International Journal of Electrical, Electronics and Data Communication (IJEEDC)
eISSN:2320-2084 , pISSN:2321-2950
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Volume-7,Issue-12  ( Dec, 2019 )
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Statistics report
Feb. 2020
Submitted Papers : 80
Accepted Papers : 10
Rejected Papers : 70
Acc. Perc : 12%
Issue Published : 84
Paper Published : 1397
No. of Authors : 3784
  Journal Paper

Paper Title
Classification Electromyography Sensor for Hand Gesture Recognition with Neural Network using Correlation-based Feature Selection

In this paper, we will present a research about hand gesture pattern recognition using electromyography sensors. Data collection process will take 100 samples in time domain data for each gesture, then transformed into frequency domain data and feature extraction to get 18 features. After getting the features, we will perform feature selection with Correlationbased Feature Selection (CFS) to reduce the total features and improve the accuracy of the classification algorithm used, which is the neural network. The analysis process will show the accuracy value of gestures used. Index Terms- Electromyography Sensor, Feature Selection, Neural Network, Pattern Recognition.

Author - Farouq Faisal Anam, Handri Santoso

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| Published on 2018-09-03
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