Blog post by Marco Altini
I was recently contacted by Kow Ping, co-founder of Well Being Digital Limited (WBD101), a company which makes heart rate sensing technology for hearables, as we have been in touch regarding PPG-based technologies for HRV analysis.
WBD101 makes technology which can be embedded in different products, with the goal of providing high quality heart rate data. The technology has been embedded in the Hera Leto Two earphones, sold by Actywell.
Kow has been kind enough to send me a device for testing, and therefore I report here an initial validation showing really good RR intervals when compared to both our camera-based solution and our trusted reference chest strap, the Polar H7.
Data was acquired using the following devices at the same time:
During data acquisition, I collected data a few minutes while breathing freely, and a few minutes while deep breathing, to elicitate higher HRV due to RSA. You will see in the plots below visually the effect of deep breathing as we get greater swings in RR intervals.
A final note on data synchronization: data cannot be perfectly synchronized because it is not timestamped by the sensors. What we can do is either to log real time and then to split data in windows based on when data was collected, then compute HRV features on these windows or to sum up RR intervals over time. For this analysis we went with the second option and also tried to visually align the data streams.
As usual, if you use the camera for your measurements, please double check our camera based measurement best practices, to make sure you'll collect high quality data on which meaningful analytics can be derived.
We will start by looking at RR intervals, the basic unit we need to compute HRV features. RR intervals (peak to peak differences in consecutive heart beats) are provided by the two sensors directly, so we don't really need to do much to collect them, apart from linking the sensor to the HRV Logger app and export the csv files.
As a third comparison, we will add also an RR intervals time series collected using the phone camera, which is the method we have introduced and normally recommend using.
What can we derive from these data? You can see clearly almost perfect correlation between Polar H7 and Hera Leto Two and Phone Camera for all conditions (relaxed vs paced breathing as highlighted by bigger oscillations in RR intervals or instantaneous heart rate), meaning that the sensor works really well.
Heart Rate Variability: rMSSD
As features, we will look only at rMSSD. rMSSD is a clear marker of parasympathetic activity and the main feature we use for our analysis in HRV4Training, from which we derive Recovery Points (check out this post for an overview of HRV features).
What we expect given the data above is to see extremely close values between the Polar H7 chest strap and Hera Leto Two, as well as the camera based measurements.
For the plot below, I computed rMSSD for each 60 seconds time window:
Results are very good considering normal variation in physiology and limitations in data synchronization.
We can see how in the first 3 minutes results also include little difference between consecutive minutes, as the protocol there involved simply self-paced breathing, similarly to what we recommend doing in the morning. rMSSD then increases as we enter the deep breathing phase, and finally reduces post deep breathing session.
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This blog is curated by
Marco Altini, founder of HRV4Training
The Ultimate Guide to HRV
1: Measurement setup
2: Interpreting your data
3: Case studies and practical examples
1. Intro to HRV
2. How to use HRV, the basics
3. HRV guided training
4. HRV and training load
5. HRV, strength & power
6. Overview in HRV4Training Pro
7. HRV in team sports
1. Context & Time of the Day
3. Paced breathing
4. Orthostatic Test
5. Slides HRV overview
6. Normal values and historical data
7. HRV features
1a. Acute Changes in HRV
1b. Acute Changes in HRV (population level)
1c. Acute Changes in HRV & measurement consistency
1d. Acute Changes in HRV in endurance and power sports
2a. Interpreting HRV Trends
2b. HRV Baseline Trends & CV
3. Tags & Correlations
4. Ectopic beats & motion artifacts
5. HRV4Training Insights
6. HRV4Training & Sports Science
7. HRV & fitness / training load
8. HRV & performance
9. VO2max models
10. Repeated HRV measurements
11. VO2max and performance
12. HR, HRV and performance
13. Training intensity & performance
14. Publication: VO2max & running performance
15. Estimating running performance
16. Coefficient of Variation
17. More on CV and the big picture
18. Case study marathon training
19. Case study injury and lifestyle stress
20. HRV and menstrual cycle
21. Cardiac decoupling
22. FTP, lactate threshold, half and full marathon time estimates
23. Training Monotony
Camera & Sensors
1. ECG vs Polar & Mio Alpha
2a. Camera vs Polar
2b. Camera vs Polar iOS10
2c. iPhone 7+ vs Polar
2d. Comparison of PPG sensors
3. Camera measurement guidelines
4. Validation paper
5. Android camera vs Chest strap
6. Scosche Rhythm24
7. Apple Watch
9. Samsung Galaxy
1. Features and Recovery Points
2. Daily advice
3. HRV4Training insights
4. Sleep tracking
5. Training load analysis
6a. Integration with Strava
6b. Integration with TrainingPeaks
6c. Integration with SportTracks
6d. Integration with Genetrainer
6e. Integration with Apple Health
6f. Integration with Todays Plan
7. Acute HRV changes by sport
8. Remote tags in HRV4T Coach
9. VO2max Estimation
10. Acute stressors analysis
11. Training Polarization
12. Lactate Threshold Estimation
13. Functional Threshold Power(FTP) Estimation for cyclists
14. Aerobic Endurance analysis
15. Intervals Analysis
16. Training Planning
17. Integration with Oura
18. Aerobic efficiency and cardiac decoupling
1. HRV normal values
2. HRV normalization by HR
3. HRV 101