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
Purpose: From a social and labor inclusion perspective, the article presents a digital prototype conceptualized to provide a “Diagnostic Page”, which delivers various prescribers and suppliers of support products th...Purpose: From a social and labor inclusion perspective, the article presents a digital prototype conceptualized to provide a “Diagnostic Page”, which delivers various prescribers and suppliers of support products that mitigate the problems of the respective patients. It also provides a “Patient Card Page”, where all the information about financing the respective products is placed, as well as all the documents likely to be needed for the commercial transactions to be carried out by all the parties involved. It also aims to provide a digital medium to grow a community in this niche market. In the action research methodology approach, the prototype was taken to funding competitions and conferences, where interviews and surveys were carried out, and a number of suggestions were collected on the type of platform to consider in order to respond to the concerns and needs of end users, such as patients, prescribers, suppliers and associations. Methods: The digital platform where the system is hosted uses algorithms that, on the diagnostic page, consider keywords used by patients and return a series of prescribers and suppliers of support products, in which the corresponding percentage of attenuation is taken into account and the best solution found to overcome the level of difficulty presented by the respective patients is delivered. Results and Conclusions: It is hoped that, with this platform, people with motor problems will be able to obtain their diagnosis instantly, through the algorithm implemented, and that they will immediately be provided with a series of prescribers, suppliers and support products best suited to their needs, as well as all the information or conditions necessary to purchase or finance them. On the other hand, prescribers, suppliers and associations have an online platform where they can offer their consultations, products and other support as freelancers who are part of a community.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Purpose: From a social and labor inclusion perspective, the article presents a digital prototype conceptualized to provide a “Diagnostic Page”, which delivers various prescribers and suppliers of support products that mitigate the problems of the respective patients. It also provides a “Patient Card Page”, where all the information about financing the respective products is placed, as well as all the documents likely to be needed for the commercial transactions to be carried out by all the parties involved. It also aims to provide a digital medium to grow a community in this niche market. In the action research methodology approach, the prototype was taken to funding competitions and conferences, where interviews and surveys were carried out, and a number of suggestions were collected on the type of platform to consider in order to respond to the concerns and needs of end users, such as patients, prescribers, suppliers and associations. Methods: The digital platform where the system is hosted uses algorithms that, on the diagnostic page, consider keywords used by patients and return a series of prescribers and suppliers of support products, in which the corresponding percentage of attenuation is taken into account and the best solution found to overcome the level of difficulty presented by the respective patients is delivered. Results and Conclusions: It is hoped that, with this platform, people with motor problems will be able to obtain their diagnosis instantly, through the algorithm implemented, and that they will immediately be provided with a series of prescribers, suppliers and support products best suited to their needs, as well as all the information or conditions necessary to purchase or finance them. On the other hand, prescribers, suppliers and associations have an online platform where they can offer their consultations, products and other support as freelancers who are part of a community.
