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A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar
by
Khan, Faheem
, Cho, Sung
in
algorithm
/ Algorithms
/ Assessments
/ Breathing
/ harmonics
/ Heart Rate
/ Humans
/ IR UWB radar
/ Monitoring
/ motion detection
/ Noise measurement
/ Radar
/ Respiration
/ respiration rate
/ Signal Processing, Computer-Assisted
/ Vital Signs
2017
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A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar
by
Khan, Faheem
, Cho, Sung
in
algorithm
/ Algorithms
/ Assessments
/ Breathing
/ harmonics
/ Heart Rate
/ Humans
/ IR UWB radar
/ Monitoring
/ motion detection
/ Noise measurement
/ Radar
/ Respiration
/ respiration rate
/ Signal Processing, Computer-Assisted
/ Vital Signs
2017
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Do you wish to request the book?
A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar
by
Khan, Faheem
, Cho, Sung
in
algorithm
/ Algorithms
/ Assessments
/ Breathing
/ harmonics
/ Heart Rate
/ Humans
/ IR UWB radar
/ Monitoring
/ motion detection
/ Noise measurement
/ Radar
/ Respiration
/ respiration rate
/ Signal Processing, Computer-Assisted
/ Vital Signs
2017
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A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar
Journal Article
A Detailed Algorithm for Vital Sign Monitoring of a Stationary/Non-Stationary Human through IR-UWB Radar
2017
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Overview
The vital sign monitoring through Impulse Radio Ultra-Wide Band (IR-UWB) radar provides continuous assessment of a patient’s respiration and heart rates in a non-invasive manner. In this paper, IR UWB radar is used for monitoring respiration and the human heart rate. The breathing and heart rate frequencies are extracted from the signal reflected from the human body. A Kalman filter is applied to reduce the measurement noise from the vital signal. An algorithm is presented to separate the heart rate signal from the breathing harmonics. An auto-correlation based technique is applied for detecting random body movements (RBM) during the measurement process. Experiments were performed in different scenarios in order to show the validity of the algorithm. The vital signs were estimated for the signal reflected from the chest, as well as from the back side of the body in different experiments. The results from both scenarios are compared for respiration and heartbeat estimation accuracy.
Publisher
MDPI AG,MDPI
Subject
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