Automatic Emotion Recognition in Robot-Children Interaction for ASD Treatment

Leo, Marco; Coco, Marco Del; Carcagnì, Pierluigi; Distante, Cosimo; Bernava, Giuseppe Massimo; Pioggia, Giovanni; Palestra, Giuseppe

doi:10.1109/iccvw.2015.76

Cited by 49 publications

(49 citation statements)

References 5 publications

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“…provided initial evidence for the potential of wearable assistive technologies to reduce hyperactivity, inattention and impulsivity in school-aged children, adolescents and young adults with ASD. Leo et al 45 and Pan et al 46 developed an automatic emotion recognition system in robot-children interaction for ASD treatment. Their results suggest that computer vision could help to improve the efficiency of behaviour analysis during interactions with robots.…”

Section: Facial Expression/emotionmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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The current state of computer vision methods applied to autism spectrum disorder (ASD) research has not been well established. Increasing evidence suggests that computer vision techniques have a strong impact on autism research. The primary objective of this systematic review is to examine how computer vision analysis has been useful in ASD diagnosis, therapy and autism research in general. A systematic review of publications indexed on PubMed, IEEE Xplore and ACM Digital Library was conducted from 2009 to 2019. Search terms included [‘autis*’ AND (‘computer vision’ OR ‘behavio* imaging’ OR ‘behavio* analysis’ OR ‘affective computing’)]. Results are reported according to PRISMA statement. A total of 94 studies are included in the analysis. Eligible papers are categorised based on the potential biological/behavioural markers quantified in each study. Then, different computer vision approaches that were employed in the included papers are described. Different publicly available datasets are also reviewed in order to rapidly familiarise researchers with datasets applicable to their field and to accelerate both new behavioural and technological work on autism research. Finally, future research directions are outlined. The findings in this review suggest that computer vision analysis is useful for the quantification of behavioural/biological markers which can further lead to a more objective analysis in autism research.

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Section: Facial Expression/emotionmentioning

confidence: 99%

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

et al. 2020

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“…In some research [110], head tracking is implemented using a Kinect sensor as an approximation of eye gaze for joint attention study. We have also seen research on using facial expressions recognition to obtain affective feedback [30,[111][112][113][114][115][116]. In some research, head orientation and movement are tracked using inertial sensors embedded in smart glasses instead of cameras [117].…”

Section: Facial Motion/expression Trackingmentioning

confidence: 99%

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Liu

Zhao

et al. 2017

Applied Sciences

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Abstract:The rapid development of computer and robotic technologies in the last decade is giving hope to perform earlier and more accurate diagnoses of the Autism Spectrum Disorder (ASD), and more effective, consistent, and cost-conscious treatment. Besides the reduced cost, the main benefit of using technology to facilitate treatment is that stimuli produced during each session of the treatment can be controlled, which not only guarantees consistency across different sessions, but also makes it possible to focus on a single phenomenon, which is difficult even for a trained professional to perform, and deliver the stimuli according to the treatment plan. In this article, we provide a comprehensive review of research on recent technology-facilitated diagnosis and treat of children and adults with ASD. Different from existing reviews on this topic, which predominantly concern clinical issues, we focus on the engineering perspective of autism studies. All technology facilitated systems used for autism studies can be modeled as human machine interactive systems where one or more participants would constitute as the human component, and a computer-based or a robotic-based system would be the machine component. Based on this model, we organize our review with the following questions: (1) What are presented to the participants in the studies and how are the content and delivery methods enabled by technologies? (2) How are the reactions/inputs collected from the participants in response to the stimuli in the studies? (3) Are the experimental procedure and programs presented to participants dynamically adjustable based on the responses from the participants, and if so, how? and (4) How are the programs assessed?

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“…Xefteris et al (2016) developed a methodology for emotion recognition using facial expressions as indicators to evaluate the overall health status of subjects suffering from neurodegenerative diseases (e.g., Mild Cognitive Impairments, Alzheimer's, dementia). Leo et al (2015) used machine learning strategies based on facial expressions during robot-child user interaction to evaluate the behaviors of children who belong to the ASD group for the development of better therapeutic protocols. Suzan and Mariofanna (2016) used computer vision and machine learning methods such as active shape models (ASM) and Support Vector Machine (SVM) to recognize facial expressions in children with ASD during playtime.…”

Section: Related Workmentioning

confidence: 99%

Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation

Flynn

Effraimidis

Angelopoulou

et al. 2020

Front. Hum. Neurosci.

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Recent success stories in automated object or face recognition, partly fuelled by deep learning artificial neural network (ANN) architectures, have led to the advancement of biometric research platforms and, to some extent, the resurrection of Artificial Intelligence (AI). In line with this general trend, inter-disciplinary approaches have been taken to automate the recognition of emotions in adults or children for the benefit of various applications, such as identification of children's emotions prior to a clinical investigation. Within this context, it turns out that automating emotion recognition is far from being straightforward, with several challenges arising for both science (e.g., methodology underpinned by psychology) and technology (e.g., the iMotions biometric research platform). In this paper, we present a methodology and experiment and some interesting findings, which raise the following research questions for the recognition of emotions and attention in humans: (a) the adequacy of well-established techniques such as the International Affective Picture System (IAPS), (b) the adequacy of state-of-the-art biometric research platforms, (c) the extent to which emotional responses may be different in children and adults. Our findings and first attempts to answer some of these research questions are based on a mixed sample of adults and children who took part in the experiment, resulting in a statistical analysis of numerous variables. These are related to both automatically and interactively captured responses of participants to a sample of IAPS pictures.

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Automatic Emotion Recognition in Robot-Children Interaction for ASD Treatment

Cited by 49 publications

References 5 publications

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Computer vision in autism spectrum disorder research: a systematic review of published studies from 2009 to 2019

Technology-Facilitated Diagnosis and Treatment of Individuals with Autism Spectrum Disorder: An Engineering Perspective

Assessing the Effectiveness of Automated Emotion Recognition in Adults and Children for Clinical Investigation

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