ArticleMedical & biological engineering & computing2018
Vision-based gait impairment analysis for aided diagnosis.
Article in Medical & biological engineering & computing, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 18 papers.
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Who cites it
18 citing papers in PubMed.
- Structuring Variability in Human Gait Datasets: A Covariate-Centered Taxonomy and Systematic Review of Image- and Depth-Based Collections.Journal of imaging · 2026Review
- AI Advancements in Healthcare: Medical Imaging and Sensing Technologies.Bioengineering (Basel, Switzerland) · 2025Article
- Towards Explainable Graph Embeddings for Gait Assessment Using Per-Cluster Dimensional Weighting.Sensors (Basel, Switzerland) · 2025Article
- Vision-based approach to knee osteoarthritis and Parkinson's disease detection utilizing human gait patterns.PeerJ. Computer science · 2025Article
- A Lightweight Pathological Gait Recognition Approach Based on a New Gait Template in Side-View and Improved Attention Mechanism.Sensors (Basel, Switzerland) · 2024Article
- FP-GCN: Frequency Pyramid Graph Convolutional Network for Enhancing Pathological Gait Classification.Sensors (Basel, Switzerland) · 2024Article
- Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Vision-based gait analysis for real-time Parkinson disease identification and diagnosis system.Health systems (Basingstoke, England) · 2024Article
- Auto-Gait: Automatic Ataxia Risk Assessment with Computer Vision on Gait Task Videos.Proceedings of the ACM on interactive, mobile, wearable and ubiquitous technologies · 2023Article
- Computer Vision and Machine Learning-Based Gait Pattern Recognition for Flat Fall Prediction.Sensors (Basel, Switzerland) · 2022Article
- Towards a Comprehensive Solution for a Vision-Based Digitized Neurological Examination.IEEE journal of biomedical and health informatics · 2022Article
- Detection and assessment of Parkinson's disease based on gait analysis: A survey.Frontiers in aging neuroscience · 2022Review
- Remote Gait Type Classification System Using Markerless 2D Video.Diagnostics (Basel, Switzerland) · 2021Article
- A Spatiotemporal Deep Learning Approach for Automatic Pathological Gait Classification.Sensors (Basel, Switzerland) · 2021Article
- Machine-learning-based children's pathological gait classification with low-cost gait-recognition system.Biomedical engineering online · 2021Article
- An Intelligent In-Shoe System for Gait Monitoring and Analysis with Optimized Sampling and Real-Time Visualization Capabilities.Sensors (Basel, Switzerland) · 2021Article
- Using a Deep Learning Method and Data from Two-Dimensional (2D) Marker-Less Video-Based Images for Walking Speed Classification.Sensors (Basel, Switzerland) · 2021Article
- Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System.Sensors (Basel, Switzerland) · 2018Article
Corrections and comments
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Gait is a firsthand reflection of health condition. This belief has inspired recent research efforts to automate the analysis of pathological gait, in order to assist physicians in decision-making. However, most of these efforts rely on gait descriptions which are difficult to understand by humans, or on sensing technologies hardly available in ambulatory services. This paper proposes a number of semantic and normalized gait features computed from a single video acquired by a low-cost sensor. Far from being conventional spatio-temporal descriptors, features are aimed at quantifying gait impairment, such as gait asymmetry from several perspectives or falling risk. They were designed to be invariant to frame rate and image size, allowing cross-platform comparisons. Experiments were formulated in terms of two databases. A well-known general-purpose gait dataset is used to establish normal references for features, while a new database, introduced in this work, provides samples under eight different walking styles: one normal and seven impaired patterns. A number of statistical studies were carried out to prove the sensitivity of features at measuring the expected pathologies, providing enough evidence about their accuracy. Graphical Abstract Graphical abstract reflecting main contributions of the manuscript: at the top, a robust, semantic and easy-to-interpret feature set to describe impaired gait patterns; at the bottom, a new dataset consisting of video-recordings of a number of volunteers simulating different patterns of pathological gait, where features were statistically assessed.
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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.