Real-Time Adaptive Human Motions for Web-Based Training

2005 ◽  
pp. 256-266
Author(s):  
Frederick W. B. Li ◽  
Becky Siu ◽  
Rynson W. H. Lau ◽  
Taku Komura
Keyword(s):  
Real Time ◽  
Web Based ◽  
Human Motions

scholarly journals Deep-Framework: A Distributed, Scalable, and Edge-Oriented Framework for Real-Time Analysis of Video Streams

Sensors ◽  
2021 ◽  
Vol 21 (12) ◽  
pp. 4045
Author(s):  
Alessandro Sassu ◽  
Jose Francisco Saenz-Cogollo ◽  
Maurizio Agelli
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Video Data ◽  
Video Analytics ◽  
Web Based ◽  
Real Time Analysis ◽  
Open Source Framework ◽  
Cluster Configuration ◽  
Time Requirements ◽  
High Level

Edge computing is the best approach for meeting the exponential demand and the real-time requirements of many video analytics applications. Since most of the recent advances regarding the extraction of information from images and video rely on computation heavy deep learning algorithms, there is a growing need for solutions that allow the deployment and use of new models on scalable and flexible edge architectures. In this work, we present Deep-Framework, a novel open source framework for developing edge-oriented real-time video analytics applications based on deep learning. Deep-Framework has a scalable multi-stream architecture based on Docker and abstracts away from the user the complexity of cluster configuration, orchestration of services, and GPU resources allocation. It provides Python interfaces for integrating deep learning models developed with the most popular frameworks and also provides high-level APIs based on standard HTTP and WebRTC interfaces for consuming the extracted video data on clients running on browsers or any other web-based platform.

scholarly journals Webinar : An Initial Experience with Web-based Real Time Interactive Clinical Seminars for Japanese Medical Students

2010 ◽  
Vol 11 (2) ◽  
pp. 87-90 ◽  
Author(s):  
Gerald H. Stein ◽  
Ayako Shibata ◽  
Miho Kojima Bautista ◽  
Yasuharu Tokuda
Keyword(s):  
Medical Students ◽  
Real Time ◽  
Initial Experience ◽  
Web Based

scholarly journals Multi-Parameter Vital Sign Telemedicine System Using Web Socket for COVID-19 Pandemics

Healthcare ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 285
Author(s):  
Chuchart Pintavirooj ◽  
Tanapon Keatsamarn ◽  
Treesukon Treebupachatsakul
Keyword(s):  
Real Time ◽  
Vital Sign ◽  
Vital Signs ◽  
Mobile Network ◽  
Cloud Platform ◽  
Telemedicine System ◽  
Web Based ◽  
The Real ◽  
Real Time Application ◽  
Commercial Platform

Telemedicine has become an increasingly important part of the modern healthcare infrastructure, especially in the present situation with the COVID-19 pandemics. Many cloud platforms have been used intensively for Telemedicine. The most popular ones include PubNub, Amazon Web Service, Google Cloud Platform and Microsoft Azure. One of the crucial challenges of telemedicine is the real-time application monitoring for the vital sign. The commercial platform is, by far, not suitable for real-time applications. The alternative is to design a web-based application exploiting Web Socket. This research paper concerns the real-time six-parameter vital-sign monitoring using a web-based application. The six vital-sign parameters are electrocardiogram, temperature, plethysmogram, percent saturation oxygen, blood pressure and heart rate. The six vital-sign parameters were encoded in a web server site and sent to a client site upon logging on. The encoded parameters were then decoded into six vital sign signals. Our proposed multi-parameter vital-sign telemedicine system using Web Socket has successfully remotely monitored the six-parameter vital signs on 4G mobile network with a latency of less than 5 milliseconds.

scholarly journals Deep Learning-Based Cattle Vocal Classification Model and Real-Time Livestock Monitoring System with Noise Filtering

Animals ◽  
2021 ◽  
Vol 11 (2) ◽  
pp. 357
Author(s):  
Dae-Hyun Jung ◽  
Na Yeon Kim ◽  
Sang Ho Moon ◽  
Changho Jhin ◽  
Hak-Jin Kim ◽  
...  
Keyword(s):  
Deep Learning ◽  
Real Time ◽  
Model Development ◽  
Classification Model ◽  
Intelligence Analysis ◽  
Noise Filtering ◽  
Meat Industry ◽  
Mel Frequency Cepstral Coefficients ◽  
Web Based ◽  
The Status

The priority placed on animal welfare in the meat industry is increasing the importance of understanding livestock behavior. In this study, we developed a web-based monitoring and recording system based on artificial intelligence analysis for the classification of cattle sounds. The deep learning classification model of the system is a convolutional neural network (CNN) model that takes voice information converted to Mel-frequency cepstral coefficients (MFCCs) as input. The CNN model first achieved an accuracy of 91.38% in recognizing cattle sounds. Further, short-time Fourier transform-based noise filtering was applied to remove background noise, improving the classification model accuracy to 94.18%. Categorized cattle voices were then classified into four classes, and a total of 897 classification records were acquired for the classification model development. A final accuracy of 81.96% was obtained for the model. Our proposed web-based platform that provides information obtained from a total of 12 sound sensors provides cattle vocalization monitoring in real time, enabling farm owners to determine the status of their cattle.

scholarly journals The Effects of Real-Time Interactive Multimedia Teleradiology System

2016 ◽  
Vol 2016 ◽  
pp. 1-9 ◽  
Author(s):  
Lilac Al-Safadi
Keyword(s):  
Real Time ◽  
Interactive Multimedia ◽  
Point Of Care ◽  
Critical Role ◽  
Web Based ◽  
Teleradiology System ◽  
Multimedia Technologies ◽  
Referring Physicians ◽  
Improving Patient Care

This study describes the design of a real-time interactive multimedia teleradiology system and assesses how the system is used by referring physicians in point-of-care situations and supports or hinders aspects of physician-radiologist interaction. We developed a real-time multimedia teleradiology management system that automates the transfer of images and radiologists’ reports and surveyed physicians to triangulate the findings and to verify the realism and results of the experiment. The web-based survey was delivered to 150 physicians from a range of specialties. The survey was completed by 72% of physicians. Data showed a correlation between rich interactivity, satisfaction, and effectiveness. The results of our experiments suggest that real-time multimedia teleradiology systems are valued by referring physicians and may have the potential for enhancing their practice and improving patient care and highlight the critical role of multimedia technologies to provide real-time multimode interactivity in current medical care.

A solution of web-based real-time control laboratories

2009 ◽  
Author(s):  
Yuliang Qiao ◽  
Guo-ping Liu ◽  
Geng Zheng ◽  
Wenshan Hu
Keyword(s):  
Real Time ◽  
Real Time Control ◽  
Web Based ◽  
Time Control
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