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DC Field | Value | Language |
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dc.contributor.author | Ji, X | - |
dc.contributor.author | Dong, Z | - |
dc.contributor.author | Zhu, L | - |
dc.contributor.author | Hu, C | - |
dc.contributor.author | Lai, CS | - |
dc.date.accessioned | 2024-10-01T08:25:36Z | - |
dc.date.available | 2024-10-01T08:25:36Z | - |
dc.date.issued | 2024-04-23 | - |
dc.identifier | ORCiD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438 | - |
dc.identifier.citation | Ji, X. et al. (2024) 'An Efficient Human Activity Recognition In-Memory Computing Architecture Development for Healthcare Monitoring', IEEE Journal of Biomedical and Health Informatics, 0 (early access), pp. 1 - 14. doi: 10.1109/JBHI.2024.3392648. | en_US |
dc.identifier.issn | 2168-2194 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/29855 | - |
dc.description.abstract | Human activity recognition has played a crucial role in healthcare information systems due to the fast adoption of artificial intelligence (AI) and the internet of thing (IoT). Most of the existing methods are still limited by computational energy, transmission latency, and computing speed. To address these challenges, we develop an efficient human activity recognition in-memory computing architecture for healthcare monitoring. Specifically, a mechanism-oriented model of Ag/a-Carbon/Ag memristor is designed, serving as the core circuit component of the proposed in-memory computing system. Then, one-transistor-two-memristor (1T2M) crossbar array is proposed to perform high-efficiency multiply-accumulate (MAC) operation and high-density memory in the proposed scheme. To facilitate understanding of the proposed efficient human activity recognition in-memory computing design, self-attention ConvLSTM module, multi-head convolutional attention module, and recognition module are proposed. Furthermore, the proposed system is applied to perform human activity recognition, which contains eleven different human activities, including five different postural falls, and six basic daily activities. The experimental results show that the proposed system has advantages in recognition performance (≥ 0.20% accuracy, ≥ 1.10% F1-score) and time consumption (approximately 8∼10 times speed up) compared to existing methods, indicating an advancement in smart healthcare applications. | en_US |
dc.description.sponsorship | This work was supported in part by the National Postdoctoral Researcher Support Program under Grant GZB20230356, the Shuimu Tsinghua Scholar program under Grant 2023SM035, the National Natural Science Foundation of China under Grant 62206062, and the Fundamental Research Funds for the Provincial University of Zhejiang under Grant GK229909299001-06. | en_US |
dc.format.extent | 1 - 14 | - |
dc.format.medium | Print-Electronic | - |
dc.language.iso | en_US | en_US |
dc.publisher | Institute of Electrical and Electronics Engineers (IEEE) | en_US |
dc.rights | Copyright © 2024 Institute of Electrical and Electronics Engineers (IEEE). Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works (see: https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/). | - |
dc.rights.uri | https://journals.ieeeauthorcenter.ieee.org/become-an-ieee-journal-author/publishing-ethics/guidelines-and-policies/post-publication-policies/ | - |
dc.subject | human activity recognition | en_US |
dc.subject | in-memory computing | en_US |
dc.subject | memristor | en_US |
dc.subject | healthcare monitoring | en_US |
dc.title | An Efficient Human Activity Recognition In-Memory Computing Architecture Development for Healthcare Monitoring | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/JBHI.2024.3392648 | - |
dc.relation.isPartOf | IEEE Journal of Biomedical and Health Informatics | - |
pubs.issue | early access | - |
pubs.publication-status | Published | - |
pubs.volume | 0 | - |
dc.identifier.eissn | 2168-2208 | - |
dc.rights.holder | Institute of Electrical and Electronics Engineers (IEEE) | - |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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