Subtype Classification and Stage Assessment of Parkinson’s Disease Based on Wearable Sensors

Subtype classification and clinical staging of Parkinson’s disease (PD) hold significant clinical importance, enabling clinicians to develop more precise treatment plans and rehabilitation strategies. Most studies rely on clinical rating scales for subtype classification and staging, lacking more scientifically rigorous and quantitative classification methods. To address this issue, we developed an IMU-based wearable device consisting of a base and wearable modules. The base module is responsible for charging and data transmission, while the wearable module collects lower limb movement data. Using this device, we collected lower limb motion data from 26 PD patients and performed data preprocessing, including posture estimation. We extracted multiple gait parameters from PD patients and investigated the effectiveness of machine learning methods in PD subtype classification and staging. Various train-test split methods were applied for validation, achieving a maximum classification accuracy of 88%. The results indicate that using a limited number of IMUs for PD subtype classification and clinical staging is feasible.