Why your nervous system wearable isn't giving you accurate information
Towards an accurate autonomic diagnostics model
I was both surprised and a little amused to learn earlier this week that many of the consumer grade health trackers that utilize photoplethysmography (PPG) sensors to compute Heart-rate Variability (HRV) – e.g., Whoop, Oura ring, etc. – cannot tell the difference between excitement and stress.
This is a pretty large problem, if your goal is to track autonomic correlates of wellbeing, since excitement is salutogenic (health-creating) and stress is pathogenic (disease-creating).
Why can they not tell the difference? Well, the sympathetic outflows of these two states are nearly identical. Both elevate heart-rate (cardio-acceleratory), increase capillary dilation, change breathing, and alter galvanic skin response in characteristic ways. It turns out that trackers that use electro-dermal activity (EDA)/ galvanic skin response (GSR) have the same issue (Healbe, etc.).
If you are pulling an HRV signal, and using spectral analysis to decompose it, which is the standard way this is done, and your model of ANS function is obsolete, which all of these models are, your data cannot parse and structure correctly.
If you do not have an accurate meta-model of the ANS, your hardware will be unable to accurately distinguish between many autonomic states. Most of these trackers are still using a model of autonomic balance– the paired antagonism of autonomic systems– as the algorithmic frame to compute state. Polyvagal Theory successfully dismantled the notion of autonomic balance in 1994, with the then-radical recognition that there are two distinct cardio-inhibitory parasympathetic systems, which means that there are three, not two autonomic neurological systems. So the fact that the most advanced wearables still use a 100-year-old model of the ANS is, at some level, a failure to stay abreast of the latest autonomic science, for like 30+ years.
(Note that Canadian neurotechnology firm Unyte’s neuro-acoustical interventions have updated to the Porgesian model, as Stephen W. Porges is their Chief Scientific Advisor.)
We are getting ready to publish a white paper explaining why the PVT model of autonomic composition, based on the concept of Jacksonian Dissolution (1958) is still incomplete. This is something we have understood for several years, but are only now preparing to publish on (I wrote about it in The Neurobiology of Connection, we are just now translating it into white papers and beginning peer-review research, though we’ve already got a data set of several thousand clients on which the initial observations were based). This is one of the primary breakthroughs that informed our new foundation model.
If your Autonomic foundation model, by which I mean your operating assumptions about the variables that come together to create autonomic state, is not accurate, you will not be able to accurately structure neurological data coming in off of any autonomic signal: HRV, EDA/GSR, pupillary reflex, etc.
On June 27 I’m going to teach our diagnostics framework for the first time. This is part of the work we are doing on rolling out new clinical applications of Autonomics. If you are interested in applying autonomics to your clinical or coaching practice, this will be a practical hand’s-on session exploring how to map autonomic composition for yourself (which will translate into being able to do it with clients) using the Autonomics foundation model.
Training is from 7 to 10 am PDT on Friday June 27, 2025.
The training is appropriate for interested laypeople, athletes, elite performers, coaches, and wellness professionals of all kinds. While there are no pre-requisites, if you haven’t read The Neurobiology of Connection, either of our handbooks: Autonomic Compass: Finding Home in your Nervous System, or Autonomic Triage: A Handbook for Responders, would be a good place to start preparing.