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Filename: drivers/Session_files_driver.php
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File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
Message: session_start(): Failed to read session data: user (path: /var/lib/php/sessions)
Filename: Session/Session.php
Line Number: 137
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File: /var/www/html/index.php
Line: 316
Function: require_once
Severity: Warning
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Filename: helpers/my_audit_helper.php
Line Number: 197
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 197
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
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Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3145
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
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Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
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Function: require_once
Choosing adequate gestures for touchless interfaces is a challenging task that has a direct impact on human-computer interaction. Such gestures are commonly determined by the designer, ad-hoc, rule-based or agreement-based methods. Previous approaches to assess agreement grouped the gestures into equivalence classes and ignored the integral properties that are shared between them. In this work, we propose a generalized framework that inherently incorporates the gesture descriptors into the agreement analysis (GDA). In contrast to previous approaches, we represent gestures using binary description vectors and allow them to be partially similar. In this context, we introduce a new metric referred to as Soft Agreement Rate ( ) to measure the level of agreement and provide a mathematical justification for this metric. Further, we performed computational experiments to study the behavior of and demonstrate that existing agreement metrics are a special case of our approach. Our method was evaluated and tested through a guessability study conducted with a group of neurosurgeons. Nevertheless, our formulation can be applied to any other user-elicitation study. Results show that the level of agreement obtained by is 2.64 times higher than the previous metrics. Finally, we show that our approach complements the existing agreement techniques by generating an artificial lexicon based on the most agreed properties.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7523555 | PMC |
http://dx.doi.org/10.1109/THMS.2020.2992216 | DOI Listing |
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