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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. 1  
  2. 2. PHYSICAL ACTIVITY & HEALTH •  Lack of physical activity is a major problem today – Epidemics quickly expanding (hypertension, diabetes, etc.) 2  
  3. 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. 4. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION 4  
  5. 5. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION
  6. 6. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION
  7. 7. WEARABLE TECHNOLOGY FOR ENERGY EXPENDITURE ESTIMATION
  8. 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. 9. PHYSIOLOGY IS PERSON-SPECIFIC Energy Expenditure 9  
  10. 10. Energy Expenditure 10   PHYSIOLOGY IS PERSON-SPECIFIC
  11. 11. Energy Expenditure 11   PHYSIOLOGY IS PERSON-SPECIFIC
  12. 12. Energy Expenditure 12   PHYSIOLOGY IS PERSON-SPECIFIC
  13. 13. Energy Expenditure Heart Rate 13   PHYSIOLOGY IS PERSON-SPECIFIC
  14. 14. Energy Expenditure Heart Rate 14   PHYSIOLOGY IS PERSON-SPECIFIC
  15. 15. Energy Expenditure Heart Rate 15   PHYSIOLOGY IS PERSON-SPECIFIC
  16. 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. 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. 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. 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. 20. CONTEXT: LOW LEVEL ACTIVITIES 20   Wearable sensors (acceleration, heart rate)
  21. 21. 21   Wearable sensors (acceleration, heart rate) Supervised learning (generalized linear models, SVMs) Activity type, walking speed CONTEXT: LOW LEVEL ACTIVITIES
  22. 22. 22   Phones (GPS) CONTEXT: LOCATIONS
  23. 23. 23   Phones (GPS) Unsupervised methods (rules) Important places CONTEXT: LOCATIONS
  24. 24. 24   Low level activities, important places CONTEXT: HIGH LEVEL ACTIVITIES
  25. 25. 25   Low level activities, important places Unsupervised methods (topic models) High level activity composites CONTEXT: HIGH LEVEL ACTIVITIES
  26. 26. HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR Activity type, walking speed, daily routine 26  
  27. 27. Activity type, walking speed, daily routine Contextualized HR 27   HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR
  28. 28. Activity type, walking speed, daily routine Contextualized HR HR normalization parameter 28   Regression model HR NORMALIZATION PARAMETER ESTIMATION USING CONTEXT-SPECIFIC HR
  29. 29. Heart Rate 29   PHYSIOLOGY IS PERSON-SPECIFIC
  30. 30. Heart Rate Heart Rate Normalized 30   PHYSIOLOGY IS PERSON-SPECIFIC
  31. 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. 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. 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. 34. Activity type, walking speed, daily routine 34   CRF ESTIMATION USING CONTEXT-SPECIFIC HR
  35. 35. Activity type, walking speed, daily routine Contextualized HR 35   CRF ESTIMATION USING CONTEXT-SPECIFIC HR
  36. 36. HR   CRF model Activity type, walking speed, daily routine Contextualized HR 36   CRF ESTIMATION USING CONTEXT-SPECIFIC HR
  37. 37. HR   CRF model Activity type, walking speed, daily routine Contextualized HR CRF   37   CRF ESTIMATION USING CONTEXT-SPECIFIC HR
  38. 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. 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. 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. 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. 42. CRF   HR   42   CRF model EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
  43. 43. CRF   EE model 43   CRF   HR   CRF model EE   HR   ACC   EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
  44. 44. CRF   EE model 44   CRF   HR   CRF model EE   HR   ACC   EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
  45. 45. CRF   EE model 45   CRF   HR   CRF model EE   HR   ACC   EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
  46. 46. CRF   EE model 46   CRF   HR   CRF model EE   HR   ACC   EE ESTIMATION PERSONALIZED BY CRF: HIERARCHICAL MODELS
  47. 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. 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. 49  

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