Proceedings of the Korean Institute of Information and Commucation Sciences Conference (한국정보통신학회:학술대회논문집)
- 2018.10a
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- Pages.477-479
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- 2018
Classification of Behavior of UTD Data using LSTM Technique
LSTM 기법을 적용한 UTD 데이터 행동 분류
- Jeung, Gyeo-wun (Korea Electronics Technology Institute) ;
- Ahn, Ji-min (Korea Electronics Technology Institute) ;
- Shin, Dong-in (Korea Electronics Technology Institute) ;
- Won, Geon (Korea Electronics Technology Institute) ;
- Park, Jong-bum (Korea Electronics Technology Institute)
- Published : 2018.10.18
Abstract
This study was carried out to utilize LSTM(Long Short-Term Memory) technique which is one kind of artificial neural network. Among the 27 types of motion data released by the UTD(University of Texas at Dallas), 3-axis acceleration and angular velocity data were applied to the basic LSTM and Deep Residual Bidir-LSTM techniques to classify the behavior.
본 연구는 인공신경망의 한 종류인 LSTM(Long Short-Term Memory) 기법을 활용하기 위하여 진행하였다. UTD(University of Texas at Dallas)가 공개한 27종 동작 데이터 중 3축 가속도 및 각속도 데이터를 기본 LSTM 및 Deep Residual Bidir-LSTM 기법에 적용하여 행동을 분류해 보았다.
Keywords
- LSTM(Long Short-Term Memory) Technique;
- Multimodal Human Action Dataset;
- acceleration;
- angular velocity;
- classification