Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder characterized by a persistence of social and communication impairment, and restricted and repetitive behaviors. However, motor disorders have also been described, but not objectively assessed. Most studies showed inefficient eye-hand coordination and motor learning in children with ASD; in other experiments, mechanisms of acquisition of internal models in self-generated movements appeared to be normal in autism. In this framework, we have developed a robotic protocol, recording gaze and hand data during upper limb tasks, in which a haptic pen-like handle is moved along specific trajectories displayed on the screen. The protocol includes trials of reaching under a perturbing force field and catching moving targets, with or without visual availability of the whole path. We acquired 16 typically-developing scholar-age children and one child with ASD as a case study. Speed-accuracy tradeoff, motor performance, and gaze-hand spatial coordination have been evaluated. Compared to typically developing peers, in the force field sequence, the child with ASD showed an intact but delayed learning, and more variable gazehand patterns. In the catching trials, he showed less efficient movements, but an intact capability of exploiting the available a-priori plan. The proposed protocol represents a powerful tool, easily tunable, for quantitative (longitudinal) assessment, and for subject-tailored training in ASD.

Casellato, C., Gandolla, M., Crippa, A., Pedrocchi, A. (2017). Robotic set-up to quantify hand-eye behavior in motor execution and learning of children with autism spectrum disorder. In 2017 International Conference on Rehabilitation Robotics (ICORR) QEII Centre, London, UK, July 17-20, 2017 (pp.953-958). IEEE Computer Society [10.1109/ICORR.2017.8009372].

Robotic set-up to quantify hand-eye behavior in motor execution and learning of children with autism spectrum disorder

CRIPPA, ALESSANDRO
Penultimo
;
2017

Abstract

Autism spectrum disorder (ASD) is a multifaceted neurodevelopmental disorder characterized by a persistence of social and communication impairment, and restricted and repetitive behaviors. However, motor disorders have also been described, but not objectively assessed. Most studies showed inefficient eye-hand coordination and motor learning in children with ASD; in other experiments, mechanisms of acquisition of internal models in self-generated movements appeared to be normal in autism. In this framework, we have developed a robotic protocol, recording gaze and hand data during upper limb tasks, in which a haptic pen-like handle is moved along specific trajectories displayed on the screen. The protocol includes trials of reaching under a perturbing force field and catching moving targets, with or without visual availability of the whole path. We acquired 16 typically-developing scholar-age children and one child with ASD as a case study. Speed-accuracy tradeoff, motor performance, and gaze-hand spatial coordination have been evaluated. Compared to typically developing peers, in the force field sequence, the child with ASD showed an intact but delayed learning, and more variable gazehand patterns. In the catching trials, he showed less efficient movements, but an intact capability of exploiting the available a-priori plan. The proposed protocol represents a powerful tool, easily tunable, for quantitative (longitudinal) assessment, and for subject-tailored training in ASD.
poster + paper
autism; motor execution; motor learning; rehabilitation
English
ICORR 2017- 15th IEEE International Conference on Rehabilitation Robotics July 17-20
2017
2017 International Conference on Rehabilitation Robotics (ICORR) QEII Centre, London, UK, July 17-20, 2017
9781538622964
2017
2017
953
958
8009372
none
Casellato, C., Gandolla, M., Crippa, A., Pedrocchi, A. (2017). Robotic set-up to quantify hand-eye behavior in motor execution and learning of children with autism spectrum disorder. In 2017 International Conference on Rehabilitation Robotics (ICORR) QEII Centre, London, UK, July 17-20, 2017 (pp.953-958). IEEE Computer Society [10.1109/ICORR.2017.8009372].
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/10281/168810
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