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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 | Han, Y | - |
dc.contributor.author | Lai, CS | - |
dc.contributor.author | Zhou, G | - |
dc.contributor.author | Qi, D | - |
dc.date.accessioned | 2023-04-02T15:13:21Z | - |
dc.date.available | 2023-04-02T15:13:21Z | - |
dc.date.issued | 2023-03-31 | - |
dc.identifier | ORCiD: Xiaoyue Ji https://orcid.org/0000-0002-3526-5215 | - |
dc.identifier | ORCiD: Zhekang Dong https://orcid.org/0000-0003-4639-3834 | - |
dc.identifier | ORCiD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438 | - |
dc.identifier | ORCiD: Donglian Qi https://orcid.org/0000-0002-6535-2221 | - |
dc.identifier.citation | Ji, X. et al. (2023) 'EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring', IEEE Transactions on Consumer Electronics, 69 (4), pp. 1005 - 1016. doi: 10.1109/tce.2023.3263672. | en_US |
dc.identifier.issn | 0098-3063 | - |
dc.identifier.uri | https://bura.brunel.ac.uk/handle/2438/26239 | - |
dc.description.abstract | Mental health problems are an increasingly common social issue severely affecting health and well-being. Multimedia processing technologies via facial expression show appealing prospects in the consumer field for mental health monitoring, while still suffer from intensive computation and low energy efficiency. This paper proposes an energy-efficiency memristive sequencer network (EMSN) for human emotion classification, which offers an environmentally friendly approach for consumers with low cost and easily deployable hardware. Firstly, two-dimensional (2D) materials are employed to construct an eco-friendly memristor, the efficacy and reliability of which are confirmed through performance testing. Then, a sequencer block is proposed using memristive circuits. Notably, it is a core component of the EMSN, consisting of a bidirectional long short-term memory circuit, normalisation circuit module, and multi-layer perception module. After combining some necessary function modules, the EMSN can be achieved. Furthermore, the proposed EMSN is applied for human emotion classification. The experimental results demonstrate that the proposed EMSN has advantages in computational efficiency and classification accuracy compared to existing mainstream methods, indicating an advancement in consumer health monitoring. | - |
dc.description.sponsorship | Fundamental Research Funds for the Provincial University of Zhejiang (Grant Number: GK229909299001-06); 10.13039/501100001809-National Natural Science Foundation of China (Grant Number: 62001149); Natural Science Foundation of Zhejiang Province (Grant Number: LQ21F010009). | en_US |
dc.format.extent | 1005 - 1016 | - |
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 © 2022 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 by sending a request to pubspermissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html | - |
dc.rights.uri | https://www.ieee.org/publications/rights/rights-policies.html | - |
dc.subject | human emotion classification | en_US |
dc.subject | memristive circuit | en_US |
dc.subject | two-dimensional (2D) materials | en_US |
dc.subject | sequencer network | en_US |
dc.title | EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring | en_US |
dc.type | Article | en_US |
dc.identifier.doi | https://doi.org/10.1109/tce.2023.3263672 | - |
dc.relation.isPartOf | IEEE Transactions on Consumer Electronics | - |
pubs.issue | 4 | - |
pubs.publication-status | Published | - |
pubs.volume | 69 | - |
dc.identifier.eissn | 1558-4127 | - |
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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FullText.pdf | Copyright © 2022 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 by sending a request to pubspermissions@ieee.org. See: https://www.ieee.org/publications/rights/rights-policies.html | 4.98 MB | Adobe PDF | View/Open |
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