A Comparative Study: Toward an Effective Convolutional Neural Network Architecture for Sensor-Based Human Activity Recognition-Reference-Cited by-同舟云学术

A Comparative Study: Toward an Effective Convolutional Neural Network Architecture for Sensor-Based Human Activity Recognition

Published:2022 Issue: Volume:10 Page:20547-20558
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Zhongkai Zhao¹^ORCID,Kobayashi Satoshi¹,Kondo Kazuma¹^ORCID,Hasegawa Tatsuhito¹^ORCID,Koshino Makoto²^ORCID

Affiliation:

1. Graduate School of Engineering, University of Fukui, Fukui, Japan

2. National Institute of Technology, Ishikawa College, Tsubata, Japan

Funder

Japan Society for the Promotion of Science (JSPS) KAKENHI Grant-in-Aid for Early-Career Scientists

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09716082.pdf?arnumber=9716082

Reference53 articles.

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3. A public domain dataset for human activity recognition using smartphones;anguita;Proc 21st Int Eur Symp Artif Neural Netw Comput Intell Mach Learn (ICACNI),2013

4. Creating and benchmarking a new dataset for physical activity monitoring

5. Introducing a New Benchmarked Dataset for Activity Monitoring

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