| --- |
| license: cc-by-nc-4.0 |
| language: |
| - en |
| pretty_name: "MIAM: Multimodal Industrial Activity Monitoring" |
| size_categories: |
| - 100GB<n<1TB |
| task_categories: |
| - video-classification |
| - robotics |
| - any-to-any |
| tags: |
| - multimodal |
| - industrial |
| - human-robot-interaction |
| - engagement-prediction |
| - imu |
| - depth |
| - rgb |
| - assembly |
| - disassembly |
| - hri |
| - activity-monitoring |
| - action-recognition |
| - multimodal-learning |
| extra_gated: true |
| extra_gated_prompt: >- |
| 📋 **MIAM Dataset — Access Request** |
| |
|
|
| Thank you for your interest in the MIAM (Multimodal Industrial Activity Monitoring) dataset. To ensure responsible use and track dataset usage, please provide the following information: |
|
|
|
|
| By requesting access, you agree to: |
|
|
| - Use this dataset for research purposes only |
|
|
| - Not redistribute the dataset or any part of it |
|
|
| - Cite the dataset in any publications resulting from its use |
|
|
| - Adhere to the CC BY-NC 4.0 license terms |
| extra_gated_fields: |
| Full Name: text |
| Institution/Organization: text |
| Position/Role: text |
| Research Area: |
| type: select |
| options: |
| - Human-Robot Interaction (HRI) |
| - Industrial AI / Robotics |
| - Multimodal Learning |
| - Computer Vision |
| - Behavioral Analytics |
| - Other (specify in Purpose field) |
| Purpose of Use (brief description of your research): text |
| Expected Duration of Use: |
| type: select |
| options: |
| - 3 months |
| - 6 months |
| - 1 year |
| I understand this is for research purposes only: |
| type: checkbox |
| I will not share or redistribute the data: |
| type: checkbox |
| I will properly cite the dataset in my work: |
| type: checkbox |
| --- |
| |
| # MIAM: A Multimodal Dataset for Enhancing Industrial Task Monitoring and Engagement Prediction |
|
|
| 290 minutes of untrimmed industrial assembly and disassembly video across 22 sessions and 8 operators, captured with synchronized third-person RGB, top-view RGB and depth, and bimanual IMU streams, and annotated for action localization, object interaction, and real-time engagement state. The release links, per session: multi-view video clips, depth streams, left- and right-hand IMU traces, and frame-accurate annotations for actions, objects, and engagement. A 70/30 train/test split is provided for reproducible benchmarking. |
|
|
| Dataset viewer: the interactive viewer does not preview rows because the dataset is distributed as per-session `.zip` archives. The `.zip` files under `train/` and `test/` are files to browse and download. Each archive contains the full multimodal data for one session. |
|
|
| Targets and scope: industrial assembly and disassembly workflows performed by 8 operators across 22 sessions. Each session includes two camera views (third-person RGB, top-view RGB and depth) and bimanual IMU data, with annotations for action segments, object interactions, and engagement states. 70% of sessions form the training split and 30% the test split. |
|
|
| ## Repository layout |
|
|
| ``` |
| README.md this card |
| train/ training split (70%) |
| session_XX.zip per-session archive (video, depth, IMU, annotations) |
| test/ test split (30%) |
| session_XX.zip per-session archive (same structure as train) |
| docs/ DATA_NOTES.md, ANNOTATION_GUIDE.md, scripts/ |
| LICENSE.md, CITATION.cff license and citation metadata |
| ``` |
|
|
| Each `session_XX.zip` extracts to: |
|
|
| ``` |
| session_XX/ |
| c1_clips/ third-person RGB videos (MP4, H.264) |
| c2_rgb_clips/ top-view RGB videos |
| c2_depth_clips/ depth streams |
| lh_imu_data.csv left-hand IMU (timestamped) |
| rh_imu_data.csv right-hand IMU (timestamped) |
| annotations.csv action + engagement labels |
| ``` |
|
|
| Files are distributed as one `.zip` per session to preserve directory structure and allow resumable downloads. Extract with `unzip session_XX.zip`. |
|
|
| ## Getting started |
|
|
| ```python |
| import pandas as pd |
| from pathlib import Path |
| |
| session = Path("./extracted/session_01") |
| |
| annotations = pd.read_csv(session / "annotations.csv") |
| lh_imu = pd.read_csv(session / "lh_imu_data.csv") |
| rh_imu = pd.read_csv(session / "rh_imu_data.csv") |
| |
| print(f"Action segments: {len(annotations)}") |
| print(f"LH IMU samples: {len(lh_imu)}") |
| print(f"RH IMU samples: {len(rh_imu)}") |
| ``` |
|
|
| ```bash |
| # annotations and docs only (small, fast) |
| hf download ArvindSihag/MIAM --repo-type dataset --include "docs/*" "README.md" |
| |
| # a single session's archive |
| hf download ArvindSihag/MIAM --repo-type dataset --include "train/session_01.zip" --local-dir ./miam_data |
| |
| # the full dataset |
| hf download ArvindSihag/MIAM --repo-type dataset --local-dir ./miam_data |
| ``` |
|
|
| ## Data description |
|
|
| Domain: industrial manufacturing and human-robot collaboration. |
| Tasks: assembly and disassembly workflows. |
| Annotation tool: VGG Video Annotator (VIA). |
| Synchronization: Camera-1 hardware timestamp. |
| Labels: actions, objects, engagement states. |
| Video format: MP4 (H.264). |
| IMU format: CSV (timestamped samples). |
| Split: 70% train, 30% test. |
|
|
| ## Engagement prediction framework |
|
|
| MIAM supports multi-level behavioral analysis: |
|
|
| - Action level: task recognition and temporal localization (RGB + depth) |
| - Object level: interaction detection and tracking (RGB + IMU) |
| - Engagement level: real-time engagement state prediction (all modalities) |
|
|
| ## License and usage policy |
|
|
| Creative Commons Attribution-NonCommercial 4.0 (CC BY-NC 4.0). |
|
|
| Allowed: academic research, non-commercial projects, derivative works (non-commercial), publication with attribution. |
|
|
| Not allowed: commercial use, redistribution, re-identification of participants, removing license notices. |
|
|
| Usage rules: |
| - Access requires manual approval. |
| - Research purposes only. |
| - Redistribution is not permitted. |
| - Citation is mandatory in any publication. |
|
|
| ## Citation |
|
|
| If you use MIAM in your research, please cite: |
|
|
| ```bibtex |
| @inproceedings{arvind2025multimodal, |
| title = {A Multimodal Dataset for Enhancing Industrial Task |
| Monitoring and Engagement Prediction}, |
| author = {Arvind and Mehta, Naval Kishore and Kumar, Himanshu |
| and Saurav, Sumeet and Singh, Sanjay}, |
| booktitle = {Proceedings of the 20th ACM/IEEE International |
| Conference on Human-Robot Interaction (HRI)}, |
| year = {2025} |
| } |
| ``` |
|
|
| ## Related resources |
|
|
| Paper (arXiv): https://arxiv.org/abs/2501.05936 |
| Code: https://github.com/arvindsihag |
| Dataset: https://huggingface.co/datasets/ArvindSihag/MIAM |
|
|
| ## Acknowledgements |
|
|
| Developed at CSIR — Central Electronics Engineering Research Institute (CEERI), Pilani, and the Academy of Scientific and Innovative Research (AcSIR), India. |
|
|
| ## Contact |
|
|
| Arvind Sihag — arvind.ceeri24a@acsir.res.in |
|
|
| For queries, collaborations, or access issues, please reach out via email or open a discussion on this repository. |
