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Activity-Recognition Model for Violence Behavior Using LSTM

Authors
Kim, S.Nam, H.Park, H.Lee, Y.-T.Yoon, Y.
Issue Date
Jan-2021
Publisher
Springer Science and Business Media Deutschland GmbH
Keywords
Abnormal detection; Fusion sensing; LSTM; Smartphone; Smartwatch
Citation
Lecture Notes in Electrical Engineering, v.715, pp.529 - 535
Journal Title
Lecture Notes in Electrical Engineering
Volume
715
Start Page
529
End Page
535
URI
https://scholarworks.sookmyung.ac.kr/handle/2020.sw.sookmyung/146849
DOI
10.1007/978-981-15-9343-7_75
ISSN
1876-1100
Abstract
Among many dangerous situations, the number of cases of violence has been growing recently. However, there is currently no research to recognize conditions such as assault. Therefore, this paper presents a VR (Violence-Recognition) model for recognition activity using LSTM. The VR model develops algorithms that can detect dangerous situations through processing and analysis of sensing data. Also, to improve accuracy by using the FFT algorithm for processing digital signals in combination with LSTM. ? 2021, Springer Nature Singapore Pte Ltd.
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