Parkinson’s Disease Classification Using Machine Learning and Wrist Rigidity Measurements from an Active Orthosis
Year: 2025
Type: article
Abstract: Background: Rigidity is a cardinal symptom of Parkinson’s Disease (PD), yet its clinical evaluation remains largely subjective and susceptible to errors. This study introduces an innovative method for objectively classifying individuals with PD by combining an active wrist orthosis with Machine Learning (ML) models. Methods: The orthosis, equipped with current and force sensors, recorded biomechanical signals during passive wrist flexion and extension, from which twelve quantitative features were extracted. Data were collected from 30 participants (15 with PD and 15 Healthy Controls). Nineteen supervised ML algorithms were systematically evaluated through feature selection, cross-validation, and hyperparameter tuning. Results: Using all twelve features, QDA achieved an accuracy of 0.889 and sensitivity of 1.000, followed by GPC (0.778) and LDA (0.778). After applying feature selection with the Correlation-based Feature Subset to reduce redundancy, Extra Trees reached 0.833 accuracy, while both QDA and GPC maintained accuracies of 0.778. This consistency across models, even with a reduced feature set, highlights the robustness of the extracted biomarkers. Conclusions: These findings confirm that wrist rigidity signals provide discriminative quantitative information between PD patients and HC and are able to support PD classification, combining engineering innovation with clinical practice that highlights the potential of integrating wearable devices and ML as a personalized healthcare in PD.
DOI: 10.3390/jsan15010001
Authors: Adriano Alves Pereira, Daniel Hilário da Silva, Caio Tonus Ribeiro, Caroline Valentini de Queiroz, Luanne Cardoso Mendes, Leandro Rodrigues da Silva Souza, Selma Terezinha Milagre, Adriano de Oliveira Andrade, Carlos Dias Maciel
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: gold
Estimated APC paid: $2,413
Awards: G4657603406, G5911270087, G6713836855, G7218604007, EFEITO NEUROMODULADOR, NEUROTRANSMISSOR E NEUROMOTOR DE OITO SEMANAS DE TREINAMENTO DE FORÇA DE ALTA INTENSIDADE ASSOCIADO À REALIDADE VIRTUAL IMERSIVA SOBRE O CONGELAMENTO DA MARCHA EM PACIENTES COM PARKINSON: UM ENSAIO CLÍNICO RANDOMIZADO., G8244817880, G8366194070
OpenAlex ID: W7116125058