ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors-Reference-Cited by-同舟云学术

ResNet-SE: Channel Attention-Based Deep Residual Network for Complex Activity Recognition Using Wrist-Worn Wearable Sensors

Published:2022 Issue: Volume:10 Page:51142-51154
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Mekruksavanich Sakorn¹^ORCID,Jitpattanakul Anuchit²^ORCID,Sitthithakerngkiet Kanokwan²^ORCID,Youplao Phichai³^ORCID,Yupapin Preecha⁴^ORCID

Affiliation:

1. Department of Computer Engineering, School of Information and Communication Technology, University of Phayao, Phayao, Thailand

2. Department of Mathematics, Faculty of Applied Science, Intelligent and Nonlinear Dynamic Innovations Research Center, King Mongkut’s University of Technology North Bangkok, Bangkok, Thailand

3. Department of Electrical Engineering, Faculty of Industry and Technology, Rajamangala University of Technology Isan Sakon Nakhon Campus, Sakon Nakhon, Thailand

4. Department of Electrical Technology, Faculty of Industrial Technology, Institute of Vocational Education Northeastern 2, Sakon Nakhon, Thailand

Funder

Thailand Science Research and Innovation Fund

University of Phayao

National Science, Research and Innovation Fund

King Mongkut’s University of Technology North Bangkok

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09771436.pdf?arnumber=9771436