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KID SIGNED

KiD: A low-cost KInematic Detector to assist early diagnosis and objective profiling of ASD

Total Cost €

0

EC-Contrib. €

0

Partnership

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Project "KID" data sheet

The following table provides information about the project.

Coordinator
FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA 

Organization address
address: VIA MOREGO 30
city: GENOVA
postcode: 16163
website: www.iit.it

contact info
title: n.a.
name: n.a.
surname: n.a.
function: n.a.
email: n.a.
telephone: n.a.
fax: n.a.

 Coordinator Country Italy [IT]
 Total cost 148˙413 €
 EC max contribution 148˙413 € (100%)
 Programme 1. H2020-EU.1.1. (EXCELLENT SCIENCE - European Research Council (ERC))
 Code Call ERC-2017-PoC
 Funding Scheme ERC-POC
 Starting year 2018
 Duration (year-month-day) from 2018-07-01   to  2019-12-31

 Partnership

Take a look of project's partnership.

# participants  country  role  EC contrib. [€] 
1    FONDAZIONE ISTITUTO ITALIANO DI TECNOLOGIA IT (GENOVA) coordinator 148˙413.00

Map

 Project objective

Autism spectrum disorders (ASDs) are a heterogeneous set of neurodevelopmental disorders characterized by deficits in social communication and reciprocal interactions, as well as stereotypic behaviours. Although early diagnosis followed by appropriate intervention appears to offer the best chance for significant health improvement and economic gain, diagnosis of autism remains complex and often difficult to obtain. Recent identification of atypical kinematic patterns in children and infants at increased risk for ASDs provides new insights into autism diagnostic and objective profiling. KiD intends to help move these insights into the development of a low cost, easy-to-use, yet reliable wearable tracking system, designed to assist detection and classification of ASDs. The novelty of KiD is to combine informed development of machine learning methods to classify kinematic data with a co-design human factor engineering. KiD holds great potential for translational possibilities into autism clinical practice. The main use of the device will be to assist clinicians to achieve expedited diagnosis, ensuring early and timely access of children at risk of autism to evidence-based intervention programs. Another use will be to examine the quantitative nature of autistic traits, enabling new forms of precision-phenotyping, which is potentially useful for stratifying patients with ASD and developing individualized treatment approaches.

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