Sounds solid. Start with a lightweight sensor stack—wearable heart‑rate, GPS, maybe a micro‑spike in skin temperature. Feed that into a predictive model; a small LSTM could roll up the last minute of data and output a set of possible next‑minute states—pace, fatigue, recovery threshold. Then map each state to a recommended exercise intensity or rest cue. The key is real‑time feedback loop: the app should be able to tweak the plan on the fly, so the user never over‑exerts or under‑works. Keep the interface minimal—just a clear “now” recommendation and a brief “why” note so the user stays engaged. And don’t forget data privacy; encrypt everything and give the user granular control over sharing. That’s the sweet spot between science, strategy, and user trust.

Got it. Start by sketching the hardware: a single chest strap for HR, a tiny IMU on the wrist for motion, and a BLE module. That’s all the body can’t feel. For the software, set up a microservice that runs a lightweight rule engine—thresholds for HR rise, movement patterns, and a simple fatigue score. When the user taps “Go,” the engine pushes the next minute’s recommendation; “Rest” turns it off and logs a recovery flag. Add the privacy toggle in the settings, a one‑click option to send anonymized data to a coach endpoint. Once we have the prototype, run a quick loop with a few volunteers to fine‑tune the thresholds. Let’s get the hardware wired, write the rule set, and build the single‑button UI—keep it simple, keep it fast. When you’re ready, I can start laying out the architecture.