PhD defense slides
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Slides for my PhD defense. Title: "Personalization of energy expenditure and cardiorespiratory fitness estimation using wearable sensors in supervised and unsupervised free-living conditions" - Full text: http://www.marcoaltini.com/uploads/1/3/2/3/13234002/20150919_thesis.pdf
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PhD defense slides
- 1. 1
- 2. PHYSICAL ACTIVITY & HEALTH • Lack of physical activity is a major problem today – Epidemics quickly expanding (hypertension, diabetes, etc.) 2
- 3. PHYSICAL ACTIVITY & HEALTH • Lack of physical activity is a major problem today – Epidemics quickly expanding (hypertension, diabetes, etc.) • Wearable technology & continuous monitoring: – Better understand relations between physical activity and health – Drive behavioral change 3
- 4. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION 4
- 5. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION
- 6. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION
- 7. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION
- 8. Combined data streams • Higher accuracy • Detect activities • Strong link between heart rate, oxygen uptake and energy expenditure WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION
- 9. PHYSIOLOGY IS PERSON-SPECIFIC Energy Expenditure 9
- 10. Energy Expenditure 10 PHYSIOLOGY IS PERSON-SPECIFIC
- 11. Energy Expenditure 11 PHYSIOLOGY IS PERSON-SPECIFIC
- 12. Energy Expenditure 12 PHYSIOLOGY IS PERSON-SPECIFIC
- 13. Energy Expenditure Heart Rate 13 PHYSIOLOGY IS PERSON-SPECIFIC
- 14. Energy Expenditure Heart Rate 14 PHYSIOLOGY IS PERSON-SPECIFIC
- 15. Energy Expenditure Heart Rate 15 PHYSIOLOGY IS PERSON-SPECIFIC
- 16. • How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level? • Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status? 16 RESEARCH QUESTIONS
- 17. • How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level? • Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status? 17 RESEARCH QUESTIONS
- 18. Current solutions: • Population based models: everyone is the same • Laboratory calibrations are performed to determine normalization parameters (e.g. running heart rate) and personalize models - i.e. context-specific HR 18 INDIVIDUAL DIFFERENCES IN PHYSIOLOGY
- 19. • Use wearable sensors and machine learning methods to determine context • Use physiological data during specific contexts to predict normalization parameters and personalize EE models without laboratory calibrations 19 OUR APPROACH
- 20. CONTEXT: LOW LEVEL ACTIVITIES 20 Wearable sensors (acceleration, heart rate)
- 21. 21 Wearable sensors (acceleration, heart rate) Supervised learning (generalized linear models, SVMs) Activity type, walking speed CONTEXT: LOW LEVEL ACTIVITIES
- 22. 22 Phones (GPS) CONTEXT: LOCATIONS
- 24. 24 Low level activities, important places CONTEXT: HIGH LEVEL ACTIVITIES
- 25. 25 Low level activities, important places Unsupervised methods (topic models) High level activity composites CONTEXT: HIGH LEVEL ACTIVITIES
- 26. HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR Activity type, walking speed, daily routine 26
- 27. Activity type, walking speed, daily routine Contextualized HR 27 HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR
- 28. Activity type, walking speed, daily routine Contextualized HR HR normalization parameter 28 Regression model HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR
- 29. Heart Rate 29 PHYSIOLOGY IS PERSON-SPECIFIC
- 30. Heart Rate Heart Rate Normalized 30 PHYSIOLOGY IS PERSON-SPECIFIC
- 31. PHYSIOLOGY IS PERSON-SPECIFIC Heart Rate Heart Rate Normalized 31 dynamic walking running biking 28% 33%29%3% 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 • Reduces error up to 33% • Does not require individual calibration or laboratory recordings
- 32. RESEARCH QUESTIONS • How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level? • Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status? 32
- 33. CARDIORESPIRATORY FITNESS ESTIMATION • Cardiorespiratory fitness is a widely used marker of overall health – Higher CRF showing lower risk of all cause mortality Current solutions: • Maximal and submaximal tests: can be risky for individuals in suboptimal health conditions, expensive, require medical supervision, laboratory equipment, spot measurement only 33
- 34. Activity type, walking speed, daily routine 34 CRF ESTIMATION USING CONTEXT-SPECIFIC HR
- 35. Activity type, walking speed, daily routine Contextualized HR 35 CRF ESTIMATION USING CONTEXT-SPECIFIC HR
- 36. HR CRF model Activity type, walking speed, daily routine Contextualized HR 36 CRF ESTIMATION USING CONTEXT-SPECIFIC HR
- 37. HR CRF model Activity type, walking speed, daily routine Contextualized HR CRF 37 CRF ESTIMATION USING CONTEXT-SPECIFIC HR
- 38. HR CRF model Activity type, walking speed, daily routine Contextualized HR CRF 38 • 10.3% error reduction when using low level context • 22.6% error reduction when combining low and high level context CRF ESTIMATION USING CONTEXT-SPECIFIC HR
- 39. RESEARCH QUESTIONS • How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level? • Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status? 39
- 40. • How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level? • Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status? 40 RESEARCH QUESTIONS
- 41. • How can we dynamically personalize heart rate-based models to improve EE estimation at the individual level? • Can we move beyond behavioral aspects of physical activity (e.g. EE, steps) and estimate cardiorespiratory fitness as a proxy to health status? 41 RESEARCH QUESTIONS
- 42. CRF HR 42 CRF model EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
- 43. CRF EE model 43 CRF HR CRF model EE HR ACC EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
- 44. CRF EE model 44 CRF HR CRF model EE HR ACC EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
- 45. CRF EE model 45 CRF HR CRF model EE HR ACC EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
- 46. CRF EE model 46 CRF HR CRF model EE HR ACC EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
- 47. EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS CRF EE model 47 CRF HR CRF model EE HR ACC • No need for explicit HR normalization • RMSE reduced by 18% on average
- 48. CONCLUSIONS • We personalized EE estimation models without the need for individual calibration in laboratory settings – reduced RMSE up to 33% (HR normalization and hierarchical modeling) • We proposed new methods for context recognition and CRF estimation in free- living without requiring laboratory tests – reduced CRF estimation error up to 22.6% 48
- 49. 49