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Patent

Processing images of at least one living being

01 Mar 2010-
TL;DR: In this article, a method of processing images of at least one living being, including obtaining a sequence (19) of digital images taken at consecutive points in time, is proposed. But the method is not suitable for the detection of individuals.
Abstract: A method of processing images of at least one living being, includes obtaining a sequence (19) of digital images taken at consecutive points in time. At least one measurement zone (26) comprising a plurality of image points is selected. For each measurement zone (26), a signal (28,30) representative of at least variations in a time- varying value of a combination of pixel values at at least a number of the image points for use in determining at least one of a presence and a frequency value of at least one peak in a spectrum of the signal (28,30) corresponding to a frequency of a periodic physiological phenomenon is obtained. The step (25) of selecting at least one measurement zone (26) includes analyzing information based on pixel data of a plurality of image parts in at least one of the images (19), each image part including at least one image point, and selecting each measurement zone (26) from contiguous parts determined to have similar characteristics.
Citations
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Journal ArticleDOI
TL;DR: This work has devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation, and has been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model.
Abstract: Remote sensing of the reflectance photoplethysmogram using a video camera typically positioned 1 m away from the patient's face is a promising method for monitoring the vital signs of patients without attaching any electrodes or sensors to them. Most of the papers in the literature on non-contact vital sign monitoring report results on human volunteers in controlled environments. We have been able to obtain estimates of heart rate and respiratory rate and preliminary results on changes in oxygen saturation from double-monitored patients undergoing haemodialysis in the Oxford Kidney Unit. To achieve this, we have devised a novel method of cancelling out aliased frequency components caused by artificial light flicker, using auto-regressive (AR) modelling and pole cancellation. Secondly, we have been able to construct accurate maps of the spatial distribution of heart rate and respiratory rate information from the coefficients of the AR model. In stable sections with minimal patient motion, the mean absolute error between the camera-derived estimate of heart rate and the reference value from a pulse oximeter is similar to the mean absolute error between two pulse oximeter measurements at different sites (finger and earlobe). The activities of daily living affect the respiratory rate, but the camera-derived estimates of this parameter are at least as accurate as those derived from a thoracic expansion sensor (chest belt). During a period of obstructive sleep apnoea, we tracked changes in oxygen saturation using the ratio of normalized reflectance changes in two colour channels (red and blue), but this required calibration against the reference data from a pulse oximeter.

381 citations

Proceedings ArticleDOI
27 Jun 2016
TL;DR: This work introduces a strategy to dynamically select face regions useful for robust HR estimation, inspired by recent advances on matrix completion theory, which significantly outperforms state-of-the-art HR estimation methods in naturalistic conditions.
Abstract: Recent studies in computer vision have shown that, while practically invisible to a human observer, skin color changes due to blood flow can be captured on face videos and, surprisingly, be used to estimate the heart rate (HR). While considerable progress has been made in the last few years, still many issues remain open. In particular, state of-the-art approaches are not robust enough to operate in natural conditions (e.g. in case of spontaneous movements, facial expressions, or illumination changes). Opposite to previous approaches that estimate the HR by processing all the skin pixels inside a fixed region of interest, we introduce a strategy to dynamically select face regions useful for robust HR estimation. Our approach, inspired by recent advances on matrix completion theory, allows us to predict the HR while simultaneously discover the best regions of the face to be used for estimation. Thorough experimental evaluation conducted on public benchmarks suggests that the proposed approach significantly outperforms state-of the-art HR estimation methods in naturalistic conditions.

280 citations

Patent
27 Mar 2012
TL;DR: In this paper, a method for extracting information from detected characteristic signals was proposed, where a data stream (76, 78, 80, 82) derived from electromagnetic radiation (14) emitted or reflected by an object (11) is received and a plurality of characteristic index elements varying over time can be extracted therefrom.
Abstract: The present invention relates to a device and a method for extracting information from detected characteristic signals. A data stream (76, 78, 80, 82) derivable from electromagnetic radiation (14) emitted or reflected by an object (11) is received and a plurality of characteristic index elements (50) varying over time can be extracted therefrom. The index elements (50) comprise physiological information (48) indicative of at least one at least partially periodic vital signal (12), and a disturbing signal component (58). For eliminating the disturbing signal component (58) to a great extent, the characteristic index elements (50) can be projected to a disturbance-reduced index element (64) having a distinct orientation in relation to a presumed orientation of the disturbing signal component (58). The disturbance-reduced index element (64) is chosen so as to reflect a dominant main orientation and length of the disturbing signal component (58) over time. Consequently, the mainly genuine physiological information (48) extracted from the data stream (76, 78, 80, 82) in this way can be utilized for determining the at least one at least partially periodic vital signal (12).

111 citations

Patent
16 Aug 2012
TL;DR: In this article, a method of remote monitoring of vital signs by detecting the PPG signal in an image of a subject taken by a video camera such as a webcam was proposed.
Abstract: A method of remote monitoring of vital signs by detecting the PPG signal in an image of a subject taken by a video camera such as a webcam. The PPG signal is identified by auto-regressive analysis of ambient light reflected from a region of interest on the subject's skin. Frequency components of the ambient light and aliasing artefacts resulting from the frame rate of the video camera are cancelled by auto-regressive analysis of ambient light reflected from a region of interest not on the subject's skin, e.g. in the background. This reveals the spectral content of the ambient light allowing identification of the subject's PPG signal. Heart rate, oxygen saturation and breathing rate are obtained from the PPG signal. The values can be combined into a wellness index based on a statistical analysis of the values.

99 citations

Journal ArticleDOI
12 Dec 2019
TL;DR: A clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care and proposes signal quality assessment algorithms to discriminate between clinically acceptable and noisy signals.
Abstract: The implementation of video-based non-contact technologies to monitor the vital signs of preterm infants in the hospital presents several challenges, such as the detection of the presence or the absence of a patient in the video frame, robustness to changes in lighting conditions, automated identification of suitable time periods and regions of interest from which vital signs can be estimated. We carried out a clinical study to evaluate the accuracy and the proportion of time that heart rate and respiratory rate can be estimated from preterm infants using only a video camera in a clinical environment, without interfering with regular patient care. A total of 426.6 h of video and reference vital signs were recorded for 90 sessions from 30 preterm infants in the Neonatal Intensive Care Unit (NICU) of the John Radcliffe Hospital in Oxford. Each preterm infant was recorded under regular ambient light during daytime for up to four consecutive days. We developed multi-task deep learning algorithms to automatically segment skin areas and to estimate vital signs only when the infant was present in the field of view of the video camera and no clinical interventions were undertaken. We propose signal quality assessment algorithms for both heart rate and respiratory rate to discriminate between clinically acceptable and noisy signals. The mean absolute error between the reference and camera-derived heart rates was 2.3 beats/min for over 76% of the time for which the reference and camera data were valid. The mean absolute error between the reference and camera-derived respiratory rate was 3.5 breaths/min for over 82% of the time. Accurate estimates of heart rate and respiratory rate could be derived for at least 90% of the time, if gaps of up to 30 seconds with no estimates were allowed.

90 citations

References
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Journal ArticleDOI
TL;DR: In this paper, a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates is described. But the detection performance is limited to 15 frames per second.
Abstract: This paper describes a face detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a simple and efficient classifier which is built using the AdaBoost learning algorithm (Freund and Schapire, 1995) to select a small number of critical visual features from a very large set of potential features. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising face-like regions. A set of experiments in the domain of face detection is presented. The system yields face detection performance comparable to the best previous systems (Sung and Poggio, 1998; Rowley et al., 1998; Schneiderman and Kanade, 2000; Roth et al., 2000). Implemented on a conventional desktop, face detection proceeds at 15 frames per second.

13,037 citations

Proceedings Article
01 Jan 2001
TL;DR: Viola et al. as mentioned in this paper proposed a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates using a new image representation called the integral image, which allows the features used by the detector to be computed very quickly.
Abstract: This paper describes a visual object detection framework that is capable of processing images extremely rapidly while achieving high detection rates. There are three key contributions. The first is the introduction of a new image representation called the “Integral Image” which allows the features used by our detector to be computed very quickly. The second is a learning algorithm, based on AdaBoost, which selects a small number of critical visual features and yields extremely efficient classifiers [4]. The third contribution is a method for combining classifiers in a “cascade” which allows background regions of the image to be quickly discarded while spending more computation on promising object-like regions. A set of experiments in the domain of face detection are presented. The system yields face detection performance comparable to the best previous systems [16, 11, 14, 10, 1]. Implemented on a conventional desktop, face detection proceeds at 15 frames per second. Author email: fPaul.Viola,Mike.J.Jonesg@compaq.com c Compaq Computer Corporation, 2001 This work may not be copied or reproduced in whole or in part for any commercial purpose. Permission to copy in whole or in part without payment of fee is granted for nonprofit educational and research purposes provided that all such whole or partial copies include the following: a notice that such copying is by permission of the Cambridge Research Laboratory of Compaq Computer Corporation in Cambridge, Massachusetts; an acknowledgment of the authors and individual contributors to the work; and all applicable portions of the copyright notice. Copying, reproducing, or republishing for any other purpose shall require a license with payment of fee to the Cambridge Research Laboratory. All rights reserved. CRL Technical reports are available on the CRL’s web page at http://crl.research.compaq.com. Compaq Computer Corporation Cambridge Research Laboratory One Cambridge Center Cambridge, Massachusetts 02142 USA

1,648 citations

Journal ArticleDOI
TL;DR: Plethysmographic signals were measured remotely (>1m) using ambient light and a simple consumer level digital camera in movie mode as discussed by the authors, which may be useful for medical purposes such as characterization of vascular skin lesions and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.
Abstract: Plethysmographic signals were measured remotely (>1m) using ambient light and a simple consumer level digital camera in movie mode. Heart and respiration rates could be quantified up to several harmonics. Although the green channel featuring the strongest plethysmographic signal, corresponding to an absorption peak by (oxy-) hemoglobin, the red and blue channels also contained plethysmographic information. The results show that ambient light photo-plethysmography may be useful for medical purposes such as characterization of vascular skin lesions (e.g., port wine stains) and remote sensing of vital signs (e.g., heart and respiration rates) for triage or sports purposes.

1,503 citations

Journal ArticleDOI
TL;DR: A novel unsupervised learning method for human action categories that can recognize and localize multiple actions in long and complex video sequences containing multiple motions.
Abstract: We present a novel unsupervised learning method for human action categories. A video sequence is represented as a collection of spatial-temporal words by extracting space-time interest points. The algorithm automatically learns the probability distributions of the spatial-temporal words and the intermediate topics corresponding to human action categories. This is achieved by using latent topic models such as the probabilistic Latent Semantic Analysis (pLSA) model and Latent Dirichlet Allocation (LDA). Our approach can handle noisy feature points arisen from dynamic background and moving cameras due to the application of the probabilistic models. Given a novel video sequence, the algorithm can categorize and localize the human action(s) contained in the video. We test our algorithm on three challenging datasets: the KTH human motion dataset, the Weizmann human action dataset, and a recent dataset of figure skating actions. Our results reflect the promise of such a simple approach. In addition, our algorithm can recognize and localize multiple actions in long and complex video sequences containing multiple motions.

1,440 citations

Book
01 Jan 2006
TL;DR: Details multi-modal biometrics and its exceptional utility for increasingly reliable human recognition systems and the substantial advantages of multimodal systems over conventional identification methods.
Abstract: Details multimodal biometrics and its exceptional utility for increasingly reliable human recognition systems. Reveals the substantial advantages of multimodal systems over conventional identification methods.

1,068 citations