justchugh/MPSC-RR

<div align="center"> <h1 style="font-size: 4em; margin-bottom: 0.2em;">MPSC-RR Dataset</h1> <h2 style="font-size: 2.5em; margin-top: 0; color: #666;">Respiratory Waveform Reconstruction using Persistent Independent Particles Tracking from Video</h2> </div>

<p align="center"> <a href="https://huggingface.co/datasets/justchugh/MPSC-RR"> <img src="https://img.shields.io/badge/Dataset-HuggingFace-yellow?style=for-the-badge" alt="Dataset"> </a> &nbsp;&nbsp; <a href="https://justchugh.github.io/RRPIPs.github.io/#paper"> <img src="https://img.shields.io/badge/Paper-CHASE%202025-blue?style=for-the-badge" alt="Paper"> </a> &nbsp;&nbsp; <a href="https://github.com/mxahan/RRPIPS"> <img src="https://img.shields.io/badge/Code-GitHub-black?style=for-the-badge" alt="Code"> </a> &nbsp;&nbsp; <a href="https://justchugh.github.io/RRPIPs.github.io/"> <img src="https://img.shields.io/badge/Website-GitHub-red?style=for-the-badge" alt="Website"> </a> &nbsp;&nbsp; <a href="LICENSE"> <img src="https://img.shields.io/badge/License-CC%20BY%204.0-green?style=for-the-badge" alt="License"> </a> </p>


Dataset Description

The MPSC-RR dataset is a comprehensive collection of RGB videos designed for contactless respiratory rate estimation and respiratory waveform reconstruction research. The dataset captures respiratory-induced movements across multiple body positions and camera angles, providing diverse scenarios for developing robust respiratory monitoring algorithms.

Key Features

• —Multi-Position Coverage: Front, back, side, and lying positions
• —Natural Breathing Patterns: Regular, slow, and fast breathing captured
• —Diverse Demographics: Adult volunteers across different age groups and backgrounds
• —Ground Truth Annotations: Vernier Go Direct Respiration Belt measurements
• —High-Quality Videos: RGB videos with clear respiratory motion visibility
• —Real-World Conditions: Varied lighting, clothing, and environmental settings

Dataset Statistics

• —Total Videos: 31 video sessions
• —Subjects: 24 adult volunteers (Subject IDs: 1-24)
• —Duration: 2.5-5 minutes per video (Average: ~4 minutes)
• —Resolution: Varied resolutions from 304×304 to 1602×1080
• —Frame Rate: Primarily 30 FPS (with some at 30.1, 29, and 24 FPS)
• —Total Duration: ~2.1 hours of respiratory monitoring data
• —Total Size: 5.18 GB

Data Structure

Video Files

s{subject_id}_{view_position}.{ext}

Naming Convention:

• —s1_front.mov - Subject 1, front view
• —s20_side.mp4 - Subject 20, side view (Note: one video in MP4 format)
• —s24_lying.mov - Subject 24, lying position

View Positions:

• —front - Frontal chest/abdomen view (6 videos)
• —back - Back/shoulder movement view (4 videos)
• —side - Side profile view (16 videos)
• —lying - Lying down position (5 videos)

Metadata Structure

{
    "video_id": str,                    # e.g., "s1_front"
    "subject_id": int,                  # Subject identifier (1-24)
    "view_position": str,               # front, back, side, lying
    "duration_seconds": float,          # Video duration
    "resolution": str,                  # e.g., "1920x1080"
    "frame_rate": float,                # 30.0, 30.1, 29.0, or 24.0 FPS
    "file_size_mb": float,              # File size in megabytes
    "filename": str                     # Actual filename with extension
}

Usage

Loading the Dataset

from datasets import load_dataset

# Load all videos
dataset = load_dataset("justchugh/MPSC-RR")

# Access video information
sample = dataset[0]
video_id = sample["video_id"]
subject_id = sample["subject_id"] 
view_position = sample["view_position"]

Filtering Videos

# Get front view videos
front_videos = dataset.filter(lambda x: x["view_position"] == "front")

# Get specific subject's videos
subject_1 = dataset.filter(lambda x: x["subject_id"] == 1)

# Get lying position videos
lying_videos = dataset.filter(lambda x: x["view_position"] == "lying")

Basic Analysis

# Count videos per position
positions = [sample["view_position"] for sample in dataset]
position_counts = {pos: positions.count(pos) for pos in set(positions)}
print("Videos per position:", position_counts)

# List all subjects
subjects = set(sample["subject_id"] for sample in dataset)
print(f"Subjects: {sorted(subjects)}")

Data Collection

Equipment

• —Camera: Standard RGB cameras (mobile phones, mounted cameras)
• —Ground Truth: Vernier Go Direct Respiration Belt for pressure measurements
• —Distance: 1-1.5 meters from subjects
• —Environment: Clinical laboratory setting with controlled conditions

Protocol

1. 1.Subject Preparation: Comfortable positioning with clear view of respiratory regions
2. 2.Baseline Recording: 30-second calibration period
3. 3.Data Collection: 3-5 minutes of natural breathing
4. 4.Ground Truth Sync: Synchronized pressure belt data collection
5. 5.Quality Check: Manual verification of respiratory cycles

View Positions Explained

Dataset Distribution

Benchmark Results

Performance of state-of-the-art methods on MPSC-RR:

Applications

This dataset supports research in:

• —Contactless Vital Sign Monitoring
• —Multi-Position Respiratory Analysis
• —Computer Vision for Healthcare
• —Sleep and Rest Monitoring
• —Wearable-Free Health Tracking
• —Clinical Decision Support Systems

Data Quality

Inclusion Criteria

• —Clear visibility of respiratory-induced motion
• —Stable video recording (minimal camera movement)
• —Synchronized ground truth data available
• —Adequate lighting conditions

Quality Metrics

• —Motion Clarity: All videos show visible respiratory movement
• —Synchronization: <50ms offset between video and pressure data
• —Duration: Minimum 148 seconds per recording
• —Resolution: Varied resolutions optimized for respiratory motion capture

Related Datasets

• —AIR-125: Infant respiratory monitoring (125 videos, infants)
• —BIDMC: PPG and respiration signals (53 recordings, clinical)
• —Sleep Database: NIR/IR respiratory data (28 videos, adults)

Ethical Considerations

• —IRB Approval: All data collection approved by institutional review board
• —Informed Consent: Written consent obtained from all participants
• —Privacy Protection: Faces blurred or cropped when necessary
• —Data Anonymization: No personally identifiable information included
• —Voluntary Participation: Participants could withdraw at any time

Citation

If you use this dataset in your research, please cite:

@inproceedings{hasan2025rrpips,
    title={RRPIPS: Respiratory Waveform Reconstruction using Persistent Independent Particles Tracking from Video},
    author={Hasan, Zahid and Ahmed, Masud and Sakib, Shadman and Chugh, Snehalraj and Khan, Md Azim and Faridee, Abu Zaher MD and Roy, Nirmalya},
    booktitle={ACM/IEEE International Conference on Connected Health: Applications, Systems and Engineering Technologies (CHASE)},
    year={2025},
    pages={1--12},
    doi={10.1145/3721201.3721366}
}

Quick Start

# Install required packages
pip install datasets

# Load and explore dataset
from datasets import load_dataset
dataset = load_dataset("justchugh/MPSC-RR")

print(f"Total videos: {len(dataset)}")
print(f"First video: {dataset[0]['video_id']}")
print(f"Positions available: {set(s['view_position'] for s in dataset)}")

# Example: Get all side view videos
side_videos = dataset.filter(lambda x: x["view_position"] == "side")
print(f"Side view videos: {len(side_videos)}")

License

This dataset is released under Creative Commons Attribution 4.0 International License.

Links

• —Code Repository: https://github.com/mxahan/RRPIPS
• —Website Repository: https://github.com/justchugh/RRPIPs.github.io

Contact

• —Technical Issues: zhasan3@umbc.edu
• —Dataset Questions: schugh1@umbc.edu

Team

<div align="center">

<table border="0" style="border: none;"> <tr> <td align="center" style="border: none; padding: 20px;"> <a href="https://mxahan.github.io/digital-cv/"> <img src="assets/authors/Zahid.png" width="180px;" style="border-radius: 50%;" alt="Zahid Hasan"/><br /> _{<b>Zahid Hasan</b>} </a> </td> <td align="center" style="border: none; padding: 20px;"> <a href="https://mahmed10.github.io/personalwebpage/"> <img src="assets/authors/Masud.png" width="180px;" style="border-radius: 50%;" alt="Masud Ahmed"/><br /> _{<b>Masud Ahmed</b>} </a> </td> <td align="center" style="border: none; padding: 20px;"> <a href="https://www.linkedin.com/in/shadman-sakib15/"> <img src="assets/authors/Shadman.png" width="180px;" style="border-radius: 50%;" alt="Shadman Sakib"/><br /> _{<b>Shadman Sakib</b>} </a> </td> <td align="center" style="border: none; padding: 20px;"> <a href="https://justchugh.github.io"> <img src="assets/authors/Snehalraj.png" width="180px;" style="border-radius: 50%;" alt="Snehalraj Chugh"/><br /> _{<b>Snehalraj Chugh</b>} </a> </td> </tr> </table>

<table border="0" style="border: none;"> <tr> <td align="center" style="border: none; padding: 20px;"> <a href="https://www.linkedin.com/in/azim-khan-10/"> <img src="assets/authors/Azim.png" width="180px;" style="border-radius: 50%;" alt="Md Azim Khan"/><br /> _{<b>Md Azim Khan</b>} </a> </td> <td align="center" style="border: none; padding: 20px;"> <a href="https://azmfaridee.github.io"> <img src="assets/authors/Zaher.png" width="180px;" style="border-radius: 50%;" alt="Abu Zaher MD Faridee"/><br /> _{<b>Abu Zaher MD Faridee</b>} </a> </td> <td align="center" style="border: none; padding: 20px;"> <a href="https://mpsc.umbc.edu/nroy"> <img src="assets/authors/Nirmalya.png" width="180px;" style="border-radius: 50%;" alt="Nirmalya Roy"/><br /> _{<b>Nirmalya Roy</b>} </a> </td> </tr> </table>

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Dataset Version: 1.0