Integrating IMU Sensors and Dual-Task Timed Up and Go to Identify Biomarkers for Early Stage Parkinson’s Disease Detection

Parkinson’s disease is a progressive neurodegenerative disorder where diagnosis is essential for effective management. However, current diagnostic approaches often lack sensitivity during the early clinical stages of the disease. This study investigates the potential of wearable inertial measurement units combined with machine learning techniques for detecting Parkinson’s disease during mobility tasks. Data were collected from 28 individuals diagnosed with Parkinson’s disease and 34 age-matched healthy controls while performing the Timed Up and Go test and its dual-task variants. A set of gait and mobility features was extracted and refined using two feature selection techniques. Four machine learning models, including Support Vector Machine, Logistic Regression, Linear Discriminant Analysis, and Gradient Boosting, were trained to classify participants. Support Vector Machine and Logistic Regression achieved the highest classification accuracy of 95%. Key discriminative features included turn duration, sit-to-stand time, and stand-to-sit time, which demonstrated potential as objective digital biomarkers for early-stage Parkinson’s disease detection. Additionally, higher motor impairment scores were associated with increased shank range of motion and prolonged turn duration, particularly during dual-task conditions. These findings highlight the potential of wearable sensor-based mobility assessments combined with machine learning as a non-invasive, accessible, and reliable tool for early-stage detection and monitoring of Parkinson’s disease.