Introduces a novel gait-state time-interval (GSTI) signal derived from multizonal plantar load distribution and an Integrative Body Intelligence framework using spectral/bispectral features from insole sensors to classify Parkinson's disease with 94.44% accuracy and enable acoustic/visual…

Provides a promising noninvasive wearable method for early PD detection and individualized gait rehabilitation that could aid monitoring and trial stratification, but it offers little mechanistic or therapeutic-target insight for drug discovery.

Multidomain and multimodal identification of the walking gait-cycle states is important for detecting and monitoring locomotion disorders such as Parkinson's disease (PD). We propose a novel multizonal clustering and multi-level thresholding method based on analyzing multizonal plantar load distribution for generating a discrete gait-state time-interval (GSTI) signal to improve PD diagnosis accuracy and the effectiveness of rehabilitation through personalized strategies. Multidomain analysis of the GSTI signal shows a novel coupled I.Baryskievic-H.Li bio-oscillator interpreted as a GSTI-derived signal-level oscillatory signature that may be associated with a central nervous system (CNS)-related locomotor rhythm organization. The bio-oscillator consists of two interconnected oscillations with distinct resonant spectral peaks at specific natural frequencies and phase coupling (nonlinearity) between two frequency components. We propose a multidomain feature level of layered Integrative Body Intelligence (IBI) framework to identify lower and higher-order interactions between gait cycle states. The proposed multimodal data level of IBI involves the proposed acoustic and visual biofeedback based on a novel acoustic harmonic plantar pressure model and a 3D gait state portrait of the GSTI signal used for walking gait monitoring and personalized rehabilitation assessment in PD. Experiments on a publicly available PD plantar-insole dataset show that the Multilayer Perceptron (MLP) model based on the selected multidomain (time-interval, spectral, and bispectral) feature subset achieves classification accuracy (94.44%), and offers a trade-off between model complexity and performance for PD recognition. This result suggests that it is possible to accurately diagnose early-stage PD through merely testing patients' GSTI signal.