Presenter: Laura Pina CSE 291

1. Presenter: Laura Pina CSE 291 Validated Caloric Expenditure Estimation using a Single Body-Worn SensorJonathan Lester, Carl Hartung, Laura Pina, Ryan Libby, GaetanoBorriello, Glen DuncanUbicomp 2009

2. Why Do We Need a System to Estimate Calorie Expenditure? • In 2007, 35% of US adults were considered overweight by the US Center for Disease Control • Serious Health Illnesses caused by an Overweight: hart diseases, heart stroke, some forms of cancer, type 2 diabetes, and hypertension • Health care costs exceeds 100 billion dollars

3. Why Do We Need a System to Estimate Calorie Expenditure? • Research has shown people overestimatethe calories they expend and underestimatethe calories they consume. • Thus, assisting in monitoring calories consumed vs. expended would be informational for individuals as well as for their doctors.

4. Have you tried to maintain two diaries for: • foods consumed throughout the day? • type and duration of physical activity throughout the day?

5. Have you tried to maintain two diaries for: • foods consumed throughout the day? • type and duration of physical activity throughout the day? VERY DIFFICULT TO BE ACCURATE AND PRECISE

6. Solution: Create a system which can automatically track and provide user feedback about their energy balance. Two components toenergy balance: • Keeping track of foods consumed throughout the day • A system able to compute caloric expenditure

7. Today we will focus on: 2. A system able to compute caloric expenditure

8. Order of Presentation • Motivation • Why Do We Need a System to Estimate Calorie Expenditure? • Experiment Details • MSP Experiment Setup • iMote/MSP • Ground Truth • Standard Medical Measure Equation for Caloric Expenditure • Activity Inference • Results • Future Work

9. Experiment Details • Laboratory and a Field Experiment • Each subject was asked to perform one field session, one lab session, a third randomly assigned to be either one of the above • Each subject wore an Intel mobile Sensing Platform (MSP) , and a VO2 mask data collecting system • Total of 51 subjects of varying age, ethnicity, sex, and body type

10. MSP Experiment Setup • Users wore the MSP on their waist • Sensors used from the MSP: accelerometer, barometer • Data from these sensors provided the data to compute caloric expenditure

11. iMote/MSP • What sensors were used: • Accelerometer sampled at 512 and features computed at 4Hz • Barometer sampled at 15 Hz

12. Ground Truth • VO2allows to measure energy consumption. • A metabolic measurement system was used to compare the energy consumption. • The system measures the flow, O2 content, and CO2 context inspired and expired gases. • This allows to estimate how much O2the body has consumed and CO2produced, and then infer metabolic rate. • VO2 is the most readily available and reliable measure of caloric expenditure.

13. Standard Medical Measure Equation for Caloric Expenditure

14. Weight is used for resting metabolic rate • Height and Gender used to compute stride length

15. What do we need to calculate calories expended? • Weight • Type of physical activity • Speed the user is performing the activity • Grade the user is performing the activity

16. What do we need to calculate calories expended? • Weight • Type of physical activity • Speed the user is performing the activity • Grade the user is performing the activity

17. Order of Presentation • Motivation • Why Do We Need a System to Estimate Calorie Expenditure? • Experiment Details • MSP Experiment Setup • iMote/MSP • Ground Truth • Standard Medical Measure Equation for Caloric Expenditure • Activity Inference • Results • Future Work

18. Activity Inference • Two separate Naïve Bayes classifiers were used. • One was trained and used on Lab Data and another was trained and used on field data • The trained classifier focused on identifying the type of activities related to the equation calculating caloric expenditure: Resting, Walking, and Running • The features used in the classifier are based are based on the 3D acceleration vector: the sum of on second FFTs using frequency bins of 2-4Hz and 1-10Hz, variance, standard deviation, range and a step speed estimate from a step counter • Step Counter implemented to find the speedat which the activity is being performed • Barometric Pressure sensor data is the simplest and lowest power consumption method of estimating the grade of the activity

19. Step Counter • Once the activity has been identified need to calculate speed and adapt to user’s walking characteristics, such as gate. • A modified version of Pan-Tompkins method using adaptive FFT energy based filter. This energy threshold allows to robustly use the same method for a variety of walking conditions, users, and sensor position. • Accuracy: • At Walking Speeds: 91% with std. dev. of 8.1% • At Running Speeds: 90% with std. dev. of 9.8%

20. Example of Step Detection Output Example accelerometer trace with the magnitude of the accelerometer shown in solid line at the bottom, extracted footfall peaks are marked with X’s, and the estimated speed from the steps is shown as a dotted line. During the first half of the trace the subject was walking at around 3MPH and then began running at 4.5MPH near the 10:44 mark

21. Grade Computation • Barometric Pressure • Barometric Pressure and GPS • GPS • GPS and Geographical Information System (GIS) Accuracy Improvement Original Estimate 86.29% - Barometer Only 95.88% 9.59% Barometer + GPS 95.25% 8.96% GPS + USGS DEM 95.65% 9.36% GPS + LIDAR Scans 88.61% 2.32% GPS Only 92.98% 6.69%

22. Why do we need to compute Slope? • Improves over all accuracy of the system (medical equation) • Reward users for their strenuous activities!More calories are burned when one is being physically active on a slope.

23. Example of Estimated Grade of Activity Top: A plot of the estimated altitudes Bottom: A plot of the estimated grades

24. How were the Results obtained? • Analysis of data was done by: • Aligning sensor data with ground truth and VO2 data • Running data through the classifier • Once an interval of activity was identified, the corresponding equation was used. Step speeds were calculated from the step detector • Weight is used for resting metabolic rate (R) • Height and Gender used to compute stride length

25. Lab ResultsAccuracy 89.52%, std dev. 7.25% Top: Example field data with the ground truth labels, as provided by the observers. Bottom: Re-labeled activities listed as either sitting (still) or walking (moving) activities. The darkened portions of the accelerometer trace are those inferred by the activity inference as being walking.

26. Field ResultsAccuracy 79.8%, std. dev. 7.25% Accelerometer magnitude (solid blue waveform) versus the smoothed ground truth V̇O2, dotted line, reported by the metabolic cart for an example laboratory data trace. The smoothed computed V̇O2 is shown as a solid dark line. The computed and estimated V̇O2 match fairly well for most of the trace. However, after the subject has jogged for a few minutes there is a noticeable cool down visible in the ground truth data that is not captured by our computed estimate.

27. A Synthetic Day • Sleep until 8AM • Go for 30 min jog before work • Walk ½ mile to the bus stop to get to work • At work spend most of the time sitting at desk with a few short walking trips to co-workers’ offices and down to the lunch room • After work walk to a restaurant for dinner • Then, catch a ride to watch a movie with friends • After, catch a ride home • Once at home, watch TV before heading off to bed.

28. Estimation of Calories Expended During Synthetic Day Histogram showing the calories burned during a hypothetical data created using a synthetic data set from one of our test subjects. The vast majority of calories and time are spent in sedentary activities, among them sitting and sleeping.

29. Future Work Classifying Sedentary Behavior: The current system classified any type of inactive activity under one single category when in fact there is medical research proving there is good, bad, and necessary sedentary behavior • Classifying sedentary behavior will lead to understanding better human physical activity as well as finding opportunistic times to encourage the user to become more active by suggesting activities they could engage in.