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
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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
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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. 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
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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)
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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
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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!
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. 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. 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
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
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24. Technology development for accurate
measurement of physiology in free
living
Validate
the
technology
Discover
new
relations
Confirm
lab-based
insights
24
26. Technology development for accurate
measurement of physiology in free
living
Validate
the
technology
Discover
new
relations
Confirm
lab-based
insights
26
27. 27
Heart rate during
labour increases due
to contractions /
pain
>
Heart rate crossings
as feature is indeed
higher during labour,
discriminative
power
28. Technology development for accurate
measurement of physiology in free
living
Validate
the
technology
Discover
new
relations
Confirm
lab-based
insights
28
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
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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. 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
49. 49
What are the key
parameters
behind better
running
performance?
Can we predict
performance?
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
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
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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
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This process has brought me to my current goal, which is to
empower the individual decision-making process using
accurate, transparent and personalized technology
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?
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56. Expected trends for a given stratification of the population
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Decrease in
HRV as normal
physiological
variation across
the cycle. Better
interpretation?
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. 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
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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. Any questions ?
You can find me at
◉ altini.marco@gmail.com
◉ marcoaltini.com
◉ HRV4Training.com
Thank you
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