Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate
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Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate
•3 likes•1,651 views
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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. Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate Marco Altini, Julien Penders, Oliver Amft
- 6. Objective Physical Activity Monitoring
- 7. Walking biking Running Sedentary Household MotionintensityEnergyExpenditure EE ACC
- 8. Walking uphill Biking Walking stairs MotionintensityEnergyExpenditure EE ACC
- 9. Walking Biking Running Sedentary Household Stairs HeartRateEnergyExpenditure EE HR
- 10. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Activity-Specific Energy Expenditure Models
- 11. EEEE 2 Subjects – Same Body Size Same Energy Expenditure
- 12. HRHR 2 Subjects – Same Body Size Different Fitness Level -> Different HR
- 13. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Activity-Specific Energy Expenditure Models
- 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. Study Design
- 18. 18 29 Subjects, 48 Activities Household Sport
- 19. VO2 max tests (gold standard) Sub-maximal tests Non-exercise tests Cardiorespiratory Fitness Assessment HR at a certain Workload -> Fitness
- 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. Heart Rate Running 10 km/h Fit Unfit Heart Rate Normalization Rest Context-Based Personalization
- 22. Heart Rate Context-Based Personalization Running 10 km/hRest Fit Unfit Heart Rate Normalization
- 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. 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. 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. EEEE 2 Subjects – Same Body Size Same Energy Expenditure
- 27. HRHR 2 Subjects – Same Body Size Different Fitness Level -> Different HR
- 28. Normalized HR – Qualitative Evaluation Normalized HR-> Better EE Estimates
- 29. Accelerometer Features Activity Recognition Anthropometric Features Activity 1 Model Activity N Model Energy Expenditure Heart Rate Normalized HR – Quantitative Evaluation
- 30. Accelerometer Features Activity Recognition Anthropometric Features Lying down Sedentary Energy Expenditure Heart Rate Normalized HR – Quantitative Evaluation Household Walking Biking Running
- 31. Accelerometer Features Activity Recognition Anthropometric Features Lying down Sedentary Energy Expenditure Heart Rate Normalized HR – Quantitative Evaluation Household Walking Biking Running
- 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. 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. 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
- 36. Limitations Inaccuracy One Model Does Not Fit All Motivations
- 37. Normalization Activities of Daily Living Limitations Motivations Inter-Individual Differences
- 39. Personalizing Energy Expenditure Estimation Using a Cardiorespiratory Fitness Predicate Marco Altini, Julien Penders, Oliver Amft Thank you