@inproceedings{4beda303b4044a94b3c381e9d5822870,
title = "Fingerspelling Classification for Robot Control",
abstract = "Improvements to human-robot interaction methods could increase the ease of use of robots in manufacturing environments. Many of these environments are noisy and therefore preclude the use of audio communication between humans or in human-robot interactions. Therefore, this paper proposes using a gesture based communication system for robot control. To that end, the VGG16 and VGG19 convolutional neural network (CNN) structures are used for gesture classification along with 3 datasets of American Sign Language (ASL) fingerspelling images. The model performance is evaluated, and modifications made to their parameters to improve performance, before applying them to robot control tasks. The results show that with parameter tuning, test accuracies of up to, 100% are achievable.",
author = "Kevin McCready and Sonya Coleman and Dermot Kerr and Nazmul Siddique and Emmett Kerr and Yiannis Aloimonos and Cornelia Ferm{\"u}ller",
year = "2024",
month = dec,
day = "12",
doi = "10.1109/indin58382.2024.10774477",
language = "English",
isbn = "979-8-3315-2748-8",
publisher = "IEEE",
pages = "1--6",
booktitle = "2024 IEEE 22nd International Conference on Industrial Informatics (INDIN)",
address = "United States",
}