Towards Affect-sensitive Assistive Intervention Technologies for Children with Autism

Conn, K.; Liu, Changchun; Sarkar, Nilanjan; Stone, Wendy L.; Warren, Zachary

doi:10.5772/6171

Cited by 8 publications

(7 citation statements)

References 56 publications

(50 reference statements)

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“…Basically, human feelings are translated to the computers, which can understand and express them. The identification of the six basic emotions can be used for developing assistive robots, as the ones which detect and processes the affective states of children suffering from autism spectrum disorder [65], intelligent tutoring systems that use automatic emotion recognition to improve learning efficiency and adapt learning contents and interfaces in order to engage students [66], virtual reality games or immersive virtual environments that act as real therapists in anxiety disorder treatment [9,10], recommender systems which know the users' mood and adapt the recommended items accordingly [67,68], public sentiments analysis about different events, economic, or political decisions [69], and assistive technology [70][71][72].…”

Section: Discussionmentioning

confidence: 99%

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

et al. 2019

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Emotions constitute an indispensable component of our everyday life. They consist of conscious mental reactions towards objects or situations and are associated with various physiological, behavioral, and cognitive changes. In this paper, we propose a comparative analysis between different machine learning and deep learning techniques, with and without feature selection, for binarily classifying the six basic emotions, namely anger, disgust, fear, joy, sadness, and surprise, into two symmetrical categorical classes (emotion and no emotion), using the physiological recordings and subjective ratings of valence, arousal, and dominance from the DEAP (Dataset for Emotion Analysis using EEG, Physiological and Video Signals) database. The results showed that the maximum classification accuracies for each emotion were: anger: 98.02%, joy:100%, surprise: 96%, disgust: 95%, fear: 90.75%, and sadness: 90.08%. In the case of four emotions (anger, disgust, fear, and sadness), the classification accuracies were higher without feature selection. Our approach to emotion classification has future applicability in the field of affective computing, which includes all the methods used for the automatic assessment of emotions and their applications in healthcare, education, marketing, website personalization, recommender systems, video games, and social media.In the 1970s, Paul Ekman identified six basic emotions [2], namely anger, disgust, fear, joy, sadness, and surprise. Russell and Mehrabian proposed a dimensional approach [3] which states that any emotion is represented relative to three fundamental dimensions, namely valence (positive/pleasurable or negative/unpleasurable), arousal (engaged or not engaged), and dominance (degree of control that a person has over their affective states).Joy or happiness is a pleasant emotional state, synonymous with contentment, satisfaction and well-being. Sadness is the opposite of happiness, being characterized by grief, disappointment, and distress. Fear emerges in the presence of a stressful or dangerous stimulus perceived by the sensory organs. When the fight or flight response appears, heart rate and respiration rate increase. Also, the muscles become more tense in order to contend with threats in the environment. Anger is defined by fury, frustration, and resentment towards others. Surprise is triggered by an unexpected outcome to a situation, ranging from amazement to shock, whereas disgust is synonymous with dislike, distaste, or repugnance, being the most visceral of all six emotions.The DEAP database [4] was created with the purpose of developing a music video recommendation system based on the users' emotional responses. The biophysical signals of 32 subjects have been recorded while they were watching 40 one-minute long excerpts of music videos eliciting various emotions. The participants rated each video in terms of valence, arousal, dominance, like/dislike and familiarity on a scale from one to nine. The physiological signals were: galvanic skin response (GSR), plethysmograph (PP...

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Section: Discussionmentioning

confidence: 99%

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

et al. 2019

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“…HRI studies have also been conducted with robots using dimensional models for affect classification using facial expressions [76][77][78], body language [5,79,80], voice [81,82], physiological signals [4,22,70,[83][84][85][86][87], and multi-modal systems [88][89][90]. The most common model used in HRI is the two-dimensional circumplex (valence-arousal) model.…”

Section: Affect Models Used In Hrimentioning

confidence: 99%

“…Automated affect detection would promote effective engagement in interactions aimed at improving a person's health and wellbeing, e.g. interventions for children with autism [22] and for the elderly [23]. Mimicry HRI consists of a robot or person imitating the verbal and/or nonverbal behaviors of the other [24].…”

Section: Introductionmentioning

confidence: 99%

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

McColl

Hong

Hatakeyama

et al. 2015

J Intell Robot Syst

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In Human-Robot Interactions (HRI), robots should be socially intelligent. They should be able to respond appropriately to human affective and social cues in order to effectively engage in bi-directional communications. Social intelligence would allow a robot to relate to, understand, and interact and share information with people in real-world humancentered environments. This survey paper presents an encompassing review of existing automated affect recognition and classification systems for social robots engaged in various HRI settings. Human-affect detection from facial expressions, body language, voice, and physiological signals are investigated, as well as from a combination of the aforementioned modes. The automated systems are described by their corresponding robotic and HRI applications, the sensors they employ, and the feature detection techniques and affect classification strategies utilized. This paper also discusses pertinent future research directions for promoting the development of socially intelligent robots capable of recognizing, classifying and responding to human affective states during real-time HRI.

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“…Comparative studies were performed to evaluate a range of machine learning algorithms to emotion recognition of three emotions, i.e., liking, engagement, and anxiety was performed Liu et al 2005). Lessons learned from human robot interaction of typical population and feature extraction and machine learning methods developed were later applied to therapy of individuals with ASD (Liu et al 2007(Liu et al , 2008aConn et al 2010).…”

Section: Adaptive Hri and Psychophysiologymentioning

confidence: 99%

Advances in Physiological Computing

Fairclough¹,

Gilleade²

2014

Human–Computer Interaction Series

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HCI is a multidisciplinary field focused on human aspects of the development of computer technology. As computer-based technology becomes increasingly pervasive-not just in developed countries, but worldwide-the need to take a human-centered approach in the design and development of this technology becomes ever more important. For roughly 30 years now, researchers and practitioners in computational and behavioral sciences have worked to identify theory and practice that influences the direction of these technologies, and this diverse work makes up the field of human-computer interaction. Broadly speaking it includes the study of what technology might be able to do for people and how people might interact with the technology. The HCI series publishes books that advance the science and technology of developing systems which are both effective and satisfying for people in a wide variety of contexts. Titles focus on theoretical perspectives (such as formal approaches drawn from a variety of behavioral sciences), practical approaches (such as the techniques for effectively integrating user needs in system development), and social issues (such as the determinants of utility, usability and acceptability).

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Towards Affect-sensitive Assistive Intervention Technologies for Children with Autism

Cited by 8 publications

References 56 publications

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

Emotion Classification Based on Biophysical Signals and Machine Learning Techniques

A Survey of Autonomous Human Affect Detection Methods for Social Robots Engaged in Natural HRI

Advances in Physiological Computing

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