Day by day,biometric-based systems play a vital role in our daily lives.This paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect huma...Day by day,biometric-based systems play a vital role in our daily lives.This paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions.This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,etc.The proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and love.The key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and LED.The proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and time.While comparing with existing technologies,the proposed algorithm gave a better error rate and less time.Error rate and time is decreased by 19.79%,5.13 s.for the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT database.The proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.展开更多
The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potenti...The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living （SHAAL）to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM （industrial, scientific, and medical） band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate （PRR） above 80% for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market.展开更多
With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeuti...With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeutic targets,traditional clinical teaching models face challenges such as outdated guideline updates,disjointed translation of scientific research,and limited skill training.This study proposes a dynamic training model integrating“guidelines,clinical practice,and scientific research.”Through stratified case-based teaching(e.g.,FibroScan simulator and metabolic sand table),dynamic guideline analysis(comparing old and new evidence),and the integration of scientific thinking(visualization of CAND1 protein mechanism),a teaching system that integrates theory and practice is constructed.Innovatively developed smart assistant tools(AI decision support system,VR liver biopsy simulator)and a multi-dimensional evaluation system(deviation analysis of diagnosis and treatment pathways,milestone assessment)are used while emphasizing metabolic medicine integration(continuous glucose monitoring and digital therapy)and ethical privacy protection(federated learning framework).This model aims to cultivate students’evidence-based decision-making skills and scientific research transformation thinking through dynamic knowledge base construction and interdisciplinary collaboration,providing sustainable teaching solutions to cope with the rapid iteration of NAFLD diagnosis and treatment.展开更多
With the rapid development of AI technology,the field of rehabilitation therapy has ushered in unprecedented opportunities for innovation.This paper provides a comprehensive review of the current applications of AI in...With the rapid development of AI technology,the field of rehabilitation therapy has ushered in unprecedented opportunities for innovation.This paper provides a comprehensive review of the current applications of AI in rehabilitation therapy and the chall enges it faces,while also exploring its future development trends.The research finds that the application of AI technology in rehabilitation therapy has significantly improved rehabilitation efficiency and patients’quality of life.AI can develop person alized rehabilitation plans based on individual patient conditions,achieve precise assessments and training through smart assistive devices,and break through the limitations of time and space with remote rehabilitation services.However,th e application of AI in rehabilitation therapy still faces several challenges,including high technological costs,data privacy concerns,and public acceptance.Looking forward,as technologies such as 5G,the Internet of Things,and brain-machine interfaces deeply integ rate with AI,rehabilitation medicine is expected to move toward a new stage of greater precision and intelligence.展开更多
基金Deanship of Scientific Research at Majmaah University for supporting this work under Project No.R-2022-166.
文摘Day by day,biometric-based systems play a vital role in our daily lives.This paper proposed an intelligent assistant intended to identify emotions via voice message.A biometric system has been developed to detect human emotions based on voice recognition and control a few electronic peripherals for alert actions.This proposed smart assistant aims to provide a support to the people through buzzer and light emitting diodes(LED)alert signals and it also keep track of the places like households,hospitals and remote areas,etc.The proposed approach is able to detect seven emotions:worry,surprise,neutral,sadness,happiness,hate and love.The key elements for the implementation of speech emotion recognition are voice processing,and once the emotion is recognized,the machine interface automatically detects the actions by buzzer and LED.The proposed system is trained and tested on various benchmark datasets,i.e.,Ryerson Audio-Visual Database of Emotional Speech and Song(RAVDESS)database,Acoustic-Phonetic Continuous Speech Corpus(TIMIT)database,Emotional Speech database(Emo-DB)database and evaluated based on various parameters,i.e.,accuracy,error rate,and time.While comparing with existing technologies,the proposed algorithm gave a better error rate and less time.Error rate and time is decreased by 19.79%,5.13 s.for the RAVDEES dataset,15.77%,0.01 s for the Emo-DB dataset and 14.88%,3.62 for the TIMIT database.The proposed model shows better accuracy of 81.02%for the RAVDEES dataset,84.23%for the TIMIT dataset and 85.12%for the Emo-DB dataset compared to Gaussian Mixture Modeling(GMM)and Support Vector Machine(SVM)Model.
基金supported by the Ministry of Higher Education,Malaysia under Grant No.R.J130000.7823.4L626
文摘The wireless sensor network （WSN） consists of sensor nodes that interact with each other to collectively monitor environmental or physical conditions at different locations for the intended users. One of its potential deployments is in the form of smart home and ambient assisted living （SHAAL）to measure patients or elderly physiological signals, control home appliances, and monitor home. This paper focuses on the development of a wireless sensor node platform for SHAAL application over WSN which complies with the IEEE 802.15.4 standard and operates in 2.4 GHz ISM （industrial, scientific, and medical） band. The initial stage of SHAAL application development is the design of the wireless sensor node named TelG mote. The main features of TelG mote contributing to the green communications include low power consumption, wearable, flexible, user-friendly, and small sizes. It is then embedded with a self-built operating system named WiseOS to support customized operation. The node can achieve a packet reception rate （PRR） above 80% for a distance of up to 8 m. The designed TelG mote is also comparable with the existing wireless sensor nodes available in the market.
文摘With the shift in the definition of disease from non-alcoholic fatty liver disease(NAFLD)to metabolism-associated fatty liver disease(MAFLD),as well as the rapid evolution of pathological classification and therapeutic targets,traditional clinical teaching models face challenges such as outdated guideline updates,disjointed translation of scientific research,and limited skill training.This study proposes a dynamic training model integrating“guidelines,clinical practice,and scientific research.”Through stratified case-based teaching(e.g.,FibroScan simulator and metabolic sand table),dynamic guideline analysis(comparing old and new evidence),and the integration of scientific thinking(visualization of CAND1 protein mechanism),a teaching system that integrates theory and practice is constructed.Innovatively developed smart assistant tools(AI decision support system,VR liver biopsy simulator)and a multi-dimensional evaluation system(deviation analysis of diagnosis and treatment pathways,milestone assessment)are used while emphasizing metabolic medicine integration(continuous glucose monitoring and digital therapy)and ethical privacy protection(federated learning framework).This model aims to cultivate students’evidence-based decision-making skills and scientific research transformation thinking through dynamic knowledge base construction and interdisciplinary collaboration,providing sustainable teaching solutions to cope with the rapid iteration of NAFLD diagnosis and treatment.
文摘With the rapid development of AI technology,the field of rehabilitation therapy has ushered in unprecedented opportunities for innovation.This paper provides a comprehensive review of the current applications of AI in rehabilitation therapy and the chall enges it faces,while also exploring its future development trends.The research finds that the application of AI technology in rehabilitation therapy has significantly improved rehabilitation efficiency and patients’quality of life.AI can develop person alized rehabilitation plans based on individual patient conditions,achieve precise assessments and training through smart assistive devices,and break through the limitations of time and space with remote rehabilitation services.However,th e application of AI in rehabilitation therapy still faces several challenges,including high technological costs,data privacy concerns,and public acceptance.Looking forward,as technologies such as 5G,the Internet of Things,and brain-machine interfaces deeply integ rate with AI,rehabilitation medicine is expected to move toward a new stage of greater precision and intelligence.
