About: A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation

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About: A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation Goto Sponge NotDistinct Permalink

An Entity of Type : schema:ScholarlyArticle, within Data Space : wasabi.inria.fr associated with source document(s)

Attributes	Values
type	Academic Article research paper schema:ScholarlyArticle
isDefinedBy	Covid-on-the-Web dataset
has title	A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation
Creator	Sun, Guanghao Ishibashi, Koichiro Le Quang, Truong Le Thi, Hoi Do Trong, Tuan Nguyen Van, Kinh Nguyen Vu, Trung Nguyen, Cuong Trong, Thanh
Source	Elsevier; Medline; PMC
abstract	OBJECTIVES: In this study, an infection screening system was developed to detect patients suffering from infectious diseases. In addition, the system was also designed to deal with the variability in age and gender, which would affect the accuracy of the detection. Furthermore, to enable a low-cost, non-contact and embedded system, multiple vital signs from a medical radar were measured and all algorithms were implemented on a Field Programmable Gate Array, named PYNQ-Z1. METHODS: The system consisted of two main stages: digital signal processing and data classification. In the former stage, Butterworth filters, with flexible cut-off frequencies depending on age and gender, and a time-domain peak detection algorithm were deployed to compute three vital signs, namely heart rate, respiratory rate, and standard deviation of heart beat-to-beat interval. For the classification problem, two machine learning models, Support Vector Machine and Quadratic Discriminant Analysis, were implemented. RESULTS: The Student's t-test showed that our proposed digital signal processing algorithms coped well with the variability of human cases in age and gender. Meanwhile, the f(1)-score of roughly 98.0% represented the high sensitivity and specificity of our proposed machine learning methods. CONCLUSION: This study outlines the implementation of an infection screening system, which achieved competent performance. The system might be beneficial for fast screening of infected patients at public health centers in underdeveloped areas, where people have little access to healthcare.
has issue date	2019-08-15(xsd:dateTime)
bibo:doi	10.1016/j.imu.2019.100225
bibo:pmid	32289073
has license	no-cc
sha1sum (hex)	780f1e8693bb6f32fd116970fee4a4887fb0ffc8
schema:url	https://doi.org/10.1016/j.imu.2019.100225
resource representing a document's title	A non-contact infection screening system using medical radar and Linux-embedded FPGA: Implementation and preliminary validation
has PubMed Central identifier	PMC7103934
has PubMed identifier	32289073
schema:publication	Inform Med Unlocked
resource representing a document's body	covid:780f1e8693bb6f32fd116970fee4a4887fb0ffc8#body_text
is schema:about of	named entity 'LINUX' named entity 'ACCURACY' named entity 'DEAL' named entity 'PROBLEM' named entity 'PERFORMANCE' named entity 'BUTTERWORTH FILTERS' named entity 'AGE' named entity 'STAGES' named entity 'MAIN' named entity 'PATIENTS' named entity 'HAVE' named entity 'NON-' named entity 'ADDITION' named entity 'METHODS' named entity 'RESULTS' named entity 'STANDARD DEVIATION' named entity 'MEASURED' named entity 'OUTLINES' named entity 'RESPIRATORY RATE' named entity 'MEDICAL' named entity 'interval' named entity 'For' named entity 'frequencies' named entity 'vital signs' named entity 'respiratory rate' named entity 'standard deviation' named entity 'FPGA' named entity 'IMPLEMENTATION' named entity 'PRELIMINARY' named entity 'DATA' named entity 'FAST' named entity 'DETECTION' named entity 'TIME' named entity 'SIGNAL PROCESSING ALGORITHMS' named entity 'CONTACT INFECTION' named entity 'VALIDATION' named entity 'USING' named entity 'MIGHT BE' named entity 'AREAS' named entity 'DIGITAL' named entity 'AFFECT' named entity 'IMPLEMENTATION' named entity 'DOMAIN' named entity 'ALGORITHM' named entity 'PUBLIC HEALTH' named entity 'SUFFERING' named entity 'MACHINE LEARNING' named entity 'CASES' named entity 'EMBEDDED' named entity 'OBJECTIVES' named entity 'INFECTED' named entity 'FIELD' named entity 'ARRAY' named entity 'SCREENING' named entity 'INFECTION SCREENING' named entity 'PEAK DETECTION' named entity 'SENSITIVITY AND SPECIFICITY' named entity 'LITTLE' named entity 'TO DETECT' named entity 'FREQUENCIES' named entity 'HEART BEAT' named entity 'INFECTION SCREENING' named entity 'QUADRATIC' named entity 'SCORE' named entity 'BEAT' named entity 'DISCRIMINANT ANALYSIS' named entity 'T-TEST' named entity 'NAMED' named entity 'PROPOSED' named entity 'CLASSIFICATION' named entity 'FLEXIBLE' named entity 'MULTIPLE' named entity 'PEOPLE'

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