scholarly journals Reliability Analysis For Finger Movement Recognition With Raw Electromyographic Signal by Evidential Convolutional Networks

Author(s):  
Yuzhou Lin ◽  
Ramaswamy Palaniappan ◽  
Philippe De Wilde ◽  
Ling Li
2019 ◽  
Vol 19 (16) ◽  
pp. 7043-7055 ◽  
Author(s):  
Ala Shaabana ◽  
Joey Legere ◽  
Jun Li ◽  
Rong Zheng ◽  
Martin V. Mohrenschildt ◽  
...  

2021 ◽  
Author(s):  
Longfei Li ◽  
Xuanyu An ◽  
Danlei Geng ◽  
Shiyi Qin ◽  
Sheng Shen

Sensors ◽  
2020 ◽  
Vol 20 (21) ◽  
pp. 6094
Author(s):  
Maria Skublewska-Paszkowska ◽  
Pawel Powroznik ◽  
Edyta Lukasik

Human movement analysis is very often applied to sport, which has seen great achievements in assessing an athlete’s progress, giving further training tips and in movement recognition. In tennis, there are two basic shots: forehand and backhand, which are performed during all matches and training sessions. Recognition of these movements is important in the quantitative analysis of a tennis game. In this paper, the authors propose using Spatial-Temporal Graph Neural Networks (ST-GCN) to challenge the above task. Recognition of the shots is performed on the basis of images obtained from 3D tennis movements (forehands and backhands) recorded by the Vicon motion capture system (Oxford Metrics Ltd, Oxford, UK), where both the player and the racket were recorded. Two methods of putting data into the ST-GCN network were compared: with and without fuzzying of data. The obtained results confirm that the use of fuzzy input graphs for ST-GCNs is a better tool for recognition of forehand and backhand tennis shots relative to graphs without fuzzy input.


2021 ◽  
Vol 33 (8) ◽  
pp. 2825
Author(s):  
Yaohui Hu ◽  
Yadong Chen ◽  
Xiaobin Yi ◽  
Jiquan Zhong ◽  
Xing Wang ◽  
...  

2018 ◽  
Vol 56 (12) ◽  
pp. 2259-2271 ◽  
Author(s):  
Pornchai Phukpattaranont ◽  
Sirinee Thongpanja ◽  
Khairul Anam ◽  
Adel Al-Jumaily ◽  
Chusak Limsakul

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