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公开(公告)号：US20200260052A1
公开(公告)日：2020-08-13
申请号：US16733065
申请日：2020-01-02
申请人： OXEHEALTH LIMITED
摘要： A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.
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公开(公告)号：US20200250816A1
公开(公告)日：2020-08-06
申请号：US16732769
申请日：2020-01-02
申请人： OXEHEALTH LIMITED
摘要： A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.
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公开(公告)号：US20200245903A1
公开(公告)日：2020-08-06
申请号：US16732979
申请日：2020-01-02
申请人： OXEHEALTH LIMITED
摘要： A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.
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公开(公告)号：US20190029600A1
公开(公告)日：2019-01-31
申请号：US16071542
申请日：2017-01-19
申请人： OXEHEALTH LIMITED
CPC分类号： A61B5/7225 , A61B5/024 , A61B5/02416 , A61B5/08 , A61B5/0816 , A61B5/1128 , A61B5/1135 , A61B5/7239 , G06T7/20 , G06T2207/10016
摘要： A method and apparatus which combines multiple simultaneous signals thought to contain a common periodic component by performing principal component analysis on each of the multiple signals, finding the weight of the first principal component, and then adding the multiple signals together in a weighted sum according to the weight of the first principal component. The method and apparatus further includes a way of combining signals from successive overlapping time windows in real time by differentiating the signal and forming as each output signal sample the value of the preceding signal sample summed with the differential of the signal, weighted by weights based on the amplitude of the differential signal at that time point.
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公开(公告)号：US20240293078A1
公开(公告)日：2024-09-05
申请号：US18593777
申请日：2024-03-01
申请人： OXEHEALTH LIMITED
发明人： Jonathan Frederick CARTER , Lionel TARASSENKO , Joao Goncalo Malveiro JORGE , Oliver John GIBSON
IPC分类号： A61B5/00 , A61B5/0205 , A61B5/11 , G06N20/00
CPC分类号： A61B5/4812 , A61B5/0077 , A61B5/0205 , A61B5/11 , A61B5/7267 , G06N20/00
摘要： A method comprising determining a sleep state of a test subject using input data from the test subject. The input data comprise: at least one measure of test subject movement; and at least one cardiorespiratory feature of the test subject and are derived from video images of the test subject. The at least one cardiorespiratory feature of the test subject is derived using a feature extractor trained using reference cardiorespiratory signals for each of a plurality of reference subjects derived from time-resolved measurements from a plurality of sensors worn by the reference subjects, the feature extractor comprising a neural network, and the reference cardiorespiratory signals. Also provided a method of training a machine-learning algorithm to determine a sleep state of a subject using training data in respect of a plurality of training subjects derived from video images of the training subjects using the feature extractor.
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公开(公告)号：US11182910B2
公开(公告)日：2021-11-23
申请号：US16334211
申请日：2017-09-19
申请人： OXEHEALTH LIMITED
摘要： In order to detect gross subject movement in a video image in a way which is not sensitive to illumination change, for example illumination changes caused by movement of shadows or sunlight, spaced pairs of image frames are selected from a video sequence and sub-divided into cells, and spatial frequency analysis is performed in each cell. The magnitude of the spatial frequency components in corresponding cells in the two selected image frames are compared. If the number of cells with high magnitude difference is high then the video image is determined as containing gross subject movement whereas if the number of cells with high magnitude differences is low, the sequence is determined as not containing gross movement, though it may contain illumination changes or no or fine movement.
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公开(公告)号：US10448900B2
公开(公告)日：2019-10-22
申请号：US15533818
申请日：2015-12-08
申请人： OXEHEALTH LIMITED
发明人： Alessandro Guazzi
IPC分类号： A61B5/00 , A61B5/0205 , A61B5/026 , A61B5/0295 , A61B5/08 , G06K9/46 , A61B5/024 , A61B5/11 , A61B5/113
摘要： Autoregressive modelling is used to identify periodic physiological signals such as heart rate or breathing rate in an image of a subject. The color channels of a video signal are windowed and normalised by dividing each signal by its mean. The ratios of the normalised channels to each other are found and principal component analyses conducted on the ratio signals. The most periodic of the principal components is selected and autoregressive models of one or more different orders are fitted to the selected component. Poles of the fitted autoregressive models of different orders are taken and pure sinusoids corresponding to the frequency of each pole are generated and their cross-correlation with the original component is found. Whichever pole corresponds to the sinusoid with the maximum cross-correlation is selected as the best estimate of the frequency of periodic physiological information in the original video signal. The method may be used in a patient monitor or in a webcam-enabled device such as a tablet computer or smart phone.
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公开(公告)号：US20190029604A1
公开(公告)日：2019-01-31
申请号：US16071570
申请日：2017-01-19
申请人： OXEHEALTH LIMITED
摘要： A method and apparatus for extracting a breathing rate estimate from video images of a respiring subject. Signals corresponding to the spatial coordinates of feature points tracked through the video sequence are filtered and excessively large changes are attenuated to reduce movement artefacts. The signals are differentiated and signals which correlate most strongly with other signals are selected. The selected signals are subject to principal component analysis and the best quality of the top five principal components is selected and its frequency is used to calculate and output a breathing rate estimate. The method is particularly suitable for detecting respiration in subject in secure rooms where the video image is of substantially the whole room and the subject is only a small part of the image, and maybe covered or uncovered.
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公开(公告)号：US20190029543A1
公开(公告)日：2019-01-31
申请号：US16071611
申请日：2017-01-23
申请人： OXEHEALTH LIMITED
摘要： A method and apparatus for estimating heart rate of a subject from a video image of the subject. Regions of interest are generated by: detecting and tracking feature points through the video image sequence, triangulating the feature points and generating square regions of interest corresponding to the in-circles of the triangles; or, according to size and location probability distributions which are defined to have a high probability for image areas away from strong intensity gradients and which generate good quality signals. In an alternative embodiment, the intensity variations from the square regions of interest through the frame sequence are taken as time series signals and those signals which have a strong peak in the power spectrum are selected and subject to principal component analysis. The principal component with a highest signal quality is selected and its frequency is found and used to estimate the heart rate.
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公开(公告)号：US20230028571A1
公开(公告)日：2023-01-26
申请号：US17878590
申请日：2022-08-01
申请人： OXEHEALTH LIMITED
摘要： A method and apparatus for monitoring a human or animal subject in a room using video imaging of the subject and analysis of the video image to detect and quantify movement of the subject and to derive an estimate of vital signs such as heart rate or breathing rate. The method includes techniques for de-correlating global intensity variations such as sunlight changes, compensating for noise, eliminating areas not of interest in the image, and quickly and automatically finding regions of interest for detecting subject movement and estimating vital signs. A logic machine is used for interpreting detected movement of the subject, and an artificial neural network is used to calculate a confidence measure for the vital signs estimates from signal quality indices. The confidence measure may be used with a normal density filter to output estimates of the vital signs.
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