Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587416
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Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3583866
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供：本刊发表的文章摘要。
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Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587422
{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2025.3587422","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587422","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"C3-C3"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098581","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716255","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-28DOI: 10.1109/JPROC.2025.3587424
{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired.","authors":"","doi":"10.1109/JPROC.2025.3587424","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3587424","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"C4-C4"},"PeriodicalIF":23.2,"publicationDate":"2025-07-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11098580","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144716256","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580189
{"title":"IEEE Membership","authors":"","doi":"10.1109/JPROC.2025.3580189","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580189","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"C3-C3"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082634","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646647","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580183
{"title":"Proceedings of the IEEE Publication Information","authors":"","doi":"10.1109/JPROC.2025.3580183","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580183","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"C2-C2"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082637","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646551","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580187
{"title":"Future Special Issues/Special Sections of the Proceedings","authors":"","doi":"10.1109/JPROC.2025.3580187","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580187","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"208-208"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082636","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646555","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3581362
Summary form only: Abstracts of articles presented in this issue of the publication.
仅以摘要形式提供：本刊发表的文章摘要。
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Pub Date : 2025-07-17DOI: 10.1109/JPROC.2025.3580191
{"title":"Proceedings of the IEEE: Stay Informed. Become Inspired.","authors":"","doi":"10.1109/JPROC.2025.3580191","DOIUrl":"https://doi.org/10.1109/JPROC.2025.3580191","url":null,"abstract":"","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 2","pages":"C4-C4"},"PeriodicalIF":23.2,"publicationDate":"2025-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=11082590","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144646592","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The electromyography (EMG) signal is particularly useful in monitoring muscle activity, and it can be acquired noninvasively on the skin surface. Thanks to these key characteristics, EMG-based human–machine interfaces (HMIs) for prosthetic myocontrol, as well as gesture recognition, are becoming widespread. A key challenge in this context is to design embedded systems to process EMG signals and generate motor commands with miniaturized, unobtrusive, and low-power devices, reliably and in real time, at a relatively low cost to provide continuous monitoring without causing stigma or discomfort. This article presents an in-depth review of the current status and future research challenges in systems and circuits for EMG acquisition and processing. We start by illustrating the sensor interfaces and acquisition systems required for signal analysis to provide efficient and effective ways of understanding the signal and its nature. We, then, focus on conventional state-of-the-art (SoA) EMG gesture recognition algorithms as well as novel architectures that tackle EMG processing challenges, i.e., hyperdimensional computing (HDC), blind source separation (BSS), and spiking neural networks (SNNs). Finally, we discuss open challenges, such as EMG variability, natural control, and efficient computation, to bring the myocontrol completely out of the laboratory, filling the gap between research prototypes and real-world applications.
{"title":"EMG Acquisition and Processing for Hand Movement Decoding on Embedded Systems: State of the Art and Challenges","authors":"Simone Benatti;Elisa Donati;Ali Moin;Marcello Zanghieri;Mattia Orlandi;Alessio Burrello;Fiorenzo Artoni;Silvestro Micera;Luca Benini;Jan M. Rabaey","doi":"10.1109/JPROC.2025.3581995","DOIUrl":"10.1109/JPROC.2025.3581995","url":null,"abstract":"The electromyography (EMG) signal is particularly useful in monitoring muscle activity, and it can be acquired noninvasively on the skin surface. Thanks to these key characteristics, EMG-based human–machine interfaces (HMIs) for prosthetic myocontrol, as well as gesture recognition, are becoming widespread. A key challenge in this context is to design embedded systems to process EMG signals and generate motor commands with miniaturized, unobtrusive, and low-power devices, reliably and in real time, at a relatively low cost to provide continuous monitoring without causing stigma or discomfort. This article presents an in-depth review of the current status and future research challenges in systems and circuits for EMG acquisition and processing. We start by illustrating the sensor interfaces and acquisition systems required for signal analysis to provide efficient and effective ways of understanding the signal and its nature. We, then, focus on conventional state-of-the-art (SoA) EMG gesture recognition algorithms as well as novel architectures that tackle EMG processing challenges, i.e., hyperdimensional computing (HDC), blind source separation (BSS), and spiking neural networks (SNNs). Finally, we discuss open challenges, such as EMG variability, natural control, and efficient computation, to bring the myocontrol completely out of the laboratory, filling the gap between research prototypes and real-world applications.","PeriodicalId":20556,"journal":{"name":"Proceedings of the IEEE","volume":"113 3","pages":"256-286"},"PeriodicalIF":23.2,"publicationDate":"2025-07-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"144629904","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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