Yeah, so imagine you’ve got a 50‑Hz accelerometer, heart‑rate, and a tiny buffer. Every 100 ms you compute a “strain score” from the z‑axis jerk and a “fatigue score” from the HR variability. If the strain score spikes above a rolling median by 20 % and the fatigue score is below a certain percentile, you trigger a load shift. Use a simple exponential moving average for the thresholds so it adapts to the day. The key is to keep the kernel under 5 ms and update the model in the background. If you can squeeze that into the watch’s main loop, you’ll shave the millisecond you’re after. It’s basically a real‑time Bayesian classifier, but with less math and more code that runs on the sensor’s firmware.