Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Num...Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.展开更多
Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,t...Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.展开更多
Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over time.Social interaction analysis aids in defining people’s emotions and a...Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over time.Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and chil-dren.In this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media.We searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on Facebook.We applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social media.We extracted a set of tex-tual,spatial,and interaction attributes from various features.Therefore,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress detection.Experiments depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in F1-measure.Also,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.展开更多
Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, a...Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, an innovative technique for early detection of potential failure and condition monitoring is urgently required by many engineers. This document describes a novel approach to improve industrial equipment safety, reliability and life cycle management. A new field portable instrument called the ＂IMS （indicator of mechanical stresses）＂ utilizes magneto-anisotropic （＂cross＂） transducers to measure anisotropy of magnetic properties in ferromagnetic material. Mechanical stresses including residual stresses in Ferro-magnetic parts, are ＂not visible＂ to most traditional NDT （non-destructive testing） methods; for example, radiography and ultrasonic inspection. Stress build-up can be the first indicator that something is faulty with a structure. This can be the result of a manufacturing defect; or as assets age and fatigue, stress loads can become unevenly distributed throughout the metal. We outline the evaluation of IMS as a fast screening tool to provide structural condition or deterioration feedback in novel applications for pipelines, petrochemical refinery, cranes, and municipal infrastructure.展开更多
Heart rate variability(HRV)that can reflect the dynamic balance between the sympathetic nervous and parasympathetic nervous of human autonomic nervous system(ANS)has attracted considerable attention.However,traditiona...Heart rate variability(HRV)that can reflect the dynamic balance between the sympathetic nervous and parasympathetic nervous of human autonomic nervous system(ANS)has attracted considerable attention.However,traditional electrocardiogram(ECG)devices for HRV analysis are bulky,and hard wires are needed to attach measuring electrodes to the chest,resulting in the poor wearable experience during the long-term measurement.Compared with that,wearable electronics enabling continuously cardiac signals monitoring and HRV assessment provide a desirable and promising approach for helping subjects determine sleeping issues,cardiovascular diseases,or other threats to physical and mental well-being.Until now,significant progress and advances have been achieved in wearable electronics for HRV monitoring and applications for predicting human physical and mental well-being.In this review,the latest progress in the integration of wearable electronics and HRV analysis as well as practical applications in assessment of human physical and mental health are included.The commonly used methods and physiological signals for HRV analysis are briefly summarized.Furthermore,we highlighted the research on wearable electronics concerning HRV assessment and diverse applications such as stress estimation,drowsiness detection,etc.Lastly,the current limitations of the integrated wearable HRV system are concluded,and possible solutions in such a research direction are outlined.展开更多
A new method of detecting stress change by temperature(DSCT),has been recently proposed on the basis of the experimental results in laboratory,and verified by field observation.In this paper,at first,physical backgrou...A new method of detecting stress change by temperature(DSCT),has been recently proposed on the basis of the experimental results in laboratory,and verified by field observation.In this paper,at first,physical background is concisely introduced,and experimental researches are followed.Then,the key techniques are reviewed,and the main results on in-situ observations are also given in detail.At last,we emphasize on the prospects of this method for being investigated further.The potential prospect includes six contents:(1)to observe the tidal force and its secondary fluid thermal effect;(2)to study temperature response to change in direction of the stress change;(3)to carry out practical engineering application;(4)to analyze the strong earthquake risk,based on bedrock temperature observation;(5)to conduct in situ experiment on DSCT;(6)to explain quantitatively the satellite thermal infrared anomaly.In short,considering that the dynamic change of the crustal stress is a key parameter of earthquake forecasting or engineering application,the method of DSCT has important practical significance for earthquake risk or engineering applications.展开更多
文摘Mental health is a significant issue worldwide,and the utilization of technology to assist mental health has seen a growing trend.This aims to alleviate the workload on healthcare professionals and aid individuals.Numerous applications have been developed to support the challenges in intelligent healthcare systems.However,because mental health data is sensitive,privacy concerns have emerged.Federated learning has gotten some attention.This research reviews the studies on federated learning and mental health related to solving the issue of intelligent healthcare systems.It explores various dimensions of federated learning in mental health,such as datasets(their types and sources),applications categorized based on mental health symptoms,federated mental health frameworks,federated machine learning,federated deep learning,and the benefits of federated learning in mental health applications.This research conducts surveys to evaluate the current state of mental health applications,mainly focusing on the role of Federated Learning(FL)and related privacy and data security concerns.The survey provides valuable insights into how these applications are emerging and evolving,specifically emphasizing FL’s impact.
基金funded by the Deanship of Scientific Research(DSR),King Abdulaziz University,Jeddah,under Grant No.(D-136-611-1443)DSR technical and financial support.
文摘Stress is now a serious disease that exists due to changes in working life and food ecosystems around the world.In general,it is difficult for a person to know if they are under stress.According to previous research,temperature,heart rate variability(HRV),humidity,and blood pressure are used to assess stress levels with the use of instruments.With the development of sensor technology and wireless connectivity,people around the world are adopting and using smart devices.In this study,a bio signal detection device with Internet of Things(IoT)capability with a galvanic skin reaction(GSR)sensor is proposed and built for real-time stress monitoring.The proposed device is based on an Arduino controller and Bluetooth communication.To evaluate the performance of the system,physical stress is created on 10 different participants with three distinct tasks namely reading,visualizing the timer clock,and watching videos.MATLAB analysis is performed for identifying the three different levels of stress and obtaining the threshold values as if the person GSR voltage i.e.,relaxed for<1.75 volts;Normal:between 1.75 and 1.44 volts and stressed:>1.44 volts.In addition,LabVIEW is used as a data acquisition system,and a Blueterm mobile application is also used to view the sensor reading received from the device through Bluetooth communication.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2022R113)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia。
文摘Social Media such as Facebook plays a substantial role in virtual com-munities by sharing ideas and ideologies among different populations over time.Social interaction analysis aids in defining people’s emotions and aids in assessing public attitudes,towards different issues such as violence against women and chil-dren.In this paper,we proposed an Arabic language prediction model to identify the issue of Violence-Induced Stress in social media.We searched for Arabic posts of many countries through Facebook application programming interface(API).We discovered that the stress state of a battered woman is usually related to her friend’s stress states on Facebook.We applied a large real database from Facebook platforms to analytically investigate the correlation of violence-induced stress states and the victim interactions on social media.We extracted a set of tex-tual,spatial,and interaction attributes from various features.Therefore,we are proposing a hybrid model–an interaction graph model incorporated in a deep learning neural model to leverage post content and interaction data for vio-lence-induced stress detection.Experiments depict that our proposed hybrid mod-el can enhance the prediction performance by 10%in F1-measure.Also,considering the user interaction information can learn an interesting phenomenon,where,the sparse social interactions of violence-induced stress stressed victims is higher by around 15%percent non-battered users,signifying that the structure of the friends of such victims is less connected than non-stressed users.
文摘Today＇s industry requires more reliable information on the current status of their hard assets; prognosis for continued usability of systems and better predictability of equipment life cycle maintenance. Therefore, an innovative technique for early detection of potential failure and condition monitoring is urgently required by many engineers. This document describes a novel approach to improve industrial equipment safety, reliability and life cycle management. A new field portable instrument called the ＂IMS （indicator of mechanical stresses）＂ utilizes magneto-anisotropic （＂cross＂） transducers to measure anisotropy of magnetic properties in ferromagnetic material. Mechanical stresses including residual stresses in Ferro-magnetic parts, are ＂not visible＂ to most traditional NDT （non-destructive testing） methods; for example, radiography and ultrasonic inspection. Stress build-up can be the first indicator that something is faulty with a structure. This can be the result of a manufacturing defect; or as assets age and fatigue, stress loads can become unevenly distributed throughout the metal. We outline the evaluation of IMS as a fast screening tool to provide structural condition or deterioration feedback in novel applications for pipelines, petrochemical refinery, cranes, and municipal infrastructure.
基金supported in part by National Science and Technology Major Project from the Minister of Science and Technology of China(2018AAA0103100).
文摘Heart rate variability(HRV)that can reflect the dynamic balance between the sympathetic nervous and parasympathetic nervous of human autonomic nervous system(ANS)has attracted considerable attention.However,traditional electrocardiogram(ECG)devices for HRV analysis are bulky,and hard wires are needed to attach measuring electrodes to the chest,resulting in the poor wearable experience during the long-term measurement.Compared with that,wearable electronics enabling continuously cardiac signals monitoring and HRV assessment provide a desirable and promising approach for helping subjects determine sleeping issues,cardiovascular diseases,or other threats to physical and mental well-being.Until now,significant progress and advances have been achieved in wearable electronics for HRV monitoring and applications for predicting human physical and mental well-being.In this review,the latest progress in the integration of wearable electronics and HRV analysis as well as practical applications in assessment of human physical and mental health are included.The commonly used methods and physiological signals for HRV analysis are briefly summarized.Furthermore,we highlighted the research on wearable electronics concerning HRV assessment and diverse applications such as stress estimation,drowsiness detection,etc.Lastly,the current limitations of the integrated wearable HRV system are concluded,and possible solutions in such a research direction are outlined.
基金supported by the National Natural Science Foundation of China (Grant No.42274079)by the Basic Research Funds from the Institute of Geology,China Earthquake Administration (Grant No.IGCEA1815).
文摘A new method of detecting stress change by temperature(DSCT),has been recently proposed on the basis of the experimental results in laboratory,and verified by field observation.In this paper,at first,physical background is concisely introduced,and experimental researches are followed.Then,the key techniques are reviewed,and the main results on in-situ observations are also given in detail.At last,we emphasize on the prospects of this method for being investigated further.The potential prospect includes six contents:(1)to observe the tidal force and its secondary fluid thermal effect;(2)to study temperature response to change in direction of the stress change;(3)to carry out practical engineering application;(4)to analyze the strong earthquake risk,based on bedrock temperature observation;(5)to conduct in situ experiment on DSCT;(6)to explain quantitatively the satellite thermal infrared anomaly.In short,considering that the dynamic change of the crustal stress is a key parameter of earthquake forecasting or engineering application,the method of DSCT has important practical significance for earthquake risk or engineering applications.
