This paper describes the development of a new ECG tele-monitoring method and system based on the embedded web server. The system consists of ECG recorders with network interface and the embedded web server, internet n...This paper describes the development of a new ECG tele-monitoring method and system based on the embedded web server. The system consists of ECG recorders with network interface and the embedded web server, internet networks and computers, with the system operating on browser/server(B/S) mode. The ECG recorder was designed by ARM9 (S3C2410X) and embedded operating system (Linux). Once the ECG recorder has been connected to the internet network, medical experts can use the internet to access the server of the ECG recorder, monitor ECG signals, and diagnose patients by browsing the dynamic web pages in the embedded web server. The experimental results reveal that the designed system is stable, reliable, and suitable for the use in real-time ECG tele-monitoring for both family and community health care.展开更多
Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,an...Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.展开更多
The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnos...The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.展开更多
This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for ar...This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.展开更多
Remote tracking the variation of air quality in an effective way will be highly helpful to decrease the health risk of human short-and long-term exposures to air pollution.However,high power consumption and poor sensi...Remote tracking the variation of air quality in an effective way will be highly helpful to decrease the health risk of human short-and long-term exposures to air pollution.However,high power consumption and poor sensing performance remain the concerned issues,thereby limiting the scale-up in deploying air quality tracking networks.Herein,we report a standalone-like smart device that can remotely track the variation of air pollutants in a power-saving way.Brevity,the created smart device demonstrated satisfactory selectivity(against six kinds of representative exhaust gases or air pollutants),desirable response magnitude(164–100 ppm),and acceptable response/recovery rate(52.0/50.5 s),as well as linear response relationship to NO2.After aging for 2 weeks,the created device exhibited relatively stable sensing performance more than 3 months.Moreover,a photoluminescence-enhanced light fidelity(Li-Fi)telecommunication technique is proposed and the Li-Fi communication distance is significantly extended.Conclusively,our reported standalone-like smart device would sever as a powerful sensing platform to construct high-performance and low-power consumption air quality wireless sensor networks and to prevent air pollutant-induced diseases via a more effective and low-cost approach.展开更多
基金Education Committee Foundation of Beijing grant number: KM200610005022+1 种基金Young Backbone Teacher Foundation of Beijing grant number: 102KB00845
文摘This paper describes the development of a new ECG tele-monitoring method and system based on the embedded web server. The system consists of ECG recorders with network interface and the embedded web server, internet networks and computers, with the system operating on browser/server(B/S) mode. The ECG recorder was designed by ARM9 (S3C2410X) and embedded operating system (Linux). Once the ECG recorder has been connected to the internet network, medical experts can use the internet to access the server of the ECG recorder, monitor ECG signals, and diagnose patients by browsing the dynamic web pages in the embedded web server. The experimental results reveal that the designed system is stable, reliable, and suitable for the use in real-time ECG tele-monitoring for both family and community health care.
文摘Emotion recognition is a growing field that has numerous applications in smart healthcare systems and Human-Computer Interaction(HCI).However,physical methods of emotion recognition such as facial expressions,voice,and text data,do not always indicate true emotions,as users can falsify them.Among the physiological methods of emotion detection,Electrocardiogram(ECG)is a reliable and efficient way of detecting emotions.ECG-enabled smart bands have proven effective in collecting emotional data in uncontrolled environments.Researchers use deep machine learning techniques for emotion recognition using ECG signals,but there is a need to develop efficient models by tuning the hyperparameters.Furthermore,most researchers focus on detecting emotions in individual settings,but there is a need to extend this research to group settings aswell since most of the emotions are experienced in groups.In this study,we have developed a novel lightweight one dimensional(1D)Convolutional Neural Network(CNN)model by reducing the number of convolution,max pooling,and classification layers.This optimization has led to more efficient emotion classification using ECG.We tested the proposed model’s performance using ECG data from the AMIGOS(A Dataset for Affect,Personality and Mood Research on Individuals andGroups)dataset for both individual and group settings.The results showed that themodel achieved an accuracy of 82.21%and 85.62%for valence and arousal classification,respectively,in individual settings.In group settings,the accuracy was even higher,at 99.56%and 99.68%for valence and arousal classification,respectively.By reducing the number of layers,the lightweight CNNmodel can process data more quickly and with less complexity in the hardware,making it suitable for the implementation on the mobile phone devices to detect emotions with improved accuracy and speed.
文摘The application of deep learning techniques in the medical field,specifically for Atrial Fibrillation(AFib)detection through Electrocardiogram(ECG)signals,has witnessed significant interest.Accurate and timely diagnosis increases the patient’s chances of recovery.However,issues like overfitting and inconsistent accuracy across datasets remain challenges.In a quest to address these challenges,a study presents two prominent deep learning architectures,ResNet-50 and DenseNet-121,to evaluate their effectiveness in AFib detection.The aim was to create a robust detection mechanism that consistently performs well.Metrics such as loss,accuracy,precision,sensitivity,and Area Under the Curve(AUC)were utilized for evaluation.The findings revealed that ResNet-50 surpassed DenseNet-121 in all evaluated categories.It demonstrated lower loss rate 0.0315 and 0.0305 superior accuracy of 98.77%and 98.88%,precision of 98.78%and 98.89%and sensitivity of 98.76%and 98.86%for training and validation,hinting at its advanced capability for AFib detection.These insights offer a substantial contribution to the existing literature on deep learning applications for AFib detection from ECG signals.The comparative performance data assists future researchers in selecting suitable deep-learning architectures for AFib detection.Moreover,the outcomes of this study are anticipated to stimulate the development of more advanced and efficient ECG-based AFib detection methodologies,for more accurate and early detection of AFib,thereby fostering improved patient care and outcomes.
文摘This study introduces a new classifier tailored to address the limitations inherent in conventional classifiers such as K-nearest neighbor(KNN),random forest(RF),decision tree(DT),and support vector machine(SVM)for arrhythmia detection.The proposed classifier leverages the Chi-square distance as a primary metric,providing a specialized and original approach for precise arrhythmia detection.To optimize feature selection and refine the classifier’s performance,particle swarm optimization(PSO)is integrated with the Chi-square distance as a fitness function.This synergistic integration enhances the classifier’s capabilities,resulting in a substantial improvement in accuracy for arrhythmia detection.Experimental results demonstrate the efficacy of the proposed method,achieving a noteworthy accuracy rate of 98% with PSO,higher than 89% achieved without any previous optimization.The classifier outperforms machine learning(ML)and deep learning(DL)techniques,underscoring its reliability and superiority in the realm of arrhythmia classification.The promising results render it an effective method to support both academic and medical communities,offering an advanced and precise solution for arrhythmia detection in electrocardiogram(ECG)data.
基金the financial support for this research from the National Key Research and Development Program of China(Grant No.2017YFA0205301)National Natural Science Foundation of China(Grant No.61771267,61774106)+6 种基金Oceanic Interdisciplinary Program of Shanghai Jiao Tong University(Grant No.BX2020208)Shanghai Natural Science Foundation(Grant No.86973)Natural Science Foundation of Ningbo City(Grant No.2017A610229)National Postdoctoral Program for Innovative Talents(Grant No.BX20190205)Special fund for science and technology innovation of Shanghai Jiao Tong University(Grant No.YG2017MS70)Shanghai Municipal Bureau of Economy and Information Technology(Grant No.XC-ZXSJ-02-2016-05)China Postdoctoral Science Foundation.
文摘Remote tracking the variation of air quality in an effective way will be highly helpful to decrease the health risk of human short-and long-term exposures to air pollution.However,high power consumption and poor sensing performance remain the concerned issues,thereby limiting the scale-up in deploying air quality tracking networks.Herein,we report a standalone-like smart device that can remotely track the variation of air pollutants in a power-saving way.Brevity,the created smart device demonstrated satisfactory selectivity(against six kinds of representative exhaust gases or air pollutants),desirable response magnitude(164–100 ppm),and acceptable response/recovery rate(52.0/50.5 s),as well as linear response relationship to NO2.After aging for 2 weeks,the created device exhibited relatively stable sensing performance more than 3 months.Moreover,a photoluminescence-enhanced light fidelity(Li-Fi)telecommunication technique is proposed and the Li-Fi communication distance is significantly extended.Conclusively,our reported standalone-like smart device would sever as a powerful sensing platform to construct high-performance and low-power consumption air quality wireless sensor networks and to prevent air pollutant-induced diseases via a more effective and low-cost approach.
