A Control Architecture of Robot-Assisted Intervention for Children with Autism Spectrum Disorders

Feng, Yongli; Jia, Qingxuan; Wei, Wei

doi:10.1155/2018/3246708

Cited by 12 publications

(9 citation statements)

References 28 publications

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“…The Haar classifiers technique has been implemented as part of a marker-less computer vision approach. The Haar Algorithm is developed by Viola and Jones [39][40][41][42][43][44] and involves two main steps (features extractions and objects detections) and is known as "The Haartraining," the normalization threshold in this experiment was set between [1,-1], and the system process can be briefly described in the following steps: The standard Haar algorithm using OpenCv for figure image processing is summarized below:…”

Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

An Intelligent Big Data Management System Using Haar Algorithm‐Based Nao Agent Multisensory Communication

Baothman

2021

Wireless Communications and Mobile Computing

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Artificial intelligence (AI) is progressively changing techniques of teaching and learning. In the past, the objective was to provide an intelligent tutoring system without intervention from a human teacher to enhance skills, control, knowledge construction, and intellectual engagement. This paper proposes a definition of AI focusing on enhancing the humanoid agent Nao’s learning capabilities and interactions. The aim is to increase Nao intelligence using big data by activating multisensory perceptions such as visual and auditory stimuli modules and speech-related stimuli, as well as being in various movements. The method is to develop a toolkit by enabling Arabic speech recognition and implementing the Haar algorithm for robust image recognition to improve the capabilities of Nao during interactions with a child in a mixed reality system using big data. The experiment design and testing processes were conducted by implementing an AI principle design, namely, the three-constituent principle. Four experiments were conducted to boost Nao’s intelligence level using 100 children, different environments (class, lab, home, and mixed reality Leap Motion Controller (LMC). An objective function and an operational time cost function are developed to improve Nao’s learning experience in different environments accomplishing the best results in 4.2 seconds for each number recognition. The experiments’ results showed an increase in Nao’s intelligence from 3 to 7 years old compared with a child’s intelligence in learning simple mathematics with the best communication using a kappa ratio value of 90.8%, having a corpus that exceeded 390,000 segments, and scoring 93% of success rate when activating both auditory and vision modules for the agent Nao. The developed toolkit uses Arabic speech recognition and the Haar algorithm in a mixed reality system using big data enabling Nao to achieve a 94% success learning rate at a distance of 0.09 m; when using LMC in mixed reality, the hand sign gestures recorded the highest accuracy of 98.50% using Haar algorithm. The work shows that the current work enabled Nao to gradually achieve a higher learning success rate as the environment changes and multisensory perception increases. This paper also proposes a cutting-edge research work direction for fostering child-robots education in real time.

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Section: Wireless Communications and Mobile Computingmentioning

confidence: 99%

An Intelligent Big Data Management System Using Haar Algorithm‐Based Nao Agent Multisensory Communication

Baothman

2021

Wireless Communications and Mobile Computing

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“…The challenge is to simulate authentic or at least appropriate human behavior while avoiding both the uncanny valley and cartoon-like over-simplification [39]. In the domain of ASD intervention, a recent work proposes a behavior control architecture for a Robot-Assisted intervention system, which enables the robot to intervene in autism therapy with high autonomy, minimizing the workload of the supervisor therapist [40]. The proposed architecture is inspired from the ACT-R cognitive architecture which is a general model of cognition and provides a framework for information processing [41].…”

Section: Robot Perception and Behavior Controlmentioning

confidence: 99%

A Taxonomy in Robot-Assisted Training: Current Trends, Needs and Challenges

et al. 2018

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In this article, we present a taxonomy in Robot-Assisted Training; a growing body of research in Human–Robot Interaction which focuses on how robotic agents and devices can be used to enhance user’s performance during a cognitive or physical training task. Robot-Assisted Training systems have been successfully deployed to enhance the effects of a training session in various contexts, i.e., rehabilitation systems, educational environments, vocational settings, etc. The proposed taxonomy suggests a set of categories and parameters that can be used to characterize such systems, considering the current research trends and needs for the design, development and evaluation of Robot-Assisted Training systems. To this end, we review recent works and applications in Robot-Assisted Training systems, as well as related taxonomies in Human–Robot Interaction. The goal is to identify and discuss open challenges, highlighting the different aspects of a Robot-Assisted Training system, considering both robot perception and behavior control.

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“…To date, there have only been a handful of studies evaluating the use of SARs in DTT-based therapies [21][22][23][24][25]. In ref.…”

Section: Introductionmentioning

confidence: 99%

“…In ref. [23], the humanoid robot Nao delivered interventions, inspired by DTT and Floortime, to teach children with ASD imitation behaviors. The intervention consisted of the robot requesting the child to imitate its arm movements and maintaining his/her engagement during the activity.…”

Section: Introductionmentioning

confidence: 99%

“…In general, current research in robot-mediated DTTbased therapies for individuals with ASD have focused on developing interventions that increase an individual's performance on an existing social skill (i.e., imitation, emotion recognition, greeting, or eye contact) within his/her repertoire using least-to-most prompting protocols [21][22][23][24][25]. To date, there has not been research on teaching a skill that is not already within an individual with ASD's existing repertoire and integrated as a part of an individual's treatment plan.…”

Section: Introductionmentioning

confidence: 99%

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A study on an applied behavior analysis-based robot-mediated listening comprehension intervention for ASD

Louie

Korneder

Abbas

et al. 2020

Paladyn, Journal of Behavioral Robotics

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Autism spectrum disorder (ASD) is a lifelong developmental condition that affects an individual’s ability to communicate and relate to others. Despite such challenges, early intervention during childhood development has shown to have positive long-term benefits for individuals with ASD. Namely, early childhood development of communicative speech skills has shown to improve future literacy and academic achievement. However, the delivery of such interventions is often time-consuming. Socially assistive robots (SARs) are a potential strategic technology that could help support intervention delivery for children with ASD and increase the number of individuals that healthcare professionals can positively affect. For SARs to be effectively integrated in real-world treatment for individuals with ASD, they should follow current evidence-based practices used by therapists such as Applied Behavior Analysis (ABA). In this work, we present a study that investigates the efficacy of applying well-known ABA techniques to a robot-mediated listening comprehension intervention delivered to children with ASD at a university-based ABA clinic. The interventions were delivered in place of human therapists to teach study participants a new skill as a part of their overall treatment plan. All the children participating in the intervention improved in the skill being taught by the robot and enjoyed interacting with the robot, as evident by high occurrences of positive affect as well as engagement during the sessions. One of the three participants has also reached mastery of the skill via the robot-mediated interventions.

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A Control Architecture of Robot-Assisted Intervention for Children with Autism Spectrum Disorders

Cited by 12 publications

References 28 publications

An Intelligent Big Data Management System Using Haar Algorithm‐Based Nao Agent Multisensory Communication

An Intelligent Big Data Management System Using Haar Algorithm‐Based Nao Agent Multisensory Communication

A Taxonomy in Robot-Assisted Training: Current Trends, Needs and Challenges

A study on an applied behavior analysis-based robot-mediated listening comprehension intervention for ASD

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