ArticleNPJ Parkinson's disease2024
Distinguishing features of Parkinson's disease fallers based on wireless insole plantar pressure monitoring.
Article in NPJ Parkinson's disease, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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Who cites it
10 citing papers in PubMed, 12 citations in OpenAlex.
- Current Trends in AI Gait Analysis for the Detection and Assessment of Parkinson's Disease Severity: Systematic Review and Meta-Analysis of Performance Using Logit Transformation.Healthcare (Basel, Switzerland) · 2026Review
- Classification of fallers in Parkinson's disease through machine learning based feature analysis.NPJ Parkinson's disease · 2026Article
- Automatic and explainable assessment for Parkinson's disease by video-based human motion understanding.Journal of neuroengineering and rehabilitation · 2026Article
- Insole-Derived Plantar Pressure Variability Reveals Dual-Task Gait Differences in Early-Stage Parkinson's Disease.IEEE journal of translational engineering in health and medicine · 2026Article
- A Preliminary Mechanics-Informed Machine Learning Framework for Objective Assessment of Parkinson's Disease and Rehabilitation Outcomes.Diagnostics (Basel, Switzerland) · 2025Article
- Multi-Domain CoP Feature Analysis of Functional Mobility for Parkinson's Disease Detection Using Wearable Pressure Insoles.Sensors (Basel, Switzerland) · 2025Article
- Flexible Smart Insole and Plantar Pressure Monitoring Using Screen-Printed Nanomaterials and Piezoresistive Sensors.ACS applied materials & interfaces · 2025Article
- A wireless, self-powered smart insole for gait monitoring and recognition via nonlinear synergistic pressure sensing.Science advances · 2025Article
- Diagnosis and Severity Rating of Parkinson's Disease Based on Multimodal Gait Signal Analysis With GLRT and ST-CNN-Transformer Networks.IEEE journal of translational engineering in health and medicine · 2025Article
- Inter-rater reliability of hand motor function assessment in Parkinson's disease: Impact of clinician training.Clinical parkinsonism & related disorders · 2024Article
Corrections and comments
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Authors and funding
4 authors at 1 institution in 1 country.
Funding
Abstract
Postural instability is one of the most disabling motor signs of Parkinson's disease (PD) and often underlies an increased likelihood of falling and loss of independence. Current clinical assessments of PD-related postural instability are based on a retropulsion test, which introduces human error and only evaluates reactive balance. There is an unmet need for objective, multi-dimensional assessments of postural instability that directly reflect activities of daily living in which individuals may experience postural instability. In this study, we trained machine-learning models on insole plantar pressure data from 111 participants (44 with PD and 67 controls) as they performed simulated static and active postural tasks of activities that often occur during daily living. Models accurately classified PD from young controls (area under the curve (AUC) 0.99+/- 0.00), PD from age-matched controls (AUC 0.99+/- 0.01), and PD fallers from PD non-fallers (AUC 0.91+/- 0.08). Utilizing features from both static and active postural tasks significantly improved classification performances, and all tasks were useful for separating PD from controls; however, tasks with higher postural threats were preferred for separating PD fallers from PD non-fallers.
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Registered trials
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.