Applying machine learning to identify autistic adults using imitation: An exploratory study

Abstract

Autism spectrum condition (ASC) is primarily diagnosed by behavioural symptoms including social, sensory and motor aspects. Although stereotyped, repetitive motor movements are considered during diagnosis, quantitative measures that identify kinematic characteristics in the movement patterns of autistic individuals are poorly studied, preventing advances in understanding the aetiology of motor impairment, or whether a wider range of motor characteristics could be used for diagnosis. The aim of this study was to investigate whether data-driven machine learning based methods could be used to address some fundamental problems with regard to identifying discriminative test conditions and kinematic parameters to classify between ASC and neurotypical controls. Data was based on a previous task where 16 ASC participants and 14 age, IQ matched controls observed then imitated a series of hand movements. 40 kinematic parameters extracted from eight imitation conditions were analysed using machine learning based methods. Two optimal imitation conditions and nine most significant kinematic parameters were identified and compared with some standard attribute evaluators. To our knowledge, this is the first attempt to apply machine learning to kinematic movement parameters measured during imitation of hand movements to investigate the identification of ASC. Although based on a small sample, the work demonstrates the feasibility of applying machine learning methods to analyse high-dimensional data and suggest the potential of machine learning for identifying kinematic biomarkers that could contribute to the diagnostic classification of autism.

Applications of Supervised Machine Learning in Autism Spectrum Disorder Research: a Review

“…Kinematic data was also used by Li et al (2017) in a model to distinguish adults with ASD from adults with TD. Participants included 16 adults with ASD and 14 adults with TD, matched on IQ.…”

Kinematic features of a simple and short movement task to predict autism diagnosis

“…The prospect of developing automated algorithms assisting with ASC identification would speed up the diagnostic process and make it more objective. Several previous studies have applied ML methods for ASC identification and a few also used kinematic data: tracking gameplay with a sensors on a tablet screen surface [5], tracking reach-and-throw a ball in basket movements [6] and tracking a simple movement imitation task [7]. Those studies achieved high classification accuracy rates of 86.7% to 96.7%.…”

The Use of Artificial Intelligence in Screening and Diagnosis of Autism Spectrum Disorder: A Literature Review

“…Third, there remains a lack of clear understanding of the technique that is being used. A number of studies used multiple algorithm approaches and report on the highest predictive value [32,33,36,37,43,44,48,49]. Before arguing on the best algorithm to use, it would be important to understand why there are such differences in the results and the reason as to which approach would be most appropriate depending on the characteristics of the dataset and what sort of an output one is trying to achieve.…”

“…Therefore, researchers have attempted to capture differences in movement patterns to use as a distinctive characteristic of ASD [41,42]. Studies by Li et al [43] and Anzulewicz et al [44], each used imitation based on observation and gesture patterns using smart tablet devices to detect kinematic parameters to use for classifying between ASD and non-ASD.…”