Sensor-Specific Transformer (PatchTST) Ensembles with Test-Matched Augmentation
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arXiv
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| Hauptverfasser: | , |
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| Format: | Preprint |
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2025
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| author | Chandankar, Pavankumar Burchard, Robin |
| author_facet | Chandankar, Pavankumar Burchard, Robin |
| contents | We present a noise-aware, sensor-specific ensemble approach for robust human activity recognition on the 2nd WEAR Dataset Challenge. Our method leverages the PatchTST transformer architecture, training four independent models-one per inertial sensor location-on a tampered training set whose 1-second sliding windows are augmented to mimic the test-time noise. By aligning the train and test data schemas (JSON-encoded 50-sample windows) and applying randomized jitter, scaling, rotation, and channel dropout, each PatchTST model learns to generalize across real-world sensor perturbations. At inference, we compute softmax probabilities from all four sensor models on the Kaggle test set and average them to produce final labels. On the private leaderboard, this pipeline achieves a macro-F1 substantially above the baseline, demonstrating that test-matched augmentation combined with transformer-based ensembling is an effective strategy for robust HAR under noisy conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2510_21282 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sensor-Specific Transformer (PatchTST) Ensembles with Test-Matched Augmentation Chandankar, Pavankumar Burchard, Robin Machine Learning We present a noise-aware, sensor-specific ensemble approach for robust human activity recognition on the 2nd WEAR Dataset Challenge. Our method leverages the PatchTST transformer architecture, training four independent models-one per inertial sensor location-on a tampered training set whose 1-second sliding windows are augmented to mimic the test-time noise. By aligning the train and test data schemas (JSON-encoded 50-sample windows) and applying randomized jitter, scaling, rotation, and channel dropout, each PatchTST model learns to generalize across real-world sensor perturbations. At inference, we compute softmax probabilities from all four sensor models on the Kaggle test set and average them to produce final labels. On the private leaderboard, this pipeline achieves a macro-F1 substantially above the baseline, demonstrating that test-matched augmentation combined with transformer-based ensembling is an effective strategy for robust HAR under noisy conditions. |
| title | Sensor-Specific Transformer (PatchTST) Ensembles with Test-Matched Augmentation |
| topic | Machine Learning |
| url | https://arxiv.org/abs/2510.21282 |