Eye movement detection has attracted increasing attention in the fields of safety driving, eye motion tracking, psychological assessment and telemedicine. Soft multi-functional electronic skin (SMFES) is designed to collect electrooculogram (EOG), skin temperature and sweat signals simultaneously for eye movement detection. Serpentine structure is adopted to ensure the stretchability of SMFES for satisfying large deformation $(>30\text{\%})$ of the soft skin surface. The paper demonstrates that EOG, skin temperature and sweat signals are successfully collected under different eye movements. The feature data from EOG, skin temperature and sweat signals are extracted with different eye movements, and the principal component analysis (PCA) method is adopted to reduce the dimensionality of the feature space. The paper also proposes an intelligent data fusion algorithm for eye movement classification whose input vector is represented by the first three principal components. Adaptive neuro fuzzy inference system (ANFIS) is built to classify and recognize the eye movements (Up, Down, Left, and Right). Furthermore, experiments have demonstrated that ANFIS algorithm achieves 90% recognition accuracy of such eye movements. This work demonstrates that SMFES integrated with data fusion algorithm can successfully solve the eye movement tracking problem, with significant impact in safety driving and wearable electronics.

眼动检测在安全驾驶，眼动跟踪，心理评估和远程医疗领域已引起越来越多的关注。柔软的多功能电子皮肤（SMFES）旨在同时收集眼电图（EOG），皮肤温度和汗液信号以进行眼动检测。采用蛇形结构以确保SMFES的可拉伸性，以满足较大的变形$（>30\text{％}）$柔软的皮肤表面。本文表明，在不同的眼球运动下，EOG，皮肤温度和汗液信号已成功收集。通过眼动的不同程度来提取EOG，皮肤温度和汗液信号中的特征数据，并采用主成分分析（PCA）方法来减小特征空间的维数。本文还提出了一种用于眼动分类的智能数据融合算法，其输入矢量由前三个主分量表示。自适应神经模糊推理系统（ANFIS）用于分类和识别眼睛的运动（上，下，左和右）。此外，实验表明，ANFIS算法可实现90％的此类眼动识别精度。