We present a feasibility study for life signs detection using a continuous-wave radar working in the band around 4 GHz. The data-processing is carried out by using two different data processing approaches, which are compared about the possibility to characterize the frequency behaviour of the breathing and heartbeat activity. The two approaches are used with the main aim to show the possibility of monitoring the vital signs activity in an accurate and reliable way.

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This paper presents a method for the detection of wakeful state, rapid eye movement sleep (REM), light sleep (N1&N2) and deep sleep (N3&N4) based on cardiorespiratory parameters. Experiments were conducted with data of 625 subjects without sleep-disordered breathing selected from the SHHS dataset. Compared to previous studies, our method considers results of neighboring epochs classification and epoch position over record time. The method demonstrates Cohen's kappa of 0.57 ± 0.13 and the accuracy of 71.4 ± 8.6 %. The results might contribute to the development of screening tools for diagnostics, prevention, and management of sleep disorders.

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A lack of effective non-contact methods for automatic fall detection, which may result in the development of health and life-threatening conditions, is a great problem of modern medicine, and in particular, geriatrics. The purpose of the present work was to investigate the advantages of utilizing a multi-bioradar system in the accuracy of remote fall detection. The proposed concept combined usage of wavelet transform and deep learning to detect fall episodes. The continuous wavelet transform was used to get a time-frequency representation of the bio-radar signal and use it as input data for a pre-trained convolutional neural network AlexNet adapted to solve the problem of detecting falls. Processing of the experimental results showed that the designed multi-bioradar system can be used as a simple and view-independent approach implementing a non-contact fall detection method with an accuracy and F1-score of 99%.

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One of the research tasks, which should be solved to develop a sleep monitor, is sleep stages classification. This paper presents an algorithm for wakefulness, rapid eye movement sleep (REM) and non-REM sleep detection based on a set of 33 features, extracted from respiratory inductive plethysmography signal, and bagging classifier. Furthermore, a few heuristics based on knowledge about normal sleep structure are suggested. We used the data from 29 subjects without sleep-related breathing disorders who underwent a PSG study at a sleep laboratory. Subjects were directed to the PSG study due to suspected sleep disorders. A leave-one-subject-out cross-validation procedure was used for testing the classification performance. The accuracy of 77.85 ± 6.63 and Cohen's kappa of 0.59 ± 0.11 were achieved for the classifier. Using heuristics we increased the accuracy to 80.38 ± 8.32 and the kappa to 0.65 ± 0.13. We conclude that heuristics may improve the automated sleep structure detection based on the analysis of indirect information such as respiration signal and are useful for the development of home sleep monitoring system.