PMID- 37425001
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230718
IS  - 1662-4548 (Print)
IS  - 1662-453X (Electronic)
IS  - 1662-453X (Linking)
VI  - 17
DP  - 2023
TI  - Efficient strategies for finger movement classification using surface 
      electromyogram signals.
PG  - 1168112
LID - 10.3389/fnins.2023.1168112 [doi]
LID - 1168112
AB  - One of the famous research areas in biomedical engineering and pattern 
      recognition is finger movement classification. For hand and finger gesture 
      recognition, the most widely used signals are the surface electromyogram (sEMG) 
      signals. With the help of sEMG signals, four proposed techniques of finger 
      movement classification are presented in this work. The first technique proposed 
      is a dynamic graph construction and graph entropy-based classification of sEMG 
      signals. The second technique proposed encompasses the ideas of dimensionality 
      reduction utilizing local tangent space alignment (LTSA) and local linear 
      co-ordination (LLC) with evolutionary algorithms (EA), Bayesian belief networks 
      (BBN), extreme learning machines (ELM), and a hybrid model called EA-BBN-ELM was 
      developed for the classification of sEMG signals. The third technique proposed 
      utilizes the ideas of differential entropy (DE), higher-order fuzzy cognitive 
      maps (HFCM), empirical wavelet transformation (EWT), and another hybrid model 
      with DE-FCM-EWT and machine learning classifiers was developed for the 
      classification of sEMG signals. The fourth technique proposed uses the ideas of 
      local mean decomposition (LMD) and fuzzy C-means clustering along with a combined 
      kernel least squares support vector machine (LS-SVM) classifier. The best 
      classification accuracy results (of 98.5%) were obtained using the LMD-fuzzy 
      C-means clustering technique classified with a combined kernel LS-SVM model. The 
      second-best classification accuracy (of 98.21%) was obtained using the DE-FCM-EWT 
      hybrid model with SVM classifier. The third best classification accuracy (of 
      97.57%) was obtained using the LTSA-based EA-BBN-ELM model.
CI  - Copyright (c) 2023 Prabhakar and Won.
FAU - Prabhakar, Sunil Kumar
AU  - Prabhakar SK
AD  - Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, 
      Republic of Korea.
FAU - Won, Dong-Ok
AU  - Won DO
AD  - Department of Artificial Intelligence Convergence, Hallym University, Chuncheon, 
      Republic of Korea.
LA  - eng
PT  - Journal Article
DEP - 20230622
PL  - Switzerland
TA  - Front Neurosci
JT  - Frontiers in neuroscience
JID - 101478481
PMC - PMC10324970
OTO - NOTNLM
OT  - BBN
OT  - EA
OT  - ELM
OT  - EWT
OT  - FCM
OT  - LMD
OT  - LS-SVM
COIS- The author declare that the research was conducted in the absence of any 
      commercial or financial relationships that could be construed as a potential 
      conflict of interest.
EDAT- 2023/07/10 06:42
MHDA- 2023/07/10 06:43
PMCR- 2023/01/01
CRDT- 2023/07/10 04:50
PHST- 2023/03/21 00:00 [received]
PHST- 2023/05/11 00:00 [accepted]
PHST- 2023/07/10 06:43 [medline]
PHST- 2023/07/10 06:42 [pubmed]
PHST- 2023/07/10 04:50 [entrez]
PHST- 2023/01/01 00:00 [pmc-release]
AID - 10.3389/fnins.2023.1168112 [doi]
PST - epublish
SO  - Front Neurosci. 2023 Jun 22;17:1168112. doi: 10.3389/fnins.2023.1168112. 
      eCollection 2023.