Deploying a Web-based Electroencephalography Data Analysis Virtual Laboratory

Alphonse, Joshy; Diwakar, Shyam

doi:10.1016/j.procs.2020.04.261

Cited by 4 publications

(2 citation statements)

References 9 publications

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“…Using just the client-side to execute the model effectively addresses this issue since no data is sent or kept outside the user's device, guaranteeing the privacy of sensitive or personal data. For example, FreddieMeter 3 is an artificial intelligence-powered singing challenge that determines how well a singer's voice (timbre, pitch, and melody) resembles Freddie Mercury's while protecting user privacy.…”

Section: Timely Responsementioning

confidence: 99%

“…Signal analysis A virtual laboratory for EEG data analysis that enables data analysis, pre-processing, and model development was built with TensorFlow.js in [3]. Using realtime sensor measurements of rib-cage movement, a separate clinical study in [108] found that an LSTM network model can predict physical effort by comparing how much air a person breathes in a minute to their perceived workout intensity.…”

Section: Video Image and Signal Analysis Appsmentioning

confidence: 99%

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Front-end deep learning web apps development and deployment: a review

Goh

Abas

2022

Appl Intell

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Machine learning and deep learning models are commonly developed using programming languages such as Python, C++, or R and deployed as web apps delivered from a back-end server or as mobile apps installed from an app store. However, recently front-end technologies and JavaScript libraries, such as TensorFlow.js, have been introduced to make machine learning more accessible to researchers and end-users. Using JavaScript, TensorFlow.js can define, train, and run new or existing, pre-trained machine learning models entirely in the browser from the client-side, which improves the user experience through interaction while preserving privacy. Deep learning models deployed on front-end browsers must be small, have fast inference, and ideally be interactive in real-time. Therefore, the emphasis on development and deployment is different. This paper aims to review the development and deployment of these deep-learning web apps to raise awareness of the recent advancements and encourage more researchers to take advantage of this technology for their own work. First, the rationale behind the deployment stack (front-end, JavaScript, and TensorFlow.js) is discussed. Then, the development approach for obtaining deep learning models that are optimized and suitable for front-end deployment is then described. The article also provides current web applications divided into seven categories to show deep learning potential on the front end. These include web apps for deep learning playground, pose detection and gesture tracking, music and art creation, expression detection and facial recognition, video segmentation, image and signal analysis, healthcare diagnosis, recognition, and identification.

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Section: Timely Responsementioning

confidence: 99%

Section: Video Image and Signal Analysis Appsmentioning

confidence: 99%