README.md

---
license: cc-by-4.0
task_categories:
  - video-classification
  - text-generation
language:
  - en
tags:
  - human-activity-recognition
  - multimodal
  - sensor-data
  - privacy-preserving
  - IMU
  - depth
  - infrared
  - thermal
  - skeleton
  - radar
  - mmwave
  - HAR
pretty_name: "CUHK-S"
size_categories:
  - 100K<n<1M
viewer: false
---

# 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 task
│   └── data/
│       ├── Depth_Color/          # Colorized depth frames (.png)
│       ├── IR/                   # Infrared frames (.png)
│       ├── Thermal/              # Thermal imaging frames (.png)
│       ├── Skeleton/             # Skeleton pose data
│       │   └── {action}/{user}/{session}/
│       │       ├── predictions/  # Keypoint JSON (.json) + overlay images (.jpg)
│       │       └── visualizations/
│       ├── IMU/                  # IMU sensor data (CSV)
│       │   └── {action}/{user}/{session}/
│       │       ├── up(LA+RA+C).csv   # Upper-body IMU (Left Arm + Right Arm + Chest)
│       │       └── down(LL+RL).csv   # Lower-body IMU (Left Leg + Right Leg)
│       └── Radar/                # mmWave radar data (CSV)
│           └── {action}/{user}/{session}/
│               └── radar_output_T{timestamp}.csv
│
├── HAU/                          # Human Action Understanding task
│   └── data/
│       ├── Depth/                # Visual modality clips as .mp4 video
│       ├── IR/
│       └── Thermal/
│           └── {user}/{session}/
│               └── {Modality}.mp4
│
├── HARn/                         # Human Action next-step Reasoning task
│   └── data/
│       ├── Depth/                # Video clips as .mp4
│       └── IR/
│           └── {action}/{user}/{session}/
│               └── Depth.mp4
│
└── source_data/                  # Raw source frames (with timestamps)
    └── data/
        ├── Depth_Color/          # Timestamped raw frames (.png)
        ├── IR/
        ├── Thermal/
        ├── Skeleton/
        ├── IMU/
        └── Radar/
            └── {user}/{session}/
                └── {Modality}_{timestamp}_{frameId}.png
```

**Path naming convention:**

| Level | Meaning | Example |
|-------|---------|---------|
| `{action}` | Action category with numeric prefix | `10_Stir_drinks` |
| `{user}` | Participant ID | `user1` |
| `{session}` | Scene–Environment–Trial index | `2-1-1` (Scene 2, Indoor, Trial 1) |

- **HAR**: Singular well-defined actions organized by action category, for traditional classification tasks
- **HAU**: Sequential action clips organized by user/session, for temporal and contextual understanding
- **HARn**: Sequential action clips organized by action/user/session, for next-action reasoning
- **source_data**: Original raw frames with full timestamps, before any task-level processing

## 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)