HiHAR: A Hierarchical Hybrid Deep Learning Architecture for Wearable Sensor-Based Human Activity Recognition-Reference-Cited by-同舟云学术

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HiHAR: A Hierarchical Hybrid Deep Learning Architecture for Wearable Sensor-Based Human Activity Recognition

Published:2021 Issue: Volume:9 Page:145271-145281
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Hoai Thu Nguyen Thi^ORCID,Han Dong Seog^ORCID

Funder

MSIT (Ministry of Science and ICT), Korea, under the ITRC (Information Technology Research Center) support program

IITP

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science

Link

http://xplorestaging.ieee.org/ielx7/6287639/9312710/09584858.pdf?arnumber=9584858

Cited by 28 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Hand-Crafted Features With a Simple Deep Learning Architecture for Sensor-Based Human Activity Recognition;IEEE Sensors Journal;2024-09-01

2. Novel hybrid optimization based adaptive deep convolution neural network approach for human activity recognition system;Multimedia Tools and Applications;2024-04-19

3. Image expression of time series data of wearable IMU sensor and fusion classification of gymnastics action;Expert Systems with Applications;2024-03

4. A multi-channel hybrid deep learning framework for multi-sensor fusion enabled human activity recognition;Alexandria Engineering Journal;2024-03

5. 3D Skeleton-Based Human Motion Prediction Using Dynamic Multi-Scale Spatiotemporal Graph Recurrent Neural Networks;IEEE Transactions on Emerging Topics in Computational Intelligence;2024-02

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