A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment-Reference-Cited by-同舟云学术

A fully connected deep learning approach to upper limb gesture recognition in a secure FES rehabilitation environment

Published:2021-02-16 Issue:5 Volume:36 Page:2387-2411
ISSN:0884-8173
Container-title:International Journal of Intelligent Systems
language:en
Short-container-title:Int J Intell Syst

Author:

Liu Qi¹^ORCID,Wu Xueyan²^ORCID,Jiang Yinghang²^ORCID,Liu Xiaodong³^ORCID,Zhang Yonghong⁴^ORCID,Xu Xiaolong¹^ORCID,Qi Lianyong⁵^ORCID

Affiliation:

1. School of Computer & Software Nanjing University of Information Science & Technology Nanjing China

2. Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET) Nanjing University of Information Science & Technology Nanjing China

3. School of Computing Edinburgh Napier University Edinburgh UK

4. School of Automation Nanjing University of Information Science & Technology Nanjing Jiangsu China

5. School of Computer Science Qufu Normal University Rizhao China

Publisher

Wiley

Subject

Artificial Intelligence,Human-Computer Interaction,Theoretical Computer Science,Software

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/int.22383

Reference42 articles.

1. Visual perception of biological motion and a model for its analysis

2. Human posture recognition approach based on ConvNets and SVM classifier

3. Entropy‐based shadowed set approximation of intuitionistic fuzzy sets

4. Fall Detection for Elderly from Partially Observed Depth-Map Video Sequences Based on View-Invariant Human Activity Representation

5. A benchmark for clothes variation in person re‐identification

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