A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson’s Disease and Rehabilitation Outcomes Article Swipe
Amirali Hanifi
,
Roozbeh Abedini-Nassab
,
Mohammed N. Ashtiani
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics15222855
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/diagnostics15222855
Background/Objectives: Non-invasive methods for evaluating rehabilitation outcomes in Parkinson’s disease (PD) remain limited. This preliminary study proposes a mechanics-informed machine learning (ML) framework integrating force-plate data with dimensionality reduction, clustering, and statistical analysis to objectively assess motor control and the effects of a targeted intervention. Methods: Twelve PD patients were randomly assigned to a PD control group performing standard exercises or an intervention group incorporating additional transverse-plane trunk motion exercises for 10 weeks. Ground reaction forces and center of pressure (COP) signals were recorded pre- and post-intervention using a force plate, alongside data from six healthy individuals as a benchmark. Features related to postural sway and COP dynamics were extracted and refined using Forward Feature Selection. Dimensionality reduction (t-SNE) and unsupervised clustering (K-means) identified group-level patterns. SHAP values and Cohen’s d quantified feature importance and effect size. Clustering robustness was assessed with bootstrapping, nested cross-validation, and permutation testing. Results: K-means clustering revealed clear pre/post-intervention separation in five of six intervention patients, with post-intervention states shifting toward the control cluster. Clustering showed strong performance (Silhouette 0.77–0.79; Calinski–Harabasz 100.8–184.9; Davies–Bouldin 0.29–0.45). The most predictive features (RMS-SML and PL-SAP) showed large effect sizes (Cohen’s d = –12.1 and –4.53, respectively) distinguishing PD patients from healthy controls. Traditional statistical tests (e.g., ANOVA) failed to detect within-group changes (p > 0.05), but ML-based methods captured subtle, nonlinear postural adaptations. Conclusions: This preliminary mechanics-informed ML framework detects PD-related motor deficits and rehabilitation-induced improvements using force-plate data, warranting validation in larger cohorts.
Related Topics
Concepts
Cluster analysis
Artificial intelligence
Machine learning
Rehabilitation
Dimensionality reduction
Robustness (evolution)
Center of pressure (fluid mechanics)
Missing data
Physical medicine and rehabilitation
Linear discriminant analysis
Computer science
Support vector machine
Pattern recognition (psychology)
Feature (linguistics)
Curse of dimensionality
Trunk
Unsupervised learning
Motion (physics)
Statistical classification
Supervised learning
Coefficient of restitution
Community-based rehabilitation
Feature selection
Physical therapy
Motor learning
Hierarchical clustering
Psychology
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/diagnostics15222855
- https://www.mdpi.com/2075-4418/15/22/2855/pdf?version=1762932756
- OA Status
- gold
- References
- 50
- OpenAlex ID
- https://openalex.org/W7105071296
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W7105071296Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.3390/diagnostics15222855Digital Object Identifier
- Title
-
A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson’s Disease and Rehabilitation OutcomesWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2025Year of publication
- Publication date
-
2025-11-12Full publication date if available
- Authors
-
Amirali Hanifi, Roozbeh Abedini-Nassab, Mohammed N. AshtianiList of authors in order
- Landing page
-
https://doi.org/10.3390/diagnostics15222855Publisher landing page
- PDF URL
-
https://www.mdpi.com/2075-4418/15/22/2855/pdf?version=1762932756Direct link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
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https://www.mdpi.com/2075-4418/15/22/2855/pdf?version=1762932756Direct OA link when available
- Concepts
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Cluster analysis, Artificial intelligence, Machine learning, Rehabilitation, Dimensionality reduction, Robustness (evolution), Center of pressure (fluid mechanics), Missing data, Physical medicine and rehabilitation, Linear discriminant analysis, Computer science, Support vector machine, Pattern recognition (psychology), Feature (linguistics), Curse of dimensionality, Trunk, Unsupervised learning, Motion (physics), Statistical classification, Supervised learning, Coefficient of restitution, Community-based rehabilitation, Feature selection, Physical therapy, Motor learning, Hierarchical clustering, PsychologyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
0Total citation count in OpenAlex
- References (count)
-
50Number of works referenced by this work
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| primary_location.raw_source_name | Diagnostics |
| primary_location.landing_page_url | https://doi.org/10.3390/diagnostics15222855 |
| publication_date | 2025-11-12 |
| publication_year | 2025 |
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