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

Parkinson’s disease (PD) 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 (IMUs) combined with machine learning (ML) techniques for detecting PD during mobility tasks. Data were collected from 28 individuals diagnosed with PD and 34 age-matched healthy controls while performing the timed up and go (TUG) test and its dual-task variants. A set of gait and mobility features was extracted and refined using two feature selection techniques. Four ML models, including support vector machine (SVM), logistic regression (LR), linear discriminant analysis (LDA), and gradient boosting (GB), were trained to classify participants. SVM and LR 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 PD detection. In addition, 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 ML as a noninvasive, accessible, and reliable tool for early stage detection and monitoring of PD.