1. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Physiological Data Modeling Contest Introductory Remarks An ICML-2004 Workshop, July 8, 2004

2. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Notes/outline Welcome (overview of techniques tried, # entrants, etc, maybe 1 slide) Schedule (including order of teams) Zodiac, DCTRI, NLM, INF, LRI, Gama, Amin, SS [ask Max about who wanted to be near edge] BodyMedia (maybe 15/20 slides) – when we started, what we do, Why the contest? Details of contest

3. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Schedule 8:30Introduction 8:50Brown-Zodiac presentation 9:15DCRTI presentation 9:35National Library of Medicine 10:00Break 10:30UTexas - Amin 10:50CMU-Informedia 11:15Univ. of Porto - Gama 11:35Laboratoire de Recherche en Informatique 12:00Lunch 14:00Smart Signal 14:25Revelation of channel and annotation names 14:40Revelation of scores 15:00Discussion of results 15:30Break 16:00Awards presentation 16:10Brainstorming 16:30Break

4. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary BodyMedia is a body monitoring solutions provider. We make tools for continuous body monitoring. 5 Years Old $20M in Venture Funding Who We Are

5. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Our Mission To be the leader in integrated products and information services that track and promote health and wellness through continuous, free- living, body monitoring.

6. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary What we make. Hardware and software that… Collects Stores Processes Represents …continuous physiologic and lifestyle information about people. 101010101010101 010101010101010 101010101010101 101010101010101 010101010101010 101010101010101 101010101010101 010101010101010

7. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary SenseWear TM Armband

8. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary 2-axis Accelerometer (inside) Heart Beat Receiver Board (inside) Timestamp Button Heat Flux Sensor Near-Body Ambient Temperature Sensor GSR Sensors Skin Temperature What it monitors: Acceleration (Motion) Galvanic Skin Response (GSR) Skin Temperature Heat Flux Heart Beats

9. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary 2-axis Accelerometer (inside) Heart Beat Receiver Board (inside) Timestamp Button Heat Flux Sensor Near-Body Ambient Temperature Sensor GSR Sensors Skin Temperature What it monitors: Acceleration (Motion) Galvanic Skin Response (GSR) Skin Temperature Heat Flux Heart Beats Machine Learning

10. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary 2-axis Accelerometer (inside) Heart Beat Receiver Board (inside) Timestamp Button Heat Flux Sensor Near-Body Ambient Temperature Sensor GSR Sensors Skin Temperature What it monitors: Acceleration (Motion) Galvanic Skin Response (GSR) Skin Temperature Heat Flux Heart Beats What we derive (today): Total Energy Expenditure Physical Activity Duration Type of Physical Activity (e.g. Resistance, Cardiovascular) Number of Steps Sleep Efficiency Contexts: Sleep, lying down, sedentary, driving, ambulation, biking, other exercise Machine Learning

11. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Collect continuous vital sign data from a set of people Feed these continuous readings (in real-time or after the fact) through constructed data models Construct each data model so that its inputs are demographic information plus streaming vital signs, and its output is a derived body state value Build and validate these data models through a supervised machine learning process This is core to BodyMedia’s business model – thus, we have good support for doing machine learning. The Data Itself Can be Modeled

12. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Low Motion High Motion Aerobic Activity? In a vehicle? Resistance Activity? Sedentary? Multi-Sensors for Disambiguation

13. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary High Heat Flux Low Heat Flux Low Motion High Motion Aerobic Activity In a vehicle Resistance Activity Sedentary Multi-Sensors for Disambiguation

14. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary High Heat Flux Low Heat Flux Low Motion High Motion Aerobic Activity In a vehicle Resistance Activity Sedentary Fever Low HR High HR Multi-Sensors for Disambiguation

15. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Multi-Sensors for Disambiguation

16. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Multi-Sensors for Disambiguation

17. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary

18. BikingDriving Office Running

19. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Accuracy VO2 ICC Model ICC Treadmill 90.3% 0.96 0.88 Biking 93.7% 0.90 0.73 Arm Ergometer 95.4% 0.76 0.88 Stepping 91.5% 0.97 0.91 University of Pittsburgh Study All results within 95% confidence interval Example: Energy Expenditure – Accuracy

20. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Collect gold standard data and wide array of high-rate sensor data Create relevant compressed data streams Build algorithm Evaluate Validate externally Training Testing Algorithm Development Process: Identify gaps, generalize to broader population Develop context detectors Sum() Variance() Peaks() Pedometer() Frequency() By subject/lab CV

21. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Challenges/opportunities for us Our data is sequential –Can’t train for my afternoon data with my morning data –Can take advantage of dynamic information Many Gigabytes of data –More than a hundred thousand hours of labeled data. –Slow to churn through even with simple algorithms Despite all our data – there are many chances to overfit –We had only one person who did “spinning” in our dataset –Curse of dimensionality… Only one left-handed female with COPD who rides a bike only holding on with her left arm. Silver, Bronze, and Tin instead of Gold standards –Noise, poor annotation are problems –Can also be very difficult to obtain (e.g. heart attack data) Sensor noise

22. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Our Markets

23. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Armband Glucose Meter Pulse Oximeter Blood Pressure Monitor Weight Scale Wireless Communication Gateway Cellular/Two-way pager/Telephone Communication BodyMedia’s Platform for Remote Healthcare Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary

24. Weight Management

25. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Diabetes Management

26. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Diabetes Management

27. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Scientific Research

28. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Scientific Research

29. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary

30. The Physiological Data Modeling Contest large amounts of data sequential data issues of sensor fusion rich domain –noise –hidden variables –context 3 domains –Gender –Very common activity –Common but not as common activity Only one left-handed female with COPD who rides a bike only holding on with her left arm. Silver, Bronze, and Tin instead of Gold standards –Noise, poor annotation are problems –Can also be very difficult to obtain (e.g. heart attack data) Sensor noise

31. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Why did we do the contest?

32. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary The PDMC Contest Details of contest and dataset, including size, tasks, number of entries, etc.

33. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary Predictions of performance We’re going to try to predict performance.

34. Copyright BodyMedia, Inc. © 2004, Confidential and Proprietary