54 research outputs found
Noncontact Detection of Sleep Apnea Using Radar and Expectation-Maximization Algorithm
Sleep apnea syndrome requires early diagnosis because this syndrome can lead
to a variety of health problems. If sleep apnea events can be detected in a
noncontact manner using radar, we can then avoid the discomfort caused by the
contact-type sensors that are used in conventional polysomnography. This study
proposes a novel radar-based method for accurate detection of sleep apnea
events. The proposed method uses the expectation-maximization algorithm to
extract the respiratory features that form normal and abnormal breathing
patterns, resulting in an adaptive apnea detection capability without any
requirement for empirical parameters. We conducted an experimental quantitative
evaluation of the proposed method by performing polysomnography and radar
measurements simultaneously in five patients with the symptoms of sleep apnea
syndrome. Through these experiments, we show that the proposed method can
detect the number of apnea and hypopnea events per hour with an error of 4.8
times/hour; this represents an improvement in the accuracy by 1.8 times when
compared with the conventional threshold-based method and demonstrates the
effectiveness of our proposed method.Comment: 8 pages, 12 figures, 3 tables. This work is going to be submitted to
the IEEE for possible publicatio
Wavelet Based Denoising of the Simulated Chest Wall Motion Detected by SFCW Radar
Low power and compact radars have emerged with the development of electronic technology. This has enabled the use of radars in indoor environments and the realization of many applications. The detection, tracking and classification of human movements by radar are among the remarkable applications. Contactless detection of human vital signs improves the quality of life of patients being kept under observation and facilitates the work of experts. In this study, it was simulated that the movement of the chest wall was modeled and detected by the SFCW radar. Gaussian, Rician and uniformly distributed random noise types were added to the modeled chest motion at different levels. The noisy signal obtained at the receiver is denoised with different mother wavelet functions and the performances of these functions are presented comparatively
Detection and analysis of human respiration using microwave Doppler radar
Non-contact detection characteristic of Doppler radar provides an unobtrusive means of respiration detection and monitoring. This avoids additional preparations such as physical sensor attachment or special clothing. Furthermore, robustness of Doppler radar against environmental factors reduce environmental constraints and strengthens the possibility of employing Doppler radar as a practical biomedical devices in the future particularly in long term monitoring applications such as in sleep studies
Detection and analysis of human respiration using microwave Doppler radar
Non-contact detection characteristic of Doppler radar provides an unobtrusive means of respiration detection and monitoring. This avoids additional preparations such as physical sensor attachment or special clothing. Furthermore, robustness of Doppler radar against environmental factors reduce environmental constraints and strengthens the possibility of employing Doppler radar as a practical biomedical devices in the future particularly in long term monitoring applications such as in sleep studies
Contact and remote breathing rate monitoring techniques: a review
ABSTRACT: Breathing rate monitoring is a must for hospitalized patients with the current coronavirus disease 2019 (COVID-19). We review in this paper recent implementations of breathing monitoring techniques, where both contact and remote approaches are presented. It is known that with non-contact monitoring, the patient is not tied to an instrument, which improves patients’ comfort and enhances the accuracy of extracted breathing activity, since the distress generated by a contact device is avoided. Remote breathing monitoring allows screening people infected with COVID-19 by detecting abnormal respiratory patterns. However, non-contact methods show some disadvantages such as the higher set-up complexity compared to contact ones. On the other hand, many reported contact methods are mainly implemented using discrete components. While, numerous integrated solutions have been reported for non-contact techniques, such as continuous wave (CW) Doppler radar and ultrawideband (UWB) pulsed radar. These radar chips are discussed and their measured performances are summarized and compared
Wearable Wireless Devices
No abstract available
ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALTH
ARTIFICIAL INTELLIGENCE-ENABLED EDGE-CENTRIC SOLUTION FOR AUTOMATED ASSESSMENT OF SLEEP USING WEARABLES IN SMART HEALT
Contactless multiscale measurement of cardiac motion using biomedical radar sensor
IntroductionA contactless multiscale cardiac motion measurement method is proposed using impulse radio ultra-wideband (IR-UWB) radar at a center frequency of 7.29 GHz.MotivationElectrocardiograph (ECG), heart sound, and ultrasound are traditional state-of-the-art heartbeat signal measurement methods. These methods suffer from defects in contact and the existence of a blind information segment during the cardiogram measurement.MethodsExperiments and analyses were conducted using coarse-to-fine scale. Anteroposterior and along-the-arc measurements were taken from five healthy male subjects (aged 25–43) when lying down or prone. In every measurement, 10 seconds of breath-holding data were recorded with a radar 55 cm away from the body surface, while the ECG was monitored simultaneously as a reference.ResultsCardiac motion detection from the front was superior to that from the back in amplitude. In terms of radar detection angles, the best cardiac motion information was observed at a detection angle of 120°. Finally, in terms of cardiac motion cycles, all the ECG information, as well as short segments of cardiac motion details named blind ECGs segments, were detected.SignificanceA contactless and multiscale cardiac motion detection method is proposed with no blind detection of segments during the entire cardiac cycle. This paves the way for a potentially significant method of fast and accurate cardiac disease assessment and diagnosis that exhibits promising application prospects in contactless online cardiac monitoring and in-home healthcare
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