Use of Machine Learning to Identify Children with Autism and Their Motor Abnormalities
Abstract
In the present work, we have undertaken a proof-of-concept study to determine whether a simple upper-limb movement could be useful to accurately classify low-functioning children with autism spectrum disorder (ASD) aged 2-4. To answer this question, we developed a supervised machine-learning method to correctly discriminate 15 preschool children with ASD from 15 typically developing children by means of kinematic analysis of a simple reach-to-drop task. Our method reached a maximum classification accuracy of 96.7% with seven features related to the goal-oriented part of the movement. These preliminary findings offer insight into a possible motor signature of ASD that may be potentially useful in identifying a well-defined subset of patients, reducing the clinical heterogeneity within the broad behavioral phenotype.
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Higher performance has been reported for behavior when constructing a model using visual face scanning (88.1%) or goal-oriented reach (96.7%) measures.
“Higher performance has been reported for behavior when constructing a model using visual face scanning (88.1%; 92) or goal-oriented reach (96.7%; 93) measures.”
From Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm (2018) · cites this paper as [93]
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Cites
Lord, C., Rutter, M., & Le Couteur, A. (1994) · Journal of Autism and Developmental Disorders · doi:10.1007/bf02172145 · Cited by 9,065 · see citing works
Motor coordination in autism spectrum disorders: A synthesis and meta-analysis
Fournier, K. A., Hass, C. J., Naik, S. K., Lodha, N., & Cauraugh, J. H. (2010) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-010-0981-3 · Cited by 1,271 · see citing works
Movement analysis in infancy may be useful for early diagnosis of autism
Teitelbaum, P., Teitelbaum, O., Nye, J., Fryman, J., & Maurer, R. G. (1998) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.95.23.13982 · Cited by 713 · see citing works
Underdevelopment of the postural control system in autism
Minshew, N. J., Sung, K., Jones, B. L., & Furman, J. M. (2004) · Neurology · doi:10.1212/01.wnl.0000145771.98657.62 · Cited by 440 · see citing works
Salvatore, C., Cerasa, A., Castiglioni, I., Gallivanone, F., Augimeri, A., Lopez, M., Arabia, G., Morelli, M., Gilardi, M. C., & Quattrone, A. (2014) · Journal of Neuroscience Methods · doi:10.1016/j.jneumeth.2013.11.016 · Cited by 299 · see citing works
Self-citationFreitag, C. M., Kleser, C., Schneider, M., & von Gontard, A. (2007) · Journal of Autism and Developmental Disorders · doi:10.1007/s10803-006-0235-6 · Cited by 146 · see citing works
Atypical diffusion tensor hemispheric asymmetry in autism
Lange, N., DuBray, M. B., Lee, J. E., Froimowitz, M. P., Froehlich, A., Adluru, N., Wright, B., Ravichandran, C., Fletcher, P. T., Bigler, E. D., et al. (2010) · Autism Research · doi:10.1002/aur.162 · Cited by 142 · see citing works
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The Heterogeneity Problem: Approaches to Identify Psychiatric Subtypes
Feczko, E., Miranda-Dominguez, O., Marr, M., Graham, A. M., Nigg, J. T., & Fair, D. A. (2019) · Trends in Cognitive Sciences · doi:10.1016/j.tics.2019.03.009 · Cited by 443 · see citing works
Liu, W., Li, M., & Yi, L. (2016) · Autism Research · doi:10.1002/aur.1615 · Cited by 297 · see citing works
Subtyping cognitive profiles in Autism Spectrum Disorder using a Functional Random Forest algorithm
Feczko, E., Balba, N. M., Miranda-Dominguez, O., Cordova, M., Karalunas, S. L., Irwin, L., Demeter, D. V., Hill, A. P., Langhorst, B. H., Grieser Painter, J., et al. (2018) · NeuroImage · doi:10.1016/j.neuroimage.2017.12.044 · Cited by 173 · see citing works
Ghasemzadeh, H., Hillman, R. E., & Mehta, D. D. (2024) · Journal of Speech, Language, and Hearing Research · doi:10.1044/2023_jslhr-23-00273 · Cited by 72 · see citing works
Costanza, C., Gallai, B., Sorrentino, M., Gnazzo, M., Pisanò, G., Parisi, L., Germanò, E., Maltese, A., Esposito, M., Roccella, M., et al. (2025) · Medicina · doi:10.3390/medicina61081346 · Cited by 5 · see citing works
“These ideas have been reinforced by previous studies indicating differences in upper limb kinematics [9] and variability in micro-movements [10] between individuals with ASD and those who are typically developing (TD).”
- Authors
- Crippa, A., Salvatore, C., Perego, P., Forti, S., Nobile, M., Molteni, M., & Castiglioni, I.
- Affiliations
- IRCCS Eugenio Medea (IT)
- Institute of Molecular Bioimaging and Physiology (IT)
- Corresponding author
- Crippa, Alessandro
- ORCID
- Venue
- Journal of Autism and Developmental Disorders
- Published
- 2015-02-05
- Volume
- 45
- Issue
- 7
- Pages
- 2146-2156
- ISSN
- 0162-3257 (ISSN-L)
- 1573-3432
- OpenAlex
- W2085724875
- Cited by
- 208
- References
- 7
- Citations by year
- 2026: 13 · 2025: 20 · 2024: 20 · 2023: 27 · 2022: 25 · 2021: 32 · 2020: 23 · 2019: 19 · 2018: 7 · 2017: 11 · 2016: 9 · 2015: 2
- FWCI
- 5.29
- Type
- journal article
- Language
- en
- Open access
- closed
- Licence
- www.springer.com/tdm (tdm)
- Locations
- Journal of Autism and Developmental Disorders (publishedVersion)
- PubMed (publishedVersion)
- BOA (University of Milano-Bicocca) (submittedVersion)
- Topics
- Life Sciences › Neuroscience › Cognitive Neuroscience › Autism Spectrum Disorder Research
- Social Sciences › Psychology › Social Psychology › Action Observation and Synchronization
- Social Sciences › Psychology › Developmental and Educational Psychology › Children's Physical and Motor Development
- Keywords
- autism spectrum disorder, low-functioning children, motor abnormalities, upper limb movement, kinematic analysis, ASD classification, preschool children, clinical heterogeneity
- MeSH terms
- Autism Spectrum Disorder, Machine Learning, Biomechanical Phenomena, Child, Preschool, Female, Humans, Infant, Male, Movement, Psychomotor Performance, Upper Extremity
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