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BCRNet-SNN: Body Channel Response-Aware Spiking Neural Network for User Recognition
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- Authors
- Taewook Kang, Chanwoo Shin, Jongseok Lee, Jae-Jin Lee, Donggyu Sim, Seong-Eun Kim
- Issue Date
- 2025-04
- Citation
- IEEE Transactions on Industrial Informatics, v.권호미정, pp.1-11
- ISSN
- 1551-3203
- Publisher
- Institute of Electrical and Electronics Engineers
- Language
- English
- Type
- Journal Article
- Abstract
- Spiking neural networks (SNNs) have been recently highlighted as an attractive approach for implementing artificial intelligence models in resource-constrained edge devices for various industrial applications. These SNNs leverage low-power and wide dynamic range processing through biologically inspired event-driven operations in a massively parallel manner. In this respect, we propose BCRNet-SNN, an SNN model designed to utilize an electric body channel response (BCR) as a biometric feature for user recognition, where each element in the BCR dataset for 15 subjects is a 1-D vector comprising 380 feature points from the measured envelope by applying chirp signals to the body. The network parameters for implementing the posttrained BCRNet-SNN are inherited from the proposed convolutional neural network (CNN) that extensively extracts BCR-based biometric features (BCRNet), followed by applying knowledge distillation-based network lightening process on BCRNet. The performance evaluation results compared to ResNet18 and ResNet6 for the BCR dataset show that BCRNet achieves a greater than 2% and 1.4% improvement, respectively, in the average classification accuracy, while significantly reducing the number of network parameters to less than 1%. The student network distilled from the teacher BCRNet (BCRNet-S) requires only 5.1% network parameters than BCRNet, which can be eligible for implementation in BCRNet-SNN. The proposed structure of BCRNet-SNN to effectively accommodate the CNN-to-SNN-converted parameters from BCRNet-S can achieve up to a maximum accuracy of 98.11%, without observable performance degradation compared to BCRNet.
- KSP Keywords
- Artificial intelligence models, Biologically inspired, Chirp signals, Convolution neural network(CNN), Edge devices, Event-driven, Feature Points, Industrial Applications, Knowledge Distillation, Massively parallel, Maximum accuracy