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https://ceur-ws.org/Vol-2564/Preface-CRoNe2019.pdf
Preface
CRoNe, the Congress on Robotics and Neuroscience, is an encounter
that acts not only as a bridge but also as a fruitful land for collabora-
tion and discussion concerning recent advances in the frontiers of ar-
tificial intelligence, robotics and neuroscience, fostering the exchange
of ideas among different, and often fairly separated, scientific fields.
The congress, part of the Latin American Robotics Week1 , organized 1
Further details in
http://www.roboticsweekla.com.
by Innovación y Robótica Estudiantil UTFSM 2 , is a meeting point for 2
a multidisciplinary group of both,
people from engineering, human and biological sciences promoting undergraduate and graduate students,
the development and understanding of complex intelligent systems. focused on R&D with emphasis on
robotics. More information in
At its 5th version, the Congress on Robotics and Neuroscience was
http://innovacionyrobotica.usm.cl.
focused on the employment of robotics in education.
Following the convergence of areas inside the Congress, the works
collected herein address open problems such as the development
of robotic platforms, novel medical devices for sample acquisition,
AI-based approaches for clinical evaluations, and feature extraction
and analysis from EEG data. There were twelve submitted works,
two works were rejected, and three works were retired by multiple
reasons argued by the authors.
This number of the Congress Proceedings starts with robotics re-
lated works, the article from Robles et al. (2020) shows current state
of development and improvements on the team of robots capable of
playing football autonomously, in the context of the Robot Soccer
World Cup, specifically on the Small Size League. Major improve-
ments from this team since their last World Cup participation in 2018
are focused on electrical and mechanical areas.
The article from Coiro et al. (2020) presents an open-source robot
designed for estimulating and fostering task scheduling for future
computational thinking development abilities on preschool children,
without needing resources such as physical equipment or computa-
tional tools for manipulating its desired behavior.
Within medical purposes, the work from Pizarro et al. (2020) in-
troduces a physical device including recent techniques for improving
sampling acquisition from lungs. Pizarro et al. (2020), based on recent
proposals, introduces a device which condense samples of exhaled
6
air, a known non-invasive technique, separating alveolar air (con-
taining biomarkers) with the air from the respiratory way (lacking
biomarkers), allowing to acquire cleaner samples and reacher sam-
ples. With similar purposes, clinical cataloging, Salinas et al. (2020)
proposes a machine learning architecture for identifying metacar-
pophalangeal deviations, related with several pathologies affecting
the functionality of the hand, such as rheumatoid arthritis or frac-
tures. In this context, Salinas et al. (2020) describes a novel machine
learning architecture including computational vision and deep learn-
ing, which analyses pictures of hands extracting relevant features for
clinical assessments.
In other medical application, Castro et al. (2020) presents the use
of image processing and convolutional neural networks to detect in-
tracranial hemorrhage in computed tomography images. Castro et al.
(2020) used two network architectures, the CNN4 proposed by the
authors and the popular VGG16 architecture, performing outstand-
ing performance over traditional methods of binary classification of
hemorrhage or non-hemorrhage. The results in Castro et al. (2020)
also show the influence of the selection of the training and test sets.
Montilla-Trochez et al. (2020) evaluates three neural network ar-
chitectures for the classification of cognitive tasks from EEG activity
measurements. The purpose of the classification is for monitoring
subjects. The tested architectures by Montilla-Trochez et al. (2020)
are a feed-forward neural network, the widely used VGG16 convolu-
tional neural network architecture and a novel hybrid convolutional
neural network proposed by the authors. The last architecture shows
better performance in the classification of the cognitive tasks: count-
ing, reading and rest. Also using EEG data, the work from Torres
et al. (2020b) detects burst of activity called sleep spindles. The work
present a detection algorithm using a single feature from the signal
and the evaluated feature is inspired from its previous usage in the
detection of relevant events of seismic registers. Torres et al. (2020b)
also compares the performance of the detection algorithm using other
signal features.
7
References
Castro JS, Chabert S, Saavedra C, Salas R. Convolutional Neu-
ral Networks for Detection Intracranial Hemorrhage in CT Im-
ages. In: Torres FA, Nettle CJ, Solis MA, editors. Proceedings
of the 5th Congress on Robotics and Neuroscience, 2019 No. 2 in
CEUR Workshop Proceedings, Aachen; 2020. p. 37–43. http:
//ceur-ws.org/Vol-##/#shortarticle_5-CRoNe2019.
Coiro F, Solis MA, Nettle CJ, Chila A. Pre-robot: an open-source ed-
ucational robotics platform for preschoolers. In: Torres FA, Nettle
CJ, Solis MA, editors. Proceedings of the 5th Congress on Robotics and
Neuroscience, 2019 No. 2 in CEUR Workshop Proceedings, Aachen;
2020. p. 18–23. http://ceur-ws.org/Vol-##/#shortarticle_
2-CRoNe2019.
Montilla-Trochez D, Salas R, Bertin A, Griskova-Bulanova I, Lis-
boa P, Saavedra C. Convolutional Neural Network for Cog-
nitive Task Prediction from EEG’s Auditory Steady State Re-
sponses. In: Torres FA, Nettle CJ, Solis MA, editors. Pro-
ceedings of the 5th Congress on Robotics and Neuroscience, 2019
No. 2 in CEUR Workshop Proceedings, Aachen; 2020. p. 44–50.
http://ceur-ws.org/Vol-##/#shortarticle_6-CRoNe2019.
Pizarro CF, Nettle CJ, Araneda OF, Tuesta M. Towards an Au-
tonomous System with Exhaled Breath Separation for Cleaner
Condensed Air Samples. In: Torres FA, Nettle CJ, Solis MA, edi-
tors. Proceedings of the 5th Congress on Robotics and Neuroscience, 2019
No. 2 in CEUR Workshop Proceedings, Aachen; 2020. p. 24–30.
http://ceur-ws.org/Vol-##/#shortarticle_3-CRoNe2019.
Robles PR, Aubel M, na NHP, Alvarez J. Mechanical, hardware
and firmware considerations for a Robocup SSL robot. In: Tor-
res FA, Nettle CJ, Solis MA, editors. Proceedings of the 5th Congress
on Robotics and Neuroscience, 2019 No. 2 in CEUR Workshop Pro-
ceedings, Aachen; 2020. p. 11–17. http://ceur-ws.org/Vol-##/
#shortarticle_1-CRoNe2019.
Salinas M, Cancino A, Zazueta A, Salas R. Computational vision
and machine learning to evaluate Metacarpophalangeal and In-
terphalangeal deviation in fingers for clinical purpose. In: Torres
FA, Nettle CJ, Solis MA, editors. Proceedings of the 5th Congress
on Robotics and Neuroscience, 2019 No. 2 in CEUR Workshop Pro-
ceedings, Aachen; 2020. p. 31–36. http://ceur-ws.org/Vol-##/
#shortarticle_4-CRoNe2019.
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Torres FA, Nettle CJ, Solis MA, editors. Proceedings of the 5th
Congress on Robotics and Neuroscience (CRoNe2019) No. 2 in
CEUR Workshop Proceedings, Aachen; 2020, http://ceur-ws.org/
Vol-##/.
Torres FA, Orio P, Escobar MJ. Online sleep spindles detection with
short and long time average ratio. In: Torres FA, Nettle CJ, Solis
MA, editors. Proceedings of the 5th Congress on Robotics and Neu-
roscience, 2019 No. 2 in CEUR Workshop Proceedings, Aachen;
2020. p. 51–57. http://ceur-ws.org/Vol-##/#shortarticle_
7-CRoNe2019.
9