Title

Categorizing Autonomic Nervous System (Ans) Emotional Signals Using Bio-Sensors For Hri Within The Maui Paradigm

Abstract

In this article, we discuss the strong relationship between affect and cognition and the importance of emotions in Multimodal Human Computer Interaction (HCI) and User-Modeling. We introduce the overall paradigm for our multimodal system that aims at recognizing its users' emotions and at responding to them accordingly depending upon the current context or application. We then describe the design of the emotion elicitation experiment we conducted by collecting, via wearable computers, physiological signals from the autonomic nervous system (galvanic skin response, heart rate, temperature) and mapping them to certain emotions (Sadness, Anger, Fear, Surprise, Frustration, and Amusement). We show the results of three different supervised learning algorithms that categorize these collected signals in terms of emotions, and generalize their learning to recognize emotions from new collections of signals. We finally discuss possible broader impact and possible applications of emotion recognition for multimodal intelligent systems. © 2006 IEEE.

Publication Date

12-1-2006

Publication Title

Proceedings - IEEE International Workshop on Robot and Human Interactive Communication

Number of Pages

277-284

Document Type

Article; Proceedings Paper

Personal Identifier

scopus

DOI Link

https://doi.org/10.1109/ROMAN.2006.314430

Socpus ID

48349142650 (Scopus)

Source API URL

https://api.elsevier.com/content/abstract/scopus_id/48349142650

This document is currently not available here.

Share

COinS