Humanoid Robotic Head Teaching a Child with Autism

Aniketh, M; Majumdar, Jharna

doi:10.1109/cimca.2018.8739603

Cited by 2 publications

(5 citation statements)

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“…They aim to achieve results from diagnostic and therapeutic interventions that are faster and more meaningful than those achieved through conventional methods are. Numerous approaches incorporating the latest advances in machine learning (ML) and deep learning (DL) have been developed to provide a personalised intelligent framework for the automatic detection of children"s affective states and engagement during a robot-assisted autism therapy when diagnosing children with autism spectrum disorder (ASD) [82][83][84] [85]. The first attempt to embed ML strategies into robot-children interactions aimed to assess the behaviours of children with ASD before introducing a metric to measure the therapy"s effectiveness [84].…”

Section: Therapy-based Social Robotmentioning

confidence: 99%

“…[66] Unknown Primary The dataset for the teaching assistant test was created from 10 students in three different groups (preprimary, primary and secondary schools). [82] EMNIST digit dataset Secondary The EMNIST dataset contains balanced handwritten digit datasets with 280,000 samples of handwritten digits (from 28,000 samples per digit) and 145,000 samples of handwritten characters (both uppercase and lowercase alphabets). [82] Custom colour ball dataset Primary Colour ball datasets are composed of approximately 500 photos that have been gathered and labelled.…”

Section: Name Of Dataset Type Descriptionmentioning

confidence: 99%

“…[82] EMNIST digit dataset Secondary The EMNIST dataset contains balanced handwritten digit datasets with 280,000 samples of handwritten digits (from 28,000 samples per digit) and 145,000 samples of handwritten characters (both uppercase and lowercase alphabets). [82] Custom colour ball dataset Primary Colour ball datasets are composed of approximately 500 photos that have been gathered and labelled. [27], [94], [75], [9], [84], [31], [45], [46], [33], [41], [42], [43], [50], [38], [39], [53] Extended Cohn-Kanade (CK+) Secondary It is a database of facial expressions that contains 327 annotated video sequences from 123 participants in eight different expression states.…”

Section: Name Of Dataset Type Descriptionmentioning

confidence: 99%

“…The majority of individuals with autism are visual thinkers. They have issues recalling successions and engine control in their grasp [82]. Performing as an application that understands and logically analyses the learning of a child with autism is a challenging task for a robot [82].…”

Section: Challenges Related To Automated Diagnosis and Therapymentioning

confidence: 99%

“…Many instructors seek assistance for the regular or administrative aspects of their jobs [16]. The development of a humanoid robot by integrating the capabilities of emotion recognition and facial expression mimicking is necessary for a teaching assistant dealing with children [68] with autism [82] [27]. Sophisticated planning techniques on NAO, such as methods to assist the robot in deciding how to progress with instructional tasks based on an assessment of the students" academic achievements, emotional state and motivation, are necessary [69].…”

Section: 41mentioning

confidence: 99%

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Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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This study presents a systematic review of the automatic vision-based assessment systems and models from related fields over the last 6 years. Many studies focus on automatic vision-based assessment in the area of robotics, artificial intelligence and virtual reality or augmented reality (AR) to enhance the communication between humans and machines for general purposes. Three reliable databases, IEEEXplore, Science Direct and Web of Science, were used to obtain relevant studies on the given topic. Several stages of filtering and scanning were applied according to the inclusion/exclusion criteria to filter the obtained 3505 papers from 2015 to 2020. Finally, 97 papers met the criteria. They were classified into four main categories based on their field by following the scientific taxonomy: computer-vision-based, robotics-based, AR-based and hybrid-based categories, accounting for 42.26% (n = 41/97 papers), 48.45% (n = 47/97 papers), 6.18% (n = 6/97 papers) and 4.12% (n = 4/97 papers), respectively. Subsequently, a deep and critical analysis of this multifield systematic review highlighted new research opportunities, motivations, challenges and recommendations that need attention to integrate interdisciplinary studies. Thus, automatic vision-based assessment, which is a field requiring automated solutions, tools and methods, enhances the ability of the assistive technology and facilitates the interaction of individuals with machines. Many studies have been conducted on the automatic vision-based assessment systems and their subtypes to promote accurate communication and performance evaluation in human-machine interaction. This study can provide researchers with useful guides and valuable information for future research. This study also addresses the ambiguity of automatic vison-based assessment models in interdisciplinary fields.

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Section: Therapy-based Social Robotmentioning

confidence: 99%

Section: Name Of Dataset Type Descriptionmentioning

confidence: 99%

Section: Name Of Dataset Type Descriptionmentioning

confidence: 99%

Section: Challenges Related To Automated Diagnosis and Therapymentioning

confidence: 99%

Section: 41mentioning

confidence: 99%

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Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

et al. 2022

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Drawing Interpretation Using Neural Networks and Accessibility Implementation in Mobile Application

Popescu

2022

Computation

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This paper continues the research of the previous work, regarding PandaSays mobile application, having its main purpose to detect the affective state of the child from his drawings, using MobileNet neural network. Children diagnosed with autism spectrum disorder, have difficulties in expressing their feelings and communicating with others. The purpose of PandaSays mobile application, is to help parents and tutors that have children diagnosed with autism, to communicate better with them and to understand their feelings. The main goal was to improve the model’s accuracy, trained with MobileNet neural network, which reached the value of 84.583%. For training the model, it was used Python programming language. The study focuses further on accessibility and its importance to children diagnosed with autism. Relevant screenshots of the mobile application are presented, in order to indicate that the application follows the accessibility guidelines and rules. Finally, there is presented the interaction with Marty robot and the efficiency of mobile application’s drawing prediction.

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Humanoid Robotic Head Teaching a Child with Autism

Cited by 2 publications

References 5 publications

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

Robotics Utilization in Automatic Vision-Based Assessment Systems From Artificial Intelligence Perspective: A Systematic Review

Drawing Interpretation Using Neural Networks and Accessibility Implementation in Mobile Application

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