The main objective of the project is to create machine learning algorithms to help identify autism using motion trackers. Potential implications of such work could be far reaching. Diagnosing autism is a long and complex process and a third of families wait more than three years for a diagnosis. Possibility to identify autism using movement characteristics instead of currently used language and social behaviour characteristics could speed up this process. Moreover, diagnoses potentially could be made earlier in the child's development as motor functions emerge before language and most social behaviours. Investigating movement differences is promising. Evidence shows that various motor impairments and especially praxis deficits are common, occurring in >70% of the people with autism. Although not all evidence is consistent, generally large effect sizes are found in controlled laboratory based tests. Recently, there is a great research interest in motor imitation in autism. Motor imitation deficits appear to be more consistent and are likely caused by top-down modulating processes not so much by the impaired ability to perform movements. Ideally, the task which will be used in the project for discriminating between autistic and healthy individuals will capture both, the ability to perform a movement, and the ability to imitate. Developing such task will be one of the main challenges of this project. Numerous theories which aim to explain movement and imitation differences exist but none of them appear to be able to explain all of the available evidence. It is likely that motor and imitative differences in autism are caused by a complex interaction of multiple factors throughout the development and these differences are heterogeneous. Machine learning methods are well suited for detecting interactions between multiple factors and even possibly for exploring heterogeneity. Indeed, another main challenge of this project will be creating new statistical, feature selection and machine learning methods to identify the most discriminative movement features that optimally describe the characteristics of different groups
Funder
Funder
Engineering and Physical Sciences Research Council
Interagency Autism Coordinating Committee (IACC)
Autism Research Database (AFD)
