University of Illinois Urbana-Champaign

Influence of environmental context on affect recognition of stylized movements

Heimerdinger, Madison Suzanne

Content Files
HEIMERDINGER-THESIS-2017.pdf

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https://hdl.handle.net/2142/99365

Description

Title
Influence of environmental context on affect recognition of stylized movements
Author(s)
Heimerdinger, Madison Suzanne
Issue Date
2017-12-05
Director of Research (if dissertation) or Advisor (if thesis)
LaViers Minnick, Amy
Department of Study
Mechanical Sci & Engineering
Discipline
Mechanical Engineering
Degree Granting Institution
University of Illinois at Urbana-Champaign
Degree Name
M.S.
Degree Level
Thesis
Date of Ingest
2018-03-13T15:48:54Z
Keyword(s)
  • Affect
  • Gait
  • Perception
  • Human-robot interaction (HRI)
Abstract
Modifying the style of movements will be an important component of robotic interaction as more and more robots move into human-facing scenarios where humans are (consciously or unconsciously) constantly monitoring the motion profile of counterparts in order to make judgments about the state of these counterparts. This thesis includes two main contributions: (1) the development of two MATLAB tools that are designed to aid in the creation and simulation of stylized movement trajectories in varied contexts and (2) three user studies that explore the effects of environmental context on a human’s perception of stylized movement. First and foremost, the results from all of the user studies indicate that environmental contexts and stylized walking sequences both impact affect recognition. In the first two studies, participants were asked to categorize stimuli as one of seven affective labels. The results show that the labels were not applied consistently and so it was concluded that the affect of a multi-dimensional stimuli cannot be adequately categorized using a single affective label. In the third study the stimuli were evaluated on multiple scales and classified using ratings of valence and arousal rather than affective labels. The results were used to create a least squares model for the dataset that decomposed the affect ratings of animations to display the compound effects of stylized walking sequences and environmental contexts on affective ratings.
Graduation Semester
2017-12
Type of Resource
text
Permalink
http://hdl.handle.net/2142/99365
Copyright and License Information
Copyright 2017 Madison Heimerdinger

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