Head Design and Optimization of An Emotionally Interactive Robot for the Treatment of Autism

Wu, Feng; Lin, Shengzhao; Cao, Xiangfei; Zhong, Huixin; Zhang, Jiaming

doi:10.1145/3351917.3351992

Cited by 5 publications

(2 citation statements)

References 23 publications

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“…FER is easily accessible to researchers of all fields and is increasingly used by the scientific community. Applications can be found, for example, in psychology, where such algorithms are used to predict mental health from social media images (Yazdavar et al, 2020), to validate interventions for autism (Wu et al, 2019), or to screen for Parkinson's disease (Jin et al, 2020). A sociological example is the assessment of collective happiness in society from social media images (Abdullah et al, 2015).…”

Section: Introductionmentioning

confidence: 99%

Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions

2021

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Emotional facial expressions can inform researchers about an individual's emotional state. Recent technological advances open up new avenues to automatic Facial Expression Recognition (FER). Based on machine learning, such technology can tremendously increase the amount of processed data. FER is now easily accessible and has been validated for the classification of standardized prototypical facial expressions. However, applicability to more naturalistic facial expressions still remains uncertain. Hence, we test and compare performance of three different FER systems (Azure Face API, Microsoft; Face++, Megvii Technology; FaceReader, Noldus Information Technology) with human emotion recognition (A) for standardized posed facial expressions (from prototypical inventories) and (B) for non-standardized acted facial expressions (extracted from emotional movie scenes). For the standardized images, all three systems classify basic emotions accurately (FaceReader is most accurate) and they are mostly on par with human raters. For the non-standardized stimuli, performance drops remarkably for all three systems, but Azure still performs similarly to humans. In addition, all systems and humans alike tend to misclassify some of the non-standardized emotional facial expressions as neutral. In sum, emotion recognition by automated facial expression recognition can be an attractive alternative to human emotion recognition for standardized and non-standardized emotional facial expressions. However, we also found limitations in accuracy for specific facial expressions; clearly there is need for thorough empirical evaluation to guide future developments in computer vision of emotional facial expressions.

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Section: Introductionmentioning

confidence: 99%

Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions

2021

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“…These robots can be programmed to mimic human behavior and gestures, as well as to simulate human emotions. A research study conducted by Feng Wu et al [32], aimed to replicate emotions by developing a humanoid robot equipped with thermoplastic elastomer (TPE) skin, which closely resembles human skin. The lifelike appearance of this robot had significant implications for clinical rehabilitation in the context of ASD.…”

Section: ) Virtual Reality Applicationsmentioning

confidence: 99%

Technology-Mediated Interventions for Autism Spectrum Disorder

Chistol,

Danubianu,

Barîla

2023

IJACSA

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According to the Diagnostic and Statistical Manual of Mental Disorders (DSM-5), Autism Spectrum Disorder (ASD) is a complex neurological and developmental condition characterized by impairments in social interaction and communication. Despite significant advancements in the research field, no pharmaceutical medication has been designed for ASD treatment. Therefore, ASD treatment relies mainly on therapeutic intervention. Interactive technologies have emerged as valuable therapy augmentation tools. This research focuses on interactive technologies developed for ASD therapeutic intervention. The study introduces a conceptual framework for understanding the full spectrum of technologies involved in the ASD context. The employed methodology encompasses expert opinions and entails a cross-sectional study that included 59 participants with significant experience in interacting with individuals diagnosed with ASD in various real-life settings, including therapists, teachers, and parents of children with ASD. The research findings revealed a broad spectrum of technologies involved in ASD interventions, including applications, devices, and robots. The results bring a new perspective on the interactive technologies used in the therapy and diagnosis of ASD and highlight their important characteristics that can serve as a standard in the development of future technological solutions.

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A Machine Learning Model for Kids’ Behavior Analysis from Facial Emotions using Principal Component Analysis

Rani

Bhambri

Chauhan

2021

2021 5th Asian Conference on Artificial Intelligence Technology (ACAIT)

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Head Design and Optimization of An Emotionally Interactive Robot for the Treatment of Autism

Cited by 5 publications

References 23 publications

Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions

Automatic Facial Expression Recognition in Standardized and Non-standardized Emotional Expressions

Technology-Mediated Interventions for Autism Spectrum Disorder

A Machine Learning Model for Kids’ Behavior Analysis from Facial Emotions using Principal Component Analysis

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