There's a lot of potential here, especially since you're already building custom firmware/controller logic and have deep experience with local AI. The torque sensor gives you a real-time force signal at ~100–200 Hz — that's a rich data stream most e-bike brands barely use beyond simple proportional assist. adventuro
Run a lightweight model (TinyML / TFLite on the controller MCU, or on a companion ESP32) that learns your pedaling signature over time: tamobykesport
- Builds a personal torque-cadence-speed profile across different terrains and conditions.
- Automatically adjusts the assist curve without you touching PAS levels — it just knows you want more on climbs and less on flats.
- Could run entirely offline, fits Omera's local-first philosophy. adventuro
Fuse torque sensor data with GPS + altimeter to anticipate what's coming: tamobykesport
- Pre-load motor torque 1–2 seconds before a hill hits, so assist feels seamless.
- Learn your commute routes and pre-cache optimal power curves per segment.
- Optimize battery spend: "You have 14 km left, 2 hills — here's the power budget."
This is the novel one. The torque signal contains subtle patterns that shift as you fatigue: hezzobike
- Pedal stroke asymmetry — left vs. right leg force imbalance increases when tired.
- Torque variability — consistent riders get jerky when fatigued.
- Cadence drift — natural RPM drops without the rider noticing.
- An on-device model could detect fatigue onset and silently increase assist to keep you safe, or nudge you to take a break.
Log torque + cadence + speed at high resolution and pipe it to a companion app: smartybikes
- Power output estimation (torque × cadence = watts) without needing a power meter.
- Stroke-by-stroke analysis like what cyclists pay €500+ for with Garmin/SRM.
- "Ghost mode" — ride against your own previous best on a route.
The torque signal is surprisingly biometric: hezzobike
- Everyone has a unique pedal stroke signature (force curve shape, L/R balance, cadence preference).
- Train a small classifier on your stroke pattern.
- If someone else pedals the bike → motor doesn't engage. No keys, no app, just pedal and it recognizes you.
| Feature | Complexity | Hardware needed |
|---|---|---|
| Adaptive assist curve | Medium | Torque + cadence + existing controller |
| Predictive terrain | Medium-high | + GPS module (ESP32 + u-blox) |
| Fatigue detection | Medium | Torque signal only, needs ML model |
| Power analytics | Low | Just logging + app |
| Rider authentication | Low-medium | Torque signal + small NN on MCU |
The power analytics + adaptive assist combo is probably the best bang-for-buck for v1 — useful immediately, differentiating vs. every other e-bike, and you can ship it with just the torque sensor you already have plus a small local model running on the controller. The rider-auth feature is a fun flex that fits Omera's anti-theft story too. tamobykesport
Want me to spec out the data pipeline for any of these (sensor → MCU → model → motor control)?