Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 143
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 143
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 209
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 994
Function: getPubMedXML
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3134
Function: GetPubMedArticleOutput_2016
File: /var/www/html/application/controllers/Detail.php
Line: 574
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 488
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Observational behavior analysis plays a key role for the discovery and evaluation of risk markers for many neurodevelopmental disorders. Research on autism spectrum disorder (ASD) suggests that behavioral risk markers can be observed at 12 months of age or earlier, with diagnosis possible at 18 months. To date, these studies and evaluations involving observational analysis tend to rely heavily on clinical practitioners and specialists who have undergone intensive training to be able to reliably administer carefully designed behavioural-eliciting tasks, code the resulting behaviors, and interpret such behaviors. These methods are therefore extremely expensive, time-intensive, and are not easily scalable for large population or longitudinal observational analysis. We developed a self-contained, closed-loop, mobile application with movie stimuli designed to engage the child's attention and elicit specific behavioral and social responses, which are recorded with a mobile device camera and then analyzed via computer vision algorithms. Here, in addition to presenting this paradigm, we validate the system to measure engagement, name-call responses, and emotional responses of toddlers with and without ASD who were presented with the application. Additionally, we show examples of how the proposed framework can further risk marker research with fine-grained quantification of behaviors. The results suggest these objective and automatic methods can be considered to aid behavioral analysis, and can be suited for objective automatic analysis for future studies.
Download full-text PDF |
Source |
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8993160 | PMC |
http://dx.doi.org/10.1109/taffc.2018.2868196 | DOI Listing |
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