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README.md

@@ -1,3 +1,57 @@
----
-license: cc-by-nc-4.0
----
+# StrokeVision-Bench: A Multimodal Video and 2D Pose Benchmark for Tracking Stroke Recovery
+
+StrokeVision-Bench is an action recognition dataset of short segments of stroke patients performing the Box-Block Test.
+
+StrokeVision-Bench contains 1,000 annotated videos (1 s @ 30 FPS)  categorized into four clinically meaningful action classes (Non-task movement, Grasping, Transport with block, Transport without block), with each sample represented in two modalities: raw video segments and 2D skeletal keypoints. We benchmark several state-of-the-art video and skeleton-based action classification methods to establish performance baselines for this domain and facilitate future research in automated stroke rehabilitation assessment.
+
+## Dataset Summary
+- Samples: 1,036 short videos (1 s @ 30 FPS)
+- Modalities: RGB frames, 2D skeleton keypoints
+- Action classes: Non-task movement, Grasping, Transport with block, Transport without block
+- Keypoints: COCO 17-keypoint format
+- Train-Test Split: 827 train segments, 209 test segments
+
+**Paper**: https://arxiv.org/abs/2509.07994
+
+## Dataset Structure
+
+StrokeVision-Bench/
+├── videos/
+│   ├── grasping/
+│   ├── non_task/
+│   ├── transport_with_block/
+│   └── transport_without_block/
+├── keypoints/
+│   ├── grasping/
+│   ├── non_task/
+│   ├── transport_with_block/
+│   └── transport_without_block/
+├── annotations/
+│   ├── train.csv
+│   └── val.csv
+
+The `videos` folder contains the raw video segments separated by class. The `keypoints` folder contains  the 2D skeletal keypoints as npy files with shape (30, 17, 2) separated by class.
+
+Each instance in the annotations contains the following features:
+- subject_id: The subject of the instance (P01-P04)
+- file_name: File name of the instance within the **videos** and **keypoints** directories, formatted as "{subject_id}_segment{segment_id}"
+- label: Action class (Non-task movement, Grasping, Transport with block, Transport without block)
+- hand: Which hand is being used (left, right)
+
+Example entry: P01,P01_segment0201,transport_with_block,left
+
+You can load the annotations directly with pandas and access files via the dataset's `videos` and `keypoints` folders.
+
+## License
+This dataset is released under the CC BY-NC 4.0 license.
+
+## Citation
+
+```bibtex
+@inproceedings{strokevisionbench,
+  title     = {StrokeVision-Bench: A Multimodal Video and 2D Pose Benchmark for Tracking Stroke Recovery},
+  author    = {David Robinson and Animesh Gupta and Rizwan Qureshi and Qiushi Fu and Mubarak Shah},
+  booktitle = {Proceedings of the IEEE International Workshop on Machine Learning for Signal Processing (MLSP)},
+  year      = {2025}
+}
+```

annotations/train.csv

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+subject_id,file_name,label,hand
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annotations/val.csv

@@ -0,0 +1,210 @@
+subject_id,file_name,label,hand
+P04,P04_segment0029,grasping,left
+P03,P03_segment0109,transport_without_block,left
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