ArticleSensors (Basel, Switzerland)2018
Automatic Classification of Gait Impairments Using a Markerless 2D Video-Based System.
Article in Sensors (Basel, Switzerland), 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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Who cites it
16 citing papers in PubMed, 60 citations in OpenAlex.
- New Gait Representation Maps for Enhanced Recognition in Clinical Gait Analysis.Bioengineering (Basel, Switzerland) · 2025Article
- Vision-based approach to knee osteoarthritis and Parkinson's disease detection utilizing human gait patterns.PeerJ. Computer science · 2025Article
- Validity and Reliability of Gait Speed and Knee Flexion Estimated by a Novel Vision-Based Smartphone Application.Sensors (Basel, Switzerland) · 2024Article
- Gait Impairment Analysis Using Silhouette Sinogram Signals and Assisted Knowledge Learning.Bioengineering (Basel, Switzerland) · 2024Article
- Empowering Lower Limb Disorder Identification through PoseNet and Artificial Intelligence.Diagnostics (Basel, Switzerland) · 2023Article
- [A two-dimensional video based quantification method and clinical application research of motion disorders].Sheng wu yi xue gong cheng xue za zhi = Journal of biomedical engineering = Shengwu yixue gongchengxue zazhi · 2023Article
- Measurement of Functional Use in Upper Extremity Prosthetic Devices Using Wearable Sensors and Machine Learning.Sensors (Basel, Switzerland) · 2023Article
- User Profiling to Enhance Clinical Assessment and Human-Robot Interaction: A Feasibility Study.International journal of social robotics · 2023Article
- Walking Speed Classification from Marker-Free Video Images in Two-Dimension Using Optimum Data and a Deep Learning Method.Bioengineering (Basel, Switzerland) · 2022Article
- 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
- Fall Detection of Elderly People Using the Manifold of Positive Semidefinite Matrices.Journal of imaging · 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
- Testing the Performance of an Innovative Markerless Technique for Quantitative and Qualitative Gait Analysis.Sensors (Basel, Switzerland) · 2020Article
- Low-Rank and Sparse Recovery of Human Gait Data.Sensors (Basel, Switzerland) · 2020Article
- Assessment of Parkinsonian gait in older adults with dementia via human pose tracking in video data.Journal of neuroengineering and rehabilitation · 2020Article
Corrections and comments
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Authors and funding
3 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Systemic disorders affecting an individual can cause gait impairments. Successful acquisition and evaluation of features representing such impairments make it possible to estimate the severity of those disorders, which is important information for monitoring patients' health evolution. However, current state-of-the-art systems perform the acquisition and evaluation of these features in specially equipped laboratories, typically limiting the periodicity of evaluations. With the objective of making health monitoring easier and more accessible, this paper presents a system that performs automatic detection and classification of gait impairments, based on the acquisition and evaluation of biomechanical gait features using a single 2D video camera. The system relies on two different types of features to perform classification: (i) feet-related features, such as step length, step length symmetry, fraction of foot flat during stance phase, normalized step count, speed; and (ii) body-related features, such as the amount of movement while walking, center of gravity shifts and torso orientation. The proposed system uses a support vector machine to decide whether the observed gait is normal or if it belongs to one of three different impaired gait groups. Results show that the proposed system outperforms existing markerless 2D video-based systems, with a classification accuracy of 98.8%.
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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.