Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.

Citation Author(s):
Ahmed
Sharshar
MBZUAI
Submitted by:
Ahmed Sharshar
Last updated:
Fri, 08/09/2024 - 10:46
DOI:
10.21227/fftf-e749
License:
0
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Abstract 

Our paper presents RespiroDynamics: A Comprehensive Multimodal Respiratory Dataset, compiled from 60 participants, recorded in two sessions labelled ’rest’ and ’exercise’. This dataset incorporates a variety of data types, including Red-Green-Blue (RGB) and Thermal videos, Heart Rate (HR), ECG readings and metadata, all synchronized with observed respiratory activities. Additionally, these data are enriched with reference values from the NHANES III (Hankinson- 1999) distribution. To construct a comprehensive and representative dataset, we engaged 60 males due to cultural factors that prevented us from collecting from females, volunteers from the Egyptian population belonging predominantly to the Caucasian race. The volunteers had high diversity in terms of age, weight, height, lifestyle, and other characteristics, thereby contributing to a well-rounded and varied sample for our research

Instructions: 

Please refer to Dataset Section for in-depth details in paper "Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning."