issai/MMHA_28

MMHAR-28: Human Action Recognition Across RGB, Depth, Thermal, and Event Modalities

Dataset Summary

MMHAR-28 is a multimodal human action recognition dataset designed for action classification across four sensing modalities:

• RGB
• Depth
• Thermal
• Event

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.

Dataset Structure

The dataset is organized into predefined splits:

• train
• val
• test

Samples are stored by modality. Typical modality folders include:

• rgb_images
• depth_images
• thermal
• event-streams

Data Instances

Each instance corresponds to one action sample and one label from the 28 action classes.

Example annotation format:

path/to/sample,label

Example paths:

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

Data Splits

The dataset provides predefined splits for:

• training
• validation
• testing

Citation

If you use the MMHAR-28 dataset in your research, please cite our paper:

@article{mmha28,
  title={MMHA-28: Human Action Recognition Across RGB, Depth, Thermal, and Event Modalities},
  author={Author 1 and Author 2 and Author 3},
  journal={...},
  year={2025}
}