1. Smart Products and Connected Health The Personal Metrics Movement Fredric Raab Sr. Systems Engineer UCSD Center for Wireless and Population Health Systems

2. The Personal Metrics Movement Wired July 2009 Know Thyself The personal metrics movement goes way beyond diet and exercise. It’s about tracking every facet of life, from sleep to mood to pain, 24/7/365. Gary Wolf quantifiedself.com

3. The Personal Metrics Movement Wired July 2009 Track your data Analyze your results Optimize your life Share with others

4. Applications Consumer driven Performance monitoring Self improvement Behavioral change Wellness Personal reflection Health provider driven Clinical monitoring Disease management Remote diagnostics Aging in place Research

5. Components Smart devices –Collect, store, upload Web-based applications –Storage –Analytics –Visualization –Sharing Mobile phones –Intervention and behavior change

6. Types of data Sensor Heart rate ECG Respiration Body temp Blood pressure Glucose levels Movement (activity) Steps Location (gps, wifi) Self Report Food intake Mood Smoking / drinking Cravings Medications Weight Level of pain

7. Consumer applications Nike+ & Garmin Forerunner

8. Omron pedometer

9. GoWear activity monitor

10. Zeo sleep monitoring

11. Research tools Heart rate and activity monitoring

12. ECG, heart rate, activity, posture respiration, skin temperature

13. Clinical real-time monitoring

15. Chronic disease management

16. Medication compliance GlowCap

17. Medication compliance Proteus Biomedical

18. Medication compliance Drug delivery patch

19. “Band-aid” sensors Corventis

20. Implantable sensors Cardiomems

21. Business challenges Medical device approvalInsurance reimbursement classification code

22. Technical challenges Power efficient radios –Proprietary protocols –Multiple intermediary devices Battery life Size Wearability and compliance Connectivity Bandwidth

23. The future More devices directly connected Smart fabrics –Sensors and power built into clothing Pattern recognition and machine learning applied to server-side analytics Personalized health intervention systems –Reminders, prompts, suggestions, feedback

24. Thank you fraab@ucsd.edu cwphs.ucsd.edu www.paceproject.org