Early Detection of Mild Cognitive Impairment Through Balance Assessment Using Multi-Location Wearable Inertial Sensors

Abstract

Early detection of Mild Cognitive Impairment (MCI), a prodromal stage of dementia, plays a pivotal role in enabling timely clinical intervention and slowing cognitive decline. This paper presents a multi-sensor balance assessment framework designed to identify MCI-related postural instabilities using a wearable inertial measurement unit (IMU) network. The proposed system employs five synchronized IMUs placed at the waist, thighs, and shanks to capture balance dynamics across four static balance tasks: Eyes-Open, Eyes-Closed, Right-Leg Lift, and Left-Leg Lift. A three-stage feature selection strategy, comprising variance and correlation pruning, univariate filtering, and embedded model selection, is implemented within a Leave-One-Subject-Out (LOSO) cross-validation scheme to extract discriminative sway features. Classification using Support Vector Machines and tree-based ensemble models consistently yields superior results, achieving accuracies between 71.7% and 79.2%, with the highest performance observed in the Eyes-Open condition. A compact 10-feature subset demonstrates stable and robust discriminative power across all tasks. Compared to a single-sensor baseline, the multi-sensor configuration provides improved classification performance, underscoring the feasibility of compact, balance-driven, non-invasive MCI screening through wearable sensor systems.