HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation
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| Main Authors: | , , , |
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| Format: | Preprint |
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2025
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| _version_ | 1866915336265138176 |
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| author | Helvaci, Halil Ismail Huber, Justin Philip Bae, Jihye Cheung, Sen-ching Samson |
| author_facet | Helvaci, Halil Ismail Huber, Justin Philip Bae, Jihye Cheung, Sen-ching Samson |
| contents | Stroke rehabilitation often demands precise tracking of patient movements to monitor progress, with complexities of rehabilitation exercises presenting two critical challenges: fine-grained and sub-second (under one-second) action detection. In this work, we propose the High Resolution Temporal Transformer (HRTR), to time-localize and classify high-resolution (fine-grained), sub-second actions in a single-stage transformer, eliminating the need for multi-stage methods and post-processing. Without any refinements, HRTR outperforms state-of-the-art systems on both stroke related and general datasets, achieving Edit Score (ES) of 70.1 on StrokeRehab Video, 69.4 on StrokeRehab IMU, and 88.4 on 50Salads. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2506_02472 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation Helvaci, Halil Ismail Huber, Justin Philip Bae, Jihye Cheung, Sen-ching Samson Computer Vision and Pattern Recognition Stroke rehabilitation often demands precise tracking of patient movements to monitor progress, with complexities of rehabilitation exercises presenting two critical challenges: fine-grained and sub-second (under one-second) action detection. In this work, we propose the High Resolution Temporal Transformer (HRTR), to time-localize and classify high-resolution (fine-grained), sub-second actions in a single-stage transformer, eliminating the need for multi-stage methods and post-processing. Without any refinements, HRTR outperforms state-of-the-art systems on both stroke related and general datasets, achieving Edit Score (ES) of 70.1 on StrokeRehab Video, 69.4 on StrokeRehab IMU, and 88.4 on 50Salads. |
| title | HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2506.02472 |