This is not curve fitting or forecasting: it's pattern matching. It's also a serious methodological approach. A fall on a sensor graph has a certain look to it

This is not curve fitting or forecasting: it's pattern matching. It's also a serious methodological approach. A fall on a sensor graph has a certain look to it just like an abnormality on an EKG that a human can detect. You can train multimodal models to detect these too with decent accuracy. What's methodologically unsound about that? If anything, it demonstrates you don't necessarily need a class of hyper-specific models to do pattern matching.