@misc{10481/71362,
year = {2021},
url = {http://hdl.handle.net/10481/71362},
abstract = {The interfaces between users and systems are evolving into a more natural communication,
including user gestures as part of the interaction, where air-writing is an emerging application for
this purpose. The aim of this work is to propose a new air-writing system based on only one array
of ultrasonic transceivers. This track will be obtained based on the pairwise distance of the hand
marker with each transceiver. After acquiring the track, different deep learning algorithms, such as
long short-term memory (LSTM), convolutional neural networks (CNN), convolutional autoencoder
(ConvAutoencoder), and convolutional LSTM have been evaluated for character recognition. It has
been shown how these algorithms provide high accuracy, where the best result is extracted from the
ConvLSTM, with 99.51% accuracy and 71.01 milliseconds of latency. Real data were used in this work
to evaluate the proposed system in a real scenario to demonstrate its high performance regarding
data acquisition and classification.},
organization = {Project “SEMULIN” (German
project number 19A20012D)},
organization = {German Federal Ministry for Economic Affairs and
Energy (BMWi)},
organization = {Project P20-00265 funded by the “Junta de Andalucia” of Spain},
publisher = {MDPI},
keywords = {Ultrasound},
keywords = {Air-writing},
keywords = {Gesture recognition},
keywords = {Deep learning},
title = {Air-Writing Character Recognition with Ultrasonic Transceivers},
doi = {10.3390/s21206700},
author = {Sáez Mingorance, Borja and Méndez Gómez, Javier and Mauro, Gianfranco and Castillo Morales, María Encarnación and Pegalajar Cuéllar, Manuel and Morales Santos, Diego Pedro},
}