There are so many devices, each have their own algorithm for sleep tracking and the device I pick largely depends on the accuracy of the device's sleep tracking.
However there are new apps, like Nukkuaa, an (EU only) sleep analysis app that uses any heart-rate tracker (like a bluetooth fitness chest strap) and infers sleep quality from the data.
A generic app expands what devices are available to me. Right now I use CGM tape and stick a Fitbit Charge 3 on my tricep - but I dislike the Fitbit app and if I change to another brand I cannot export my logs.
A third party app has the potential to train against more data than any individual single brand could and, with appropriate tagging, could possible offer better accuracy. Open source would be icing on the cake.
Additionally, it would be great if devices like the Fitbit Charge could be used as a bluetooth heart rate monitor that can be used on apps that consume trackers on a (presumably) standard API (like TrainerRoad, Zwift, etc).
I use the Oura ring [1] for sleep tracking, it's by far the best I know of for sleep. The fact that it's a ring is convenient for comfort, and very precise as it adheres well to the finger even during night movements.
> Additionally, it would be great if devices like the Fitbit Charge could be used as a bluetooth heart rate monitor that can be used on apps that consume trackers on a (presumably) standard API (like TrainerRoad, Zwift, etc).
Not open source but Sleep as Android is a great app that integrates with many different devices to track your sleep. They also have their own devices that can help improve tracking.
I understood sleep trackers are useless. Here your doctor can lend you some professional watches you can wear for 2 weeks that cost 700 eur, but those are not sold to the public. Unless things did change in the past 4 years.
And even then, they can not really measure your sleep. If you want to measure sleep, you will need to have wires on your scalp that measure what happens inside your brain.
Studies show that some device (like the Fitbit Charge 6, Apple watch) infer sleep stages with a greater than 80% agreement compared to an ECG sleep study.
--- For primarily younger white males
But still pretty good seeing as they infer this largely from heart rate and movement.
Go do an actual sleep study some time. You'll be hooked up to a ton of sensors, and in the morning a group of doctors pour over the results, and they'll basically vote to decide on what the data means. "Oh this looks like REM".
So, to calibrate a sleep tracking device, you have a person wear the device, while also doing the sleep study. You do this a bunch of times. You train some ML models to try and make the outputs from the sensor data, after processing, the same as the study data.
After some degree of accuracy you declare success.
Now, does it work? In broad strokes, yes. You can (easily!!) see the effect of alcohol on sleep quality. If you have a crap night vs a good night, sure, a wrist based consumer device can figure that out.
Actual details? Eh. I wouldn't trust the devices for anything but directional data.
The more sensors devices get, the better than ML model can be trained.
Now it has been awhile since I last worked on this stuff (I actually just sat next to the people doing the work), so maybe there is some revolutionary new technique out there, but if not, it is still ML models trying to correlate things and match them up to what a bunch of fancier sensors said during studies.
Typical watch based sleep trackers and even my Withings sleep pad can’t really track my sleep properly, particularly REM sleep. I think I move too much. I bought a Dreem2 EEG device that measures brain waves and it could detect my REM sleep correctly, and determined that my sleep is actually fine, not great, but good enough.
I've been using FitoTrack from the F-Droid appstore. It works fantastic, offline, private, and looking at the screenshots of the Outrun app have very similar look & layout.
I’ve been wanting something like this but so long, and had it on my list of projects to potentially try making. All I need is basic tracking of my walks/runs to motivate myself, and have zero desire for all the other bloat most fitness apps have.
It's a name for two (admittedly entirely unrelated use case) pieces of software. Just because something may or may not stand up in court doesn't automatically preclude lawyers from sending Cease & Desist letters or what ever. Just look at the nonsense from Take Two claiming that Remedy's new logo is too close to the Rock Star logo [1]. Any reasonable person wouldn't confuse the two, but still the lawyers are involved.
I started building something like this for myself (as a CLI tool, so way less ambitious). It’s a pretty fun project with a tight feedback loop.
Parsing GPS data is surprisingly simple.
It surprised me to learn that different apps will “smooth” out measures like elevation and distance — it’s actually quite a rich problem space with a few interesting solutions.
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There are so many devices, each have their own algorithm for sleep tracking and the device I pick largely depends on the accuracy of the device's sleep tracking.
However there are new apps, like Nukkuaa, an (EU only) sleep analysis app that uses any heart-rate tracker (like a bluetooth fitness chest strap) and infers sleep quality from the data.
A generic app expands what devices are available to me. Right now I use CGM tape and stick a Fitbit Charge 3 on my tricep - but I dislike the Fitbit app and if I change to another brand I cannot export my logs.
A third party app has the potential to train against more data than any individual single brand could and, with appropriate tagging, could possible offer better accuracy. Open source would be icing on the cake.
Additionally, it would be great if devices like the Fitbit Charge could be used as a bluetooth heart rate monitor that can be used on apps that consume trackers on a (presumably) standard API (like TrainerRoad, Zwift, etc).
- https://www.nukkuaa.com/en
[1] https://ouraring.com/raf/34be98b1fd?utm_medium=iac
At such a price (more than my Apple Watch) I expect all the analysis to be done on the phone.
> research-grade sensors
Are these people really this shallow? What does that even mean
The new charge 6 does have this capability. [0]
[0](https://support.google.com/fitbit/answer/14236705)
And even then, they can not really measure your sleep. If you want to measure sleep, you will need to have wires on your scalp that measure what happens inside your brain.
--- For primarily younger white males
But still pretty good seeing as they infer this largely from heart rate and movement.
So, to calibrate a sleep tracking device, you have a person wear the device, while also doing the sleep study. You do this a bunch of times. You train some ML models to try and make the outputs from the sensor data, after processing, the same as the study data.
After some degree of accuracy you declare success.
Now, does it work? In broad strokes, yes. You can (easily!!) see the effect of alcohol on sleep quality. If you have a crap night vs a good night, sure, a wrist based consumer device can figure that out.
Actual details? Eh. I wouldn't trust the devices for anything but directional data.
The more sensors devices get, the better than ML model can be trained.
Now it has been awhile since I last worked on this stuff (I actually just sat next to the people doing the work), so maybe there is some revolutionary new technique out there, but if not, it is still ML models trying to correlate things and match them up to what a bunch of fancier sensors said during studies.
What else works well with gadgetbridge?
Any ios equivalent?
[0]: https://opentracksapp.com/
That’s not ideal, but I’m definitely not the target market as I’m happy to buy one of the higher end Garmin models.
[1] https://www.gamesindustry.biz/take-two-reportedly-in-tradema...
Parsing GPS data is surprisingly simple.
It surprised me to learn that different apps will “smooth” out measures like elevation and distance — it’s actually quite a rich problem space with a few interesting solutions.