Consider a scenario for an IMU sensor system, you test the AI model by comparing its output to ground truth.
1. Train / test split
Train the model on one dataset, then test it on different (unseen) motion data.
2. Compare to ground truth
Check:
– Orientation error (deg)
– Position/velocity RMSE
– Drift over time
– Noise or stability
– Real-time performance (latency)
Ground truth can come from motion capture, a turntable, or known trajectories.
3. If it’s not working:
– Collect more varied data (different motions, conditions)
– Clean/normalise data and ensure proper calibration
– Tune hyperparameters
– Improve input features (e.g., time windows, bias terms)
– Change the model
4. Retrain and test again
Process: train → test vs ground truth → measure error/drift → improve data/model → retrain.
Generally, it all depends on the kind of data you have and what you are trying to do.
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