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
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)}")
# 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:
@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.