{"title":"Integration of a Lightweight Customized 2D CNN Model to an Edge Computing System for Real-Time Multiple Gesture Recognition","authors":"Hulin Jin, Zhiran Jin, Yong-Guk Kim, Chunyang Fan","doi":"10.1007/s10723-023-09715-5","DOIUrl":null,"url":null,"abstract":"<h3>Abstract</h3> <p>The human-machine interface (HMI) collects electrophysiology signals incoming from the patient and utilizes them to operate the device. However, most applications are currently in the testing phase and are typically unavailable to everyone. Developing wearable HMI devices that are intelligent and more comfortable has been a focus of study in recent times. This work developed a portable, eight-channel electromyography (EMG) signal-based device that can distinguish 21 different types of motion. To identify the EMG signals, an analog front-end (AFE) integrated chip (IC) was created, and an integrated EMG signal acquisition device combining a stretchy wristband was made. Using the EMG movement signals of 10 volunteers, a SIAT database of 21 gestures was created. Using the SIAT dataset, a lightweight 2D CNN-LSTM model was developed and specialized training was given. The signal recognition accuracy is 96.4%, and the training process took a median of 14 min 13 s. The model may be used on lower-performance edge computing devices because of its compact size, and it is anticipated that it will eventually be applied to smartphone terminals.</p>","PeriodicalId":54817,"journal":{"name":"Journal of Grid Computing","volume":"181 1","pages":""},"PeriodicalIF":3.6000,"publicationDate":"2023-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Grid Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s10723-023-09715-5","RegionNum":2,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"COMPUTER SCIENCE, INFORMATION SYSTEMS","Score":null,"Total":0}
引用次数: 0
Abstract
The human-machine interface (HMI) collects electrophysiology signals incoming from the patient and utilizes them to operate the device. However, most applications are currently in the testing phase and are typically unavailable to everyone. Developing wearable HMI devices that are intelligent and more comfortable has been a focus of study in recent times. This work developed a portable, eight-channel electromyography (EMG) signal-based device that can distinguish 21 different types of motion. To identify the EMG signals, an analog front-end (AFE) integrated chip (IC) was created, and an integrated EMG signal acquisition device combining a stretchy wristband was made. Using the EMG movement signals of 10 volunteers, a SIAT database of 21 gestures was created. Using the SIAT dataset, a lightweight 2D CNN-LSTM model was developed and specialized training was given. The signal recognition accuracy is 96.4%, and the training process took a median of 14 min 13 s. The model may be used on lower-performance edge computing devices because of its compact size, and it is anticipated that it will eventually be applied to smartphone terminals.
期刊介绍：
Grid Computing is an emerging technology that enables large-scale resource sharing and coordinated problem solving within distributed, often loosely coordinated groups-what are sometimes termed "virtual organizations. By providing scalable, secure, high-performance mechanisms for discovering and negotiating access to remote resources, Grid technologies promise to make it possible for scientific collaborations to share resources on an unprecedented scale, and for geographically distributed groups to work together in ways that were previously impossible. Similar technologies are being adopted within industry, where they serve as important building blocks for emerging service provider infrastructures.
Even though the advantages of this technology for classes of applications have been acknowledged, research in a variety of disciplines, including not only multiple domains of computer science (networking, middleware, programming, algorithms) but also application disciplines themselves, as well as such areas as sociology and economics, is needed to broaden the applicability and scope of the current body of knowledge.
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