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.

DOI Link: 
10.21227/fftf-e749
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[1] Ahmed Sharshar, "Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.", IEEE Dataport, 2023. [Online]. Available: http://dx.doi.org/10.21227/fftf-e749. Accessed: Jan. 20, 2025.
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author = {Ahmed Sharshar },
publisher = {IEEE Dataport},
title = {Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.},
year = {2023} }
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Ahmed Sharshar. (2023). Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.. IEEE Dataport. http://dx.doi.org/10.21227/fftf-e749
Ahmed Sharshar, 2023. Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.. Available at: http://dx.doi.org/10.21227/fftf-e749.
Ahmed Sharshar. (2023). "Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.." Web.
1. Ahmed Sharshar. Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning. [Internet]. IEEE Dataport; 2023. Available from : http://dx.doi.org/10.21227/fftf-e749
Ahmed Sharshar. "Respiro Dynamics: A Multifaceted Dataset for Enhanced Lung Health Assessment Using Deep Learning.." doi: 10.21227/fftf-e749