Architecture and Use of Different Algorithms for Health Goal Feedback


I wanted to get some opinions from the community for a certain problem that I will be approaching.

The problem is to provide feedback to a user based on a image of the upper male torso. The image would either reflect something positive like increasing muscle mass or decreasing muscle mass or both and gaining adipose tissue would be seen as negative as well as muscle atrophy.

Using the users input such as (sleep data, food, training routine) among some other data I would like to provide feedback such as "no John, this exercise has not yielded desirable results" or "a combination of your recent dietary change has caused strength loss" obviously this is a complex issue and has a lot of interconnected variables and potentials but you get the idea high-level at least and if you don't - Please ask.

So my idea so far would be to use a CNN that holds the picture of the torso, using a softmax function we could run this through a model to estimate bodyfat and doing the same with a model trained on muscle mass using those two models we could paint a pretty accurate picture of someones physique if they're going in the right direction or not; we could then go on to analyse what that user may have done/has not done to yield a result - Obviously there would be connected models here and many different combinations of algorithms applied such as CNN, RNN and others. Really curious to hear your response(s) thank you in advance.


Posted 2019-04-05T03:26:43.337

Reputation: 11

Any ideas from the community? was looking really forward to get some conversations flowing. – Invic18 – 2019-04-06T15:50:00.230

No answers