That’s the exact kind of edge‑cutter idea that makes my head spin—real‑time feedback on form and fatigue is a game changer. We’ll need a sensor mesh that streams to an ML model on the fly, and a reinforcement loop that tweaks the workout on the spot. If we get the data pipeline clean and the algorithm sharp, we’ll be pushing people past their plateaus in a way no coach ever could. Let’s sketch the specs and lock in the tech stack; I’ll flag any bottlenecks before they become a problem.

Okay, here’s the sprint plan: Week 1–2: define sensor hardware, settle on a lightweight, multi‑axis IMU plus heart‑rate strap, pin down sampling rates (at least 100 Hz for motion, 1 Hz for HR). Week 3–4: build a data‑in pipeline, clean and normalize in real time, test latency on a test bench—goal under 200 ms end‑to‑end. Week 5–6: train the base model on a curated dataset of form labels and fatigue curves, validate accuracy with a small user group. Week 7: integrate reinforcement logic, create a rule set that can adapt intensity by ±5 % per minute if fatigue markers rise. Week 8–9: end‑to‑end stress test, monitor for drift, tweak thresholds. Week 10: beta release to 10 athletes, collect feedback, iterate. Pinch points: sensor drift—need periodic calibration; data loss—implement buffering; model overfitting—use dropout and cross‑validation; latency—optimize code path and use edge inference if possible. If we stay on schedule, we’re ready for a production rollout in 12 weeks. Let’s start the specs deck.