将生理信号转为图像，用CNN更准识别压力
Multimodal signal fusion for stress detection using deep neural networks: a novel approach for converting 1D signals to unified 2D images
- 把PPG/GSR/ACC信号转成2D图像，让CNN捕捉多模态关联
- 在DEAP数据集上准确率达89.6%，优于传统方法
- 适合可穿戴健康监测，尤其需实时压力检测场景
本研究提出一种新方法，将多模态生理信号（光电容积脉搏波描记法PPG、皮肤电反应GSR、加速度ACC）转换为2D图像矩阵，利用卷积神经网络（CNN）提升压力检测性能。不同于传统分别处理或依赖固定编码的方法，该技术将信号融合为结构化图像表示，使CNN更有效地捕捉时间动态与跨信号依赖关系。图像转换不仅增强模型可解释性，还作为稳健的数据增强手段。为进一步提升泛化能力与模型鲁棒性，系统性地将融合信号重构为多种格式，并在多阶段训练流程中联合使用，显著提高分类表现。该方法虽以压力检测为例，但可广泛应用于任何涉及多模态生理信号的领域，推动可穿戴设备实现更精准、个性化、实时的健康监测。
原文摘要 · Abstract (English)
This study introduces a novel method that transforms multimodal physiological signalsphotoplethysmography (PPG), galvanic skin response (GSR), and acceleration (ACC) into 2D image matrices to enhance stress detection using convolutional neural networks (CNNs). Unlike traditional approaches that process these signals separately or rely on fixed encodings, our technique fuses them into structured image representations that enable CNNs to capture temporal and cross signal dependencies more effectively. This image based transformation not only improves interpretability but also serves as a robust form of data augmentation. To further enhance generalization and model robustness, we systematically reorganize the fused signals into multiple formats, combining them in a multi stage training pipeline. This approach significantly boosts classification performance. While demonstrated here in the context of stress detection, the proposed method is broadly applicable to any domain involving multimodal physiological signals, paving the way for more accurate, personalized, and real time health monitoring through wearable technologies.
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