Summary: Subtle hand movements during grasping tasks may help diagnose autism spectrum disorder (ASD) with high accuracy. Using machine learning, researchers analyzed how autistic and non-autistic individuals used their fingers to grasp objects, achieving approximately 85% classification accuracy.
These motor differences, often detectable early in life, could complement current diagnostic methods that rely on later-developing behavioral signs. The findings may lead to simpler, scalable tools for earlier diagnosis and faster intervention, improving support for autistic individuals.
Key Facts:
- 85% Accuracy: Machine learning classified autism based on grasping motions with high precision.
- Early Motor Signals: Subtle movement differences may allow for earlier ASD diagnosis than current methods.
- Scalable Potential: Natural hand movement analysis offers a simpler, accessible diagnostic approach.
Source: York University
Getting a timely diagnosis of autism spectrum disorder is a major challenge, but new research out of York University shows that how young adults, and potentially children, grasp objects could offer a simpler way to diagnose someone on the autism spectrum.
The team, part of an international collaboration, used machine learning to analyze naturalistic hand movements – specifically, finger motions during grasping – in autistic and non-autistic individuals.
“Our models were able to classify autism with approximately 85 per cent accuracy, suggesting this approach could potentially offer simpler, scalable tools for diagnosis,” says lead author, Associate Professor Erez Freud of York’s Department of Psychology and the Centre for Vision Research.
“Autism currently affects about one in 50 Canadian children, and timely, accessible diagnosis remains a major challenge. Our findings add to the growing body of research suggesting that subtle motor patterns may provide valuable diagnostic signals – something not yet widely leveraged in clinical practice.”
In addition to social and communication challenges, autism, a neurodevelopmental disorder,
can include motor abnormalities which often show up in early childhood. The researchers say testing for these motor movements early could lead to faster diagnoses and intervention.
“The main behaviours markers for diagnosis are focused on those with relatively late onset and the motor markers that can be captured very early in childhood may thus lower age of diagnosis,” says Professor Batsheva Hadad of the University of Haifa, an expert in autism research and a key collaborator in this study.
Autistic and non-autistic young adult participants were asked to use their thumbs and index fingers, which had tracking markers attached, to grasp different blocks of varying size, lift each one and replace it in the same spot, and put their hand back in the starting position.
The researchers used machine learning to analyze the participants’ finger movements as they made grasping motions.
Both groups of participants had normal IQ and were matched on age and intelligence. Young adults were used instead of children to rule out any differences in the findings due to delayed development.
The research found that subtle motor control differences can be captured effectively with more than 84 per cent accuracy. The study also showed there were distinct kinematic properties in the grasping movements between autistic and non-autistic participants.
Analysis of naturalistic precision grasping tasks has not typically been used in previous studies, says Freud. Machine learning, however, provides researchers with a powerful new tool to analyze motor patterns, opening new ways to use movement data in the assessment of autism spectrum disorder.
The findings, says Freud, could lead to the development of more accessible and reliable diagnostic tools as well as timely intervention and support that could improve outcomes for autistic individuals in the future.
About this Autism research news
Author: Sandra McLean
Source: York University
Contact: Sandra McLean – York University
Image: The image is credited to Neuroscience News
Original Research: Open access.
“Effective autism classification through grasping kinematics” by Erez Freud et al. Autism Research
Abstract
Effective autism classification through grasping kinematics
Autism is a complex neurodevelopmental condition, where motor abnormalities play a central role alongside social and communication difficulties.
These motor symptoms often manifest in early childhood, making them critical targets for early diagnosis and intervention.
This study aimed to assess whether kinematic features from a naturalistic grasping task could accurately distinguish autistic participants from non-autistic ones.
We analyzed grasping movements of autistic and non-autistic young adults, tracking two markers placed on the thumb and index finger. Using a subject-wise cross-validated classifiers, we achieved accuracy scores of above 84%.
Receiver operating characteristic analysis revealed strong classification performance with area under the curve values of above 0.95 at the subject-wise analysis and above 0.85 at the trial-wise analysis.
These findings indicate strong reliability in accurately distinguishing autistic participants from non-autistic ones.
These findings suggest that subtle motor control differences can be effectively captured, offering a promising approach for developing accessible and reliable diagnostic tools for autism.
