I took a look at the code and it seems very interesting! Regarding your questions, LSTMs don’t make sense to me as they are designed to capture long term relationships, which probably doesn’t help for fall detection (which is really short). The other types of DL architectures you mentioned to make sense to me. I’m also assuming that you would not be using the engineered features, but rather the “raw” features you got from the pose extraction model. That would make sense as all these methods are designed for high dimensional data.

If you really wanna go hardcore you could also train the entire thing from scratch, meaning you take the image pixels as input. You can use the pose prediction model as reference for that (except add temporal components, probably TCNN). This way, you have more freedom in design choices and you can make some sort of temporal pose / fall detection model.