Severity: Warning
Message: file_get_contents(https://...@pubfacts.com&api_key=b8daa3ad693db53b1410957c26c9a51b4908&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
Filename: helpers/my_audit_helper.php
Line Number: 176
Backtrace:
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 176
Function: file_get_contents
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 250
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3122
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 575
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 489
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 316
Function: require_once
Two tri-axial accelerometers were placed on the wrists (one on each hand) of the patients with Parkinson's disease (PD) and a non-PD control group. Subjects were asked to perform three of the upper extremity motor function tasks from the Unified Parkinson's Disease Rating Scale (UPDRS) test. The tasks were: 1) finger tapping, 2) opening and closing of palms, and 3) pronation-supination movements of the forearms. The inertia signals were wirelessly received and stored on a computer for further off-line analysis. Various features such as range, standard deviation, entropy, time to accomplish the task, and maximum frequency present in the signal were extracted and compared. The results showed that among the studied population, "standard deviation", "range", "entropy", "time" and "max frequency" are the best to worst features, respectively, to distinguish between the non-PD and PD subjects. Furthermore, using the mentioned features, it is more probable to distinguish between the non-PD and PD subjects from tasks 2 and 3 as opposed to task 1.
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Source |
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http://dx.doi.org/10.1109/EMBC.2014.6944833 | DOI Listing |
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