Synergy-Driven Myoelectric Control for EMG-Based Prosthetic Manipulation: A Case Study

2014 ◽  
Vol 11 (02) ◽  
pp. 1450013 ◽  
Author(s):  
Shunchong Li ◽  
Jiayuan He ◽  
Xinjun Sheng ◽  
Honghai Liu ◽  
Xiangyang Zhu

The paper proposes a synergy-based myoelectric control strategy for prosthetic hands. Synergy is first reviewed in the context of hand movement, then postural synergy-based proportional and simultaneous control has been introduced to prosthetic manipulation via the principal component analysis (PCA) framework. Experiments have been comprehensively carried out on lab-developed prosthetic hand called SJU-5 to evaluate the proposed method. It is evident that the synergy driven myoelectric control achieves the targeted objectives and performs well on the SJU-5 prosthetic hand.

2010 ◽  
Vol 4 (1-2) ◽  
pp. 239-247 ◽  
Author(s):  
Emmanuel A. Ariyibi ◽  
Samuel L. Folami ◽  
Bankole D. Ako ◽  
Taye R. Ajayi ◽  
Adebowale O. Adelusi

Water ◽  
2018 ◽  
Vol 10 (4) ◽  
pp. 437 ◽  
Author(s):  
Ana Marín Celestino ◽  
Diego Martínez Cruz ◽  
Elena Otazo Sánchez ◽  
Francisco Gavi Reyes ◽  
David Vásquez Soto

Author(s):  
Petr Praus

In this chapter the principals and applications of principal component analysis (PCA) applied on hydrological data are presented. Four case studies showed the possibility of PCA to obtain information about wastewater treatment process, drinking water quality in a city network and to find similarities in the data sets of ground water quality results and water-related images. In the first case study, the composition of raw and cleaned wastewater was characterised and its temporal changes were displayed. In the second case study, drinking water samples were divided into clusters in consistency with their sampling localities. In the case study III, the similar samples of ground water were recognised by the calculation of cosine similarity, the Euclidean and Manhattan distances. In the case study IV, 32 water-related images were transformed into a large image matrix whose dimensionality was reduced by PCA. The images were clustered using the PCA scatter plots.


2020 ◽  
pp. 1-10 ◽  
Author(s):  
Alexandre Teixeira de Souza ◽  
Lucas Augusto T. X. Carneiro ◽  
Osmar Pereira da Silva Junior ◽  
Sérgio Luís de Carvalho ◽  
Juliana Heloisa Pinê Américo-Pinheiro

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