Prof. Miguel Cazorla
Intitute for computing research, Universidad de Alicante, Alicante, Valencia, Spain
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Academic Career Length
30Years (1997-Present)
(Calculated once Scilit publication data is synced: current year − year of first publication + 1)
Representative Publications
View All Publications3 November 2016
Conference Paper
AGAlberto Garcia-Garcia
Alberto Garcia-Garcia
Francisco Gomez-DonosoJGJ. Garcia-Rodriguez
J. Garcia-Rodriguez
SOS. Orts-Escolano
S. Orts-Escolano
Miguel CazorlaJAJ. Azorin-Lopez
J. Azorin-Lopez
During the last few years, Convolutional Neural Networks are slowly but surely becoming the default method solve many computer vision related problems. This is mainly due to the continuous success that they have achieved when applied to certain tasks such as image, speech, or object recognition.
Journal Article
Ester Martinez-Martin
Felix Escalona MoncholiMiguel CazorlaOver one billion people in the world suffer from some form of disability. Nevertheless, according to the World Health Organization, people with disabilities are particularly vulnerable to deficiencies in services, such as health care, rehabilitation, support, and assistance. In this sense, recent technological developments can mitigate these deficiencies, offering less-expensive assistive systems to meet users’ needs. This paper reviews and summarizes the research efforts toward the development of these kinds of systems, focusing on two social groups: older adults and children with autism.
Journal Article
In recent years the advances in Artificial Intelligence (AI) have been seen to play an important role in human well-being, in particular enabling novel forms of human-computer interaction for people with a disability. In this paper, we propose a sEMG-controlled 3D game that leverages a deep learning-based architecture for real-time gesture recognition. The 3D game experience developed in the study is focused on rehabilitation exercises, allowing individuals with certain disabilities to use low-cost sEMG sensors to control the game experience. For this purpose, we acquired a novel dataset of seven gestures using the Myo armband device, which we utilized to train the proposed deep learning model. The signals captured were used as an input of a Conv-GRU architecture to classify the gestures. Further, we ran a live system with the participation of different individuals and analyzed the neural network’s classification for hand gestures. Finally, we also evaluated our system, testing it for 20 rounds with new participants and analyzed its results in a user study.
24 February 2018 in Virtual Reality
Journal Article
Edmanuel CruzSOSergio Orts-Escolano
Sergio Orts-Escolano
Francisco Gomez-DonosoCMCarlos Rizo Maestre
Carlos Rizo MaestreJOJose Carlos Rangel Ortiz
Jose Carlos Rangel OrtizMiguel Cazorla +1
1 January 2014
Book Chapter
BCBarbara Caputo
Barbara CaputoHMHenning Müller
Henning Müller
Jesus Martínez-GómezMVMauricio Villegas
Mauricio VillegasBABurak Acar
Burak Acar
Vicente Morell Giménez +6
1 January 2019 in Image and Vision Computing
Journal Article
Francisco Gomez-DonosoSOSergio Orts-Escolano
Sergio Orts-Escolano
Miguel CazorlaAbout Me
Prof. Miguel Cazorla is a Computer Engineer from the University of Alicante (1995) and holds a PhD in Computer Engineering from the same University (2000). In 1995, he started as an assistant professor at the University of Alicante and since 2017 he has been a full professor. He is an IEEE senior member. He has published more than 70 papers indexed in JCR (with more than 20 in Q1) and more than 100 publications in national and international conferences. He has supervised 19 PhD theses and is a principal investigator in several national projects (CICYT, Challenges), as well as having completed multiple transfer contracts with the industry. He is a member of different program committees of national and international conferences. His research line has always focused on computer vision. From the beginning he applied these skills to try to solve robotics tasks. In recent years, he has diversified his lines to apply deep learning techniques to different areas (medical image, object recognition, depth estimation, identification of traffic objects, etc.). All his research in recent years has focused on social robotics, that is, applying these techniques to help dependent persons.