Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD)

Bekele, Esubalew; Wade, Joshua; Bian, Dayi; Fan, Jing; Swanson, Amy; Warren, Zachary; Sarkar, Nilanjan

doi:10.1109/vr.2016.7504695

Cited by 54 publications

(56 citation statements)

References 41 publications

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“…When a user is immersed in a VE, they can be systematically exposed to specific feared stimuli within a contextually relevant setting [4,6,16,17]. VEs have also been shown to help children with Autism Spectrum Disorders (ASD) improve their social functioning [3]. These examples indicate where a system that uses emotional modulation could be useful: to help the physician analyse the emotional states and development of the patient's condition, as well as to use that information to adapt the treatment in real-time, avoiding possible over exposure of the patient.…”

Section: Applications In Medicinementioning

confidence: 99%

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Menezes

Samara

Galway

et al. 2017

Pers Ubiquit Comput

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One of the challenges in virtual environments is the difficulty users have in interacting with these increasingly complex systems. Ultimately, endowing machines with the ability to perceive users emotions will enable a more intuitive and reliable interaction. Consequently, using the electroencephalogram as a bio-signal sensor, the affective state of a user can be modelled and subsequently utilised in order to achieve a system that can recognise and react to the user's emotions. This paper investigates features extracted from electroencephalogram signals for the purpose of affective state modelling based on Russell's Circumplex Model. Investigations are presented that aim to provide the foundation for future work in modelling user affect to enhance interaction experience in virtual environments. The DEAP dataset was used within this work, along with a Support Vector Machine and Random Forest, which yielded reasonable classification accuracies for Valence and Arousal using feature vectors based on statistical measurements and band power from the α, β, δ, and θ waves and High Order Crossing of the EEG signal.

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Section: Applications In Medicinementioning

confidence: 99%

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Menezes

Samara

Galway

et al. 2017

Pers Ubiquit Comput

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“…Nevertheless, in the long run, obtaining responses through EEG has the potential to develop brain-computer interfaces to improve social skills of individuals with ASD [39]. EEG data were also collected in [141][142][143][144]. In [145], Ozcan et al reported a design for wearable companion toys in which biosensors can be embedded to measure the participant's physiological and emotional state.…”

Section: Physiological Data Collectionmentioning

confidence: 99%

“…In [146], physiological data, including PPG, Galvanic Skin Response (GSR), and Respiration (RSP), are collected while a participant with ASD engages in a driving task. Note that in some studies, multimodal data are collected [143]. In addition to physiological data, eye gaze and spoken conversations are also collected [143] to enable adaptive social interaction.…”

Section: Physiological Data Collectionmentioning

confidence: 99%

“…Note that in some studies, multimodal data are collected [143]. In addition to physiological data, eye gaze and spoken conversations are also collected [143] to enable adaptive social interaction.…”

Section: Physiological Data Collectionmentioning

confidence: 99%

“…In addition to eye gaze, we have seen studies that employ various methods to obtain user feedback for adaptive control of a treatment/education program, including facial expression, head orientation, human gesture tracking, and physiological data collection [135,136,141,143,146,151].…”

Section: Automatic Adaption Of Programsmentioning

confidence: 99%

See 2 more Smart Citations

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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Architecture for Cooperative Interacting Robotic Systems Towards Assisted Living: A Preliminary Study

Ciuccarelli

Freddi

Iarlori

et al. 2019

Lecture Notes in Electrical Engineering

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Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD)

Cited by 54 publications

References 41 publications

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

Towards emotion recognition for virtual environments: an evaluation of eeg features on benchmark dataset

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

Architecture for Cooperative Interacting Robotic Systems Towards Assisted Living: A Preliminary Study

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