Build an Edge-AI OpenPose CPR Feedback System

1. 1

   Prove the accuracy claim

   The premise is that pose estimation on edge hardware can reach roughly 90 percent accuracy versus gold-standard reference feedback systems. Reproduce and validate that accuracy on your target hardware before anything else, because the entire value proposition is accuracy-at-lower-cost. Document the benchmark rigorously.

2. 2

   Optimize for cheap edge hardware

   The point is running strong feedback on lower-cost hardware than dedicated devices, so optimize the model to run in real time on an affordable edge board or camera. The cost gap versus premium devices is your market. Balance accuracy against the price you can hit.

3. 3

   Package it for classrooms

   Turn the model into a deployable unit an instructor can set up quickly and read at a glance, with clear per-student feedback. Ruggedness and simplicity matter for real training rooms. Provide setup and calibration that a non-engineer can handle.

4. 4

   Position against both neighbors

   You are more accurate and instructor-ready than a plain phone app and cheaper than a proprietary sensor device; make that positioning explicit. Buyers choosing feedback tools weigh exactly this tradeoff. Show the accuracy and price side by side.

5. 5

   Sell to accuracy-conscious buyers

   Training providers, schools, and healthcare programs that need reliable feedback but cannot justify premium-device prices are your customers. Offer purchase or device-plus-support pricing. Honest, validated accuracy is the whole sale.

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