@conference {230, title = {Aplicaciones de Internet de las Cosas SIPIA6-Red de Sensores Inal{\'a}mbricos con IPv6}, booktitle = {XV Workshop de Investigadores en Ciencias de la Computaci{\'o}n}, year = {2013}, author = {Mercado, Gustavo and Borgo, Roberto and Gonzalez Antivilo, Francisco and Taffernaberry, Juan Carlos and Diedrichs, Ana and Aguirre, Mat{\'\i}as and Robles, Mar{\'\i}a In{\'e}s and Grunwaldt, Guillermo and Tabacchi, Germ{\'a}n and Tromer, Sebasti{\'a}n and others} } @conference {168, title = {Arm muscular effort estimation from images using Computer Vision and Machine Learning}, booktitle = {1st International Conference on Ambient Intelligence for Health}, year = {2015}, month = {Dec}, address = {Puerto Varas, Chile}, abstract = {

A problem of great interest in disciplines like occupational\ medicine, ergonomics, and sports, is the measurement of biomechanical\ variables involved in human movement and balance such as internal\ muscle forces and joint torques. This problem \ is solved by a two-step process: data capturing using impractical, intrusive and expensive devices\ that is then used as input in \ complex models for obtaining the biomechanical variables of interest. In this work we present a first step towards\ capturing input \ data through a more automated, non-intrusive and economic process, specifically weight held by an arm subject to isometric\ contraction as a measure of muscular effort. We do so, by processing\ RGB images of the arm with computer vision (Local Binary Patterns\ and Color Histograms) and estimating the effort with machine learning\ algorithms (SVM and Random \ Forests). In the best case we obtained an\ FMeasure= 70.68\%, an Accuracy= 71.66\% and a mean absolute error in\ the predicted weights of 554.16 grs (over 3 possible levels of effort). Considering the difficulty of the problem , it is enlightening to achieve over\ random results indicating that, despite the simplicity of the approach,\ it is possible to extract meaningful information for the \ predictive task.\ Moreover, the simplicity of the approach suggests many lines of further\ improvements: on the image capturing \ side with other kind of images;\ on the feature extraction side with more sophisticated algorithms and\ features; and on the\ knowledge extraction side with more sophisticated\ learning algorithms.\ 

}, keywords = {color histograms, LBP, muscle arm effort, random forests, SVM}, author = {Abraham, Leandro and Bromberg, Facundo and Forradellas, Raymundo} } @conference {183, title = {Low-power wireless sensor network for frost monitoring in agriculture research}, booktitle = {Biennial Congress of Argentina (ARGENCON), 2014 IEEE}, year = {2014}, publisher = {IEEE}, organization = {IEEE}, address = {Bariloche}, abstract = {

This work presents the development of a wireless sensor network (WSN), based on IEEE-802.15.4, in order to be used for frost characterization in precision agriculture by measuring temperature. Our key objective is to reduce the power consumption of the network to the minimum, allowing several measurement points per node and the remote monitoring of the sensors behaviour. For the communication interface between a WSN node and the sensors, we have developed a serial protocol inspired in SDI-12. Preliminary results show a low-cost and low-power WSN. The user can access and use the data for agronomic research.

}, isbn = {978-1-4799-4270-1}, doi = {10.1109/ARGENCON.2014.6868546}, url = {http://dx.doi.org/10.1109/ARGENCON.2014.6868546}, author = {Diedrichs, Ana Laura and Tabacchi, Germ{\'a}n and Grunwaldt, Guillermo and Pecchia, Mat{\'\i}as and Mercado, Gustavo and Gonzalez Antivilo, Francisco} } @conference {232, title = {RED SIPIA: Red de Sensores Inal{\'a}mbricos para Investigaci{\'o}n Agron{\'o}mica}, booktitle = {XIII Workshop de Investigadores en Ciencias de la Computaci{\'o}n}, year = {2011}, author = {Mercado, Gustavo and Borgo, Roberto and Gonzalez Antivilo, Francisco and Ortiz Uriburu, Gisela and Diedrichs, Ana and Farreras, Pablo and Aguirre, Mat{\'\i}as and Battaglia, Fernando and Tabacchi, Germ{\'a}n and Tromer, Sebasti{\'a}n} } @conference {115, title = {Sistema SCADA para monitoreo y control de una central hidroel{\'e}ctrica}, booktitle = {CNEISI}, year = {2011}, month = {09/2011}, address = {C{\'o}rdoba, Argentina}, author = {Abraham, L and Botta, A and Fratte, D and Oca{\~n}a, P} } @conference {80, title = {Strategies for piecing-together Local-to-Global Markov network learning algorithms}, booktitle = {ASAI 2011}, year = {2011}, month = {08/2011}, publisher = {Proceedings of ASAI 2011, Argentinean Symposioum of Artificial Intelligence}, organization = {Proceedings of ASAI 2011, Argentinean Symposioum of Artificial Intelligence}, address = {C{\'o}rdoba, Argentina}, author = {Schl{\"u}ter, F and Bromberg, F and Abraham, L} } @proceedings {177, title = {Estimaci{\'o}n de carga muscular mediante im{\'a}genes}, journal = {Argentinean Symposium of Artificial Intelligence (ASAI) - Jornadas Argentinas de Inform{\'a}tica}, year = {2014}, pages = {91--98}, publisher = {Sociedad Argentina de Inform{\'a}tica}, address = {Buenos Aires, Argentina}, abstract = {

Un problema de gran inter{\'e}s en disciplinas como la ocupacional, ergon{\'o}mica y deportiva, es la medici{\'o}n de variables biomec{\'a}nicas involucradas en\ el movimiento humano (como las fuerzas musculares internas y torque de articulaciones).\ Actualmente este problema se resuelve en un proceso de dos pasos.\ Primero capturando datos con dispositivos\ poco pr{\'a}cticos, intrusivos y costosos.\ Luego estos datos son usados como entrada en modelos complejos para obtener\ las variables biomec{\'a}nicas como salida. El presente trabajo representa una alternativa automatizada, no intrusiva y econ{\'o}mica al primer paso, proponiendo la\ captura de estos datos a trav{\'e}s de im{\'a}genes. En trabajos futuros la idea es automatizar todo el proceso de c{\'a}lculo de esas variables. En este trabajo elegimos un\ caso particular de medici{\'o}n de variables biomec{\'a}nicas: el problema de estimar\ el nivel discreto de carga muscular que est{\'a}n ejerciendo los m{\'u}sculos de un brazo. Para \ estimar a partir de im{\'a}genes est{\'a}ticas del brazo ejerciendo la fuerza de\ sostener la carga, el nivel de la misma, realizamos un\ proceso de clasificaci{\'o}n.\ Nuestro enfoque utiliza Support Vector Machines para clasificaci{\'o}n, combinada\ con una etapa de pre-procesamiento que extrae caracter{\'\i}sticas visuales utilizando variadas t{\'e}cnicas (Bag of Keypoints, Local Binary Patterns, Histogramas de\ Color, Momentos de Contornos) En los mejores casos (Local Binary Patterns y\ Momentos de Contornos) obtenemos medidas de performance en la clasificaci{\'o}n\ (Precision, Recall, F-Measure y Accuracy) superiores al 90 \%.

}, url = {http://43jaiio.sadio.org.ar/proceedings/ASAI/search.html}, author = {Abraham, Leandro and Bromberg, Facundo and Forradellas, Raymundo} } @proceedings {257, title = {Multi species weed detection with Retinanet one-step network in a maize field}, journal = {Precision agriculture{\textquoteright}21}, year = {2021}, month = {06/2021}, pages = {2223{\textendash}2228}, publisher = {Wageningen Academic Publishers}, address = {Budapest, Hungary}, abstract = {

Weed density and composition are not uniform throughout the field, nevertheless, the conventional approach is to carry out a uniform application. Object Detection Networks have already arrived in agricultural applications that can be used for weed management. The current study developed a detection and classification of weeds system in a one-step procedure using RetinaNet Object Detection Network. The procedure was based on identifying Solanum nigrum L., Cyperus rotundus L. and Echinochloa crus-galli L. and two growth stages both for a broadleaf species (S. nigrum) as well as narrow-leaved species (C. rotundus) in a maize field. The predictions were evaluated by mAP metric. The result obtained was 0.88 with values between 0.98 and 0.75 depending on the class.

}, keywords = {deep learning, object detection networks, RetinaNet, site-specific weed management}, isbn = {978-90-8686-916-9}, issn = {978-90-8686-363-1}, doi = {https://doi.org/10.3920/978-90-8686-916-9}, url = {https://www.wageningenacademic.com/doi/abs/10.3920/978-90-8686-916-9}, author = {Correa, JM L{\'o}pez and Todeschini, M and P{\'e}rez, DS and Karouta, J and Bromberg, F and Ribeiro, A and And{\'u}jar, D} } @article {219, title = {Ensemble of shape functions and support vector machines for the estimation of discrete arm muscle activation from external biceps 3D point clouds}, journal = {Computers in Biology and Medicine}, volume = {95}, year = {2018}, month = {04/2018}, pages = {129-139}, chapter = {129}, abstract = {

Background: Muscle activation level is currently being captured using im- practical and expensive devices which make their use in telemedicine settings extremely difficult. To address this issue, a prototype is presented of a non- invasive, easy-to-install system for the estimation of a discrete level of muscle activation of the biceps muscle from 3D point clouds captured with RGB-D cameras.

Methods: A methodology is proposed that uses the ensemble of shape functions point cloud descriptor for the geometric characterization of 3D point clouds, together with support vector machines to learn a classifier that, based on this geometric characterization for some points of view of the biceps, provides a model for the estimation of muscle activation for all neighboring points of view. This results in a classifier that is robust to small perturba- tions in the point of view of the capturing device, greatly simplifying the installation process for end-users.

Results: In the discrimination of five levels of effort with values up to the maximum voluntary contraction (MVC) of the biceps muscle (3800 g),\ \ the best variant of the proposed methodology achieved mean absolute errors of about 9.21 \% MVC {\textemdash} an acceptable performance for telemedicine settings where the electric measurement of muscle activation is impractical.

Conclusions: The results prove that the correlations between the exter- nal geometry of the arm and biceps muscle activation are strong enough to consider computer vision and supervised learning an alternative with great potential for practical applications in tele-physiotherapy.\ 

}, keywords = {3d point clouds, biceps activation estimation, biomechanics, ensemble of shape functions, support vector machines, Tele-physiotherapy}, issn = {0010-4825}, doi = {https://doi.org/10.1016/j.compbiomed.2018.02.011}, url = {https://www.sciencedirect.com/science/article/pii/S0010482518300416}, author = {Leandro Abraham and Facundo Bromberg and Raymundo Forradellas} } @mastersthesis {243, title = {Visi{\'o}n computacional y aprendizaje de m{\'a}quinas aplicado a la estimaci{\'o}n de activaci{\'o}n muscular del b{\'\i}ceps braquial}, volume = {Computer Science Ph.D}, year = {2018}, month = {2018}, pages = {63}, abstract = {

Este trabajo de tesis complementa los requisitos de la carrera de Doctorado en Ciencias de la Computaci{\'o}n de la Facultad de Ciencias Exactas de la Universidad Nacional del Centro de la Provincia de Buenos Aires. El problema general en el que se encuentra enmarcada la presente tesis es el de estimar el nivel de activaci{\'o}n muscular ejecutado por el b{\'\i}ceps de un brazo humano al ejercer diferentes niveles de esfuerzos discretos, por medio de un sistema de visi{\'o}n artificial alimentado exclusivamente con im{\'a}genes externas del brazo. El sistema mencionado tiene embebido un componente de aprendizaje de m{\'a}quinas supervisado que le permite predecir la medida deseada, a partir de ejemplos conocidos de la activaci{\'o}n real. En un primer paso del m{\'e}todo, se aplican t{\'e}cnicas de visi{\'o}n computacional para la descripci{\'o}n de im{\'a}genes de manera de generar vectores de caracter{\'\i}sticas de las mismas. Luego, estos vectores son usados como entrada en un proceso de aprendizaje de m{\'a}quinas supervisado por la fuerza externa aplicada, un valor altamente correlacionado al nivel de activaci{\'o}n real que el b{\'\i}ceps est{\'a} ejerciendo. Se evaluaron t{\'e}cnicas maduras y com{\'u}nmente usadas en estas {\'a}reas para las cuales existen implementaciones computacionales. Como respuesta a la pregunta de {\textquestiondown}por qu{\'e} es importante resolver el problema planteado?, se presenta una introducci{\'o}n al mismo y a las {\'a}reas disciplinares de aplicaci{\'o}n. Por otro lado, pretendiendo responder {\textquestiondown}qu{\'e} soluciones existen para el problema planteado?, se presenta una revisi{\'o}n bibliogr{\'a}fica de los trabajos relacionados al problema y el m{\'e}todo propuesto para resolverlo. Para responder a la pregunta de {\textquestiondown}qu{\'e} debo aprender para resolver el problema mediante el m{\'e}todo propuesto?, se presentan las tecnolog{\'\i}as de soporte evaluadas en esta tesis para resolver el problema usando el m{\'e}todo propuesto, puntualmente en las {\'a}reas de conocimiento de descripci{\'o}n de im{\'a}genes y aprendizaje de m{\'a}quinas supervisado. Finalmente para responder a la pregunta de {\textquestiondown}c{\'o}mo aplicar estas tecnolog{\'\i}as para resolver el problema?, se presentan distintas instancias del m{\'e}todo que pretenden resolver el problema de estimaci{\'o}n planteado. Una de las instancias del m{\'e}todo presentadas, logra obtener una precisi{\'o}n aceptable en la tarea de medici{\'o}n de la activaci{\'o}n en un setting lo suficientemente pr{\'a}ctico como para ser usado en ciertos ejercicios de tele-rehabilitaci{\'o}n. La precisi{\'o}n en la medici{\'o}n de la activaci{\'o}n as{\'\i} como la correlaci{\'o}n estimada entre las mediciones obtenidas con este m{\'e}todo y la real, son comparables a los m{\'e}todos actuales y a trabajos similares.\ 

}, author = {Abraham, Leandro}, editor = {Bromberg, Facundo} }