Automated quantification of stereotypical motor movements in autism using persistent homology
4 affiliations
- 1Department of Mathematics, Northeastern University
- 2Department of Mathematics, Khoury College of Computer Sciences, Northeastern University
- 3Department of Mathematics and Computer Sciences, Ursinus College
- 4Bouve College of Health Sciences, Khoury College of Computer Sciences, Northeastern University
Abstract
Stereotypical motor movements (SMM) are a core diagnostic feature of autism and remain difficult to quantify across individuals and developmental stages in an efficient and valid way. The current paper presents a novel pipeline that leverages Topological Data Analysis to quantify and characterize recurrent movement patterns. Specifically, we use persistent homology to construct low-dimensional, interpretable feature vectors that capture geometric properties associated with SMM in autistic people by extracting periodic structure from time series derived from pose estimation landmarks in video data and accelerometer signals from wearable sensors. We demonstrate that these features, combined with simple classifiers, enable accurate automated quantification of SMM in autistic people. Visualization of the learned feature space reveals that extracted features generalize across individuals and are not dominated by person-specific SMM. Our results highlight the potential of using mathematically principled features to support more scalable, interpretable, and person-agnostic characterization of SMM in autistic people in naturalistic settings.
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BibTeX
@article{mbaye2026automated,
title = {{Automated quantification of stereotypical motor movements in autism using persistent homology}},
author = {Austin A. MBaye and Jose A. Perea and Christopher J. Tralie and Matthew S. Goodwin},
journal = {Scientific Reports},
year = {2026},
month = jul,
doi = {10.1038/s41598-026-60095-8},
url = {https://doi.org/10.1038/s41598-026-60095-8},
note = {CC BY},
}
RIS
TY - JOUR
TI - Automated quantification of stereotypical motor movements in autism using persistent homology
AU - Austin A. MBaye
AU - Jose A. Perea
AU - Christopher J. Tralie
AU - Matthew S. Goodwin
PY - 2026
DA - 2026/07/01
JO - Scientific Reports
DO - 10.1038/s41598-026-60095-8
UR - https://doi.org/10.1038/s41598-026-60095-8
AB - Stereotypical motor movements (SMM) are a core diagnostic feature of autism and remain difficult to quantify across individuals and developmental stages in an efficient and valid way. The current paper presents a novel pipeline that leverages Topological Data Analysis to quantify and characterize recurrent movement patterns. Specifically, we use persistent homology to construct low-dimensional, interpretable feature vectors that capture geometric properties associated with SMM in autistic people by extracting periodic structure from time series derived from pose estimation landmarks in video data and accelerometer signals from wearable sensors. We demonstrate that these features, combined with simple classifiers, enable accurate automated quantification of SMM in autistic people. Visualization of the learned feature space reveals that extracted features generalize across individuals and are not dominated by person-specific SMM. Our results highlight the potential of using mathematically principled features to support more scalable, interpretable, and person-agnostic characterization of SMM in autistic people in naturalistic settings.
SN - 2045-2322
ER -