A Multichannel CNN-GRU Model for Human Activity Recognition-Reference-Cited by-同舟云学术

A Multichannel CNN-GRU Model for Human Activity Recognition

Published:2022 Issue: Volume:10 Page:66797-66810
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lu Limeng¹^ORCID,Zhang Chuanlin²^ORCID,Cao Kai¹^ORCID,Deng Tao¹^ORCID,Yang Qianqian³^ORCID

Affiliation:

1. Key Laboratory of China's Ethnic Languages and Information Technology of Ministry of Education, Northwest Minzu University, Lanzhou, China

2. School of Mathematics and Computer Science, Northwest Minzu University, Lanzhou, China

3. Faculty of Nursing, Kunming Medical University, Kunming, China

Funder

National Natural Science Foundation of China, China

Natural Science Foundation of Gansu Province, China

Fundamental Research Funds for the Central Universities, China

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/ielx7/6287639/9668973/09802089.pdf?arnumber=9802089

Reference45 articles.

1. iSPLInception: An Inception-ResNet Deep Learning Architecture for Human Activity Recognition

2. HHARNet: Taking inspiration from Inception and Dense Networks for Human Activity Recognition using Inertial Sensors

3. Human Activity Recognition Based on Wearable Sensor Using Hierarchical Deep LSTM Networks

4. Real-time human activity recognition from accelerometer data using convolutional neural networks;andrey;Appl Soft Comput,2018

5. Deep convolutional neural networks on multichannel time series for human activity recognition;yang;Proc Intern Joint Conf Artificial Intel (IJCAI),2015

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