New Test-Time Scenario for Biosignal: Concept and Its Approach
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2024
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| Subjects: | |
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| _version_ | 1866909406025744384 |
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| author | Jo, Yong-Yeon Lee, Byeong Tak Kim, Beom Joon Hong, Jeong-Ho Lee, Hak Seung Kwon, Joon-myoung |
| author_facet | Jo, Yong-Yeon Lee, Byeong Tak Kim, Beom Joon Hong, Jeong-Ho Lee, Hak Seung Kwon, Joon-myoung |
| contents | Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing. In healthcare, OTTA is vital for real-time tasks like predicting blood pressure from biosignals, which demand continuous adaptation. We introduce a new test-time scenario with streams of unlabeled samples and occasional labeled samples. Our framework combines supervised and self-supervised learning, employing a dual-queue buffer and weighted batch sampling to balance data types. Experiments show improved accuracy and adaptability under real-world conditions. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2411_17785 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | New Test-Time Scenario for Biosignal: Concept and Its Approach Jo, Yong-Yeon Lee, Byeong Tak Kim, Beom Joon Hong, Jeong-Ho Lee, Hak Seung Kwon, Joon-myoung Signal Processing Machine Learning Online Test-Time Adaptation (OTTA) enhances model robustness by updating pre-trained models with unlabeled data during testing. In healthcare, OTTA is vital for real-time tasks like predicting blood pressure from biosignals, which demand continuous adaptation. We introduce a new test-time scenario with streams of unlabeled samples and occasional labeled samples. Our framework combines supervised and self-supervised learning, employing a dual-queue buffer and weighted batch sampling to balance data types. Experiments show improved accuracy and adaptability under real-world conditions. |
| title | New Test-Time Scenario for Biosignal: Concept and Its Approach |
| topic | Signal Processing Machine Learning |
| url | https://arxiv.org/abs/2411.17785 |