A Two-fold PCA-Approach for Inter-Individual Recognition of Emotions in Natural Walking

This paper describes recognition of emotions of an unkown person during natural walking. As gait data is redundant, high dimensional and variable, effective feature extraction is essential. A combination of two consecutive Principal Component Analyses (PCA) and Fourier Transformation is used for data reduction. Naive Bayes, 1-Nearest Neighbor and Support Vector Machine (SVM) are compared for classification. Best accuracy is achieved for Naive Bayes with 72% for the four emotions sad, neutral, happy and angry within 1.65 seconds recording time. Slight increase in recognition accuracy is achieved, if the first PCA is replaced by Kernel PCA for SVM classification. The emotions angry and sad, both with high contrast in arousal, are better recognizable than neutral and happy. It is concluded that gait is capable to reveal the emotional state of a walker above chance level.