MIHR: A Human-Robot Interaction Model

Episodic memory is crucial to develop complex cognitive abilities like learning or reasoning, and non-complex cognitive abilities like calling the name of someone or remembering an appointment. It is known that an intellectual disability implies a deficit over tasks related to episodic memory, however, in the literature, there is no approach to stimulate episodic memory in children with intellectual disabilities. Because interactions with social robots have generated positive effects in children with intellectual disabilities, we propose an approach composed of three training sessions based on social robotics. In this paper, we present an exploratory study to know the effects of our approach on episodic memory in children with intellectual disabilities. The results have shown that our approach can enhance episodic memory in these children when they interact with interest and improve their performance in sessions.

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“…▪ Premisa 3. Considerar la adaptación de la interacción según la persona que interactúa con el robot [30].…”

Section: Intervención Para El Estímulo De La Memoria Episódicaunclassified

Effects of Social Robotics on Episodic Memory in Children With Intellectual Disabilities

Angulo

Azuaje

Leon

et al. 2021

IEEE R. Iberoamericana Tecnologias Aprendizaje

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“…I N recent years automatization and robotics have been used in more and more areas besides the industrial sector. The rapid spreading of robots in daily living environments directs the attention to the field of human-robot interactions (HRI) [1][2] [3], human-robot interaction models (MIHR) [4] and social behavior of robots [5][6] [7]. The so-called service or social robots are becoming part of everyday life, and this coexistence between humans and robots raises some questions.…”

Section: Introductionmentioning

confidence: 99%

Deep Learning-Based Recognition and Analysis of Limb-Independent Dog Behavior for Ethorobotical Application

Nagy

Köröndi²

2022

IEEE Access

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This paper presents a Behavior Transfer System (BTS) to model the behavior patterns of dogs and make it possible to implement the behavior patterns on mobile robots. The system relies on an iSpace based measurement system and a deep learning prediction algorithm. With the help of the measurement system, ethological measurements can be automatized to eliminate human coding errors and make the data collection process more robust and consistent. The trained neural networks have a dual purpose. First, the neural networks can be utilized to analyze ethological measurements and predict different behavior patterns of the dog. Test results show that the implemented neural networks can effectively predict the attention of the dog with 88% accuracy, the tail waging with 82% accuracy, and the contact seeking behavior with 88% accuracy. Second, implementing the neural networks previously trained on dogs can serve as a robot operational behavioral model which mimics the behavior pattern of a dog after an adequate mathematical abstraction that maps the movements of the dog into a robot movement set. The presented method of this paper can be applied to automatize the behavior coding work of ethologists and the trained neural network can be used as an abstract robot behavior control module.

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LeSeVen: A Web Application for Learning Written Vocabulary Through the Venezuelan Sign Language

Pérez,

Nuñez,

Pérez-Pérez

2023

Lecture Notes in Educational Technology

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MIHR: A Human-Robot Interaction Model

Cited by 6 publications

References 33 publications

Effects of Social Robotics on Episodic Memory in Children With Intellectual Disabilities

Effects of Social Robotics on Episodic Memory in Children With Intellectual Disabilities

Deep Learning-Based Recognition and Analysis of Limb-Independent Dog Behavior for Ethorobotical Application

LeSeVen: A Web Application for Learning Written Vocabulary Through the Venezuelan Sign Language

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