Activity-Recognition Model for Violence Behavior Using LSTM

Kim, Svetlana; Nam, Hyejeong; Park, Hyunho; Lee, Yong-Tae; Yoon, Yongik

doi:10.1007/978-981-15-9343-7_75

상세 보기

Activity-Recognition Model for Violence Behavior Using LSTM

Kim, Svetlana;
Nam, Hyejeong;
Park, Hyunho;
Lee, Yong-Tae;
Yoon, Yongik

Citations

SCOPUS

2

초록

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.

키워드

Abnormal detection; Fusion sensing; LSTM; Smartphone; Smartwatch; Ubiquitous computing; Activity recognition; Dangerous situations; Digital signals; FFT algorithm; Sensing data; Long short-term memory

제목: Activity-Recognition Model for Violence Behavior Using LSTM

저자: Kim, Svetlana; Nam, Hyejeong; Park, Hyunho; Lee, Yong-Tae; Yoon, Yongik

DOI: 10.1007/978-981-15-9343-7_75

발행일: 2021-01

유형: Conference Paper

저널명: Lecture Notes in Electrical Engineering

권: 715

페이지: 529 ~ 535

언어: ENG

출판사: Springer Science and Business Media Deutschland GmbH

발행국가: 독일

분량: 7 페이지

ISSN: P 1876-1100