5 ms·
Beyond sensor data: Foundation models of behavioral data from wearables
- fiduciarytemp 1y agoHas anyone seen the publishing of the weights or even an API release?
- brandonb 1y agoIn the paper, they say they can't release the weights due to terms of consent with study participants (this is from the Apple Heart and Movement study).
- brandonb 1y agoI worked on one of the first wearable foundation models in 2018. The innovation of this 2025 paper from Apple is moving up to a higher level of abstraction: instead of training on raw sensor data (PPG, accelerometer), it trains on a timeseries of behavioral biomarkers derived from that data (e.g., HRV, resting heart rate, and so on.). They find high accuracy in detecting many conditions: diabetes (83%), heart failure (90%), sleep apnea (85%), etc.
- puppymaster 1y agoreminds me of Jim Simons of Renaissance advise when it comes to data science - sort first, then regress.
- deleted 1y ago[deleted]
- dinobones 1y agoNot sort in the literal way right? https://stats.stackexchange.com/questions/185507/what-happens-if-the-explanatory-and-response-variables-are-sorted-independently https://stats.stackexchange.com/questions/185507/what-happen...
- clickety_clack 1y agoThe guy was sorting the X separately from y? That can’t be a real.
- falcor84 1y ago"Nothing is foolproof to a sufficiently talented fool"
- tomrod 1y agoNot every day you find pseudo permutation in the wild!
- throwaway314155 1y agoHad the phrase "foundation model" become a term of art yet?
- brandonb 1y agoBy 2018, the concept was definitely in the air since you had GPT-1 (2018) and BERT (2018). You could argue even Word2Vec (2013) had the core concept of pre-training on an unsupervised or self-supervised objective leading to performance on a downstream semantic task. However, the phrase "foundation model" wasn't coined until 2021, to my knowledge.
- throwaway314155 1y agoI guess I just find the whole "foundation model" phrasing to be designed in a way to pat the backs of the "winners" who would of course be those with the most money. I'm sure there are foundation models from groups that aren't e.g. OpenAI, but the origins felt egotistical and asserting that you made one prior to the phrase's inception only feels more-so. Had you merely called it an early instance of pretraining, I'd be fine with it.
- crorella 1y agoInsurance and health insurance companies must be super interested in this research and its applications.
- jeron 1y agoI'm sure they're also interested in the data. Imagine raising premiums based on conditions they detect from your wearables. That's why it's of utmost importance to secure biometrics data
- brandonb 1y agoAt least in the US, health insurers can’t raise rates or deny coverage based on pre-existing conditions. That was a major part of the Affordable Care Act.
- abenga 1y agoThe ACA will not survive the next couple of years.
- daveguy 1y agoThat's what they said in 2016.
- abenga 1y agoThis time he has control over all the arms of government + the support of much of the private sector though. The last time there was at least some push-back.
- daveguy 1y agoThey had the exact same thing last time too. Control over all arms + support of much of the private sector. We told them to go fuck themselves then and we will again.
- teiferer 1y agoWhat is an "accuracy" of 83%? Do 83% of predicted diabetes cases actually have diabetes? Or did 83% of those who have diabetes get diagnosed as such? It's about precision vs. recall. You can improve one by sacrificing the other. Boiling it down to one number is hard.
- topaz0 1y agoThey use the area under the receiver operating curve, which is a pretty standard way to boil that down to one number.
- teiferer 1y agoAh, thanks for the pointer! https://en.m.wikipedia.org/wiki/Receiver_operating_characteristic https://en.m.wikipedia.org/wiki/Receiver_operating_character... So, 83% is actually not that great, given that you can achieve 50% by guessing randomly.
- dyauspitr 1y agoIs there a way to run this on your own data? I’ve been wearing my Apple Watch for years and would love to be able to use it better.
- brandonb 1y agoNot yet -- this one is just a research study. Some of their previous research has made it into product features. For example, Apple Watch VO2Max (cardio fitness) is based on a deep neural network published in 2023: https://www.empirical.health/blog/how-apple-watch-cardio-fitness-vo2max-works/ https://www.empirical.health/blog/how-apple-watch-cardio-fit...
- pricklyprice 1y agoApple was reporting VO2max for a very long time (much before 2023). I wonder what the accuracy was back then? Maybe they should the option for users to re-compute those past numbers based on the latest and greatest algorithm.
- llm_nerd 1y agoApple's VO2Max measures are not based upon that deep neural network development, and empirical seems to be conflating a few things. And FWIW, just finding the actual paper is almost impossible as that same site has SEO-bombed Google so thoroughly you end up in the circular-reference empirical world where all of their pages reference each other as authorities. Apple and Columbia did recently collaborate on a heart rate response model -- one which can be downloaded and trialed -- but that was not related to the development of their VO2Max calculations. Apple is very shrouded about how they calculate VO2Max, but it likely is a pretty simple calculation (e.g. how much is your heart responding based upon the level of activity assumed based upon your motion, method of exercise and movements). The most detail they provide is in https://www.apple.com/healthcare/docs/site/Using_Apple_Watch_to_Estimate_Cardio_Fitness_with_VO2_max.pdf https://www.apple.com/healthcare/docs/site/Using_Apple_Watch..., which mostly is a validation that it's providing decent enough accuracy.
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- vibecodermcswag 1y agoi love this because I build in medtech, but the big problem is no open weights, nor open data. you can export your own apple XML data for usage and processing, but if you want to create an application and request apple XML data from users, that likely crosses into clinical research territory with data security policy requirements and de-identification needs.
- pricklyprice 1y agowhat is the best way for non-big tech to buy such data for research and product development?
- guzik 1y agoSome are for free: - aidlab.com/datasets - physionet.org
- pricklyprice 1y agothanks for sharing. I also found wearable dataset of ~1k users https://cseweb.ucsd.edu/~jmcauley/datasets/fitrec.html https://cseweb.ucsd.edu/~jmcauley/datasets/fitrec.html
- autoexec 1y agodata brokers.
- piratesAndSons 1y agoTrusting your health data with AI brothers is... extremely ill-advised. I don't even trust Apple themselves, which will sell your health data any insurance company any minute now.
- kridsdale1 1y agoWhat do you base that suspicion on?
- MangoToupe 1y agoCan someone explain what "wearable foundation" means?
- compiler-guy 1y agoIt's a "Foundation Model" for wearable devices. So "wearable" describes where it is to be used, rather than describing "foundation".
- memming 1y agoInteresting to see contrastive loss instead of a reconstruction loss.
- LPisGood 1y agoIs anyone else surprised by how poorly performing the results are for the vast majority of cases? The foundation model which had access to sensor data and behavioral biomarkers actually _underperformed_ the baseline predictor that just uses nonspecific demographic data in almost 10 areas. In fact, even when the wearable foundation model was better, it was only marginally better. I was expecting much more dramatic improvements with such rich data available.
- Herring 1y agoI worked with similar data in grad school. I'm not surprised. You can have a lot of data, but sometimes the signal (or signal quality) just isn't present in that haystack, and there's nothing you can do about it. Sometimes you just have to use ultrasound or MRI or stick a camera in the body, because everything else might as well be reading tea leaves, and people generally demand very high accuracy when it comes to their health.
- bumby 1y agoI wonder how much of that is driven by poor performing behavioral models. There was a HN article from a few weeks back and it only had an accuracy of about 70% determining if someone was awake or asleep. I would guess that the secondary behavioral data used in this data (like cardiovascular fitness) are much harder to predict from raw sensor data than being awake or asleep.
- deleted 1y ago[deleted]
- aanet 1y agoThanks for posting this. This looks promising... I have about 3-3.5 years worth of Apple Health + Fitness data (via my Apple Watch) encompassing daily walks / workouts / runs / HIIT / weight + BMI / etc. I started collecting this religiously during pandemic. The exported Fitness data is ~3.5GB I'm looking to do some longitudinal analysis - for my own purposes first, to see how certain indicators have evolved. Has anyone done something similar? Perhaps in R, Python? Would love to do some tinkering. Any pointers appreciated! Thanks!!
- kridsdale1 1y agoIt might actually be worth writing your analysis in Swift with the actual HealthKit API and visualization libraries. Bonus: when you’re done, you’ll have an app you can sell.
- aanet 1y ago:thumbs_up.gif: My sentiments, exactly. Though I'm looking to scratch my own itch for now...
- bob_theslob646 1y agoHow hard is it to write in swift?
- brandonb 1y agoFWIW, we're working on something similar (you wouldn't necessarily need to write R or Python). Feel free to email me at bmb@empirical.health and I can add you to a beta once we have it ready!
- aanet 1y agoThanks, I'll reach out. I am curious to do my own analysis, for two main reasons: - some data is confidential (I'd hate for it to leave my devices) - wanna DIY / learn / iterate Will ping you in any case. Thanks
- rsanek 1y agoCool way of integrating the two approaches. For those on mobile, I created an infographic that's a bit more accessible: https://studyvisuals.com/artificial-intelligence/beyond-sensor-data-foundation-models-of-behavioral-data-from-wearables-improve-health-predictions.html https://studyvisuals.com/artificial-intelligence/beyond-sens...