ArticleDigital biomarkers2022
Video-Based Pose Estimation for Gait Analysis in Stroke Survivors during Clinical Assessments: A Proof-of-Concept Study.
Article in Digital biomarkers, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
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Who cites it
47 citing papers in PubMed, 1 synthesis or guideline pooled it.
- A systematic review of the applications of markerless motion capture (MMC) technology for clinical measurement in rehabilitation.Journal of neuroengineering and rehabilitation · 2023Pooled it
- Validation of a Deep Learning-Based Markerless Video System for Gait Speed Assessment During the 10-Meter Walk Test in Outdoor Clinical Settings.Journal of functional morphology and kinesiology · 2026Article
- Evaluation Protocols and Validation for Cameras in Indoor Healthcare Monitoring.Sensors (Basel, Switzerland) · 2026Article
- Use of Computer Vision with Conventional Video Recordings of Gait in Older Adults: A Scoping Review.Healthcare (Basel, Switzerland) · 2026Review
- IMU- and Vision-Based Measurement Techniques for Joint Kinematics: A Narrative Review.Sensors (Basel, Switzerland) · 2026Review
- Automated gait classification: Comparison of automated algorithms to expert classification.Clinical biomechanics (Bristol, Avon) · 2026Article
- Quantitative assessment of the 30-second sit-to-stand test using computer vision technology for physical therapists.Journal of neuroengineering and rehabilitation · 2026Article
- Portable biomechanics laboratory enables clinically accessible movement analysis from a handheld smartphone.NPJ digital medicine · 2026Article
- HierarchicalNets for multi level hierarchical classification of yoga poses.Scientific reports · 2026Article
- Smartphone video-based knee extension moments during chair rise relate to MRI measures of muscle function.medRxiv : the preprint server for health sciences · 2026Article
- A Picture is Worth a Thousand Clinical Assessments: Use of Video-Based Pose Estimation to Augment Post-Stroke Upper Limb Assessment of Bilateral Tasks.Restorative neurology and neuroscience · 2026Article
- Validating Single-Camera Pose Estimation Against Multi-Camera Motion Capture for Accessible Biomechanical Assessment.IEEE access : practical innovations, open solutions · 2026Article
- 3D pose estimation for scalable remote gait kinematics assessment.NPJ digital medicine · 2025Article
- A graphical user interface for editing keypoints from human pose estimation algorithms.medRxiv : the preprint server for health sciences · 2025Article
- Differentiating post-stroke patients from healthy individuals via vision-based skeleton-optical fusion.Journal of neuroengineering and rehabilitation · 2025Article
- Validity of AI-Driven Markerless Motion Capture for Spatiotemporal Gait Analysis in Stroke Survivors.Sensors (Basel, Switzerland) · 2025Article
- Effect of an Ankle Stabilization Strap Using a Badaging Technique on Ankle Range of Motion, Balance, and Spatiotemporal Gait Parameters in Patients with Chronic Stroke: A Randomized Controlled Trial.Life (Basel, Switzerland) · 2025Article
- Vision-Based Assessment of Skeletal Muscle Decline: Correlating Gait Variance with SPPB Performance.Healthcare (Basel, Switzerland) · 2025Article
- Anti-drift pose tracker (ADPT), a transformer-based network for robust animal pose estimation cross-species.eLife · 2025Article
- Gradual restoration of gait following unicompartmental knee arthroplasty: a prospective study.Journal of orthopaedic surgery and research · 2025Article
Corrections and comments
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Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Recent advancements in deep learning have produced significant progress in markerless human pose estimation, making it possible to estimate human kinematics from single camera videos without the need for reflective markers and specialized labs equipped with motion capture systems. Such algorithms have the potential to enable the quantification of clinical metrics from videos recorded with a handheld camera. Here we used DeepLabCut, an open-source framework for markerless pose estimation, to fine-tune a deep network to track 5 body keypoints (hip, knee, ankle, heel, and toe) in 82 below-waist videos of 8 patients with stroke performing overground walking during clinical assessments. We trained the pose estimation model by labeling the keypoints in 2 frames per video and then trained a convolutional neural network to estimate 5 clinically relevant gait parameters (cadence, double support time, swing time, stance time, and walking speed) from the trajectory of these keypoints. These results were then compared to those obtained from a clinical system for gait analysis (GAITRite®, CIR Systems). Absolute accuracy (mean error) and precision (standard deviation of error) for swing, stance, and double support time were within 0.04 ± 0.11 s; Pearson's correlation with the reference system was moderate for swing times (
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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.