Lessons From Building an Edge ML System for Tennis Swing Detection
I spent most of my career building data systems at enterprise scale. Big warehouses, big pipelines, models trained on tables that other people had already cleaned before they ever reached me. I did not realize how much that setup was cushioning me until I strapped a sensor to a tennis racquet and tried to make it think.
The idea was simple on paper. Put a small motion sensor on the racquet, capture the swing, and tell the player what shot they just hit and how well they hit it. In real time, on their phone, on the court. I figured the machine learning would be the hard part.
The machine learning was the easy part. Here is what actually humbled me.
Before the lessons, here is the whole system end-to-end, because every hard problem lives somewhere in this pipeline:
- Racquet IMU (accel + gyro):the raw motion stream, most of...
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