Wearable Technology for Early Parkinson's Disease Biomarker Identification - A Cross-Sectional Controlled Trial...

Parkinson's Disease (PD) is a progressive neurodegenerative disorder characterized by motor and nonmotor symptoms. Early diagnosis and effective management are crucial but often hindered by limitations in subjective clinical assessments. This paper introduces a novel wearable-based data collection protocol that integrates Inertial Measurement Units (IMUs) and Electromyography (EMG) sensors to objectively assess motor impairments in patients with PD. This single-center, non-randomized, cross-sectional study involves 30 PD patients at varying stages and 30 healthy controls. Participants perform standardized motor tasks from the MDS-UPDRS Part III motor assessment while equipped with sensors that capture kinematic and muscle activity data, providing a comprehensive view of motor function. With this study, we aim to generate high-quality data for advanced analysis, identifying key movement features that distinguish PD symptoms from healthy controls and detecting early-stage markers. By combining kinematic and muscle activity data with patient-specific attributes, the protocol supports the development of personalized musculoskeletal models. This protocol establishes a framework for transitioning from subjective assessments to precise, data-driven methodologies in PD diagnosis. By emphasizing whole-body movements and the synergy of IMUs and EMG sensors, the study addresses critical gaps in PD care, enhancing diagnostic precision, reducing delays, and improving patient outcomes while alleviating the burdens of the healthcare system.