Machine learning discrimination of Parkinson's Disease stages from walker-mounted sensors data

TL;DR

This study demonstrates that machine learning can accurately classify Parkinson's Disease stages using low-cost, walker-mounted sensors, providing a practical and interpretable tool for clinical gait assessment and disease progression monitoring.

Contribution

It introduces a novel feature selection approach with high interpretability and demonstrates the feasibility of low-cost sensors for accurate PD stage classification.

Findings

Achieved 93% accuracy in classifying PD stages

ANOVA feature selection reduces computation time while maintaining performance

Features reveal insights into gait deterioration in PD

Abstract

Clinical methods that assess gait in Parkinson's Disease (PD) are mostly qualitative. Quantitative methods necessitate costly instrumentation or cumbersome wearable devices, which limits their usability. Only few of these methods can discriminate different stages in PD progression. This study applies machine learning methods to discriminate six stages of PD. The data was acquired by low cost walker-mounted sensors in an experiment at a movement disorders clinic and the PD stages were clinically labeled. A large set of features, some unique to this study are extracted and three feature selection methods are compared using a multi-class Random Forest (RF) classifier. The feature subset selected by the Analysis of Variance (ANOVA) method provided performance similar to the full feature set: 93% accuracy and had significantly shorter computation time. Compared to PCA, this method also enabled clinical interpretability of the selected features, an essential attribute to healthcare applications. All selected-feature sets are dominated by information theoretic features and statistical features and offer insights into the characteristics of gait deterioration in PD. The results indicate a feasibility of machine learning to accurately classify PD severity stages from kinematic signals acquired by low-cost, walker-mounted sensors and implies a potential to aid medical practitioners in the quantitative assessment of PD progression. The study presents a solution to the small and noisy data problem, which is common in most sensor-based healthcare assessments.