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本研究提出RespiroDynamics：多模态呼吸综合数据集。该数据集共收录60名受试者的数据，采集分为「静息」与「运动」两个阶段。数据集涵盖多种模态数据，包括红-绿-蓝（RGB）影像、热成像视频、心率（HR）数据、心电图（ECG）读数及元数据，所有数据均与同步记录的呼吸活动对齐。此外，该数据集还补充了源自NHANES III（Hankinson，1999）分布的参考数值。为构建全面且具有代表性的数据集，由于文化因素限制无法招募女性受试者，本研究共招募了60名来自以高加索人种为主体的埃及人群的男性志愿者。这些志愿者在年龄、体重、身高、生活方式及其他特征上均具有较高多样性，从而为本研究提供了兼具全面性与多样性的研究样本。

搜集汇总

背景与挑战

背景概述

该数据集是一个多模态呼吸健康数据集，包含60名埃及男性的RGB视频、热成像视频、心率和心电图数据，分为休息和运动两种状态。数据与呼吸活动同步，并参考了NHANES III标准，旨在通过深度学习技术提升肺部健康评估能力。