A System to Measure Physiological Response During Social Interaction in VR for Children With ASD

2019 ◽  
pp. 1-33
Author(s):  
Karla Conn Welch ◽  
Uttama Lahiri ◽  
Zachary E. Warren ◽  
Nilanjan Sarkar
Keyword(s):  
Social Interaction ◽  
Social Communication ◽  
Physiological Response ◽  
Affective Response ◽  
Autism Spectrum ◽  
Matched Pair ◽  
Signal Acquisition ◽  
Affective States ◽  
Physiological Signal ◽  
Physiological Sensing

This chapter presents work aimed at investigating interactions between virtual reality (VR) and children with autism spectrum disorder (ASD) using physiological sensing of affective cues. The research objectives are two-fold: 1) develop VR-based social communication tasks and integrate them into the physiological signal acquisition module to enable the capture of one's physiological responses in a time-synchronized manner during participation in the task and 2) conduct a pilot usability study to evaluate a VR-based social interaction system that induces an affective response in ASD and typically developing (TD) individuals by using a physiology-based approach. Physiological results suggest there is a different physiological response in the body in relation to the reported level of the affective states. The preliminary results from a matched pair of participants could provide valuable information about specific affect-eliciting aspects of social communication, and this feedback could drive individualized interventions that scaffold skills and improve social wellbeing.

scholarly journals Fostering Emotion Recognition in Children with Autism Spectrum Disorder

2021 ◽  
Vol 5 (10) ◽  
pp. 57
Author(s):  
Vinícius Silva ◽  
Filomena Soares ◽  
João Sena Esteves ◽  
Cristina P. Santos ◽  
Ana Paula Pereira
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Social Communication ◽  
Autism Spectrum ◽  
Spectrum Disorder ◽  
Support Vector ◽  
Robotic Platform ◽  
Children With Asd

Facial expressions are of utmost importance in social interactions, allowing communicative prompts for a speaking turn and feedback. Nevertheless, not all have the ability to express themselves socially and emotionally in verbal and non-verbal communication. In particular, individuals with Autism Spectrum Disorder (ASD) are characterized by impairments in social communication, repetitive patterns of behaviour, and restricted activities or interests. In the literature, the use of robotic tools is reported to promote social interaction with children with ASD. The main goal of this work is to develop a system capable of automatic detecting emotions through facial expressions and interfacing them with a robotic platform (Zeno R50 Robokind® robotic platform, named ZECA) in order to allow social interaction with children with ASD. ZECA was used as a mediator in social communication activities. The experimental setup and methodology for a real-time facial expression (happiness, sadness, anger, surprise, fear, and neutral) recognition system was based on the Intel® RealSense™ 3D sensor and on facial features extraction and multiclass Support Vector Machine classifier. The results obtained allowed to infer that the proposed system is adequate in support sessions with children with ASD, giving a strong indication that it may be used in fostering emotion recognition and imitation skills.

scholarly journals Behavioural and psychological characteristics in Pitt-Hopkins syndrome: a comparison with Angelman and Cornelia de Lange syndromes

2019 ◽  
Vol 11 (1) ◽  
Author(s):  
Alice Watkins ◽  
Stacey Bissell ◽  
Jo Moss ◽  
Chris Oliver ◽  
Jill Clayton-Smith ◽  
...  
Keyword(s):  
Social Interaction ◽  
Social Communication ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Sensory Profile ◽  
Psychological Characteristics ◽  
Genetic Syndrome ◽  
Reciprocal Social Interaction ◽  
Cornelia De Lange ◽  
Challenging Behaviours

Abstract Background Pitt-Hopkins syndrome (PTHS) is a genetic neurodevelopmental disorder associated with intellectual disability. Although the genetic mechanisms underlying the disorder have been identified, description of its behavioural phenotype is in its infancy. In this study, reported behavioural and psychological characteristics of individuals with PTHS were investigated in comparison with the reported behaviour of age-matched individuals with Angelman syndrome (AS) and Cornelia de Lange syndrome (CdLS). Methods Questionnaire data were collected from parents/caregivers of individuals with PTHS (n = 24), assessing behaviours associated with autism spectrum disorder (ASD), sociability, mood, repetitive behaviour, sensory processing, challenging behaviours and overactivity and impulsivity. For most measures, data were compared to data for people with AS (n = 24) and CdLS (n = 24) individually matched by adaptive ability, age and sex. Results Individuals with PTHS evidenced significantly higher levels of difficulties with social communication and reciprocal social interaction than individuals with AS, with 21 of 22 participants with PTHS meeting criteria indicative of ASD on a screening instrument. Individuals with PTHS were reported to be less sociable with familiar and unfamiliar people than individuals with AS, but more sociable with unfamiliar people than individuals with CdLS. Data also suggested areas of atypicality in sensory experiences. Challenging behaviours were reported frequently in PTHS, with self-injury (70.8%) occurring at significantly higher rates than in AS (41.7%) and aggression (54.2%) occurring at significantly higher rates than in CdLS (25%). Individuals with PTHS also evidenced lower reported mood than individuals with AS. Conclusions Behaviours which may be characteristic of PTHS include those associated with ASD, including deficits in social communication and reciprocal social interaction. High rates of aggression and self-injurious behaviour compared to other genetic syndrome groups are of potential clinical significance and warrant further investigation. An atypical sensory profile may also be evident in PTHS. The specific aetiology of and relationships between different behavioural and psychological atypicalities in PTHS, and effective clinical management of these, present potential topics for future research.

Outcomes of real-world social interaction for autistic adults paired with autistic compared to typically developing partners

Autism ◽  
2019 ◽  
Vol 24 (5) ◽  
pp. 1067-1080 ◽  
Author(s):  
Kerrianne E Morrison ◽  
Kilee M DeBrabander ◽  
Desiree R Jones ◽  
Daniel J Faso ◽  
Robert A Ackerman ◽  
...  
Keyword(s):  
Social Interaction ◽  
Real World ◽  
Social Communication ◽  
Blind Spot ◽  
Autism Spectrum ◽  
Typically Developing ◽  
Interaction Styles ◽  
Autistic People ◽  
Autistic Adults

Differences in social communication and interaction styles between autistic and typically developing have been studied in isolation and not in the context of real-world social interaction. The current study addresses this “blind spot” by examining whether real-world social interaction quality for autistic adults differs when interacting with typically developing relative to autistic partners. Participants (67 autism spectrum disorder, 58 typically developing) were assigned to one of three dyadic partnerships (autism–autism: n = 22; typically developing–typically developing: n = 23; autism–typically developing: n = 25; 55 complete dyads, 15 partial dyads) in which they completed a 5-min unstructured conversation with an unfamiliar person and then assessed the quality of the interaction and their impressions of their partner. Although autistic adults were rated as more awkward, less attractive, and less socially warm than typically developing adults by both typically developing and autistic partners, only typically developing adults expressed greater interest in future interactions with typically developing relative to autistic partners. In contrast, autistic participants trended toward an interaction preference for other autistic adults and reported disclosing more about themselves to autistic compared to typically developing partners. These results suggest that social affiliation may increase for autistic adults when partnered with other autistic people, and support reframing social interaction difficulties in autism as a relational rather than an individual impairment.

scholarly journals Affective State Assistant for Helping Users with Cognition Disabilities Using Neural Networks

Electronics ◽  
2020 ◽  
Vol 9 (11) ◽  
pp. 1843
Author(s):  
Luis Muñoz-Saavedra ◽  
Francisco Luna-Perejón ◽  
Javier Civit-Masot ◽  
Lourdes Miró-Amarante ◽  
Anton Civit ◽  
...  
Keyword(s):  
Affective State ◽  
Cognitive Disorders ◽  
Extraction Process ◽  
Autism Spectrum ◽  
Verbal Communication ◽  
Communication Process ◽  
Discrete Wavelet ◽  
Affective States ◽  
Physiological Signal ◽  
Additional Information

Non-verbal communication is essential in the communication process. This means that its lack can cause misinterpretations of the message that the sender tries to transmit to the receiver. With the rise of video calls, it seems that this problem has been partially solved. However, people with cognitive disorders such as those with some kind of Autism Spectrum Disorder (ASD) are unable to interpret non-verbal communication neither live nor by video call. This work analyzes the relationship between some physiological measures (EEG, ECG, and GSR) and the affective state of the user. To do that, some public datasets are evaluated and used for a multiple Deep Learning (DL) system. Each physiological signal is pre-processed using a feature extraction process after a frequency study with the Discrete Wavelet Transform (DWT), and those coefficients are used as inputs for a single DL classifier focused on that signal. These multiple classifiers (one for each signal) are evaluated independently and their outputs are combined in order to optimize the results and obtain additional information about the most reliable signals for classifying the affective states into three levels: low, middle, and high. The full system is carefully detailed and tested, obtaining promising results (more than 95% accuracy) that demonstrate its viability.

An initial examination of the psychometric properties of the Diagnostic Instrument for Social and Communication Disorders (DISCO-11) in a clinical sample of children with a diagnosis of Autism spectrum disorder

2020 ◽  
pp. 1-10
Author(s):  
Margo Anglim ◽  
Emma Victoria Conway ◽  
Myra Barry ◽  
Muhammad Kashif ◽  
Pauline Ackermann ◽  
...  
Keyword(s):  
Autism Spectrum Disorder ◽  
Social Interaction ◽  
Psychometric Properties ◽  
Social Communication ◽  
Communication Disorders ◽  
Autism Spectrum ◽  
Cronbach’S Alpha ◽  
Cronbach's Alpha ◽  
Dsm 5 ◽  
Over Time

Introduction: The diagnostic interview for social and communication disorders (DISCO – 11; Wing 2006), is a semi-structured, interview-based instrument used in the diagnosis of children with autism spectrum disorder (ASD). This paper explores the psychometric properties of the DISCO-11 used in a specialist Paediatric clinical setting. Two key research questions were examined; (1) Does the factor structure of the DISCO-11 reflect the diagnostic and statistical manual 5th edition (DSM-5, American Psychiatric Association [APA], 2013) dyad of impairment in ASD? (2) Is there evidence of diagnostic stability over time using the DISCO? Methods: Review assessments of 65 children with ASD were carried out using standardised measures including the DISCO-11 and the autism diagnostic observation schedule. Results: The results revealed two factors resembling the DSM-5 algorithms, as used in DISCO-11, which were named as social-communication, and restricted and repetitive behaviours. The reliability, for the overall DISCO score was good (Cronbach’s alpha = 0.78). The social communication and social interaction subscale showed good reliability (Cronbach’s Alpha = 0.77) as did the restricted and repetitive patterns of behaviour, interests or activities subscale (Cronbach’s Alpha = 0.74). Acceptable internal reliability was found for the overall DISCO score and the subscales of social communication and social interaction and the restricted and repetitive patterns of behaviour, interests or activities. Test–retest showed good stability of diagnosis over time. Discussion: This study supports that the DISCO-11 shows potential as a valid and reliable instrument that can be used both for clinical and research purposes.

scholarly journals Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

2021 ◽  
Author(s):  
Sawon Pratiher ◽  
Ananth Radhakrishnan ◽  
Karuna P. Sahoo ◽  
SAZEDUL ALAM ◽  
Scott E. Kerick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Statistical Hypothesis ◽  
Signal Acquisition ◽  
Classification Problems ◽  
Statistical Hypothesis Testing ◽  
Physiological Signal ◽  
Induced Stress ◽  
Physiological Sensing ◽  
The Impact

<p>"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible."</p><p><br></p><p>Physiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.</p>

scholarly journals Social Isolation Alters Social and Mating Behavior in the R451C Neuroligin Mouse Model of Autism

2017 ◽  
Vol 2017 ◽  
pp. 1-9 ◽  
Author(s):  
E. L. Burrows ◽  
A. F. Eastwood ◽  
C. May ◽  
S. C. Kolbe ◽  
T. Hill ◽  
...  
Keyword(s):  
Social Interaction ◽  
Aggressive Behavior ◽  
Social Interactions ◽  
Mating Behavior ◽  
Social Isolation ◽  
Social Communication ◽  
Neurodevelopmental Disorder ◽  
Autism Spectrum ◽  
Repetitive Behaviors ◽  
Neural Mechanisms

Autism spectrum disorder (ASD) is a neurodevelopmental disorder typified by impaired social communication and restrictive and repetitive behaviors. Mice serve as an ideal candidate organism for studying the neural mechanisms that subserve these symptoms. The Neuroligin-3 (NL3) mouse, expressing a R451C mutation discovered in two Swedish brothers with ASD, exhibits impaired social interactions and heightened aggressive behavior towards male mice. Social interactions with female mice have not been characterized and in the present study were assessed in maleNL3R451Cand WT mice. Mice were housed in social and isolation conditions to test for isolation-induced increases in social interaction. Tests were repeated to investigate potential differences in interaction in naïve and experienced mice. We identified heightened interest in mating and atypical aggressive behavior inNL3R451Cmice.NL3R451Cmice exhibited normal social interaction with WT females, indicating that abnormal aggressive behavior towards females is not due to altered motivation to engage. Social isolation rearing heightened interest in social behavior in all mice. Isolation housing selectively modulated the response to female pheromones inNL3R451Cmice. This study is the first to show altered mating behavior in theNL3R451Cmouse and has provided new insights into the aggressive phenotype in this model.

scholarly journals Classification of VR-Gaming Difficulty Induced Stress Levels using Physiological (EEG & ECG) Signals and Machine Learning

2021 ◽  
Author(s):  
Sawon Pratiher ◽  
Ananth Radhakrishnan ◽  
Karuna P. Sahoo ◽  
SAZEDUL ALAM ◽  
Scott E. Kerick ◽  
...  
Keyword(s):  
Machine Learning ◽  
Well Being ◽  
Statistical Hypothesis ◽  
Signal Acquisition ◽  
Classification Problems ◽  
Statistical Hypothesis Testing ◽  
Physiological Signal ◽  
Induced Stress ◽  
Physiological Sensing ◽  
The Impact

<p>"This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessible."</p><p><br></p><p>Physiological sensing has long been an indispensable fixture for virtual reality (VR) gaming studies. Moreover, VR induced stressors are increasingly being used to assess the impact of stress on an individual’s health and well-being. This study discusses the results of experimental research comprising multimodal physiological signal acquisition from 31 participants during a Go/No-Go VR-based shooting exercise where participants had to shoot the enemy and spare the friendly targets. The study encompasses multiple sessions, including orientation, thresholding, and shooting. The shooting sessions consist of tasks under low & high difficulty induced stress conditions with in-between baseline segments. Machine learning (ML) performance with heart rate variability (HRV) from electrocardiogram (ECG) and electroencephalogram (EEG) features outperform the prevalent methods for four different VR gaming difficulty-induced stress (GDIS) classification problems (CPs). Further, the significance of the HRV predictors and different brain region activations from EEG is deciphered using statistical hypothesis testing (SHT). The ablation study shows the efficacy of multimodal physiological sensing for different gaming difficulty-induced stress classification problems (GDISCPs) in a VR shooting task.</p>

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