How does AI affect sport?

Artificial intelligence - just take it sporty

One thing is immediately noticeable, for us and for the app: Jan is not athletic and clearly overweight. Individual training units and exercises that match these parameters are selected from the collection of exercises. For example, full-body units that are (or should be) high-intensity for weight loss. After the first unit, the app gets feedback from Jan: Was it exhausting? Yes, it was already too much. Saved. The execution was disastrous, Jan is honest about that. Saved. His squats were painful. That is also saved. Due to the pain, the app suggests another type of execution or a completely different exercise.

The situation is different for Daniel. He does sport regularly, has an ideal weight and wants to build muscle. Based on these parameters, completely different exercises come into play, such as modified push-ups that address specific chest or shoulder areas, various types of knee bends and pull-ups, the latter also with one arm. After all, the goal here is the demand for isolated muscles and groups. If we apply the principle from the first figure, the basic logic of such an app could look like this:

With the help of the input of the user, the offered exercises and their intensity are also adapted. Firstly, in order to optimally adapt to the new circumstances and secondly, to remain demanding without hurting the user. From the feedback shown in Figure 2, new training units are then put together, also based on the initial data of the user. In theory it looks like this: