The Experts below are selected from a list of 25659 Experts worldwide ranked by ideXlab platform
Jane Z Wang - One of the best experts on this subject based on the ideXlab platform.
-
illumination variation resistant video based Heart Rate Measurement using joint blind source separation and ensemble empirical mode decomposition
IEEE Journal of Biomedical and Health Informatics, 2017Co-Authors: Juan Cheng, Lingxi Xu, Xun Chen, Jane Z WangAbstract:Recent studies have demonstRated that Heart Rate (HR) could be estimated using video data [e.g., exploring human facial regions of interest (ROIs)] under well-controlled conditions. However, in practice, the pulse signals may be contaminated by motions and illumination variations. In this paper, tackling the illumination variation challenge, we propose an illumination-robust framework using joint blind source separation (JBSS) and ensemble empirical mode decomposition (EEMD) to effectively evaluate HR from webcam videos. The framework takes the hypotheses that both facial ROI and background ROI have similar illumination variations. The background ROI is then considered as a noise reference sensor to denoise the facial signals by using the JBSS technique to extract the underlying illumination variation sources. Further, the reconstructed illumination-resisted green channel of the facial ROI is detrended and decomposed into a number of intrinsic mode functions using EEMD to estimate the HR. Experimental results demonstRated that the proposed framework could estimate HR more accuRately than the state-of-the-art methods. The Bland–Altman plots showed that it led to better agreement with HR ground truth with the mean bias 1.15 beats/min (bpm), with 95% limits from $-$ 15.43 to 17.73 bpm, and the correlation coefficient 0.53. This study provides a promising solution for realistic noncontact and robust HR Measurement applications.
-
illumination variation resistant video based Heart Rate Measurement using joint blind source separation and ensemble empirical mode decomposition
IEEE Journal of Biomedical and Health Informatics, 2017Co-Authors: Juan Cheng, Xun Chen, Jane Z WangAbstract:Recent studies have demonstRated that Heart Rate (HR) could be estimated using video data [e.g., exploring human facial regions of interest (ROIs)] under well-controlled conditions. However, in practice, the pulse signals may be contaminated by motions and illumination variations. In this paper, tackling the illumination variation challenge, we propose an illumination-robust framework using joint blind source separation (JBSS) and ensemble empirical mode decomposition (EEMD) to effectively evaluate HR from webcam videos. The framework takes the hypotheses that both facial ROI and background ROI have similar illumination variations. The background ROI is then considered as a noise reference sensor to denoise the facial signals by using the JBSS technique to extract the underlying illumination variation sources. Further, the reconstructed illumination-resisted green channel of the facial ROI is detrended and decomposed into a number of intrinsic mode functions using EEMD to estimate the HR. Experimental results demonstRated that the proposed framework could estimate HR more accuRately than the state-of-the-art methods. The Bland–Altman plots showed that it led to better agreement with HR ground truth with the mean bias 1.15 beats/min (bpm), with 95% limits from $-$ 15.43 to 17.73 bpm, and the correlation coefficient 0.53. This study provides a promising solution for realistic noncontact and robust HR Measurement applications.
-
video based human Heart Rate Measurement using joint blind source separation
Biomedical Signal Processing and Control, 2017Co-Authors: Zhenyu Guo, Xun Chen, Zhiqi Shen, Jane Z WangAbstract:Abstract Remote (non-contact) Measurements of human cardiopulmonary physiological parameters based on photoplethysmography (PPG) can lead to efficient and comfortable medical assessment, which is important in human healthcare. It was shown that human facial blood volume variation during cardiac cycle can be indirectly captured by common Red–Green–Blue (RGB) cameras. In this paper, we show that it is promising to incorpoRate data from different facial sub-regions to improve remote Measurement performance. We propose a novel method for non-contact video-based human Heart Rate (HR) Measurement by exploring correlations among facial sub-regions via joint blind source separation (J-BSS). To our knowledge, this is the first time that J-BSS approaches, instead of prevailing BSS techniques such as independent component analysis (ICA), is successfully applied in non-contact physiological parameter Measurement. We test the proposed method on a large public database, which provides the subjects’ left-thumb plethysmograph signals as ground truth. Experimental results show that the proposed J-BSS method outperforms previous ICA-based methodologies.
Jawatankuasa Kerja Psm Uthm - One of the best experts on this subject based on the ideXlab platform.
-
Miniaturized and portable home-based vital sign monitor design with android mobile application
International Journal of Integrated Engineering Universiti Tun Hussein Onn Malaysia, 2019Co-Authors: Jawatankuasa Kerja Psm UthmAbstract:Frequent or continuous vital sign monitoring could help to decrease mortality Rate as early detection of vital sign abnormality allow prompt medical action to be taken for early prevention Measurement, especially to elderly people and patients who suffer from chronic disease or infectious disease. However, most of the vital sign monitor are designed for hospital usage and opeRated by healthcare professionals, which the devices are generally heavy duty, cost-expensive, and complicated user interface for home user. This paper proposes a miniaturized and portable home-based vital sign monitor, named myVitalGear, which can accuRately measure Heart Rate using electrocardiogram (ECG), body temperature and blood oxygen saturation (SpO2), based on Arduino Nano technology. This device aims to enable frequent vital sign monitoring at home by reducing long distance travel to hospital and long waiting hour at hospital. The device consists of an AD8232 chip to acquire ECG for Heart Rate Measurement and further Heart rhythm abnormality detection, a high precision DS18B20 temperature sensor for body temperature Measurement, and a MAX30100 pulse sensor for SpO2monitoring. In this device, the Arduino Nano microcontroller acts as the master controller to control all the system peripherals and biomedical sensors to acquire and process all the vital signs. The device also equipped with simple interface like light emitting diode (LED), liquid crystal display (LCD) and buzzer as the status indicator for layman user. A mobile application which targeted to Android-based smart phone is also developed to communicate with myVitalGearthrough Bluetooth wireless communication. The mobile app support the functionalities of displaying the vital sign Measurement result, automated short message service (SMS) notification message and user location sending to the healthcare provider or guardian, in case of any vital sign abnormality is detected. Validation result has shown that the system able to measure vital sign with accuracy of 99.4%, 99.7% and 98.1% for Heart Rate, body temperature, and SpO2 respectively
Wen Hau Yuan - One of the best experts on this subject based on the ideXlab platform.
-
Test Miniaturized and Portable Home-Based Vital Sign Monitor Design with Android Mobile Application
'Penerbit UTHM', 2019Co-Authors: Yusof, Mas Azalya, Xin Fung Shu, Li Low Wee, Wen Lim Chiao, Wen Hau YuanAbstract:Frequent or continuous vital sign monitoring could help to decrease mortality Rate as early detection of vital sign abnormality allow prompt medical action to be taken for early prevention Measurement, especially to elderly people and patients who suffer from chronic disease or infectious disease. However, most of the vital sign monitor are designed for hospital usage and opeRated by healthcare professionals, which the devices are generally heavy duty, cost-expensive, and complicated user interface for home user. This paper proposes a miniaturized and portable home-based vital sign monitor, named myVitalGear, which can accuRately measure Heart Rate using electrocardiogram (ECG), body temperature and blood oxygen saturation (SpO2), based on Arduino Nano technology. This device aims to enable frequent vital sign monitoring at home by reducing long distance travel to hospital and long waiting hour at hospital. The device consists of an AD8232 chip to acquire ECG for Heart Rate Measurement and further Heart rhythm abnormality detection, a high precision DS18B20 temperature sensor for body temperature Measurement, and a MAX30100 pulse sensor for SpO2 monitoring. In this device, the Arduino Nano microcontroller acts as the master controller to control all the system peripherals and biomedical sensors to acquire and process all the vital signs. The device also equipped with simple interface like light emitting diode (LED), liquid crystal display (LCD) and buzzer as the status indicator for layman user. A mobile application which targeted to Android-based smart phone is also developed to communicate with myVitalGear through Bluetooth wireless communication. The mobile app support the functionalities of displaying the vital sign Measurement result, automated short message service (SMS) notification message and user location sending to the healthcare provider or guardian, in case of any vital sign abnormality is detected. Validation result has shown that the system able to measure vital sign with accuracy of 99.4%, 99.7% and 98.1% for Heart Rate, body temperature, and SpO2 respectively
Xun Chen - One of the best experts on this subject based on the ideXlab platform.
-
illumination variation resistant video based Heart Rate Measurement using joint blind source separation and ensemble empirical mode decomposition
IEEE Journal of Biomedical and Health Informatics, 2017Co-Authors: Juan Cheng, Lingxi Xu, Xun Chen, Jane Z WangAbstract:Recent studies have demonstRated that Heart Rate (HR) could be estimated using video data [e.g., exploring human facial regions of interest (ROIs)] under well-controlled conditions. However, in practice, the pulse signals may be contaminated by motions and illumination variations. In this paper, tackling the illumination variation challenge, we propose an illumination-robust framework using joint blind source separation (JBSS) and ensemble empirical mode decomposition (EEMD) to effectively evaluate HR from webcam videos. The framework takes the hypotheses that both facial ROI and background ROI have similar illumination variations. The background ROI is then considered as a noise reference sensor to denoise the facial signals by using the JBSS technique to extract the underlying illumination variation sources. Further, the reconstructed illumination-resisted green channel of the facial ROI is detrended and decomposed into a number of intrinsic mode functions using EEMD to estimate the HR. Experimental results demonstRated that the proposed framework could estimate HR more accuRately than the state-of-the-art methods. The Bland–Altman plots showed that it led to better agreement with HR ground truth with the mean bias 1.15 beats/min (bpm), with 95% limits from $-$ 15.43 to 17.73 bpm, and the correlation coefficient 0.53. This study provides a promising solution for realistic noncontact and robust HR Measurement applications.
-
illumination variation resistant video based Heart Rate Measurement using joint blind source separation and ensemble empirical mode decomposition
IEEE Journal of Biomedical and Health Informatics, 2017Co-Authors: Juan Cheng, Xun Chen, Jane Z WangAbstract:Recent studies have demonstRated that Heart Rate (HR) could be estimated using video data [e.g., exploring human facial regions of interest (ROIs)] under well-controlled conditions. However, in practice, the pulse signals may be contaminated by motions and illumination variations. In this paper, tackling the illumination variation challenge, we propose an illumination-robust framework using joint blind source separation (JBSS) and ensemble empirical mode decomposition (EEMD) to effectively evaluate HR from webcam videos. The framework takes the hypotheses that both facial ROI and background ROI have similar illumination variations. The background ROI is then considered as a noise reference sensor to denoise the facial signals by using the JBSS technique to extract the underlying illumination variation sources. Further, the reconstructed illumination-resisted green channel of the facial ROI is detrended and decomposed into a number of intrinsic mode functions using EEMD to estimate the HR. Experimental results demonstRated that the proposed framework could estimate HR more accuRately than the state-of-the-art methods. The Bland–Altman plots showed that it led to better agreement with HR ground truth with the mean bias 1.15 beats/min (bpm), with 95% limits from $-$ 15.43 to 17.73 bpm, and the correlation coefficient 0.53. This study provides a promising solution for realistic noncontact and robust HR Measurement applications.
-
video based human Heart Rate Measurement using joint blind source separation
Biomedical Signal Processing and Control, 2017Co-Authors: Zhenyu Guo, Xun Chen, Zhiqi Shen, Jane Z WangAbstract:Abstract Remote (non-contact) Measurements of human cardiopulmonary physiological parameters based on photoplethysmography (PPG) can lead to efficient and comfortable medical assessment, which is important in human healthcare. It was shown that human facial blood volume variation during cardiac cycle can be indirectly captured by common Red–Green–Blue (RGB) cameras. In this paper, we show that it is promising to incorpoRate data from different facial sub-regions to improve remote Measurement performance. We propose a novel method for non-contact video-based human Heart Rate (HR) Measurement by exploring correlations among facial sub-regions via joint blind source separation (J-BSS). To our knowledge, this is the first time that J-BSS approaches, instead of prevailing BSS techniques such as independent component analysis (ICA), is successfully applied in non-contact physiological parameter Measurement. We test the proposed method on a large public database, which provides the subjects’ left-thumb plethysmograph signals as ground truth. Experimental results show that the proposed J-BSS method outperforms previous ICA-based methodologies.
Shifeng Yan - One of the best experts on this subject based on the ideXlab platform.
-
a Measurement of illumination variation resistant noncontact Heart Rate based on the combination of singular spectrum analysis and sub band method
Computer Methods and Programs in Biomedicine, 2021Co-Authors: Jongsong Ryu, Sunchol Hong, Shili Liang, Sinil Pak, Qingyue Chen, Shifeng YanAbstract:Abstract Background and Objective The imaging photoplethysmography method is a non-contact and non-invasive Measurement method that usually uses surrounding illumination as an illuminant, which can be easily influenced by the surrounding illumination variations. Thus, it has a practical value to develop an efficient method of Heart Rate Measurement that can remove the interference of illumination variations robustly. Method We propose a novel framework of Heart Rate Measurement that is robust to illumination variations by combining singular spectrum analysis and sub-band method. At first, we extract the blood volume pulse signal by applying the modified sub-band method to the raw facial RGB trace signals. Then the spectra for the interference of illumination variations are extracted from the raw signal obtained from facial regions of interest by using singular spectrum analysis. Finally, we estimate the more robust Heart Rate through comparison analysis between the spectra of the extracted blood volume pulse signal and the illumination variations. Results We compared our method with several state-of-the-art methods through the analysis using the self-collected data and the UBFC-RPPG database. Bland-Altman plots and Pearson correlation coefficients pointed out that the proposed method could measure the Heart Rate more accuRately than the state-of-the-art methods. For the self-collected data and the UBFC-RPPG database, Bland-Altman plots showed that proposed method caused better agreement with 95% limits from -4 bpm to 10 bpm and from -2 bpm to 4 bpm respectively than the other state-of-the-art methods. It also revealed that the highly linear relation was held between the estimated Heart Rate and ground truth with the correlation coefficients of 0.89 and 0.99, respectively. Conclusion By extracting illumination variation directly from the facial region of interest rather than from the background region of interest, the proposed method demonstRates that it can overcome the drawbacks of the previous methods due to the illumination variation difference between the background and facial regions of interest. It can be found that the proposed method has a relatively good robustness regardless of whether illumination variation exists or not.
