[D] Models that can be used to reverse engineer understanding of a new area where no one has domain knowledge?

I'm working on classifying accelerometer time series data to detect stroke. Accelerometers are placed on a patient's head, and the idea is that the irregularities in cranial blood flow will move the head differently enough from normal blood flow so that a model can classify stroke or not stroke based on the accelerometer data.

Basically no one has domain knowledge in this area because it's a relatively new thing, so I'm at a loss as to how I can engineer features that I'll be able to explain to stakeholders. I've been able to build a model using tsfresh and xgboost that achieves good metrics so I know it's possible, and I can use eli5 or SHAP to tell what tsfresh features are important. But that isn't enough for stakeholders yet because I can't really interpret the tsfresh features back into biological phenomena that my stakeholders will be happy with.

What kind of time series approaches can I use instead that is explainable enough to be able to reverse engineer some sort of understanding of stroke accelerometry?