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Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition

Wi-Fi Channel State Information (CSI) based human activity recognition (HAR) which using channel disturbances caused by signal reflection is a novel way of environment sensing and motion recognition. The collected channels characteristics are heavily influenced by the environment, human activity pa...

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Main Authors: Wong, Yan Chiew, Saw, Chia Yee
Format: Article
Language:English
Published: Elsevier Ltd. 2023
Online Access:http://eprints.utem.edu.my/id/eprint/27440/2/0129827102023.PDF
http://eprints.utem.edu.my/id/eprint/27440/
https://www.sciencedirect.com/science/article/pii/S0045790623003415
https://doi.org/10.1016/j.compeleceng.2023.108917
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spelling my.utem.eprints.274402024-07-11T13:10:55Z http://eprints.utem.edu.my/id/eprint/27440/ Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition Wong, Yan Chiew Saw, Chia Yee Wi-Fi Channel State Information (CSI) based human activity recognition (HAR) which using channel disturbances caused by signal reflection is a novel way of environment sensing and motion recognition. The collected channels characteristics are heavily influenced by the environment, human activity patterns and subject’s weight and height. These signal variations reflected from body components are mainly affected by static multipath effects comprises random noise and behave differently in individuals, and thus an active field of research. To reach further for achieving automated real-time classification, lower computational cost and easy adaptability to hardware are necessary. In this work, a CSI-based HAR with hybrid framework, Convolutional Neural Network (CNN)-Stochastic Reservoir (SR) (CNN-SR) has been proposed, enabling a subject adaptable and more efficient hardware implementation with minimal computational complexity. A subcarrier correlation matrix is first computed and portrayed in image without preprocessing based on the reflection of the raw CSI signal induced by human activities at regular intervals, allowing visual observation of whole pattern changes. The time-based features are subsequently extracted through CNN and these feature arrays are then feed into SR which based on stochastic spiking neural network (SSNN) in simple cycle reservoir architecture for template matching. SR offers attractive power savings over typical von Neumann systems, by doing stochastic computations. The proposed method has also been demonstrated that is capable for HAR based on partially captured signals. The signal pattern of each segment can be observed in a single sight and then employed for person-to-person template recognition. This enables HAR with minimal computational complexity and solving the inter-person variability concerns. The results demonstrate that the proposed CNN-SR achieves impressive performance in recognizing human activities and surpasses existing models with an average accuracy of 93.49%. Elsevier Ltd. 2023-10 Article PeerReviewed text en http://eprints.utem.edu.my/id/eprint/27440/2/0129827102023.PDF Wong, Yan Chiew and Saw, Chia Yee (2023) Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition. Computers and Electrical Engineering, 111. pp. 1-11. ISSN 0045-7906 https://www.sciencedirect.com/science/article/pii/S0045790623003415 https://doi.org/10.1016/j.compeleceng.2023.108917
institution Universiti Teknikal Malaysia Melaka
building UTEM Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Teknikal Malaysia Melaka
content_source UTEM Institutional Repository
url_provider http://eprints.utem.edu.my/
language English
description Wi-Fi Channel State Information (CSI) based human activity recognition (HAR) which using channel disturbances caused by signal reflection is a novel way of environment sensing and motion recognition. The collected channels characteristics are heavily influenced by the environment, human activity patterns and subject’s weight and height. These signal variations reflected from body components are mainly affected by static multipath effects comprises random noise and behave differently in individuals, and thus an active field of research. To reach further for achieving automated real-time classification, lower computational cost and easy adaptability to hardware are necessary. In this work, a CSI-based HAR with hybrid framework, Convolutional Neural Network (CNN)-Stochastic Reservoir (SR) (CNN-SR) has been proposed, enabling a subject adaptable and more efficient hardware implementation with minimal computational complexity. A subcarrier correlation matrix is first computed and portrayed in image without preprocessing based on the reflection of the raw CSI signal induced by human activities at regular intervals, allowing visual observation of whole pattern changes. The time-based features are subsequently extracted through CNN and these feature arrays are then feed into SR which based on stochastic spiking neural network (SSNN) in simple cycle reservoir architecture for template matching. SR offers attractive power savings over typical von Neumann systems, by doing stochastic computations. The proposed method has also been demonstrated that is capable for HAR based on partially captured signals. The signal pattern of each segment can be observed in a single sight and then employed for person-to-person template recognition. This enables HAR with minimal computational complexity and solving the inter-person variability concerns. The results demonstrate that the proposed CNN-SR achieves impressive performance in recognizing human activities and surpasses existing models with an average accuracy of 93.49%.
format Article
author Wong, Yan Chiew
Saw, Chia Yee
spellingShingle Wong, Yan Chiew
Saw, Chia Yee
Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition
author_facet Wong, Yan Chiew
Saw, Chia Yee
author_sort Wong, Yan Chiew
title Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition
title_short Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition
title_full Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition
title_fullStr Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition
title_full_unstemmed Neuromorphic computing with hybrid CNN-stochastic reservoir for time series WiFi based human activity recognition
title_sort neuromorphic computing with hybrid cnn-stochastic reservoir for time series wifi based human activity recognition
publisher Elsevier Ltd.
publishDate 2023
url http://eprints.utem.edu.my/id/eprint/27440/2/0129827102023.PDF
http://eprints.utem.edu.my/id/eprint/27440/
https://www.sciencedirect.com/science/article/pii/S0045790623003415
https://doi.org/10.1016/j.compeleceng.2023.108917
_version_ 1806429013886894080
score 13.211869

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