Fall detection in humans is critical in the prevention of life-threatening conditions. This is especially important for elderly people who are living alone. Therefore, automatic fall detection is one of the most relevant problems in geriatrics.
View Article and Find Full Text PDFSensors (Basel)
December 2019
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.
View Article and Find Full Text PDFDiagnostics (Basel)
October 2018
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.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2017
This paper presents a model for the estimation of a priori probabilities of sleep epoch classes based on the epoch location in a sleep cycle. These probabilities are used as additional features for sleep stage classification based on the analysis of respiratory effort. The model was validated with data of 685 subjects selected from the Sleep Heart Health Study dataset.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
July 2017
The paper deals with a problem of a remote prolonged temperature monitoring of biological objects. It presents an algorithm for automatic analysis and processing of video recording from a thermographic camera. Special attention is paid to the limitation of the method taking into account the possibility of its utilizing in laboratory conditions.
View Article and Find Full Text PDFThis paper presents a method for classifying wakefulness, REM, light and deep sleep based on the analysis of respiratory activity and body motions acquired by a bioradar. The method was validated using data of 32 subjects without sleep-disordered breathing, who underwent a polysomnography study in a sleep laboratory. We achieved Cohen's kappa of 0.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
June 2017
This paper presents an algorithm for the detection of wakeful state, rapid eye movement sleep (REM) and non-REM sleep based on the analysis of respiratory movements acquired through a bioradar. We used the data from 29 subjects without sleep-related breathing disorders who underwent a polysomnography study at a sleep laboratory. A leave-one-subject-out cross-validation procedure was used for testing the classification performance.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
June 2017
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.
View Article and Find Full Text PDFAnnu Int Conf IEEE Eng Med Biol Soc
August 2016
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.
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