Signal-to-Noise Ratio in Doppler Radar System for Heart and Respiratory Rate Measurements

Droitcour, Amy D.; Borić–Lubecke, Olga; Kovacs, G.T.A.

doi:10.1109/tmtt.2009.2029668

Cited by 131 publications

(82 citation statements)

References 18 publications

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“…The optimization neither assumes activities, 46 and vital signs. 6,15 Our work is closest to prior art that uses RF signals to extract a person's breathing rate and average heart rate. 6,15,19,23,33 In contrast to this past work, which recovers the average period of a heartbeat (which is of the order of a second), emotion recognition requires extracting the individual heartbeats and measuring small variations in the beat-to-beat intervals with millisecondscale accuracy.…”

Section: Beat Extraction Algorithmmentioning

confidence: 99%

“…6,15 Our work is closest to prior art that uses RF signals to extract a person's breathing rate and average heart rate. 6,15,19,23,33 In contrast to this past work, which recovers the average period of a heartbeat (which is of the order of a second), emotion recognition requires extracting the individual heartbeats and measuring small variations in the beat-to-beat intervals with millisecondscale accuracy. Unfortunately, prior research that aims to segment RF reflections into individual beats either cannot achieve sufficient accuracy for emotion recognition 11,20,31 or requires the monitored subjects to hold their breath.…”

Section: Beat Extraction Algorithmmentioning

confidence: 99%

“…Recent research has shown that such RF reflections can be used to measure a person's breathing and average heart rate without body contact. 6,15,19,23,33 However, the periodicity of the heart signal (i.e., its running average) is not sufficient for emotion recognition. To recognize emotions, we need to measure the minute variations in each individual beat length.…”

Section: Introductionmentioning

confidence: 99%

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Emotion recognition using wireless signals

2018

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This paper demonstrates a new technology that can infer a person's emotions from RF signals reflected off his body. EQ-Radio transmits an RF signal and analyzes its reflections off a person's body to recognize his emotional state (happy, sad, etc.). The key enabler underlying EQ-Radio is a new algorithm for extracting the individual heartbeats from the wireless signal at an accuracy comparable to on-body ECG monitors. The resulting beats are then used to compute emotion-dependent features which feed a machine-learning emotion classifier. We describe the design and implementation of EQ-Radio, and demonstrate through a user study that its emotion recognition accuracy is on par with state-of-theart emotion recognition systems that require a person to be hooked to an ECG monitor.

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Section: Beat Extraction Algorithmmentioning

confidence: 99%

Section: Beat Extraction Algorithmmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Emotion recognition using wireless signals

2018

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“…It comprises a signal processing module, amplifying and filtering modules, level move, A/D conversion, MCU data processing modules and wireless communication module. Since signals detected by Doppler radar sensor have small amplitude with low signal to noise ratio [3], the multiple amplifier filters were designed to improve the signal to noise ratio as well as lifting signal to an acceptable level by MCU. Our system uses 2.4G wireless RF modules communicating with PC, the PC displays and stores information of respiration and heartbeat in a real-time manner.…”

Section: The Basic System Components and Working Principlementioning

confidence: 99%

Non-contact physiological signal monitoring system based on Doppler radar

Chen

Yang

et al. 2017

IEICE Electron. Express

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A non-contact physiological signal monitoring system was proposed using Doppler radar sensor to detect the movement of human thoracic cavity. The system consists of Doppler radar sensor, signal conditioning, MCU data processing and wireless communication. The system collects human chest movement due to heart and respiration (pleura fretting signals for conditioning). The experimental results show that the Doppler radar that the system used with the zero phase IIR digital filter can achieve real-time monitoring, store the physiological data, separate respiration and heartbeat signals, eliminate the distortion of the signal phase and have advantages of small size, low power consumption, strong penetration.

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“…Research has already been reported on using microwave Doppler radar for measuring respiratory function [10], [16], [8], [15], [3], [4] as well as other approaches using ultra wideband (UWB) radar [13], [11]. Such non-contact monitoring has significant potential for a range of applications from rescue operation in disaster management (earthquake and tsunami) to sleep pattern monitoring and sudden infant death syndrome (SIDS).…”

Section: Introductionmentioning

confidence: 99%

Further applications of Doppler radar for non-contact respiratory assessment

Lee

Pathirana

Caelli

et al. 2013

2013 35th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC)

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This paper further investigates the use of Doppler radar for detecting and identifying certain human respiratory characteristics from observed frequency and phase modulations. Specifically, we show how breathing frequencies can be determined from the demodulated signal leading to identifying abnormalities of breathing patterns using signal derivatives, optimal filtering and standard statistical measures. Specifically, we report results on a robust method for distinguishing cessation of the normal breathing cycle. The proposed approach can have potential application in the management of sudden infant death syndrome(SIDS) and sleep apnea.

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Signal-to-Noise Ratio in Doppler Radar System for Heart and Respiratory Rate Measurements

Cited by 131 publications

References 18 publications

Emotion recognition using wireless signals

Emotion recognition using wireless signals

Non-contact physiological signal monitoring system based on Doppler radar

Further applications of Doppler radar for non-contact respiratory assessment

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