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README.md

@@ -1,31 +1,151 @@
 ---
-license: cc-by-nc-4.0
+license: cc-by-4.0
 task_categories:
-- video-classification
-- text-generation
-modalities:
-- depth
-- infrared
-- thermal
-- imu
-- radar
-- skeleton
+  - video-classification
+  - text-generation
 language:
-- en
+  - en
 tags:
-- human-activity-recognition
-- multimodal
-- sensor-data
-- IMU
-- depth
-- infrared
-- thermal
-- skeleton
-- radar
-- mmwave
-- HAR
-- privacy-preserving
-pretty_name: CUHK-S
+  - human-activity-recognition
+  - multimodal
+  - sensor-data
+  - privacy-preserving
+  - IMU
+  - depth
+  - infrared
+  - thermal
+  - skeleton
+  - radar
+  - mmwave
+  - HAR
+pretty_name: "CUHK-S"
 size_categories:
-- 100M<n<1B
----
+  - 100K<n<1M
+---
+
+# CUHK-S: A Privacy-Preserving Multimodal Dataset for Human Action Recognition
+
+[![Paper](https://img.shields.io/badge/Paper-arXiv:2512.07136-red)](https://www.arxiv.org/abs/2512.07136)
+[![Project Page](https://img.shields.io/badge/Project-Page-blue)](https://siyang-jiang.github.io/CUHK-X/)
+
+## Dataset Description
+
+CUHK-S is a **privacy-preserving subset** of the [CUHK-X](https://siyang-jiang.github.io/CUHK-X/) dataset, a large-scale multimodal benchmark for Human Action Recognition (HAR), Understanding (HAU), and Reasoning (HARn). CUHK-X was accepted at **MobiSys 2026**.
+
+Compared to the full CUHK-X dataset, CUHK-S:
+- **Removes all RGB video** to prevent facial identification
+- **Downscales** all visual modalities to 320 × 240
+- **Selects 18 out of 30** participants while preserving full action coverage (40 categories)
+
+## Dataset Summary
+
+| Attribute         | Value                                          |
+|-------------------|------------------------------------------------|
+| Participants      | 18 (selected from 30 in CUHK-X)               |
+| Action Categories | 40                                              |
+| Modalities        | 6 (Depth, IR, Thermal, IMU, Radar, Skeleton)   |
+| Visual Resolution | 320 × 240                                      |
+| Total Size        | ~146 GB (18 zip files, one per participant)     |
+
+## Modalities
+
+| Modality   | Format      | Description                                     |
+|------------|-------------|-------------------------------------------------|
+| Depth      | PNG (color) | Colorized depth maps from Vzense NYX 650        |
+| IR         | PNG         | Infrared images, robust to lighting changes      |
+| Thermal    | PNG         | Heat signature from thermal camera               |
+| IMU        | CSV         | 5-sensor accelerometer/gyroscope/magnetometer    |
+| Radar      | Binary      | mmWave radar point cloud (TI Radar)              |
+| Skeleton   | JSON/CSV    | 3D joint positions from pose estimation          |
+
+> **Note**: RGB video is intentionally excluded from CUHK-S to protect participant privacy.
+
+## Dataset Structure
+
+Each participant's data is packaged as a zip file: `CUHK-S_userN-userN.zip`
+
+```
+CUHK-S/
+├── HAR/                    # Human Action Recognition (per-action organized)
+│   ├── data/
+│   │   ├── depth_color/
+│   │   ├── ir/
+│   │   ├── thermal/
+│   │   ├── imu/
+│   │   ├── radar/
+│   │   └── skeleton/
+│   └── GT/                 # Ground truth annotations
+├── HARn/                   # Human Action Reasoning (per-modality organized)
+│   ├── data/
+│   └── GT/
+├── HAU/                    # Human Action Understanding (per-modality organized)
+│   ├── data/
+│   └── GT/
+└── source_data/            # Raw source data
+    ├── data/
+    └── GT/
+```
+
+- **HAR**: Singular well-defined actions for traditional classification tasks
+- **HAU**: Sequential actions for temporal and contextual understanding
+- **HARn**: Sequential actions for next-action reasoning and prediction
+- **source_data**: Raw unprocessed sensor data
+
+## IMU Sensor Layout
+
+Five IMU sensors are placed on the body:
+
+| Sensor | Position   | Channels (per sensor)                     |
+|--------|------------|-------------------------------------------|
+| WTLA   | Left Arm   | Acc(X/Y/Z), Gyro(X/Y/Z), Mag(X/Y/Z)     |
+| WTC    | Chest      | Acc(X/Y/Z), Gyro(X/Y/Z), Mag(X/Y/Z)     |
+| WTRA   | Right Arm  | Acc(X/Y/Z), Gyro(X/Y/Z), Mag(X/Y/Z)     |
+| WTRL   | Right Leg  | Acc(X/Y/Z), Gyro(X/Y/Z), Mag(X/Y/Z)     |
+| WTLL   | Left Leg   | Acc(X/Y/Z), Gyro(X/Y/Z), Mag(X/Y/Z)     |
+
+## Benchmarks & Tasks
+
+| Task                    | Type            | Metrics                         |
+|-------------------------|-----------------|----------------------------------|
+| Action Recognition      | Classification  | Accuracy, F1, Precision, Recall  |
+| Action Selection        | Multiple Choice | Accuracy                         |
+| Action Captioning       | Generation      | BLEU, METEOR                     |
+| Emotion Analysis        | Classification  | Accuracy                         |
+| Sequential Reordering   | Ordering        | Accuracy                         |
+| Next Action Reasoning   | Reasoning       | Accuracy                         |
+
+## Citation
+
+If you use CUHK-S in your research, please cite:
+
+```bibtex
+@inproceedings{jiang2026cuhkx,
+  title={CUHK-X: A Large-Scale Multimodal Dataset and Benchmark for Human Action Recognition, Understanding and Reasoning},
+  author={Jiang, Siyang and others},
+  booktitle={Proceedings of ACM MobiSys},
+  year={2026}
+}
+```
+
+## Ethics & Privacy
+
+We obtained approval from an Institutional Review Board (IRB) to conduct this study and collect data from human subjects.
+
+**Privacy measures in CUHK-S:**
+- No RGB video is included to prevent facial identification
+- All visual modalities are downscaled to 320 × 240
+- Participants are identified only by numeric IDs (e.g., user1, user2)
+- No personally identifiable information is linked to individual records
+- IMU, Radar, and Skeleton modalities do not capture visual appearance
+
+## License
+
+Code is released under the MIT License. The dataset is available for non-commercial research under a Data Use Agreement (DUA) and is not redistributable. Our derived annotations/splits are released under CC BY 4.0.
+
+**Note**: This dataset is designed for research and educational purposes. Please ensure compliance with your institution's ethics guidelines when using human activity data.
+
+## Contact
+
+- **Email**: syjiang [AT] ie.cuhk.edu.hk
+- **Project Page**: [https://siyang-jiang.github.io/CUHK-X/](https://siyang-jiang.github.io/CUHK-X/)
+- **Lab**: [CUHK AIoT Lab](https://aiot.ie.cuhk.edu.hk)