ArticleAnnual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference2024
Video-based Clinical Gait Analysis in Parkinson's Disease: A Novel Approach Using Frontal Plane Videos and Machine Learning.
Article in Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Use of Computer Vision with Conventional Video Recordings of Gait in Older Adults: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- Remote Assessment of Parkinson Disease Using Deep Learning on Structured Mouse-Trace Data From Suspected Cases: Machine-Learning Pilot Feasibility Study.JMIR formative research · 2026Article
- From RGB-D to RGB-Only: Reliability and Clinical Relevance of Markerless Skeletal Tracking for Postural Assessment in Parkinson's Disease.Sensors (Basel, Switzerland) · 2026Article
- VisionMD-Gait: scalable clinical gait assessment from smartphone videos.Scientific reports · 2026Article
Corrections and comments
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Authors and funding
7 authors.
Funding
Abstract
Gait can be significantly impaired by neurological conditions such as Parkinson's disease (PD). Gait impairments can be quantified by using instrumented gait analysis techniques, but these instrumented techniques are expensive and suffer from limited accessibility in clinical practice. This paper introduces a method utilizing machine learning algorithms for video-based clinical gait analysis of frontal plane videos recorded with readily available devices. Our approach leverages open-source body pose (Google's MediaPipe) and metric depth (ZoeDepth) estimators to calculate spatiotemporal parameters (STP) of gait from videos and demonstrated high agreement and correlation with traditional marker-based motion capture (MoCap). We applied our proposed methodology to gait videos of healthy controls and people with PD (PWP). Our results agree with those obtained with high-end instrumental gait analysis techniques and support the detection of PD from gait videos.Clinical Relevance- This study presents an innovative, user-friendly, and cost-effective solution for clinical gait analysis. It will enable automated gait analysis via videos in gait-impaired individuals such as PWP, paving the way for telemedicine evaluation for movement decline. The future impact is early detection, effective remote monitoring, and new treatments for increased fall risk in an already mobility-challenged population.
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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.