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A3: HCI Coding Guideline for Research Using Video Annotation to Assess Behavior of Nonverbal Subjects with Computer-Based Intervention

A3: HCI Coding Guideline for Research Using Video Annotation to Assess Behavior of Nonverbal... HCI studies assessing nonverbal individuals (especially those who do not communicate through traditional linguistic means: spoken, written, or sign) are a daunting undertaking. Without the use of directed tasks, interviews, questionnaires, or question-answer sessions, researchers must rely fully upon observation of behavior, and the categorization and quantification of the participant’s actions. This problem is compounded further by the lack of metrics to quantify the behavior of nonverbal subjects in computer-based intervention contexts. We present a set of dependent variables called A3 (pronounced A-Cubed) or Annotation for ASD Analysis, to assess the behavior of this demographic of users, specifically focusing on engagement and vocalization. This paper demonstrates how theory from multiple disciplines can be brought together to create a set of dependent variables, as well as demonstration of these variables, in an experimental context. Through an examination of the existing literature, and a detailed analysis of the current state of computer vision and speech detection, we present how computer automation may be integrated with the A3 guidelines to reduce coding time and potentially increase accuracy. We conclude by presenting how and where these variables can be used in multiple research areas and with varied target populations. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png ACM Transactions on Accessible Computing (TACCESS) Association for Computing Machinery

A3: HCI Coding Guideline for Research Using Video Annotation to Assess Behavior of Nonverbal Subjects with Computer-Based Intervention

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Publisher
Association for Computing Machinery
Copyright
Copyright © 2009 by ACM Inc.
ISSN
1936-7228
DOI
10.1145/1530064.1530066
Publisher site
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Abstract

HCI studies assessing nonverbal individuals (especially those who do not communicate through traditional linguistic means: spoken, written, or sign) are a daunting undertaking. Without the use of directed tasks, interviews, questionnaires, or question-answer sessions, researchers must rely fully upon observation of behavior, and the categorization and quantification of the participant’s actions. This problem is compounded further by the lack of metrics to quantify the behavior of nonverbal subjects in computer-based intervention contexts. We present a set of dependent variables called A3 (pronounced A-Cubed) or Annotation for ASD Analysis, to assess the behavior of this demographic of users, specifically focusing on engagement and vocalization. This paper demonstrates how theory from multiple disciplines can be brought together to create a set of dependent variables, as well as demonstration of these variables, in an experimental context. Through an examination of the existing literature, and a detailed analysis of the current state of computer vision and speech detection, we present how computer automation may be integrated with the A3 guidelines to reduce coding time and potentially increase accuracy. We conclude by presenting how and where these variables can be used in multiple research areas and with varied target populations.

Journal

ACM Transactions on Accessible Computing (TACCESS) – Association for Computing Machinery

Published: Jun 1, 2009

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