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
In this paper, we present a novel unobtrusive multi-modal sensor for monitoring of physiological parameters featuring capacitive electrocardiogram (cECG), reflective photoplethysmogram (rPPG), and magnetic induction monitoring (MI) in a single sensor. The sensor system comprises sensor nodes designed and optimized for integration into a grid-like array of multiple sensors in a bed and a central controller box for data collection and processing. Hence, it is highly versatile in application and suitable for unobtrusive monitoring of vital signs, both in a professional setting and a home-care environment. The presented hardware design takes both inter-modal interference between cECG and MI into account as well as intra-modal interference due to cross talk between two MI sensors in close vicinity. In a lab study, we evaluated a prototype of our new multi-modal sensor with two sensor nodes on four healthy subjects. The subjects were lying on the sensors and exercising with a hand grip in order to increase heart rate and thus evaluate our sensor both during changing physiological parameters as well as a wider range of those. Heart beat intervals and heart rate variability were derived from both cECG and rPPG. Breathing intervals were derived from the MI sensor. For heart beat intervals, we achieved an RMSE of 2.3 ms and a correlation of 0.99 using cECG. Similarly, using rPPG, an RMSE of 18.9 ms with a correlation of 0.99 was achieved. With regard to breathing intervals derived from MI, we achieved an RMSE of 1.12 s and a correlation of 0.90.
Download full-text PDF |
Source |
---|---|
http://dx.doi.org/10.1109/TBCAS.2019.2911199 | DOI Listing |
Enter search terms and have AI summaries delivered each week - change queries or unsubscribe any time!