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| # MMHAR-28: Human Action Recognition Across RGB, Depth, Thermal, and Event Modalities |
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| ## Dataset Summary |
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| MMHAR-28 is a multimodal human action recognition dataset designed for action classification across four sensing modalities: |
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| - RGB |
| - Depth |
| - Thermal |
| - Event |
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| The MMHAR-28 dataset contains 28 human action classes collected in two sessions. Session 1 focuses on single-person sports and exercise actions, while Session 2 focuses on two-person interaction activities. |
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| ## Dataset Structure |
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| The dataset is organized into predefined splits: |
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| - `train` |
| - `val` |
| - `test` |
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| Samples are stored by modality. Typical modality folders include: |
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| - `rgb_images` |
| - `depth_images` |
| - `thermal` |
| - `event-streams` |
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| ## Data Instances |
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| Each instance corresponds to one action sample and one label from the 28 action classes. |
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| Example annotation format: |
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| ```text |
| path/to/sample,label |
| ``` |
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| Example paths: |
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| ```text |
| data/train/session_1/sub_18/d_rgb/28/rgb_images,13 |
| data/train/session_1/sub_7/d_rgb/26/depth_images,12 |
| data/train/session_1/sub_33/thermal/9_1_0,8 |
| data/train/session_1/sub_55/event-streams/15,7 |
| ``` |
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| ## Data Splits |
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| The dataset provides predefined splits for: |
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| - training |
| - validation |
| - testing |
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| ## Citation |
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| If you use the MMHAR-28 dataset in your research, please cite our paper: |
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| ```bibtex |
| @ARTICLE{11447325, |
| author={Rakhimzhanova, Tomiris and Kuzdeuov, Askat and Muratov, Artur and Varol, Huseyin Atakan}, |
| journal={IEEE Transactions on Biometrics, Behavior, and Identity Science}, |
| title={MMHAR-28: Human Action Recognition Across RGB, Thermal, Depth, and Event Modalities}, |
| year={2026}, |
| volume={}, |
| number={}, |
| pages={1-1}, |
| keywords={Videos;Cameras;Event detection;Thermal sensors;Sensors;Web sites;Video on demand;Three-dimensional displays;Software;Lighting;Human action recognition (HAR);multimodal learning;RGB;depth;thermal;event-based camera;multimodal dataset;video classification;deep learning}, |
| doi={10.1109/TBIOM.2026.3675639}} |
| |
| ``` |
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