Summary
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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More information & hyperlinks
| Web resources: | https://cordis.europa.eu/project/id/789601 |
| Start date: | 01-07-2018 |
| End date: | 31-12-2019 |
| Total budget - Public funding: | 148 413,00 Euro - 148 413,00 Euro |
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Original description
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.Status
CLOSEDCall topic
ERC-2017-PoCUpdate Date
27-04-2024
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