Abstract
For afiection recognition problems of GSR (Galvanic Skin Response) signal, this paper presents a method of afiection recognition based on a improved K-nearest neighbor algorithm. In this research, on the flrst step, the group collected 175 participants’ GSR signals on the test of emotional arousal which was acquired in the lab environment; then used the Batterworth low pass fllter to remove GSR signals noise, and extracted the original features of GSR signals at the same time; then recognized the flve feelings of happiness, sadness, fear, anger and calm with the improved K- nearest neighbor classifler. The experimental results show that the method can improves the accuracy of identiflcation indeed.
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 callDisclaimer: 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.
