Activity Recognition Based on Pattern Recognition of Myoelectric Signals for Rehabilitation

2017 ◽  
pp. 427-442 ◽  
Author(s):  
Oluwarotimi Williams Samuel ◽  
Peng Fang ◽  
Shixiong Chen ◽  
Yanjuan Geng ◽  
Guanglin Li
Keyword(s):  
Pattern Recognition ◽  
Activity Recognition ◽  
Myoelectric Signals

scholarly journals Improved pattern recognition classification accuracy for surface myoelectric signals using spectral enhancement

2015 ◽  
Vol 18 ◽  
pp. 61-68 ◽  
Author(s):  
Paul McCool ◽  
Lykourgos Petropoulakis ◽  
John J. Soraghan ◽  
Navin Chatlani
Keyword(s):  
Pattern Recognition ◽  
Classification Accuracy ◽  
Myoelectric Signals ◽  
Spectral Enhancement

Effects of Long-Term Myoelectric Signals on Pattern Recognition

2013 ◽  
pp. 396-404 ◽  
Author(s):  
Jiayuan He ◽  
Dingguo Zhang ◽  
Xinjun Sheng ◽  
Xiangyang Zhu
Keyword(s):  
Pattern Recognition ◽  
Myoelectric Signals

Granular Region-oriented Fuzzy-Rough based kNN Improvization for Activity Recognition Modeling

2015 ◽  
Vol 3 (1) ◽  
Author(s):  
Vijay Borges ◽  
Wilson Jeberson
Keyword(s):  
Pattern Recognition ◽  
Activity Recognition ◽  
Human Activity ◽  
Test Object ◽  
Feature Space ◽  
Nearest Neighbors ◽  
Human Activity Recognition ◽  
Similarity Relations ◽  
Fuzzy Similarity ◽  
Knn Classifier

Activity recognition is a complex task of the Human Computer Interaction (HCI) domain with ever-increasing research interest. Human activity recognition has been specially addressed by the advances in pattern recognition. k-Nearest Neighbors(kNN) is a non-parametric classifier from pattern recognition theory, that mimics human decision making by taking previous experiences into consideration for segregating unknown objects. A novel fuzzy-rough model, based on granular computing for improvisation of the kNN classifier is proposed herewith. In this model, feature-wise fuzzy memberships are generated to fuzzify the feature space of the nearest neighbors of the test object. These neighbors fuzzified feature space are then aggregated into granules, based on their class-belongingness. From these, lower and upper approximation granules are generated using rough set theory to classify the test object. It is shown experimentally that this model outperforms the traditional kNN by 16.43% and Fuzzy-kNN by 10.25%, in the human activity recognition domain. Another novelty is in the efficient use of the fuzzy similarity relations in class-dependent granulated feature space, and, fuzzy-rough lower/upper approximations in the hybridization of the kNN classifier.

Prothesis Movements Pattern Recognition of Surface Myoelectric Signals

2012 ◽  
Vol 7 (1) ◽  
pp. 332-335
Author(s):  
Cheng Gao ◽  
Jiaoying Huang ◽  
Guangying Yang
Keyword(s):  
Pattern Recognition ◽  
Myoelectric Signals
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