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Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate

Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate

Presentation for Pervasive Health 2013.

Paper Abstract: Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily physical activity (PA) patterns affect health, especially in today’s sedentary society. Wearable accelerometers (ACC) and heart rate (HR) sensors have been widely used to monitor physical activity and estimate EE. However, current EE estimation algorithms have not taken into account a person’s cardiorespiratory fitness (CRF), even though CRF is the main cause of inter-individual variation in HR during exercise. In this paper we propose a new algorithm, which is able to significantly reduce EE estimate error and inter-individual variability, by automatically modeling CRF, without requiring users to perform specific fitness tests. Results show a decrease in Root Mean Square Error (RMSE) between 28 and 33% for walking, running and biking activities, compared to state of the art activity-specific EE algorithms combining ACC and HR.

Presentation for Pervasive Health 2013.

Paper Abstract: Accurate Energy Expenditure (EE) estimation is key in understanding how behavior and daily physical activity (PA) patterns affect health, especially in today’s sedentary society. Wearable accelerometers (ACC) and heart rate (HR) sensors have been widely used to monitor physical activity and estimate EE. However, current EE estimation algorithms have not taken into account a person’s cardiorespiratory fitness (CRF), even though CRF is the main cause of inter-individual variation in HR during exercise. In this paper we propose a new algorithm, which is able to significantly reduce EE estimate error and inter-individual variability, by automatically modeling CRF, without requiring users to perform specific fitness tests. Results show a decrease in Root Mean Square Error (RMSE) between 28 and 33% for walking, running and biking activities, compared to state of the art activity-specific EE algorithms combining ACC and HR.

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Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate

  1. 1. Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate Marco Altini, Julien Penders, Oliver Amft
  2. 2. A Sedentary Society
  3. 3. Disease TrendsObesity Trends
  4. 4. Diabetes Hypertension Metabolic Syndrome Cardiovascular Disease Obesity
  5. 5. Wearable Sensors Measure Manage
  6. 6. Objective Physical Activity Monitoring
  7. 7. Walking biking Running Sedentary Household MotionintensityEnergyExpenditure EE ACC
  8. 8. Walking uphill Biking Walking stairs MotionintensityEnergyExpenditure EE ACC
  9. 9. Walking Biking Running Sedentary Household Stairs HeartRateEnergyExpenditure EE HR
  10. 10. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Activity-Specific Energy Expenditure Models
  11. 11. EEEE 2 Subjects – Same Body Size Same Energy Expenditure
  12. 12. HRHR 2 Subjects – Same Body Size Different Fitness Level -> Different HR
  13. 13. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Activity-Specific Energy Expenditure Models
  14. 14. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Activity-Specific Energy Expenditure Models Personalized Heart Rate Normalization
  15. 15. Study Design
  16. 16. 16 O2 CO2 Indirect Calorimeter
  17. 17. 17 ECG Necklace ACC HR
  18. 18. 18 29 Subjects, 48 Activities Household Sport
  19. 19. VO2 max tests (gold standard) Sub-maximal tests Non-exercise tests Cardiorespiratory Fitness Assessment HR at a certain Workload -> Fitness
  20. 20. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Activity-Specific Energy Expenditure Models Personalized Heart Rate Normalization HR at a certain Workload -> Fitness
  21. 21. Heart Rate Running 10 km/h Fit Unfit Heart Rate Normalization Rest Context-Based Personalization
  22. 22. Heart Rate Context-Based Personalization Running 10 km/hRest Fit Unfit Heart Rate Normalization
  23. 23. Heart Rate Context-Based Personalization Rest Fit Unfit Walking HR 4 km/h HR 5 km/h HR 6 km/h Running 10 km/h
  24. 24. Walking Speed Estimator Activity Recognition HR Walking HR at Rest Heart Rate Features Accelerometer Features Anthropometric Features Heart Rate Normalization Factor Heart Rate Normalization Factor Estimator Age, Height Automatic Normalization Factor Estimation
  25. 25. 120 140 160 180 200 120140160180200 Measured Normalization Factor PreidictedNormalizationFactor 130 140 150 160 170 180 -30-20-100102030 Normalization Factor Residuals Automatic Normalization Factor Estimation RMSE 8.3 bpm
  26. 26. EEEE 2 Subjects – Same Body Size Same Energy Expenditure
  27. 27. HRHR 2 Subjects – Same Body Size Different Fitness Level -> Different HR
  28. 28. Normalized HR – Qualitative Evaluation Normalized HR-> Better EE Estimates
  29. 29. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Normalized HR – Quantitative Evaluation
  30. 30. Accelerometer Features Activity Recognition Anthropometric Features Lying down Sedentary Energy Expenditure Heart Rate Normalized HR – Quantitative Evaluation Household Walking Biking Running
  31. 31. Accelerometer Features Activity Recognition Anthropometric Features Lying down Sedentary Energy Expenditure Heart Rate Normalized HR – Quantitative Evaluation Household Walking Biking Running
  32. 32. Normalized HR – Quantitative Evaluation dynamic walking running biking RMSE 1.02 kcal/min 0.60 kcal/min 1.13 kcal/min 1.25 kcal/min 1.38 kcal/min
  33. 33. Accelerometer Features Activity Recognition Anthropometric Features Lying down Sedentary Energy Expenditure Heart Rate Heart Rate Normalization Normalized HR – Quantitative Evaluation Household Walking Biking Running
  34. 34. Normalized HR – Quantitative Evaluation dynamic walking running biking RMSE 0.81 kcal/min 28% 33%29%3% 26% 0.60 kcal/min 0.58 kcal/min 1.13 kcal/min 0.81 kcal/min 1.25 kcal/min 0.89 kcal/min 1.38 kcal/min 0.92 kcal/min
  35. 35. Motivations Sedentary Society Disease Trends
  36. 36. Limitations Inaccuracy One Model Does Not Fit All Motivations
  37. 37. Normalization Activities of Daily Living Limitations Motivations Inter-Individual Differences
  38. 38. Normalization Limitations Motivations Personalized Models
  39. 39. Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate Marco Altini, Julien Penders, Oliver Amft Thank you

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