There are multiple apps in the App Store that do this. I spent last year implementing pose detection in an exercise app and we used both Apple’s pose detection and a 3rd party’s. The pose (each point of the human form) itself was sent to a machine learning backend at around 30 fps, analyzed, and data returned at about the same speed using gRPC. Each exercise had a set of specific feedback for both positioning (“Stand facing the camera with you arms at your side/Stand sideways to the camera/etc”) and form correction (“Raise your right arm higher above your head etc”). Feedback was spoken out loud to the user and there was a relatively complex set of rules governing which feedback got priority and how often feedback was spoken. I also implemented an on-screen “skeleton” of the user’s human form points that rendered on top of the camera view. Pretty fun project from a tech point of view.
The signal to noise in fitness apps is high. The mainstream ones don’t do this, or if they do the implementation is so bad it’s not worth using, and discovery of anything else is fraught with shitware that wants a subscription to “unlock” it’s unknown potential.
Did you mean to say signal-to-noise [ratio] is _low_? Meaning that you get way too much noise for the amount of signal. Or did you mean to say it needs to be high (I.e. low noise) to be useful?
For every good fitness app that does what it promises (-> signal) there are at least 50 bad fitness apps that promise too much and let you pre-pay for the (broken) features you wanted, money you'll never get back (-> noise).
The amount of noise in Fitness apps is so high, nobody really dares to try out small apps. Therefore cool implementations from small devs like the workout-correction might stay unnoticed for years.
