Severity: Warning
Message: file_get_contents(https://...@gmail.com&api_key=61f08fa0b96a73de8c900d749fcb997acc09&a=1): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
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
Line: 271
Function: simplexml_load_file_from_url
File: /var/www/html/application/helpers/my_audit_helper.php
Line: 3165
Function: getPubMedXML
File: /var/www/html/application/controllers/Detail.php
Line: 597
Function: pubMedSearch_Global
File: /var/www/html/application/controllers/Detail.php
Line: 511
Function: pubMedGetRelatedKeyword
File: /var/www/html/index.php
Line: 317
Function: require_once
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Traditional exercise fatigue monitoring technologies have faced challenges, including high costs, operational complexity, and invasiveness, which limit their practicality for daily training and health management. A biobased pulp wool triboelectric nanogenerator (PW-TENG) has been developed and integrated with a heart rate strap (HRS) and inertial measurement unit (IMU) to establish a multimodal fatigue monitoring system. Doping BaTiO particles, spraying conductive graphite, and bidirectional W-shape encapsulation have enhanced its output performance by 100%, tripled the contact-separation efficiency, and achieved ultrafast response and recovery times (8.4 and 4.6 ms) with 6200 cycles stability. In speed experiments, nine metrics collected by PW-TENG and IMU at six speeds were analyzed using principal component analysis (PCA), which showed that PC1 and PC2 contributed 76.4 and 13.4% of the variance to the speed, respectively. In fatigue experiments, the trend of human fatigue-induced changes in locomotor characteristics is verified, indicating that PC1, PC2, and PC3 contributed 29.1, 21.5, and 14.4% to the three phases of fatigue, respectively. In contrast, the PC1 contribution of the PW-TENG alone is as high as 53%, demonstrating excellent fatigue sensitivity. This noninvasive, robust system has provided a practical solution for optimizing training and personalized health management, showing significant potential in sports science and medical applications.
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Source |
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http://dx.doi.org/10.1021/acssensors.5c01574 | DOI Listing |