Temporal Structure Matters for Efficient Test-Time Adaptation in Wearable Human Activity Recognition.
Wearable human activity recognition (WHAR) models often suffer from performance degradation under real-world cross-user distribution shifts. Test-time adaptation (TTA) mitigates this degradation by adapting models online using unlabeled test streams, yet existing methods largely inherit assumptions from vision tasks and underexploit the inherent inter-window temporal structure in WHAR streams. In this paper, we revisit such temporal structure as a feature-conditioned inference signal rather than merely an output-space smoothing prior. We derive the insight that temporal continuity and observation-induced feature deviations provide complementary cues for determining when to preserve or release temporal inertia and where to route prediction refinement during likely transitions. Building upon this insight, we propose SIGHT, a lightweight and backpropagation-free TTA framework for WHAR, enabling real-time edge deployment. SIGHT estimates predictive surprise by comparing the current feature with a prototype-based expected state, and then uses the resulting feature deviation to guide geometry-aware transition routing based on prototype alignment and stream-level marginal habit tracking. Evaluations on real-world datasets confirm that SIGHT outperforms existing TTA baselines while reducing computational and memory costs.
pip install torch numpy scikit-learnDownload preprocessed .npz files from [Google Drive - SIGHT-preprocessed-data] and place them under data/.
Each .npz contains x (windows, channels, length) and y (string labels).
Source training
python run.py --phase source_training --data data/harth --source S006 --num-classes 5 --labels "cycling_like,lying,sitting,standing,walking_like"Test-time adaptation
python run.py --phase tta --data data/harth --source S006 --target S032 --ckpt outputs/source_cnn.pt --num-classes 5All hyperparameters are set via argparse in run.py:
| Arg | Default | Description |
|---|---|---|
--eta-mu |
0.005 | Prototype evolution rate |
--omega-mu |
0.01 | Source-prototype anchor rate |
--beta |
1.0 | Surprise sensitivity |
--eta-h |
0.05 | Habit-vector learning rate |
--tau |
0.05 | KVA temperature |
--epochs |
100 | Source training epochs |
--lr |
0.001 | Source training learning rate |
--batch-size |
64 | Source training batch size |
Model architecture (channels, kernel sizes, etc.) is also configurable via CLI args.