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All Journal Journal of Information Technology and Computer Engineering Andalas Journal of Electrical and Electronic Engineering Technology
Minoru Sasaki
Unknown Affiliation
Computer Science & IT Control & Systems Engineering Education Electrical & Electronics Engineering Energy Engineering

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Hand Gesture to Control Virtual Keyboard using Neural Network Arrya Anandika; Muhammad Ilhamdi Rusydi; Pepi Putri Utami; Rizka Hadelina; Minoru Sasaki
JITCE (Journal of Information Technology and Computer Engineering) Vol 7 No 01 (2023): Journal of Information Technology and Computer Engineering
Publisher : Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/jitce.7.01.40-48.2023

Abstract

Disability is one of a person's physical and mental conditions that can inhibit normal daily activities. One of the disabilities that can be found in disability is speech without fingers. Persons with disabilities have obstacles in communicating with people around both verbally and in writing. Communication tools to help people with disabilities without finger fingers continue to be developed, one of them is by creating a virtual keyboard using a Leap Motion sensor. The hand gestures are captured using the Leap Motion sensor so that the direction of the hand gesture in the form of pitch, yaw, and roll is obtained. The direction values are grouped into normal, right, left, up, down, and rotating gestures to control the virtual keyboard. The amount of data used for gesture recognition in this study was 5400 data consisting of 3780 training data and 1620 test data. The results of data testing conducted using the Artificial Neural Network method obtained an accuracy value of 98.82%. This study also performed a virtual keyboard performance test directly by typing 20 types of characters conducted by 15 respondents three times. The average time needed by respondents in typing is 5.45 seconds per character.
Electroencephalography on Controlling Assistive Device: A Systematic Literature Review Salisa 'Asyarina Ramadhani; Muhammad Ilhamdi Rusydi; Andrivo Rusydi; Minoru Sasaki; Luxfy Roya Azmi
Andalas Journal of Electrical and Electronic Engineering Technology Vol. 4 No. 2 (2024): November 2024
Publisher : Electrical Engineering Dept, Engineering Faculty, Universitas Andalas

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25077/ajeeet.v4i2.42

Abstract

The present article delves into the practical applications of electroencephalography (EEG) in assistive devices. The article thoroughly summarizes the current state of the art, research trends, methods, and implementation. The focus is primarily on how EEG can operate various assistive devices effectively, incorporating artificial intelligence, machine learning, and several computing methods. The authors emphasize the importance of conducting more research and development in the field and offer valuable insights into its prospective directions. A complete search of the Scopus database from 2017 to 2022, including journals and proceedings such as IEEE Xplore, MDPI, Springer, Frontiers, and ScienceDirect, was conducted to ensure the findings are as comprehensive as possible. Conferring to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) methodology, 4397 metadata were transformed into 45. Based on the data synthesis, the following study execution must prioritize determining whether the observed signals are attributable to EEG artifacts or actual EEG signals. The derivation of input signals for controlling helpful devices can be enhanced by utilizing familiar activities, such as facial muscle movements, and employing various machine-learning techniques to ensure high levels of accuracy.
Co-Authors Andrivo Rusydi Arrya Anandika Azmi, Luxfy Roya Pepi Putri Utami Ramadhani, Salisa 'Asyarina Rizka Hadelina Rusydi, Muhammad Ilhamdi