Abstract
Emotion recognition is attracting considerable interest among the research community. In this work, Empirical Mode Decomposition has been implemented to derive both statistical and nonlinear features from Wrist Pulse Signal to classifying emotions namely anxiety and boredom. Wrist Pulse signals were extracted from 24 subjects using TETRIS game as a stimulus using Fission and Fusion approach. The acquired signals were pre-processed to remove unwanted noise and artefacts present within the signal. In addition, various classifiers namely Naiive Byes, Support Vector Machine, K-Nearest Neighbour, Logistic Regression, Linear Discriminant Analysis, Quadratic Discriminant Analysis were considered. Results from these classifiers indicate that both Logistic Regression and Quadratic Discriminant Analysis gave an indistinguishable accuracy of 99.71% (fission) and 77.08% (fusion) for anxiety state. Moreover, for boredom state, the highest classification accuracy was 66.67 % for Naiive Bayes using fission and 64.58% for fusion. Results highlight the impact of empirical mode decomposition with hilbert transform for the recognition of emotion from wrist pulse signals.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: IOP Conference Series: Materials Science and Engineering
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
