@inproceedings{83e1e43d35f94495944afea60471fd3a,
title = "MAST: Myo Armband Sign-Language Translator for Human Hand Activity Classification",
abstract = "As Computer Science has grown into an encompassed field in various scientific areas, the need for developing a computer aided and artificially intelligent device has become more important especially in the medical field. Artificial Intelligence (AI) plays a vital role not only in accelerating and optimizing common tasks but also in performing tasks that humans are incapable of. This paper presents a Myo Armband Sign-Language Translator (MAST), which is a novel algorithm to translate a hand's gestures into medical sign language using a Myo armband sensor which collects muscles' electromyography signals and then to classify them using an enhanced version of a dynamic random forest. Our experimental results indicate that a systematic fine tuning of MAST parameters leads to an accuracy improvement of 13\% over the state-of-the-art scheme such as SCIKIT's random forest. Other comparison results show an improvement of over 20\% compared to a popular classification scheme such as Support Vector Machines (SVM) and a deep learning technique such as Convolutional Neural Network (CNN).",
keywords = "Convolutional Neural Network, Electromyography, Myo Armband, Random Forest, Signlanguage Translator, Support Vector Machine",
author = "Shakeel, \{Zuhaib Muhammad\} and Soonhyuk So and Patrick Lingga and Jeong, \{Jaehoon Paul\}",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 11th International Conference on Information and Communication Technology Convergence, ICTC 2020 ; Conference date: 21-10-2020 Through 23-10-2020",
year = "2020",
month = oct,
day = "21",
doi = "10.1109/ICTC49870.2020.9289153",
language = "English",
series = "International Conference on ICT Convergence",
publisher = "IEEE Computer Society",
pages = "494--499",
booktitle = "ICTC 2020 - 11th International Conference on ICT Convergence",
}