Date post: | 17-Feb-2017 |
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Engineering |
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Heart Rate Variability, Training & Performance
@marco_alt Lead Data Scientist @ Bloom Technologies
Maker HRV4Training.com PhD Candidate applied Machine Learning @ TU/e
[Marco Altini]
2012 - 2015
“Runner” / Scientist
2012 - 2015
“Runner”
Genetics?
Yeah, genetics
Almost there
2012 - 2015
Scientist
2009
Cardiorespiratory Fitness Estimation (VO2max)
Energy Expenditure Estimation (kcals) Activity Recognition
PhD (defense next week!) Applied Machine Learning
Eindhoven University of Technology
Making it smaller
Prediction of pregnancy
complications Labour detection
Load Data Scientist Bloom Technologies
Making it smaller
Heart Rate + Heart Rate Variability + Electrohysterography +
Blood Pressure Gestational hypertension prediction
Labour detection Preterm birth
Head of Data Science Bloom Technologies HRV4Training
60 Seconds PPG Measurements
Adapted from Tamura et al. Wearable Photoplethysmographic Sensors—Past and Present
Adapted from Tamura et al. Wearable Photoplethysmographic Sensors—Past and Present
• Accessibility – camera-based data acquisition
• User generated data & research – Pushing the boundaries on what we
know about the relations between training, lifestyle, physiology and performance
– More users, more parameters, more stratifications (lifestyle factors)
HRV4Training
• What is heart rate variability (HRV)?
• How to get the most out of HRV (best practices)
• What can we do with the data
• Opportunities from user generated data
Quick outline
2012 - 2015
What is HRV?
Beat to Beat Variation
Heart Rate Variability (HRV) • Regulated by sympathetic /
parasympathetic branches of the ANS
• Clear proxy to parasympathetic activity / recovery / body functions at rest – Understand how we react to stressors
Autonomic Nervous System
Higher HRV
Less physiologically stressed
Ready to perform
Lower HRV
More physiologically stressed
Tiredness
This slide is an oversimplification
• What is heart rate variability (HRV)?
• How to get the most out of HRV (best practices)
• What can we do with the data
• Opportunities from user generated data
Quick outline
2012 - 2015
How to get the most out of HRV measurements?
60 Seconds PPG Measurements
• Quick snapshot of your physiology (HR+HRV) – Parasympathetic activity
• Low barrier (fast, convenient, no sensors)
• Insightful – day to day variability due to external stressors
(training, travel, etc.), long term baseline changes (physical condition, chronic stress)
– If done properly!
Best Practices for 60 seconds PPG Measurements
• When to take the measurement
– Morning, during the day?, etc.
• What type of measurement – Lying down, sitting, orthostatic?
• Paced breathing – Constrained, unconstrained?
• What metric to use?
– Time domain, frequency domain?
• Are 60 seconds really enough?
When to take the measurement
• First thing after waking up – Relaxed physiological state – Limit all external stressors – Closest to what we do in research /
clinical studies – Don’t read your email before the
measurement!
What type of measurement
• Lying down while still in bed – Limits other factors like not waiting
enough after standing up – Performed in clinical studies – Sitting/Standing also valid, however for
simplicity I’d recommend lying down
Paced Breathing (1/3)
• Improves reliability and repeatability of the measurement – Breathing patterns and RSA have an
impact on HRV values – Using paced breathing provides more
consistent settings (same context!) – Use what works for you (8-12 breaths per
minute typically)
Paced Breathing (2/3)
Paced Breathing (3/3)
Paced Breathing (3/3)
Consistency!
• Choose: – A body position – A paced breathing rate – Waking time (more or less) / measurement
routine
Stick to those
What metric to use?
• HRV is not a single number • Use rMSSD or ln rMSSD – Marker of parasympathetic activity (only
thing you can reliably measure). There is no clear sympathetic marker
– HF, LF, HF/LF or other frequency domain features require more time (and are computed differently by everyone, difficult to generalize/compare)
Are 60 seconds really enough?
• Yes. Just follow the best practices
HRV4Training - measurement
Camera view
PPG view
60 seconds timer Breathing bar for paced breathing
Instantaneous heart rate
• What is heart rate variability (HRV)?
• How to get the most out of HRV (best practices)
• What can we do with the data
• Opportunities from user generated data
Quick outline
2012 - 2015
What can we do with the data in the context of training &
performance?
What to do with HRV data
• Acute HRV changes
• Multi parameter trends
Acute HRV changes Day to day variability
Acute HRV changes Rest or easy trainings
Higher HRV
Acute HRV changes Average or intense trainings
Lower HRV
Acute HRV changes
Acute HRV changes
Acute HRV changes
Acute HRV changes
Multi-parameter trends
• In the long term things get more complicated
• Higher HRV not necessarily linked to better condition/performance
• Understanding the big picture requires more parameters and context – Training load, other stressors
Multi-parameter trends
• HRV baseline and variation
• More variation could be indicative of maladaptation to training (weekly values all over the place)
Multi-parameter trends
• Detects: – Coping well – Maladaptations – Accumulated
fatigue
• What is heart rate variability (HRV)?
• How to get the most out of HRV (best practices)
• What can we do with the data
• Opportunities from user generated data
Quick outline
2012 - 2015
User generated data
User generated data Dataset
User generated data Acute HRV changes
User generated data Acute HRV changes
User generated data Acute HRV changes & consistency
User generated data Acute HRV changes & consistency
User generated data Acute HRV changes & consistency
User generated data
• What’s next – Better understand relation between
physiological parameters and physical condition in the long term
– Build better individual models
– Stratify on more parameters / include different samples of the population
Questions?
HRV4Training.com/faq
@marco_alt