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Title: | EMSN: An Energy-Efficient Memristive Sequencer Network for Human Emotion Classification in Mental Health Monitoring |
Authors: | Ji, X Dong, Z Han, Y Lai, CS Zhou, G Qi, D |
Keywords: | human emotion classification;memristive circuit;two-dimensional (2D) materials;sequencer network |
Issue Date: | 31-Mar-2023 |
Publisher: | Institute of Electrical and Electronics Engineers (IEEE) |
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. |
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. |
URI: | https://bura.brunel.ac.uk/handle/2438/26239 |
DOI: | https://doi.org/10.1109/tce.2023.3263672 |
ISSN: | 0098-3063 |
Other Identifiers: | ORCiD: Xiaoyue Ji https://orcid.org/0000-0002-3526-5215 ORCiD: Zhekang Dong https://orcid.org/0000-0003-4639-3834 ORCiD: Chun Sing Lai https://orcid.org/0000-0002-4169-4438 ORCiD: Donglian Qi https://orcid.org/0000-0002-6535-2221 |
Appears in Collections: | Dept of Electronic and Electrical Engineering Research Papers |
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