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

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