Evidence map›Paper›PMID 42505467›Full record

ArticleBiosensors2026

Region-Specific Information-Theoretic Feature Representation of Wearable Plantar Insole Signals for Parkinson's Disease Gait Assessment.

Hao Li, Xinyu Zhang, Qikai Wang, Jun Ma

Abstract read
In one paragraph

Article in Biosensors, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Hao LiDepartment of Infocommunication Technologies, Belarusian State University of Informatics and Radioelectronics, 220013 Minsk, Belarus.ORCID 0009-0000-9627-5284
Xinyu ZhangDepartment of Infocommunication Technologies, Belarusian State University of Informatics and Radioelectronics, 220013 Minsk, Belarus.ORCID 0009-0002-0029-9770
Qikai WangDepartment of Infocommunication Technologies, Belarusian State University of Informatics and Radioelectronics, 220013 Minsk, Belarus.ORCID 0009-0004-8450-7876
Jun MaDepartment of Infocommunication Technologies, Belarusian State University of Informatics and Radioelectronics, 220013 Minsk, Belarus.ORCID 0000-0002-5326-462X

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Parkinson's disease (PD) is associated with gait impairment, bilateral asymmetry, and increased gait variability, highlighting the need for objective and interpretable wearable gait assessment. Plantar insole recordings directly capture foot-ground loading, but their use in PD assessment is often limited by global or low-order descriptors that do not fully represent regional loading organization. This study proposes a region-specific information-theoretic framework for PD gait assessment using wearable plantar-pressure insoles. Bilateral plantar insole signals were reorganized into five anatomical regions: heel, rearfoot, midfoot, forefoot, and toe. Self-information index (SII), Shannon entropy (EN), negentropy (NEG), sample entropy (SEN), and Kullback-Leibler divergence (KL) features were extracted to characterize self-information fluctuation, probabilistic uncertainty, non-Gaussian organization, temporal irregularity, and directional distributional discrepancy in plantar-pressure dynamics. The resulting feature representation was evaluated at gait-cycle, walking-recording, and subject-independent levels using conventional classifiers, ablation analysis, subject-balanced cycle aggregation, and an information-theoretic three-dimensional feature-space rule model (ITFS-RM). KNN achieved an accuracy of 0.9668 at the gait-cycle level, and MLP achieved an accuracy of 0.9344 at the walking-recording level. Under stricter subject-independent evaluation, the accuracy was 0.8475, and subject-balanced-cycle aggregation achieved an accuracy of 0.8655. Region-specific analysis and ablation experiments showed spatially heterogeneous HC-PD differences, with the toe region showing the most consistent contribution. SII, KL, and NEG provided stable discriminative contributions, particularly in toe-related and regional-transition features. ITFS-RM provided explicit feature combinations, value ranges, and spatial rule boundaries for interpretable walking-recording level and subject-grouped separation. These results support region-specific information-theoretic analysis as an interpretable representation of plantar-pressure dynamics for PD gait assessment and emphasize the need for subject-wise validation when repeated walking recordings are available.

Indexed as

GaitParkinson DiseaseWearable Electronic DevicesFootHumansInformation Theorygait assessmentinformation-theoretic analysisParkinson’s diseaseplantar insole sensorsself-information indexwearable sensing

Identifiers

PMID42505467
PMCPMC13406867

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.