REFIT: Recognize, Fix, and Test Wearable Sensor Placement Shifts without Labels
We present REFIT, an input calibration for frozen activity-recognition models whose inertial sensors are worn differently at deployment than in training.
ProofPaper ↗
Key points
- When users move a watch to the other wrist or put a strap sensor back on turned, the model sees the same motion on changed axes.
- REFIT undoes such shifts without labels or retraining.
- It tests the fixed model with a label-free accuracy estimate and asks the user to re-wear the sensor when it is low.
- Experiments on real left/right sensor pairs and on real and simulated re-attachment show that REFIT outperforms label-free test-time adaptation methods on every dataset and restores most of the accuracy lost to re-attachment.
Sources (1)
- [1]REFIT: Recognize, Fix, and Test Wearable Sensor Placement Shifts without LabelsarXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 6, 06:49 PM
We present REFIT, an input calibration for frozen activity-recognition models whose inertial sensors are worn differently at deployment than in training.
When users move a watch to the other wrist or put a strap sensor back on turned, the model sees the same motion on changed axes.
Extractive summary: sentences quoted from the sources.