Combining computational techniques with movement data to predict adult autism diagnosis

Autism is a life-long developmental condition that affects how a person communicates and interacts with people. In addition to these social aspects, ~80% of autistic individuals have coordination difficulties such as poor eye-hand coordination, unstable balance and unusual gait. The healthcare aim of this project is to uncover whether these coordination difficulties can be used to diagnose autistic adults.
Currently, diagnosis of autistic adults is difficult and time consuming and autistic adults have placed the need for earlier and improved diagnosis in their top 10 research priorities. This is because existing diagnostic criteria have not been validated in an adult population, autistic adults have developed compensatory strategies and the subjective nature of the observational inventories mean that diagnosis can vary between clinicians. Consequently, access to valuable support is delayed. This project will combine motion tracking data collected during movement tasks with data science methods to investigate whether an automated test based on coordination skills could provide added value for diagnostic precision, when used in combination with current observational inventories. Using movement tasks to diagnose adults is advantageous over current methods as coordination difficulties occur throughout the lifespan and movement can be measured quantitatively and objectively, providing a rich dataset to identify discriminating features.
Our recent published EPSRC-funded work demonstrates the potential of this approach: Machine Learning (ML) techniques were successfully applied to motion tracking data from 44 autistic and non-autistic participants. We now need to create new models on a wider repertoire of movements and larger sample size to enable identification of consistent motor patterns that will increase classification accuracy.
Objectives
Objective 1: To collect data on a wider range of motor tasks and develop robust classification tools
Objective 2: To collect data and test the robustness of the models on a larger group of autistic individuals, as well as those with motor disorders (Parkinson's Disease (PD), Developmental Coordination Disorder (DCD)).
Objective 3: To identify whether coordination difficulties can be divided into different subgroups, using deep clustering approaches.
Approach
Autistic and non-autistic adults will perform different actions (e.g. grasping a cup, walking, balancing) while motion sensors track their movements. Algorithms will be developed to extract the relevant movement parameters for each task. First, group means and discriminative features for classification will be identified for each movement task using feature extraction and selection methods. Second, supervised learning, classification and deep neural learning methods will be investigated to generate robust classifiers, which enable the detection of autistics from controls. We will continue our recent developments on interpretable deep learning (understanding why the network made a particular decision) by comparing variability based classical statistics with deep learning approaches. New autistic and non-autistic participants will then be used to test the robustness of models along with participants with PD and DCD. Clustering techniques such as K-nearest neighbours will be used to identify any subgroups within the autistic data, which differ from non-autistic individuals.
Novelty/Potential outcomes
The result will be a prototype ML/artificial intelligence tool to identify those at risk from having autism, supporting clinical decision making and leading to earlier and quicker diagnosis. In addition, the project will develop novel analytical science tools using ML and deep neural learning to create robust models. The research aligns with the grand challenge within the Healthcare Technologies theme: "Optimising Treatment: Optimising care through effective diagnosis, patient-specific prediction and evidence-based intervention"