Enhancing skeleton-based human motion recognition with Lie algebra and memristor-augmented LSTM and CNN

Lately, as a subset of human-centric studies, vision-oriented human action recognition has emerged as a pivotal research area, given its broad applicability in fields like healthcare, video surveillance, autonomous driving, sports, and education. This brief applies Lie algebra and standard bone leng...

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Bibliographic Details
Published inAIMS mathematics Vol. 9; no. 7; pp. 17901 - 17916
Main Authors Fan, Zhencheng, Yan, Zheng, Cao, Yuting, Yang, Yin, Wen, Shiping
Format Journal Article
LanguageEnglish
Published AIMS Press 01.01.2024
Subjects
human motion recognition
lie algebre
lstm
memristor
neural network
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Summary:Lately, as a subset of human-centric studies, vision-oriented human action recognition has emerged as a pivotal research area, given its broad applicability in fields like healthcare, video surveillance, autonomous driving, sports, and education. This brief applies Lie algebra and standard bone length data to represent human skeleton data. A multi-layer long short-term memory (LSTM) recurrent neural network and convolutional neural network (CNN) are applied for human motion recognition. Finally, the trained network weights are converted into the crossbar-based memristor circuit, which can accelerate the network inference, reduce energy consumption, and obtain an excellent computing performance.
ISSN:2473-6988
2473-6988
DOI:10.3934/math.2024871