content  Introduction  Data Collection  Data Processing and Modeling  Conclusion  Comparison  Q & A

3. Introduction  Current Study Current Study  Motivation Motivation  Related Work Related Work  Key Challenge Key Challenge

4. Current Study  Mobile health is more popular in health study Wearable, inexpensive Collect data Many platforms  Problem: How to tell story from data collected from clients?

5. Motivation  Study on mobile health helps with several application areas: Identify physical activity Drug use Smoke and alcohol event Craving and Mental study

6. Related Work  Cocaine study 1.Effect of different doses of cocaine in different way 2.Nonlinear regression model to identify drug use which is not readily to use 3.“iHeal”: Drugs craving, not reported yet

7. Key Challenges  Cocaine study 1.Incorrect placement and poor attachment 2.Don’t wear sensor 3.Hard to find cocaine patient 4.Difficult to collect ground truth 5.Dosage and method of injection 6.Unconstrained environment

8. Data Collection  Lab and Field Lab and Field  Sensor Suite Sensor Suite  Data Collected Data Collected

9. Lab and Field  Johns Hopkins University Medical School (JHU Lab Study)  3 cocaine dependent volunteers  National Institute on Drug Abuse (NIDA Lab Study)  6 cocaine using volunteers  National Institute on Field Study  42 active poly-drug users at NIDA

10. Sensor Suite  AutoSense  Flexible band on chest  Respiration data : 21.33 Hz  ECG: 64 Hz  Accelerometer: 10.67 Hz  Ambient and skin temperature: 1 Hz  Galvanic skin response: 10.67 Hz

11. Data Collected  JHU Lab Study:  554 hours of good data collected from 3 subject  NIDA Lab Study  280 hours of good ECG collected from 6 subject  NIDA Field Study  10,449 hours of good ECG collected from 42 subjects  Urine Reports  385 potential cocaine of the 922 days of data collection

12. Data Processing and Modeling  Pipeline Pipeline  RR Intervals Detection RR Intervals Detection  Activity Detection Activity Detection  Model Development Model Development  Windows Selection Windows Selection  ANS ANS

13. Pipeline

14. RR Intervals Detection  Heart rate variability (HRV) is the variation of beat to beat intervals, also known as R-R intervals  Detect R peak  Outlier removal

15. Activity Detection  Research on accelerometer  7 participants wore AutoSense  266 minutes moving  183 minutes stationary  Threshold of 0.35 to classify moving and stationary

16. Model Development  Candidate windows selection  MACD line (Moving average convergence divergence)  EMA (Exponential Moving Averages)  Autonomous Nervous System (ANS) Model

17. Windows Selection  MACD  EMA slow - EMA fast = MACD Line  Slow: [1, 180]; Fast: [2, 90]  Input: smoothed RR intervals

18. Windows Selection

19. Autonomous Nervous System

23. Conclusion  Parameter Estimation Parameter Estimation  Parameter Discussion Parameter Discussion  Result Discussion Result Discussion  Future Work Future Work

24. Pipeline

25. Parameter Estimation

26. Parameter Discussion  Different treatment has different recovery time  Calculate recovery time in each window

27. Result Discussion

28. Future work  Generalizable approaches  Detecting events  Smooth noisy data  More powerful model

29. Comparison  Key challenge is almost same  Effect on body with alcohol smaller  Alcohol study have more variables  Do not have so much related work  Windows selection is usable for alcohol

30. Thank you !

31. Any questions ?