A long short‐term memory based Schaeffer gesture recognition system

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Title: A long short‐term memory based Schaeffer gesture recognition system
Authors: Oprea, Sergiu | Garcia-Garcia, Alberto | Orts-Escolano, Sergio | Villena Martínez, Víctor | Castro-Vargas, John Alejandro
Research Group/s: Robótica y Visión Tridimensional (RoViT)
Center, Department or Service: Universidad de Alicante. Departamento de Tecnología Informática y Computación | Universidad de Alicante. Departamento de Ciencia de la Computación e Inteligencia Artificial
Keywords: Gesture recognition | Recurrent neural networks | Schaeffer language
Knowledge Area: Arquitectura y Tecnología de Computadores | Ciencia de la Computación e Inteligencia Artificial
Issue Date: Apr-2018
Publisher: John Wiley & Sons
Citation: Expert Systems. 2018, 35(2): e12247. doi:10.1111/exsy.12247
Abstract: In this work, a Schaeffer language recognition system is proposed in order to help autistic children overcome communicative disorders. Using Schaeffer language as a speech and language therapy, improves children communication skills and at the same time the understanding of language productions. Nevertheless, the teaching process of children in performing gestures properly is not straightforward. For this purpose, this system will teach children with autism disorder the correct way to communicate using gestures in combination with speech reproduction. The main purpose is to accelerate the learning process and increase children interest by using a technological approach. Several recurrent neural network‐based approaches have been tested, such as vanilla recurrent neural networks, long short‐term memory networks,and gated recurrent unit‐based models. In order to select the most suitable model, an extensive comparison has been conducted reporting a 93.13% classification success rate over a subset of 25 Schaeffer gestures by using an long short‐term memory‐based approach. Our dataset consists on pose‐based features such as angles and euclidean distances extracted from the raw skeletal data provided by a Kinect v2 sensor.
Sponsor: This work has been funded by the Spanish Government TIN2016-76515-R grant for the COMBAHO project, supported with Feder funds. This work has also been supported by a Spanish national grant for PhD studies FPU15/04516 and the grant "Ayudas para Estudios de Máster e Iniciación a la Investigación" from the University of Alicante. Experiments were made possible by a generous hardware donation from NVIDIA (Tesla K40).
URI: http://hdl.handle.net/10045/75096
ISSN: 0266-4720 (Print) | 1468-0394 (Online)
DOI: 10.1111/exsy.12247
Language: eng
Type: info:eu-repo/semantics/article
Rights: © 2017 John Wiley & Sons, Ltd.
Peer Review: si
Publisher version: https://doi.org/10.1111/exsy.12247
Appears in Collections:INV - RoViT - Artículos de Revistas

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