Sign Language Recognition Based on EMG Signals through a Hibrid Intelligent System

Abstract

Non-verbal communication is an important part of everyday interactions and human-computer interaction. Vision techniques and instrumented gloves for sign language recognition are commonly used, but these are often expensive and considered invasive to the user. This research proposes the recognition of words from the American Sign Language (ASL) using the SCEPTRE database acquired by two Myoelectrical bracelets. Computational intelligence techniques were used to optimize the number of attributes using Principal Component Analysis (PCA) and a classifier based on Neural Networks (NN). The results suggest that it is possible to reduce the attributes using PCA without significantly losing the quality in classification. This allows faster processing, a convenient feature for classifiers for real-time SL recognition.

Sign Language Recognition Using the Electromyographic Signal: A Systematic Literature Review

“…Findings from the results obtained in the different studies examined in this article indicate that the use of the Myo armband can yield accurate results.. In the study published by the authors of [41], the researchers utilized two Myo armbands and assessed the performance of their approach by experimenting with different combinations of characteristics. Remarkably, they achieved 100% accuracy using surface electromyography (sEMG) in conjunction with accelerometer, gyroscope, and magnetometer sensors.…”

“…Regarding ANN models, various types have been utilized, including the emotional artificial neural network (E-ANN), multilayer perceptron, and backpropagation neural network. The research conducted by the authors of [41] reports the highest accuracy of 100% using a multilayer perceptron neural network classifier with data from the Myo armbands. CNNs have been identified as the third most popular classifier for sign language gesture classification using EMG signals.…”

“…In the study published by the authors of [41], the researchers employed the principal component analysis (PCA) filter, along with a multilayer perceptron neural network (MPN), for the classification of surface EMG signals associated with nine words from American sign language (ASL). The experiments were conducted using the SCEPTRE database, which consisted of data captured from two Myo armbands placed on the forearms of two hands belonging to three subjects.…”