Application of step-frequency radars in medicine

Anishchenko, Lesya; Alekhin, Maksim; Tataraidze, Alexander; Ivashov, Sergey; Бугаев, А. С.; Soldovieri, Francesco

doi:10.1117/12.2049523

Cited by 16 publications

(4 citation statements)

References 9 publications

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“…Low power Frequency Modulated Continuous Wave (FMCW) radar and micro-Doppler tracks have been recently used with various scopes, such as discriminating armed from unarmed people [15], identifying people on the basis of their gait characteristics [16][17][18] or their movements [19], and for gestures recognition [20]. Moreover, radar technology has been successfully applied to the medical field [21,22], for example to remotely monitor the cardiac and respiratory frequency [23]. Principal Component Analysis (PCA) has been often exploited in radar applications, as an instrument to reduce the dimensionality of the available feature space and to automatize the feature extraction and selection procedure [24,25], together with deep learning algorithms for fall detection [26] and human activity recognition [27].…”

Section: Related Workmentioning

confidence: 99%

People Walking Classification Using Automotive Radar

et al. 2020

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Automotive radars are able to guarantee high performances at the expenses of a relatively low cost, and recently their application has been extended to several fields in addition to the original one. In this paper we consider the use of this kind of radars to discriminate different types of people’s movements in a real context. To this end, we exploit two different maps obtained from radar, that is, a spectrogram and a range-Doppler map. Through the application of dimensionality reduction methods, such as principal component analysis (PCA) and t-distributed stochastic neighbor embedding (t-SNE) algorithm, and the use of machine learning techniques we prove that is possible to classify with a very good precision people’s way of walking even employing commercial devices specifically designed for other purposes.

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Section: Related Workmentioning

confidence: 99%

People Walking Classification Using Automotive Radar

et al. 2020

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“…The chest wall displacement of a person is detected first. The respiratory and heartbeat rate can be determined with this information [18][19][20]. The displacement is calculated using the delay between the signals sent from the transmitter and received from the target.…”

Section: Received Signalmentioning

confidence: 99%

“…This level is calculated depending on the standard deviation of the noise. The standard deviation ( ) estimation of the noises are evaluated as in Equation (18). After determining the the level is calculated as in Equation (19).…”

Section: Thresholdingmentioning

confidence: 99%

Wavelet Based Denoising of the Simulated Chest Wall Motion Detected by SFCW Radar

Acar

Seflek

Yaldiz

2019

AEM

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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.

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“…One of the methods that can be used for prolonged daily unobtrusive stress detection and monitoring is the bioradar technique [23]. This method has been known since the 1970s [24,25].…”

Section: Introductionmentioning

confidence: 99%

Challenges and Potential Solutions of Psychophysiological State Monitoring with Bioradar Technology

Anishchenko

2018

Diagnostics

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Psychophysiological state monitoring provides a promising way to detect stress and accurately assess wellbeing. The purpose of the present work was to investigate the advantages of utilizing a new unobtrusive multi-transceiver system on the accuracy of remote psychophysiological state monitoring by means of a bioradar technique. The technique was tested in laboratory conditions with the participation of 35 practically healthy volunteers, who were asked to perform arithmetic and physical workload tests imitating different types of stressors. Information about any variation in vital signs, registered by a bioradar with two transceivers, was used to detect mental or physical stress. Processing of the experimental results showed that the designed two-channel bioradar can be used as a simple and relatively easy approach to implement a non-contact method for stress monitoring. However, individual specificity of physiological responses to mental and physical workloads makes the creation of a universal stress-detector classifier that is suitable for people with different levels of stress tolerance a challenging task. For non-athletes, the proposed method allows classification of calm state/mental workload and calm state/physical workload with an accuracy of 89% and 83% , respectively, without the usage of any additional a priori information on the subject.

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Application of step-frequency radars in medicine

Cited by 16 publications

References 9 publications

People Walking Classification Using Automotive Radar

People Walking Classification Using Automotive Radar

Wavelet Based Denoising of the Simulated Chest Wall Motion Detected by SFCW Radar

Challenges and Potential Solutions of Psychophysiological State Monitoring with Bioradar Technology

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