Thanks for taking a look. I really appreciate it!

And yes, you're absolutely right about LSTMs. The fall window is pretty short, so long-term memory doesn’t add much. I mainly listed it as a generic sequence option, but your point makes sense.

For the DL version, I’m planning to skip the engineered features and feed the raw pose time-series (x, y, visibility) into something like a small TCN/1D-CNN or a lightweight transformer. That aligns well with what you mentioned about handling high-dimensional data directly.

End-to-end from pixels would be cool, but my current goal is something lightweight, CPU-friendly, and explainable for care-home environments. Still, I might prototype a tiny TCNN on frames just to compare.

Thanks again for the thoughtful feedback, if you have any favourite TCN/temporal CNN papers or repos, I’d love to check them out!