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dc.contributor.editorYang, Jong-Ryul
dc.contributor.editorHyun, Eugin
dc.contributor.editorKim, Sun Kwon
dc.date.accessioned2022-01-11T13:38:34Z
dc.date.available2022-01-11T13:38:34Z
dc.date.issued2021
dc.identifierONIX_20220111_9783036517667_405
dc.identifier.urihttps://directory.doabooks.org/handle/20.500.12854/76670
dc.description.abstractSensor technology for monitoring vital signs is an important topic for various service applications, such as entertainment and personalization platforms and Internet of Things (IoT) systems, as well as traditional medical purposes, such as disease indication judgments and predictions. Vital signs for monitoring include respiration and heart rates, body temperature, blood pressure, oxygen saturation, electrocardiogram, blood glucose concentration, brain waves, etc. Gait and walking length can also be regarded as vital signs because they can indirectly indicate human activity and status. Sensing technologies include contact sensors such as electrocardiogram (ECG), electroencephalogram (EEG), photoplethysmogram (PPG), non-contact sensors such as ballistocardiography (BCG), and invasive/non-invasive sensors for diagnoses of variations in blood characteristics or body fluids. Radar, vision, and infrared sensors can also be useful technologies for detecting vital signs from the movement of humans or organs. Signal processing, extraction, and analysis techniques are important in industrial applications along with hardware implementation techniques. Battery management and wireless power transmission technologies, the design and optimization of low-power circuits, and systems for continuous monitoring and data collection/transmission should also be considered with sensor technologies. In addition, machine-learning-based diagnostic technology can be used for extracting meaningful information from continuous monitoring data.
dc.languageEnglish
dc.subject.classificationthema EDItEUR::T Technology, Engineering, Agriculture, Industrial processes::TB Technology: general issuesen_US
dc.subject.classificationthema EDItEUR::K Economics, Finance, Business and Management::KN Industry and industrial studies::KNB Energy industries and utilitiesen_US
dc.subject.othercardiopulmonary resuscitation (CPR)
dc.subject.otherelectroencephalogram (EEG)
dc.subject.otherhemodynamic data
dc.subject.othercarotid blood flow (CBF)
dc.subject.othercerebral circulation
dc.subject.otherfrequency-shift keying radar
dc.subject.othercross-correlation
dc.subject.otherenvelope detection
dc.subject.othercontinuous-wave radar
dc.subject.otherfrequency discrimination
dc.subject.othervital-signs monitoring
dc.subject.otherheartbeat accuracy improvement
dc.subject.otherheartbeat detection
dc.subject.otherabsolute distance measurement
dc.subject.otherradar signal processing
dc.subject.other3D+t modeling
dc.subject.othercoronary artery
dc.subject.othernon-rigid registration
dc.subject.othercage deformation
dc.subject.other4D CT
dc.subject.otherpassenger detection
dc.subject.otherCW radar
dc.subject.otherradar feature vector
dc.subject.otherradar machine learning
dc.subject.otherwearable sensors
dc.subject.otherphysiology
dc.subject.othermedical monitoring
dc.subject.othervital signs
dc.subject.othercompensatory reserve
dc.subject.otherultra-high resolution
dc.subject.othercone-beam computed tomography
dc.subject.otherlow-contrast object
dc.subject.otheroptimal filter
dc.subject.othermodulation transfer function
dc.subject.othernoise power spectrum
dc.subject.otherdoppler cardiogram
dc.subject.otherwavelet transform
dc.subject.otherdenoising
dc.subject.othermother wavelet function
dc.subject.otherdecomposition level
dc.subject.othersignal decomposition
dc.subject.othersignal-to-noise-ratio
dc.titleSensors for Vital Signs Monitoring
dc.typebook
oapen.identifier.doi10.3390/books978-3-0365-1765-0
oapen.relation.isPublishedBy46cabcaa-dd94-4bfe-87b4-55023c1b36d0
oapen.relation.isbn9783036517667
oapen.relation.isbn9783036517650
oapen.pages141
oapen.place.publicationBasel, Switzerland


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