Population-invariant measurement of IMU-based gait biomarkers for automated Parkinson’s disease diagnosis

Abstract

Accurate and scalable Parkinson’s disease (PD) screening currently demands extensive clinician time and costly imaging or laboratory resources. Gait analyze have emerged as a promising digital biomarker, yet cohort-specific variability often obscures disease signals and undermines cross-group performance. We present a population-invariant IMU signal measurement framework that extracts robust gait biomarkers for PD diagnosis. Using two shank-mounted inertial measurement units (IMUs), our method applies Multivariate Singular Spectrum Analysis (MSSA) to five consecutive gait cycles, systematically isolates and removes cohort-confounding modes, and then reconstructs purified gait signals. Statistical validation via the Bhattacharyya distance demonstrates a marked reduction in inter-cohort variance while preserving diagnostic features. Evaluated on a diverse population of 127 subjects—spanning young healthy, middle-aged healthy, older healthy, middle-aged PD, and older PD groups—this lightweight, low-cost pipeline achieves 94.5 % cross-validated diagnostic accuracy. By delivering universal gait biomarkers that transcend age and demographic differences, our approach minimizes cohort bias, enhances generalizability, and paves the way toward automated, precision-diagnostic tools for Parkinson’s disease.