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Decoding human physiology: a decade of research

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From hardware development to large-scale user-generated data and insights

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Decoding human physiology: a decade of research

  1. 1. Decoding human physiology Marco Altini, PhD February 13th, 2020 Tampere University of Technology
  2. 2. Marco Altini PhD in machine learning (2015) MSc, BSc in computer science engineering (2010) MSc in human movement sciences, high performance coaching (work in progress, 2020) ~50 publications and patents in the field Founder of HRV4Training, formerly at imec, Bloomlife bio 2
  3. 3. Currently in Amsterdam 3
  4. 4. “ My research interest is in the application of machine learning methods to healthcare and sports applications. Particularly, in using technology and data science to advance our knowledge on the relation between physiological, behavioral, lifestyle parameters and health & performance, therefore empowering the individual in the decision-making process 4
  5. 5. 5 2009
  6. 6. 6 2020
  7. 7. Why physiological data? ◉ Unique window into individual responses to stressors (physical, psychological, etc.) ◉ Actionable feedback (can drive behavioral change) ◉ Measurable 7
  8. 8. Challenges ◉ Unique window into individual responses to stressors (physical, psychological, etc.) ○ Double edge sword, very person-specific ◉ Actionable feedback (can drive behavioral change) ○ Difficulty in determining relevant changes and optimal action plan ◉ Measurable ○ Very prone to noise and high day to day variability (movement, artifacts, normal values, etc.) 8
  9. 9. Main topics covered in my research on human physiology ◉ Physiological data and personalization ◉ Technology development for accurate measurement of physiology in free living ◉ Insights from large scale user-generated data 9
  10. 10. Main topics covered in my research on human physiology ◉ Physiological data and personalization ◉ Technology development for accurate measurement of physiology in free living ◉ Insights from large scale user-generated data > Bringing it back to the individual, empowering decision making (N=1) 10
  11. 11. Physiological data and personalization Unsupervised normalization of physiological parameters to improve estimates at the individual level 11
  12. 12. Physiological data and personalization: example ◉ Energy expenditure estimation ○ Activity-specific models combining heart rate and accelerometer data (+activity recognition) ○ Heart rate normalization parameter estimation or cardiorespiratory fitness estimation to model inter- individual differences in heart rate responses to exercise without requiring individual calibration 12
  13. 13. Energy expenditure 13 Two individuals with similar body size and composition expend the same energy
  14. 14. Physiology is person-specific 14 Energy Expenditure Heart Rate Heart rate however can differ greatly depending on fitness: poor predictor
  15. 15. 15 Activity recognition + walking speed estimation + model to estimate submaximal heart rate
  16. 16. Automatic estimation of a heart-rate normalization factor: focus on the underlying physiological principle 16 Use submaximal heart rate to normalize heart rate Maximal heart rate is not representative of fitness!
  17. 17. Physiology is person-specific 17 Heart Rate Heart Rate Normalized
  18. 18. Personalization ◉ Energy expenditure estimation ○ No individual calibration required, large error reduction 18
  19. 19. Personalization ◉ Energy expenditure estimation ○ No individual calibration, large error reduction 19 Model VO2max directly instead of using a heart rate normalization parameter Domain knowledge!
  20. 20. Technology development for accurate measurement of physiology in free living Validated, easy to use consumer products. Help individuals manage their health + generated population-level insights 20
  21. 21. Technology development for accurate measurement of physiology in free living: prenatal health and training Validate the technology Discover new relations Confirm lab-based insights 21
  22. 22. Bloomlife Prenatal monitoring 22
  23. 23. First consumer product able to measure uterine and cardiac activity ◉ Carried out research on: ○ Contractions monitoring ○ Labour detection ○ Fetal movement detection ○ Pregnancy complications (preeclampsia, preterm birth) ◉ Physiological stress estimation 23
  24. 24. Technology development for accurate measurement of physiology in free living Validate the technology Discover new relations Confirm lab-based insights 24
  25. 25. 25 Rooijakkers et al. Intrauterine pressure measured and estimated using a non-invasive wearable sensor
  26. 26. Technology development for accurate measurement of physiology in free living Validate the technology Discover new relations Confirm lab-based insights 26
  27. 27. 27 Heart rate during labour increases due to contractions / pain > Heart rate crossings as feature is indeed higher during labour, discriminative power
  28. 28. Technology development for accurate measurement of physiology in free living Validate the technology Discover new relations Confirm lab-based insights 28
  29. 29. 29
  30. 30. 30 87% accuracy on balanced dataset
  31. 31. 31 Real life settings, higher noise and data imbalance
  32. 32. 32 Correctly identifies actual labour recordings with higher probability
  33. 33. HRV4Training Stress & recovery 33
  34. 34. Only validated app able to measure heart rate variability reliably using the phone camera ◉ Carried out research on: ○ Acute stressors and physiological responses (training, travel, sick days, alcohol intake) ○ Injury risk ○ VO2max estimation ○ Running performance estimation 34
  35. 35. Technology development Validate the technology Discover new relations Confirm lab-based insights 35
  36. 36. 36
  37. 37. 37
  38. 38. 38 0.99 correlation
  39. 39. Technology development Validate the technology Discover new relations Confirm lab-based insights 39
  40. 40. 40 Decrease in rMSSD across the cycle
  41. 41. Technology development Validate the technology Discover new relations Confirm lab-based insights 41
  42. 42. 42 ACWR linked to injuries only when HRV was low
  43. 43. Technology development for accurate measurement of physiology in free living: prenatal health and training Validate the technology Discover new relations Confirm lab-based insights 43
  44. 44. Technology development for accurate measurement of physiology in free living: prenatal health and training Validate the technology Discover new relations Confirm lab-based insights 44 What’s next? Scaling up
  45. 45. User-generated data Providing consumers with clinical-grade tools and data and analyzing such data at a scale beyond what is possible in regular studies 45
  46. 46. 46 Does the relation between HRV and training hold across age groups and genders?
  47. 47. 47 800 people. Consistent results across genders HRV more sensitive than heart rate
  48. 48. 48 800 people. Consistent results across age groups HRV more sensitive than heart rate
  49. 49. 49 What are the key parameters behind better running performance? Can we predict performance?
  50. 50. 50 Identify most important parameters in a large set of physiological and workouts data 220 measurements/person 450 000 measurements 300 000 workouts 2 years
  51. 51. 51 Develop accurate model Prediction: 4% error on 10 km time estimation
  52. 52. Place your screenshot here Bring it back to the user Knowledge acquired thanks to the development of a validated tool, released on the market, can be provided to the user to aid decision-making at the individual level 52
  53. 53. Main topics covered in my research on human physiology ◉ Physiological data and personalization ◉ Technology development for accurate measurement of physiology in free living ◉ Insights from large scale user-generated data 53 This process has brought me to my current goal, which is to empower the individual decision-making process using accurate, transparent and personalized technology
  54. 54. N = 1 Empowering the individual 54
  55. 55. How do we bring the insights back to the individual? ◉ Expected trends for a given stratification of the population ◉ Visualizations and interpretations highlighting the importance of comparing changes within an individual’s historical data and the expected measurement to measurement variability ◉ Actionability: when should we make changes? 55
  56. 56. Expected trends for a given stratification of the population 56 Decrease in HRV as normal physiological variation across the cycle. Better interpretation?
  57. 57. Visualizations and interpretation: context & big picture 57
  58. 58. Visualizations 58 Tell a story using all available context Historical data, recent trends, training load, subjective parameters…
  59. 59. Actionability ◉ What is the effect of heart rate variability biofeedback and mindfulness practice on chronic physiological stress and performance? ◉ When is the best time for high intensity training if we want in the context of improving performance for our target event? ◉ … 59
  60. 60. Main topics covered in my research on human physiology ◉ Physiological data and personalization ◉ Technology development for accurate measurement of physiology in free living ◉ Insights from large scale user-generated data 60 This process has brought me to my current goal, which is to empower the individual decision-making process using accurate, transparent and personalized technology. Work in progress!
  61. 61. Any questions ? You can find me at ◉ altini.marco@gmail.com ◉ marcoaltini.com ◉ HRV4Training.com Thank you 61
  62. 62. Decoding human physiology Marco Altini, PhD February 13th, 2020 Tampere University of Technology

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