In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health infor...In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.展开更多
Activity recognition of indoor occupants using indirect sensing with less privacy violation is one of the hot research topics. This paper proposes a CO<sub>2</sub> sensor-based indoor occupant activity mon...Activity recognition of indoor occupants using indirect sensing with less privacy violation is one of the hot research topics. This paper proposes a CO<sub>2</sub> sensor-based indoor occupant activity monitoring system. Using the IoT sensor node that contains CO<sub>2</sub> sensors, the measured CO<sub>2</sub> concentrations in three locations (laboratory, office, and bedroom) were stored in a cloud server for up to 35 days starting July 1, 2023. The CO<sub>2</sub> measurements stored at 30-second intervals were statistically processed to produce a heat-mapped display of the hourly average or maximum CO<sub>2</sub> concentration. From the heatmap visualizations of CO<sub>2</sub> concentration, the proposed system estimated meeting, heating water using a portable stove, and sleep for the occupants’ activity recognition.展开更多
Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and e...Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.展开更多
Internet addiction is associated with an increased risk of suicidal behavior and can lead to brain dysfunction among adolescents.However,whether brain dysfunction occurs in adolescents with Internet addiction who atte...Internet addiction is associated with an increased risk of suicidal behavior and can lead to brain dysfunction among adolescents.However,whether brain dysfunction occurs in adolescents with Internet addiction who attempt suicide remains unknown.This observational cross-sectional study enrolled 41 young Internet addicts,aged from 15 to 20 years,from the Department of Psychiatry,the First Affiliated Hospital of Chongqing Medical University,China from January to May 2018.The participants included 21 individuals who attempted suicide and 20 individuals with Internet addiction without a suicidal attempt history.Brain images in the resting state were obtained by a 3.0 T magnetic resonance imaging scanner.The results showed that activity in the gyrus frontalis inferior of the right pars triangularis and the right pars opercularis was significantly increased in the suicidal attempt group compared with the non-suicidal attempt group.In the resting state,the prefrontal lobe of adolescents who had attempted suicide because of Internet addiction exhibited functional abnormalities,which may provide a new basis for studying suicide pathogenesis in Internet addicts.The study was authorized by the Ethics Committee of Chongqing Medical University,China(approval No.2017 Scientific Research Ethics(2017-157))on December 11,2017.展开更多
Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous acti...Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activ-ity.Therefore,a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor(NIoTS)and transfer learning is proposed.First,the NIoTS is installed in the athlete’s leg muscles to collect activity signals.Second,the noise component in the activity signal is removed using the de-noising method based on mathematical morphology.Finally,the depth feature of the activity signal is extracted through the deep transfer learning model,and the Euclidean distance between the extracted feature and the depth feature of the frontal emergency stops dangerous activity signal is compared.If the European distance is small,it can be judged as the frontal emergency stops dangerous activity,and the frontal emergency stops dangerous activity recognition is realized.The results show that the average time delay of activity signal acquisition of the algorithm is low,the signal-to-noise ratio of the action signal is high,and the activity signal mean square error is low.The variance of the frontal emergency stops dangerous activity recognition does not exceed 0.5.The difference between the appearance time of the dangerous activity and the recognition time of the algorithm is 0.15 s,it can accurately and quickly recognize the frontal emergency stops the dangerous activity.展开更多
With the development of the Internet of Things(IoT)and the popularization of commercial WiFi,researchers have begun to use commercial WiFi for human activity recognition in the past decade.However,cross-scene activity...With the development of the Internet of Things(IoT)and the popularization of commercial WiFi,researchers have begun to use commercial WiFi for human activity recognition in the past decade.However,cross-scene activity recognition is still difficult due to the different distribution of samples in different scenes.To solve this problem,we try to build a cross-scene activity recognition system based on commercial WiFi.Firstly,we use commercial WiFi devices to collect channel state information(CSI)data and use the Bi-directional long short-term memory(BiLSTM)network to train the activity recognition model.Then,we use the transfer learning mechanism to transfer the model to fit another scene.Finally,we conduct experiments to evaluate the performance of our system,and the experimental results verify the accuracy and robustness of our proposed system.For the source scene,the accuracy of the model trained from scratch can achieve over 90%.After transfer learning,the accuracy of cross-scene activity recognition in the target scene can still reach 90%.展开更多
Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' beha...Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' behaviors and automatically response the corresponding activities to satisfy users' actual demands. However, activity models in the existing approaches are usually defined separately through knowledge-driven methods. These approaches cause that the activity models can't be matched with the services dynamically. To address the problem, we develop the semantic association model and a novel approach of activity recognition and guidance is presented. In our approach, the smart devices and users' requirements are described by semantic models. When the requirements are detected and understood, smart gateway can provide appropriate services, achieving activity assistance. The semantic association model allows all related elements in smart home connect with each other logically. The approach has been implemented and the results show that the success rate of the approach based on semantic association model is higher than 33% at average as compared to the approach based on predefined models. The proposed approach can effectively help people who are in trouble with learning or remembering in the common life.展开更多
基金supported by Princess Nourah bint Abdulrahman University Researchers Supporting Project Number(PNURSP2022R194)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘In this present time,Human Activity Recognition(HAR)has been of considerable aid in the case of health monitoring and recovery.The exploitation of machine learning with an intelligent agent in the area of health informatics gathered using HAR augments the decision-making quality and significance.Although many research works conducted on Smart Healthcare Monitoring,there remain a certain number of pitfalls such as time,overhead,and falsification involved during analysis.Therefore,this paper proposes a Statistical Partial Regression and Support Vector Intelligent Agent Learning(SPR-SVIAL)for Smart Healthcare Monitoring.At first,the Statistical Partial Regression Feature Extraction model is used for data preprocessing along with the dimensionality-reduced features extraction process.Here,the input dataset the continuous beat-to-beat heart data,triaxial accelerometer data,and psychological characteristics were acquired from IoT wearable devices.To attain highly accurate Smart Healthcare Monitoring with less time,Partial Least Square helps extract the dimensionality-reduced features.After that,with these resulting features,SVIAL is proposed for Smart Healthcare Monitoring with the help of Machine Learning and Intelligent Agents to minimize both analysis falsification and overhead.Experimental evaluation is carried out for factors such as time,overhead,and false positive rate accuracy concerning several instances.The quantitatively analyzed results indicate the better performance of our proposed SPR-SVIAL method when compared with two state-of-the-art methods.
文摘Activity recognition of indoor occupants using indirect sensing with less privacy violation is one of the hot research topics. This paper proposes a CO<sub>2</sub> sensor-based indoor occupant activity monitoring system. Using the IoT sensor node that contains CO<sub>2</sub> sensors, the measured CO<sub>2</sub> concentrations in three locations (laboratory, office, and bedroom) were stored in a cloud server for up to 35 days starting July 1, 2023. The CO<sub>2</sub> measurements stored at 30-second intervals were statistically processed to produce a heat-mapped display of the hourly average or maximum CO<sub>2</sub> concentration. From the heatmap visualizations of CO<sub>2</sub> concentration, the proposed system estimated meeting, heating water using a portable stove, and sleep for the occupants’ activity recognition.
基金The authors would like to acknowledge the support of the Deputy for Research and Innovation-Ministry of Education,Kingdom of Saudi Arabia,for this research through a grant(NU/IFC/ENT/01/020)under the institutional Funding Committee at Najran University,Kingdom of Saudi Arabia。
文摘Obesity poses several challenges to healthcare and the well-being of individuals.It can be linked to several life-threatening diseases.Surgery is a viable option in some instances to reduce obesity-related risks and enable weight loss.State-of-the-art technologies have the potential for long-term benefits in post-surgery living.In this work,an Internet of Things(IoT)framework is proposed to effectively communicate the daily living data and exercise routine of surgery patients and patients with excessive weight.The proposed IoT framework aims to enable seamless communications from wearable sensors and body networks to the cloud to create an accurate profile of the patients.It also attempts to automate the data analysis and represent the facts about a patient.The IoT framework proposes a co-channel interference avoidance mechanism and the ability to communicate higher activity data with minimal impact on the bandwidth requirements of the system.The proposed IoT framework also benefits from machine learning based activity classification systems,with relatively high accuracy,which allow the communicated data to be translated into meaningful information.
基金supported by a grant from Chongqing Science and Technology Commission of China,Nos.CSTC2018jxj1130009,cstc2019 jscx-msxmX0279(both to YH)the Traditional Chinese Medicine Scientific Research Fund from Chongqing Health Committee of China,No.2019ZY023315(to YH)
文摘Internet addiction is associated with an increased risk of suicidal behavior and can lead to brain dysfunction among adolescents.However,whether brain dysfunction occurs in adolescents with Internet addiction who attempt suicide remains unknown.This observational cross-sectional study enrolled 41 young Internet addicts,aged from 15 to 20 years,from the Department of Psychiatry,the First Affiliated Hospital of Chongqing Medical University,China from January to May 2018.The participants included 21 individuals who attempted suicide and 20 individuals with Internet addiction without a suicidal attempt history.Brain images in the resting state were obtained by a 3.0 T magnetic resonance imaging scanner.The results showed that activity in the gyrus frontalis inferior of the right pars triangularis and the right pars opercularis was significantly increased in the suicidal attempt group compared with the non-suicidal attempt group.In the resting state,the prefrontal lobe of adolescents who had attempted suicide because of Internet addiction exhibited functional abnormalities,which may provide a new basis for studying suicide pathogenesis in Internet addicts.The study was authorized by the Ethics Committee of Chongqing Medical University,China(approval No.2017 Scientific Research Ethics(2017-157))on December 11,2017.
文摘Currently,it is difficult to extract the depth feature of the frontal emergency stops dangerous activity signal,which leads to a decline in the accuracy and efficiency of the frontal emergency stops the dangerous activ-ity.Therefore,a recognition for frontal emergency stops dangerous activity algorithm based on Nano Internet of Things Sensor(NIoTS)and transfer learning is proposed.First,the NIoTS is installed in the athlete’s leg muscles to collect activity signals.Second,the noise component in the activity signal is removed using the de-noising method based on mathematical morphology.Finally,the depth feature of the activity signal is extracted through the deep transfer learning model,and the Euclidean distance between the extracted feature and the depth feature of the frontal emergency stops dangerous activity signal is compared.If the European distance is small,it can be judged as the frontal emergency stops dangerous activity,and the frontal emergency stops dangerous activity recognition is realized.The results show that the average time delay of activity signal acquisition of the algorithm is low,the signal-to-noise ratio of the action signal is high,and the activity signal mean square error is low.The variance of the frontal emergency stops dangerous activity recognition does not exceed 0.5.The difference between the appearance time of the dangerous activity and the recognition time of the algorithm is 0.15 s,it can accurately and quickly recognize the frontal emergency stops the dangerous activity.
基金This work was supported in part by the Key Program of the National Natural Science Foundation of China(Grant Nos.61932013 and 61803212)The National Natural Science Foundation of China(Grant Nos.61873131 and 61803212)+2 种基金Natural Science Foundation of Jiangsu Province(BK20180744)China Postdoctoral Science Foundation(2019M651920 and 2020T130315)The Research Foundation of Jiangsu for“333 High Level Talents Training Project”(BRA2020065).
文摘With the development of the Internet of Things(IoT)and the popularization of commercial WiFi,researchers have begun to use commercial WiFi for human activity recognition in the past decade.However,cross-scene activity recognition is still difficult due to the different distribution of samples in different scenes.To solve this problem,we try to build a cross-scene activity recognition system based on commercial WiFi.Firstly,we use commercial WiFi devices to collect channel state information(CSI)data and use the Bi-directional long short-term memory(BiLSTM)network to train the activity recognition model.Then,we use the transfer learning mechanism to transfer the model to fit another scene.Finally,we conduct experiments to evaluate the performance of our system,and the experimental results verify the accuracy and robustness of our proposed system.For the source scene,the accuracy of the model trained from scratch can achieve over 90%.After transfer learning,the accuracy of cross-scene activity recognition in the target scene can still reach 90%.
基金supported by Electric energy data mining and intelligent analysis technology research and application projects of Shenzhen Power Supply Bureau, Ltd
文摘Wireless smart home system is to facilitate people's lives and it trend to adopt a more intelligent way to provide services. It is very desirable in the recent SH market for the system to recognize users' behaviors and automatically response the corresponding activities to satisfy users' actual demands. However, activity models in the existing approaches are usually defined separately through knowledge-driven methods. These approaches cause that the activity models can't be matched with the services dynamically. To address the problem, we develop the semantic association model and a novel approach of activity recognition and guidance is presented. In our approach, the smart devices and users' requirements are described by semantic models. When the requirements are detected and understood, smart gateway can provide appropriate services, achieving activity assistance. The semantic association model allows all related elements in smart home connect with each other logically. The approach has been implemented and the results show that the success rate of the approach based on semantic association model is higher than 33% at average as compared to the approach based on predefined models. The proposed approach can effectively help people who are in trouble with learning or remembering in the common life.
