scholarly journals Pattern Recognition and Characterization of Upper Limb Neuromuscular Dynamics during Driver-Vehicle Interactions

iScience ◽  
2020 ◽  
Vol 23 (9) ◽  
pp. 101541
Author(s):  
Yang Xing ◽  
Chen Lv ◽  
Yifan Zhao ◽  
Yahui Liu ◽  
Dongpu Cao ◽  
...  
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Neuromuscular Dynamics

INTELLIGENT UPPER-LIMB PROSTHETIC CONTROL (iULP) WITH NOVEL FEATURE EXTRACTION METHOD FOR PATTERN RECOGNITION USING EMG

2021 ◽  
pp. 2150043
Author(s):  
SIDHARTH PANCHOLI ◽  
AMIT M. JOSHI
Keyword(s):  
Pattern Recognition ◽  
Upper Limb ◽  
Classification Accuracy ◽  
Time Derivative ◽  
Machine Learning Algorithms ◽  
Prosthetic Control ◽  
Myoelectric Prosthesis ◽  
Feature Extraction Method ◽  
Similar Work ◽  
Emg Signal

EMG signal-based pattern recognition (EMG-PR) techniques have gained lots of focus to develop myoelectric prosthesis. The performance of the prosthesis control-based applications mainly depends on extraction of eminent features with minimum neural information loss. The machine learning algorithms have a significant role to play for the development of Intelligent upper-limb prosthetic control (iULP) using EMG signal. This paper proposes a new technique of extracting the features known as advanced time derivative moments (ATDM) for effective pattern recognition of amputees. Four heterogeneous datasets have been used for testing and validation of the proposed technique. Out of the four datasets, three datasets have been taken from the standard NinaPro database and the fourth dataset comprises data collected from three amputees. The efficiency of ATDM features is examined with the help of Davies–Bouldin (DB) index for separability, classification accuracy and computational complexity. Further, it has been compared with similar work and the results reveal that ATDM features have excellent classification accuracy of 98.32% with relatively lower time complexity. The lower values of DB criteria prove the good separation of features belonging to various classes. The results are carried out on 2.6[Formula: see text]GHz Intel core i7 processor with MATLAB 2015a platform.

Characterization of upper limb movement-related EEG dynamics through fractional integrated autoregressive modeling

2021 ◽  
Author(s):  
Laura Cavaliere ◽  
Vincenzo Catrambone ◽  
Matteo Bianchi ◽  
Ana Paula Rocha ◽  
Gaetano Valenza
Keyword(s):  
Upper Limb ◽  
Limb Movement ◽  
Autoregressive Modeling
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