TY - GEN
T1 - Human Activity Recognition Using CNN & LSTM
AU - Shiranthika, Chamani
AU - Premakumara, Nilantha
AU - Chiu, Huei Ling
AU - Samani, Hooman
AU - Shyalika, Chathurangi
AU - Yang, Chan Yun
N1 - Funding Information:
The authors gratefully acknowledge the support grants from Ministry of Science and Technology of Taiwan through its grant 108-2221-E-305-012, the National Taipei University through its grant 109-NTPU-0RDA-F-006 and the University System of Taipei Joint Research Program through its grant USTP-NTPU-TMU-109-01.
Publisher Copyright:
© 2020 IEEE.
Copyright:
Copyright 2021 Elsevier B.V., All rights reserved.
PY - 2020/12/2
Y1 - 2020/12/2
N2 - In identifying objects, understanding the world, analyzing time series and predicting future sequences, the recent developments in Artificial Intelligence (AI) have made human beings more inclined towards novel research goals. There is a growing interest in Recurrent Neural Networks (RNN) by AI researchers today, which includes major applications in the fields of speech recognition, language modeling, video processing and time series analysis. Recognition of Human Behavior or the Human Activity Recognition (HAR) is one of the difficult issues in this wonderful AI field that seeks answers. As an assistive technology combined with innovations such as the Internet of Things (IoT), it can be primarily used for eldercare and childcare. HAR also covers a broad variety of real-life applications, ranging from healthcare to personal fitness, gaming, military applications, security fields, etc. HAR can be achieved with sensors, images, smartphones or videos where the advancement of Human Computer Interaction (HCI) technology has become more popular for capturing behaviors using sensors such as accelerometers and gyroscopes. This paper introduces an approach that uses CNN and Long Short-Term Memory (LSTM) to predict human behaviors on the basis of the WISDM dataset.
AB - In identifying objects, understanding the world, analyzing time series and predicting future sequences, the recent developments in Artificial Intelligence (AI) have made human beings more inclined towards novel research goals. There is a growing interest in Recurrent Neural Networks (RNN) by AI researchers today, which includes major applications in the fields of speech recognition, language modeling, video processing and time series analysis. Recognition of Human Behavior or the Human Activity Recognition (HAR) is one of the difficult issues in this wonderful AI field that seeks answers. As an assistive technology combined with innovations such as the Internet of Things (IoT), it can be primarily used for eldercare and childcare. HAR also covers a broad variety of real-life applications, ranging from healthcare to personal fitness, gaming, military applications, security fields, etc. HAR can be achieved with sensors, images, smartphones or videos where the advancement of Human Computer Interaction (HCI) technology has become more popular for capturing behaviors using sensors such as accelerometers and gyroscopes. This paper introduces an approach that uses CNN and Long Short-Term Memory (LSTM) to predict human behaviors on the basis of the WISDM dataset.
KW - Convolutional Neural Networks (CNN)
KW - Human Activity Recognition
KW - Long Short-Term Memory (LSTM)
UR - http://www.scopus.com/inward/record.url?scp=85100081393&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85100081393&partnerID=8YFLogxK
U2 - 10.1109/ICITR51448.2020.9310792
DO - 10.1109/ICITR51448.2020.9310792
M3 - Conference contribution
AN - SCOPUS:85100081393
T3 - Proceedings of ICITR 2020 - 5th International Conference on Information Technology Research: Towards the New Digital Enlightenment
BT - Proceedings of ICITR 2020 - 5th International Conference on Information Technology Research
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 5th International Conference on Information Technology Research, ICITR 2020
Y2 - 2 December 2020 through 4 December 2020
ER -
