Robot Control on the Basis of Bio-electrical Signals

Ierache, Jorge Salvador; Pereira, Gustavo; Sattolo, Iris; Irribaren, Juan; Suiffet, Nicolás

doi:10.1007/978-3-642-37374-9_33

Cited by 7 publications

(4 citation statements)

References 5 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…En el trabajo titulado: Proposal of a Multimodal Model for emotional assessment within affective computing in gastronomic settings [12], los autores se basan en un enfoque propuesto por el psicólogo Paul Ekman, en el cual clasifican las siguientes emociones: ira, asco, alegría, miedo, tristeza, sorpresa y desprecio. En esta investigación se utiliza el conjunto de imágenes estandarizadas de alimentos OLAF (Open Library of Affective Foods).…”

Section: Trabajos Relacionadosunclassified

Heuristics for the analysis of emotions using encephalographic signals

Gelvez García,

Rojas Contreras,

Montenegro Marín

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

View full text Add to dashboard Cite

Resumen-Las emociones humanas son características que definen su comportamiento, a lo largo del tiempo se han venido estudiando con más frecuencia en diversas áreas. Este articulo presenta una heurística para validar el análisis de emociones en el momento en que un ser humano es expuesto a visualización de contenido, el método de captura de emociones propuesto es la diadema Emotiv Epoc X.El objetivo es definir una métrica que permita comparar el resultado de las señales obtenidas, con un test de clasificación de emociones, en este caso se propone utilizar el test SAM.

show abstract

Section: Trabajos Relacionadosunclassified

Heuristics for the analysis of emotions using encephalographic signals

Gelvez García,

Rojas Contreras,

Montenegro Marín

2023

Proceedings of the 21th LACCEI International Multi-Conference for Engineering, Education and Technology (LACCEI 2023): “Leaders

View full text Add to dashboard Cite

show abstract

“…Aprendizaje de Robots por Refuerzo con supervisión y Control a Través de Bioseñales. (Ierache, Pereira, Iribarren, & Sattolo, 2012), (Ierache, Pereira, & Iribarren, Navigation Control of a Robot from a Remote Location via the Internet Using Brain-Machine Interface, 2014). Los anteriores trabajos sirvieron como base para el Control de artefactos y robots centrado en emociones: La aplicación desarrollada permitió la captura y almacenamiento de las señales de emociones leídas por el BCI (Emotiv EPOC), durante un tiempo determinado mientras se estimuló al usuario con una fuente externa (presentación con imagen y sonido, llamadas 911) (Ierache, Nervo, Pereira, & Iribarren, 2014).…”

Section: Antecedentes Del Trabajo Propuestounclassified

Framework multimodal emocional en el contexto de ambientes dinámicos

Ierache

Sattolo²,

Chapperón³

2020

risti

View full text Add to dashboard Cite

En este trabajo, se presentan los antecedentes entorno a instrumentos, métodos y modelos en la temática de computación afectiva. En particular se exploran los modelos emocionales deductivos y categóricos. Se describe el Framework multimodal (MM) emocional desarrollado y su arquitectura conceptual. Finalmente se exhiben y se discuten los resultados generales de las experimentaciones realizadas, en particular las que se realizaron con un simulador de vuelo. El resultado de las experiencias permitió verificar la correlación entre los registros SAM y los valores del BCI (excitación y relajación, EEG) registrados por el Framework para el caso de prueba presentado.

show abstract

“…Such communication is typically afforded by a brain-computer interface (BCI), which converts physiological signals into control inputs. BCIs have been developed to exploit a variety of physiological signals, including electroencephalographic signals (Jones, Middendorf, McMillan, Calhoun, & Warm, 2003; Middendorf, McMillan, Calhoun, & Jones, 2000; Millan et al, 2003; Serruya, Hatsopoulos, Paniski, Fellows, & Donoghue, 2002; Wessberg et al, 2000); combinations of electrocorticographic signals, skin biopotentials, and facial muscle tensions (Saulnier, Sharlin, & Greenberg, 2009); and electrooculographic signals (Ierache, Dittler, Pereira, & Martinez, 2009).…”

Section: Review Of Past Researchmentioning

confidence: 99%

Human-Robot Interaction

Jones¹,

Schmidlin²

2011

Reviews of Human Factors and Ergonomics

View full text Add to dashboard Cite

The widespread adoption of personal service robots will likely depend on how well they interact with users. This chapter was motivated by a desire to facilitate the design of usable personal service robots. Toward that end, this chapter reviews the literature concerning people interacting with personal service robots. First, ongoing research related to the design of personal service robots is discussed. This material is organized around generic activities that would take place when a user initiates interaction with a future personal service robot, for example, understanding the robot's affordances or its cognitive capabilities, as well as when a personal service robot initiates interaction with a user, for example, understanding the user's intent or engaging and communicating with the user. Second, research areas that deserve more attention from the human-robot interaction community are discussed, for example, understanding when people do and do not treat robots as if they were people. Throughout the chapter, recommendations for the design of future personal service robots are offered along with recommendations for future research. S imple robots that perform a household chore or entertain are becoming commonplace, and more sophisticated versions of those robots are just on the horizon. Such sophistication, however, could translate into difficulty interacting with those robots. To avoid that difficulty, designers must be concerned about human-robot interaction. Toward that end, this chapter reviews the literature concerning how robots that perform chores or entertain should interact with people. THE DIvERSIfIcATIon of commERcIAl RoboTS The United Nations categorizes commercial robots as industrial robots, professional service robots, or personal service robots (United Nations, 2008). An industrial robot is an "automatically controlled, reprogrammable, multipurpose manipulator programmable in three or more axes" (International Organization for Standardization, 1994). For example, the first industrial robot, known as the Unimate, was a large programmable arm that handled and stacked hot pieces of die-cast metal at a General Motors plant. Unlike industrial robots, professional service robots and personal service robots do not have standardized definitions. For the most part, they are differentiated from one another on the basis

show abstract

Robot Control on the Basis of Bio-electrical Signals

Cited by 7 publications

References 5 publications

Heuristics for the analysis of emotions using encephalographic signals

Heuristics for the analysis of emotions using encephalographic signals

Framework multimodal emocional en el contexto de ambientes dinámicos

Human-Robot Interaction

Contact Info

Product

Resources

About