标题： PulmoFusion：通过高效的多模态融合推进肺部健康 显示英文标题

标题： PulmoFusion: Advancing Pulmonary Health with Efficient Multi-Modal Fusion

摘要： 传统远程肺功能检测缺乏有效肺部监测所需的精度。 我们提出了一种新颖的非侵入性方法，使用多模态预测模型，将RGB或热成像视频数据与患者元数据相结合。 我们的方法利用节能的脉冲神经网络(SNNs)对峰值呼气流量(PEF)进行回归，并对用力呼气容积(FEV1)和用力肺活量(FVC)进行分类，使用轻量级CNN来克服SNN在回归任务中的局限性。 通过多头注意力层改进多模态数据集成，并采用K折验证和集成学习来提高鲁棒性。 使用热成像数据，我们的SNN模型在呼吸周期基础上达到92%的准确率，按患者计算达到99.5%。 PEF回归模型的相对RMSE分别为0.11(热成像)和0.26(RGB)，FEV1/FVC预测的MAE为4.52%，确立了最先进的性能。 代码和数据集可在https://github.com/ahmed-sharshar/RespiroDynamics.git找到 显示英文摘要

摘要： Traditional remote spirometry lacks the precision required for effective pulmonary monitoring. We present a novel, non-invasive approach using multimodal predictive models that integrate RGB or thermal video data with patient metadata. Our method leverages energy-efficient Spiking Neural Networks (SNNs) for the regression of Peak Expiratory Flow (PEF) and classification of Forced Expiratory Volume (FEV1) and Forced Vital Capacity (FVC), using lightweight CNNs to overcome SNN limitations in regression tasks. Multimodal data integration is improved with a Multi-Head Attention Layer, and we employ K-Fold validation and ensemble learning to boost robustness. Using thermal data, our SNN models achieve 92% accuracy on a breathing-cycle basis and 99.5% patient-wise. PEF regression models attain Relative RMSEs of 0.11 (thermal) and 0.26 (RGB), with an MAE of 4.52% for FEV1/FVC predictions, establishing state-of-the-art performance. Code and dataset can be found on https://github.com/ahmed-sharshar/RespiroDynamics.git