Sprint form analysis without a server
Can a phone analyse sprint mechanics well enough to coach from, without uploading the video anywhere?
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The obvious architecture for video analysis is to upload the clip and process it in the cloud. It is also the architecture with the worst properties for this particular product: an athlete records on a track with poor signal, the clips are large, the processing cost scales linearly with use, and the footage is of a person’s body.
SPRIVO runs the pose detection natively on the device instead, through a MediaPipe integration rather than a hosted inference service. The sprint never leaves the phone to be scored.
The open question is not whether it runs — it does — but where the accuracy ceiling sits for the specific mechanical faults worth coaching, and how far the analysis can go before a cloud model would genuinely be better. That is what keeps this in the Lab while the product it sits inside is on the App Store — the question is the accuracy ceiling, not whether the app ships.
What we learned
Findings so far.
- 01
On-device analysis removes a whole category of problems at once: no upload wait on a bad connection, no per-clip processing bill, and no server holding video of somebody’s body.
- 02
It moves the cost from infrastructure to integration. The native layer is the substantial engineering, and it is platform-specific in a way a hosted API would not have been.
About SPRIVO
This experiment came out of building SPRIVO.