Zobrazeno 1 - 10
of 63
pro vyhledávání: '"Francisco Gomez-Donoso"'
A kinematic, imaging and electromyography dataset for human muscular manipulability index prediction
Autor:
Óscar G. Hernández, Jose M. Lopez-Castellanos, Carlos A. Jara, Gabriel J. Garcia, Andres Ubeda, Vicente Morell-Gimenez, Francisco Gomez-Donoso
Publikováno v:
Scientific Data, Vol 10, Iss 1, Pp 1-11 (2023)
Abstract Human Muscular Manipulability is a metric that measures the comfort of an specific pose and it can be used for a variety of applications related to healthcare. For this reason, we introduce KIMHu: a Kinematic, Imaging and electroMyography da
Externí odkaz:
https://doaj.org/article/acb559dd52a54363a8cb0c82d2ba53f1
Autor:
Francisco Gomez-Donoso, Felix Escalona, Sergio Orts-Escolano, Alberto Garcia-Garcia, Jose Garcia-Rodriguez, Miguel Cazorla
Publikováno v:
IEEE Access, Vol 10, Pp 15378-15392 (2022)
Convolutional Neural Networks (CNNs) have become the default paradigm for addressing classification problems, especially, but not only, in image recognition. This is mainly due to their high success rate. Although a number of approaches currently app
Externí odkaz:
https://doaj.org/article/67f6038836ba414a9a35ba1271b03986
Publikováno v:
Data in Brief, Vol 42, Iss , Pp 108172- (2022)
In the past years, several works on urban object detection from the point of view of a person have been made. These works are intended to provide an enhanced understanding of the environment for blind and visually challenged people. The mentioned app
Externí odkaz:
https://doaj.org/article/c4b40844ecae48ed87d5432a214d1398
Publikováno v:
IEEE Access, Vol 8, Pp 161958-161968 (2020)
Currently, state-of-the-art methods for 3D object recognition rely in a deep learning-pipeline. Nonetheless, these methods require a large amount of data that is not easy to obtain. In addition to that, the majority of them exploit features of the da
Externí odkaz:
https://doaj.org/article/603869f5b5b24aee85090cdea03f2180
Publikováno v:
IEEE Access, Vol 7, Pp 185076-185085 (2019)
As it is well known, some versions of the Pepper robot provide poor depth perception due to the lenses it has in front of the tridimensional sensor. In this paper, we present a method to improving that faulty 3D perception. Our proposal is based on a
Externí odkaz:
https://doaj.org/article/0d0a59d1ceb1451b8e65f7758270e885
Publikováno v:
Applied Sciences, Vol 11, Iss 7, p 2943 (2021)
In this work, we introduce HaReS, a hand rehabilitation system. Our proposal integrates a series of exercises, jointly developed with a foundation for those with motor and cognitive injuries, that are aimed at improving the skills of patients and the
Externí odkaz:
https://doaj.org/article/b35bc72239234442bb1ffee9766253b2
Publikováno v:
Applied Sciences, Vol 10, Iss 10, p 3409 (2020)
Deep learning-based methods have proven to be the best performers when it comes to object recognition cues both in images and tridimensional data. Nonetheless, when it comes to 3D object recognition, the authors tend to convert the 3D data to images
Externí odkaz:
https://doaj.org/article/346923d21dd844e58f1cef3ea22ace23
Publikováno v:
Sensors, Vol 19, Iss 2, p 371 (2019)
Every year, a significant number of people lose a body part in an accident, through sickness or in high-risk manual jobs. Several studies and research works have tried to reduce the constraints and risks in their lives through the use of technology.
Externí odkaz:
https://doaj.org/article/dbe8c2610ecf451b8ccd5180682c4f75
Autor:
Álvaro Belmonte-Baeza, Francisco Gomez-Donoso, Felix Escalona, Rosabel Roig-Vila, Rosabel Martinez-Roig, Miguel Cazorla
Publikováno v:
INTED2023 Proceedings.
Autor:
Francisco Gomez-Donoso, Felix Escalona, Bessie Dominguez-Dager, Monica Pina-Navarro, Francisco Morillas-Espejo, Miguel Cazorla
Publikováno v:
INTED2023 Proceedings.