Using gradient saliency and pretrained video/time-series models, frame-level stroke exercise quality can be learned from video-level labels, with the best configuration reaching 72% AUC versus 69% for a ground-truth-trained baseline.
Challenges in applying evidence-based practice in stroke rehabilita- tion: a qualitative description of health professional experience in low, middle, and high-income countries,
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Frame-Level Real-Time Assessment of Stroke Rehabilitation Exercises from Video-Level Labeled Data: Task-Specific vs. Foundation Models
Using gradient saliency and pretrained video/time-series models, frame-level stroke exercise quality can be learned from video-level labels, with the best configuration reaching 72% AUC versus 69% for a ground-truth-trained baseline.