arXiv Open Access 2025

HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation

Halil Ismail Helvaci Justin Philip Huber Jihye Bae Sen-ching Samson Cheung

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Abstrak

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.

Topik & Kata Kunci

cs.CV

Penulis (4)

Halil Ismail Helvaci

Justin Philip Huber

Jihye Bae

Sen-ching Samson Cheung

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APA MLA BibTeX

Helvaci, H.I., Huber, J.P., Bae, J., Cheung, S.S. (2025). HRTR: A Single-stage Transformer for Fine-grained Sub-second Action Segmentation in Stroke Rehabilitation. https://arxiv.org/abs/2506.02472

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Tahun Terbit: 2025
Bahasa: en
Sumber Database: arXiv
Akses: Open Access ✓