Week 40: added support for iPhone 16
hi there 👋
I hope everything is well.
We have been working on adding compatibility for all new iPhone 16 models (with two or three cameras), and have already released updates for HRV4Training, HRV4Biofeedback (our deep breathing tool), and Camera HRV (our research app).
Below is some data collected using an iPhone 16 Pro and compared against a chest strap, the Polar H10. The protocol consisted of either normal breathing at rest, while seated, or deep breathing exercises. For data collection, I used the HRV Logger app to collect RR intervals from the chest strap, and the Camera HRV app to collect RR intervals using our camera-based algorithms. Both apps are available here.
RR and PP intervals:
rMSSD computed from RR and PP intervals for the data above:
For participant 001 we have an ECG-derived rMSSD of 54.6 ms and a PPG-derived rMSSD of 53.2 ms. For participant 002 we have an ECG-derived rMSSD of 95.6 ms and a PPG-derived rMSSD of 96.8 ms. Overall, rMSSD over the 8 minutes of recordings is 75.1 ms for the Polar H10 and 75 ms for the camera-based algorithms. Not bad.
Here you can also see a short video with the raw PPG data in HRV4Training.
As always, we report signal quality at the end of the measurement, so that you can ensure the collected data is not artifacted (which you can also see visually in the PPG signal). Needless to say, you don’t get any of that using wearables.
The methods shown here are very similar to what we have described in our validation paper, which you can find here.
Enjoy.
Ambassador Program 2025
Applications to become HRV4Training ambassadors for next year are still open. You can learn more and apply, here.
Discount for Pro: 20% off 🖥️
HRV4Training Pro is the ultimate platform to help you analyze and interpret your physiological data, for individuals and teams.
You can find a guide here.
Try HRV4Training Pro for free at HRVTraining.web.app or use promo code SCIENCE for 20% off.
In the app, Pro brings the normal values view, which can help contextualizing longer-term changes, as well as rMSSD on the homepage, see an example below:
See you next week!
Marco holds a PhD cum laude in applied machine learning, a M.Sc. cum laude in computer science engineering, and a M.Sc. cum laude in human movement sciences and high-performance coaching.
He has published more than 50 papers and patents at the intersection between physiology, health, technology, and human performance.
He is co-founder of HRV4Training, advisor at Oura, guest lecturer at VU Amsterdam, and editor for IEEE Pervasive Computing Magazine. He loves running.
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