ArticlePloS one2017
Vertical ground reaction force marker for Parkinson's disease.
Article in PloS one, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers, 1 of them a synthesis that pooled it.
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Who cites it
40 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Machine Learning for the Diagnosis of Parkinson's Disease: A Review of Literature.Frontiers in aging neuroscience · 2021Pooled it
- A comprehensive survey on diagnosis and assessment of Parkinson's disease via plantar pressure analysis.NPJ Parkinson's disease · 2026Article
- Longitudinal characterization of biomechanical alterations in a Parkinson's disease mouse model using three-dimensional ground reaction forces.Clinical parkinsonism & related disorders · 2026Article
- A new parallel-path ConvMixer neural network for predicting neurodegenerative diseases from gait analysis.Medical & biological engineering & computing · 2025Article
- Parkinson disease detection based on in-air dynamics feature extraction and selection using machine learning.Scientific reports · 2025Article
- Using machine learning to identify Parkinson's disease severity subtypes with multimodal data.Journal of neuroengineering and rehabilitation · 2025Article
- Detection and Severity Assessment of Parkinson's Disease Through Analyzing Wearable Sensor Data Using Gramian Angular Fields and Deep Convolutional Neural Networks.Sensors (Basel, Switzerland) · 2025Article
- Comparison of feetme insoles with a motion capture system coupled to force plates for assessing gait and posture.Scientific reports · 2025Article
- Estimating gait parameters from sEMG signals using machine learning techniques under different power capacity of muscle.Scientific reports · 2025Article
- Wearable fall risk assessment by discriminating recessive weak foot individual.Journal of neuroengineering and rehabilitation · 2025Article
- Beyond clinical scales: an observational study on instrumental gait analysis and biomechanical patterns in patients with Parkinson's disease.Frontiers in bioengineering and biotechnology · 2025Article
- Machine learning approach to gait analysis for Parkinson's disease detection and severity classification.Frontiers in robotics and AI · 2025Article
- Vision-based approach to knee osteoarthritis and Parkinson's disease detection utilizing human gait patterns.PeerJ. Computer science · 2025Article
- CRISP: a correlation-filtered recursive feature elimination and integration of SMOTE pipeline for gait-based Parkinson's disease screening.Frontiers in computational neuroscience · 2025Article
- Deep learning techniques for detecting freezing of gait episodes in Parkinson's disease using wearable sensors.Frontiers in physiology · 2025Article
- Exploring the Potential Imaging Biomarkers for Parkinson's Disease Using Machine Learning Approach.Bioengineering (Basel, Switzerland) · 2024Article
- Gait classification for early detection and severity rating of Parkinson's disease based on hybrid signal processing and machine learning methods.Cognitive neurodynamics · 2024Article
- The ground reaction force pattern during walking under vestibular-demanding task with/without mastoid vibration: implication for future sensorimotor training in astronauts.Frontiers in physiology · 2024Article
- Vertical Ground Reaction Forces in Parkinson's Disease: A Speed-Matched Comparative Analysis with Healthy Subjects.Sensors (Basel, Switzerland) · 2023Article
- Inverse distance weighting to rapidly generate large simulation datasets.Journal of biomechanics · 2023Article
Corrections and comments
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Authors and funding
5 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Parkinson's disease (PD) patients regularly exhibit abnormal gait patterns. Automated differentiation of abnormal gait from normal gait can serve as a potential tool for early diagnosis as well as monitoring the effect of PD treatment. The aim of current study is to differentiate PD patients from healthy controls, on the basis of features derived from plantar vertical ground reaction force (VGRF) data during walking at normal pace. The current work presents a comprehensive study highlighting the efficacy of different machine learning classifiers towards devising an accurate prediction system. Selection of meaningful feature based on sequential forward feature selection, the swing time, stride time variability, and center of pressure features facilitated successful classification of control and PD gaits. Support Vector Machine (SVM), K-nearest neighbor (KNN), random forest, and decision trees classifiers were used to build the prediction model. We found that SVM with cubic kernel outperformed other classifiers with an accuracy of 93.6%, the sensitivity of 93.1%, and specificity of 94.1%. In comparison to other studies, utilizing same dataset, our designed prediction system improved the classification performance by approximately 10%. The results of the current study underscore the ability of the VGRF data obtained non-invasively from wearable devices, in combination with a SVM classifier trained on meticulously selected features, as a tool for diagnosis of PD and monitoring effectiveness of therapy post pathology.
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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.