Finger language recognition based on ensemble artificial neural network learning using armband EMG sensors

Seongjung Kim, Jongman Kim, Soonjae Ahn, Youngho Kim

Research output: Contribution to journalConference articlepeer-review

13 Scopus citations

Abstract

BACKGROUND: Deaf people use sign or finger languages for communication, but these methods of communication are very specialized. For this reason, the deaf can suffer from social inequalities and financial losses due to their communication restrictions. OBJECTIVE: In this study, we developed a finger language recognition algorithm based on an ensemble artificial neural network (E-ANN) using an armband system with 8-channel electromyography (EMG) sensors. METHODS: The developed algorithm was composed of signal acquisition, filtering, segmentation, feature extraction and an E-ANN based classifier that was evaluated with the Korean finger language (14 consonants, 17 vowels and 7 numbers) in 17 subjects. E-ANN was categorized according to the number of classifiers (1 to 10) and size of training data (50 to 1500). The accuracy of the E-ANN-based classifier was obtained by 5-fold cross validation and compared with an artificial neural network (ANN)-based classifier. RESULTS AND CONCLUSIONS: As the number of classifiers (1 to 8) and size of training data (50 to 300) increased, the average accuracy of the E-ANN-based classifier increased and the standard deviation decreased. The optimal E-ANN was composed with eight classifiers and 300 size of training data, and the accuracy of the E-ANN was significantly higher than that of the general ANN.

Original languageEnglish
Pages (from-to)S249-S258
JournalTechnology and Health Care
Volume26
Issue numberS1
DOIs
StatePublished - May 29 2018
Externally publishedYes
Event6th International Conference on Biomedical Engineering and Biotechnology, iCBEB 2017 - Guangzhou, China
Duration: Oct 17 2017Oct 20 2017

Keywords

  • Armband sensor
  • Finger language recognition
  • Surface electromyography (EMG)

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