Using FSR Data for Refined Early Detection of Parkinson’s Disease with Gait Analysis
DOI:
https://doi.org/10.58445/rars.2453Abstract
Early detection of Parkinson’s disease (PD) is important for improving patient quality of life and slowing disease progression. This research explores the use of force-sensitive resistors (FSRs) in gait analysis to identify early indicators of PD. We use the Gait and Neurological Disorders (GaitND) dataset from PhysioNet. This dataset includes gait data from individuals with Parkinson’s disease, Huntington’s disease, amyotrophic lateral sclerosis, and healthy controls. Analyzed key time-series features such as stride intervals, swing intervals, stance intervals, and double support intervals for the left and right limb. Also we used advanced machine learning techniques like ensemble methods and optimization algorithms to enhance classification accuracy. Using a genetic algorithm we achieved an accuracy of 81.08%. The findings indicate the potential of FSR-based wearable technologies for non-invasive and continuous monitoring of gait patterns. This research also shows the feasibility of using FSRs into other types of diagnostic tools for better PD detection and for early prevention.
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