ArticleSensors (Basel, Switzerland)2022
A Vision-Based System for Stage Classification of Parkinsonian Gait Using Machine Learning and Synthetic Data.
Article in Sensors (Basel, Switzerland), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers, 1 of them a synthesis that pooled it.
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Who cites it
6 citing papers in PubMed, 1 synthesis or guideline pooled it, 17 citations in OpenAlex.
- AI Video Analysis in Parkinson's Disease: A Systematic Review of the Most Accurate Computer Vision Tools for Diagnosis, Symptom Monitoring, and Therapy Management.Sensors (Basel, Switzerland) · 2025Pooled it
- Gait stability prediction through synthetic time-series and vision-based data.Frontiers in sports and active living · 2025Article
- Severity Classification of Parkinson's Disease via Synthesis of Energy Skeleton Images from Videos Produced in Uncontrolled Environments.Diagnostics (Basel, Switzerland) · 2024Article
- HydraGAN: A Cooperative Agent Model for Multi-Objective Data Generation.ACM transactions on intelligent systems and technology · 2024Article
- Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning.Bioengineering (Basel, Switzerland) · 2024Article
- A Review of Embedded Machine Learning Based on Hardware, Application, and Sensing Scheme.Sensors (Basel, Switzerland) · 2023Review
Corrections and comments
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Authors and funding
2 authors at 1 institution in 1 country.
Funding
Abstract
Parkinson's disease is characterized by abnormal gait, which worsens as the condition progresses. Although several methods have been able to classify this feature through pose-estimation algorithms and machine-learning classifiers, few studies have been able to analyze its progression to perform stage classification of the disease. Moreover, despite the increasing popularity of these systems for gait analysis, the amount of available gait-related data can often be limited, thereby, hindering the progress of the implementation of this technology in the medical field. As such, creating a quantitative prognosis method that can identify the severity levels of a Parkinsonian gait with little data could help facilitate the study of the Parkinsonian gait for rehabilitation. In this contribution, we propose a vision-based system to analyze the Parkinsonian gait at various stages using linear interpolation of Parkinsonian gait models. We present a comparison between the performance of a k-nearest neighbors algorithm (KNN), support-vector machine (SVM) and gradient boosting (GB) algorithms in classifying well-established gait features. Our results show that the proposed system achieved 96-99% accuracy in evaluating the prognosis of Parkinsonian gaits.
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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.