u/Fickle_Structure9936

I'm building a fitness app called Trainio that is using WearOS watch sensor data (acceleration sensors) to automatically count repetitions of gym exercises (push ups, bicep curls, lateral raises..). And, eventually, also identify execution form issues (like using too much momentum).

However, I have only really tested it on my Pixel Watch 2. Does anyone know how strong the differences in sensor data between WearOS watches are? Does the acceleration data from Galaxy watch, One plus, etc. look very different from Pixel Watch data? Because if so, my current exercise recognition AI model would probably struggle with these devices and I'd need to develop device specific models.

If you do have a different watch than the Pixel, I would obviously very much appreciate if you could try if the repcounting for one of the above exercises works for you. With the Pixel my reps are identified with ca. 90% accuracy (probably will be lower for people with other body shapes, workout speeds etc.)

Hey guys,

I've launched my app Trainio in the PlayStore 4 weeks ago. I now have first data points on the app performance:

• Installations: 65
• Active Devices: 47
• Monthly Active Users: 37
• Active Users per Day: 5

As I have absolutely no reference, is that good/bad?

Which metric should I optimize for, i.e. should I start actively marketing to get more installs and potentially feedback or should I focus on retention by improving my apps quality?

Are there other KPIs I am missing? I have started implementing Firebase controls to see which critical flows the user is completing (e.g. are users dropping off during onboarding because it is too complex?).

Not sure if that's relevant for the data assessment, but it's a fitness app that includes a WearOS companion app on which you can complete the workout offline without phone and I'm also experimenting with automated repetition counting for some exercises. Also some agentic AI features that analyze user workouts and tells the user what he should focus on. So, basically, a fitness app on steroids if you will..haha.

Grateful for any advice. If you wanna try it out, you're more than welcome as well obviously. THX!

I’m building a Wear OS companion app for my fitness app Trainio and have been experimenting with rep counting / exercise recognition from smartwatch motion sensor data.

Current setup:

• collect accelerometer + gyroscope data on the watch during exercises
• segment the motion data into workout/set windows
• run a trained model to classify the exercise and estimate repetitions
• sync the result back to the Android phone app

For now I’m testing this on a limited set of exercises where the accuracy is pretty reasonable (>90%), e.g. push-ups, bicep curls, lateral raises, etc. I have also been able to identify further exercises like bench press, overhead press, squats, but with less accuracy. (ca. 80%)

The hard parts are not really the model training itself, but the product/engineering edge cases:

• differences in sensor data from different watches
• different rep speeds / partial reps
• distinguishing exercise movement from random wrist movement ('ghost reps')
• battery impact if sensors run too aggressively (1 hour workout consumes ca. 30% of watch battery)
• deciding what should run on-device vs after sync on the phone/backend
• avoiding false confidence when the model is uncertain
• CSV file size for a 1 hour workout is currently around 30mb which is also not small. But I think that's ok

Any good advice/experience on these points?

I've been lifting for a while and the thing that always annoyed me was logging. On the one hand, I love data and strongly believe that documenting your numbers is key for staying motivated and making progress. But either I'd forget sets, or I'd break my flow fumbling with my phone between them. Most apps Strong, Hevy, etc. are great but still phone-first — the watch is an afterthought.

So I started building my own thing on the side. It's called Trainio. Android only mobile + WearOS.

What it does well right now:

Full workout logging from the watch. You can start, run, and finish an entire session without ever pulling out your phone — log weight and reps, swap exercises, add sets, rest timer, the whole thing. Online, offline, LTE, no LTE... doesn't matter. The watch app is the part I'm most happy with.

Some features of the mobile app:

• An AI coach with access to your history that can generate / modify plans and answer questions. This is not another retarded version of ChatGPT, but you can actually ask the Trainio AI whether it can spot any muscular dysbalances based on your workout history, analyze workout trends, and how you can improve your current workout routines.
• I have started building an automated repcounting feature. I've built an AI model that takes acceleration data from the watch as input and can count repetitions automatically for some exercises (push ups, bicep curls, lateral raises). I only tried this with the Galaxy Watch so far, so I hope this works for other devices just as well.
• Offline-first. Your data lives on your device, optional cloud sync.

I'm a solo dev, no team, no funding. There will be bugs.

iOS doesn't exist and won't anytime soon.

I'm also experimenting with form feedback from the watch's sensors, but that's very early prototype — works on my Pixel Watch for some exercises, nowhere near ready for prime time.

I want maybe 20–30 people who actually train consistently to use it for a few weeks and tell me what sucks. If something annoys you I'll fix it or argue with you about it. You can actively help me shape Trainio :)

Comment or DM if you want a Play Store testing link. A Wear OS watch makes it nicer but isn't required.