On line-affective state reporting device: A tool for evaluating affective state inference systems

Susana Zoghbi, Dana Kulić, Elizabeth Croft, Machiel Van Der Loos

Research output: Chapter in Book/Report/Conference proceedingConference PaperOther

Abstract

The monitoring of human affective state is a key part of developing responsive and naturally behaving human-robot interaction systems. However, evaluation and calibration of physiologically monitored affective state data is typically done using offline questionnaires and user reports. In this paper we investigate the use of an online-device for collecting real-time user reports of affective state during interaction with a robot. These reports are compared to both previous survey reports taken after the interaction, and the affective states estimated by an inference system. The aim is to evaluate and characterize the physiological signal-based inference system and determine which factors significantly influence its performance. This analysis will be used in future work, to fine tune the affective estimations by identifying what kind of variations in physiological signals precede or accompany the variations in reported affective states.Copyright is held by the author/owner(s).Copyright is held by the author/owner(s).

Original languageEnglish
Title of host publicationProceedings of the 4th ACM/IEEE International Conference on Human-Robot Interaction, HRI'09
Pages311-312
Number of pages2
DOIs
Publication statusPublished - 1 Dec 2008
Externally publishedYes
EventAnnual ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2009 - San Diego, United States of America
Duration: 11 Mar 200913 Mar 2009
Conference number: 4th
https://dl.acm.org/doi/proceedings/10.1145/1514095 (Proceedings)

Conference

ConferenceAnnual ACM/IEEE International Conference on Human-Robot Interaction (HRI) 2009
Abbreviated titleHRI 2009
CountryUnited States of America
CitySan Diego
Period11/03/0913/03/09
Internet address

Keywords

  • Affective state estimation
  • Human's responses to robots-affective responses
  • Human-robot interaction
  • Physiological signal monitoring

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