论文标题
SPD歧管上的生理和行为信号融合，并应用于压力和疼痛检测
Fusion of Physiological and Behavioural Signals on SPD Manifolds with Application to Stress and Pain Detection
论文作者
论文摘要
现有的多模式应力/疼痛识别方法通常独立地从不同的方式中提取特征，因此忽略了交叉模式相关性。本文提出了一个新型的几何框架，用于使用对称阳性（SPD）矩阵作为一种表示的多模式应力/疼痛检测，该代表性结合了协方差和交叉稳定性的生理和行为信号的相关关系。考虑到SPD矩阵的Riemannian流形的非线性，众所周知的机器学习技术不适合对这些矩阵进行分类。因此，采用了切线空间映射方法将派生的SPD矩阵序列映射到可将基于LSTM的网络用于分类的切线空间中的向量序列。提出的框架已在两个公共多模式数据集上进行了评估，这两者都取得了压力和疼痛检测任务的最新结果。
Existing multimodal stress/pain recognition approaches generally extract features from different modalities independently and thus ignore cross-modality correlations. This paper proposes a novel geometric framework for multimodal stress/pain detection utilizing Symmetric Positive Definite (SPD) matrices as a representation that incorporates the correlation relationship of physiological and behavioural signals from covariance and cross-covariance. Considering the non-linearity of the Riemannian manifold of SPD matrices, well-known machine learning techniques are not suited to classify these matrices. Therefore, a tangent space mapping method is adopted to map the derived SPD matrix sequences to the vector sequences in the tangent space where the LSTM-based network can be applied for classification. The proposed framework has been evaluated on two public multimodal datasets, achieving both the state-of-the-art results for stress and pain detection tasks.
