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EngageME

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Cerrado

Automated Measurement of Engagement Level of Children with Autism Spectrum Condi...

Automated Measurement of Engagement Level of Children with Autism Spectrum Conditions during Human robot Interaction Engaging children with ASC (Autism Spectrum Conditions) in communication centred activities during educational therapy is one of the cardinal challenges by ASC and contributes to its poor outcome. To this end, therapists recently... ver más

30/09/2019

UAU

240K€

Presupuesto del proyecto: 240K€

Líder del proyecto

UNIVERSITAET AUGSBURG No se ha especificado una descripción o un objeto social para esta compañía.

Fecha límite participación Sin fecha límite de participación.

Ver 4 Participantes

Financiación concedida El organismo H2020 notifico la concesión del proyecto el día 2019-09-30

MSCA-IF-2015-GF: Marie Skłodowska-Curie Individual F...

I+D

Cerrada hace 9 años

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Información adicional privada

No hay información privada compartida para este proyecto. Habla con el coordinador.

4 Participantes

UAU

239.86K€ | Lider

MASSACHUSETTS INSTITUTE OF T...

UNI PASSAU

N.D. | Participante

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Duración del proyecto: 42 meses Fecha Inicio: 2016-03-11
Fecha Fin: 2019-09-30

Líder del proyecto

UNIVERSITAET AUGSBURG No se ha especificado una descripción o un objeto social para esta compañía.

Presupuesto del proyecto 240K€

Fecha límite de participación Sin fecha límite de participación.

Descripción del proyecto Engaging children with ASC (Autism Spectrum Conditions) in communication centred activities during educational therapy is one of the cardinal challenges by ASC and contributes to its poor outcome. To this end, therapists recently started using humanoid robots (e.g., NAO) as assistive tools. However, this technology lacks the ability to autonomously engage with children, which is the key for improving the therapy and, thus, learning opportunities. Existing approaches typically use machine learning algorithms to estimate the engagement of children with ASC from their head-pose or eye-gaze inferred from face-videos. These approaches are rather limited for modeling atypical behavioral displays of engagement of children with ASC, which can vary considerably across the children. The first objective of EngageME is to bring novel machine learning models that can for the first time effectively leverage multi-modal behavioural cues, including facial expressions, head pose, vocal and physiological cues, to realize fully automated context-sensitive estimation of engagement levels of children with ASC. These models build upon dynamic graph models for multi-modal ordinal data, based on state-of-the-art machine learning approaches to sequence classification and domain adaptation, which can adapt to each child, while still being able to generalize across children and cultures. To realize this, the second objective of EngageME is to provide the candidate with the cutting-edge training aimed at expanding his current expertise in visual processing with expertise in wearable/physiological, and audio technologies, from leading experts in these fields. EngageME is expected to bring novel technology/models for endowing assistive robots with ability to accurately ‘sense’ engagement levels of children with ASC during robot-assisted therapy, while providing the candidate with a set of skills needed to become one of the frontiers in the emerging field of affect-sensitive assistive technology.

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