ArticleScientific reports2024
Validation of portable in-clinic video-based gait analysis for prosthesis users.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed.
- Synergy-based Functional Electrical Stimulation for gait rehabilitation in chronic stroke: a pilot study.Frontiers in rehabilitation sciences · 2026Trial
- Portable biomechanics laboratory enables clinically accessible movement analysis from a handheld smartphone.NPJ digital medicine · 2026Article
- VisionMD-Gait: scalable clinical gait assessment from smartphone videos.Scientific reports · 2026Article
- A graphical user interface for editing keypoints from human pose estimation algorithms.medRxiv : the preprint server for health sciences · 2025Article
- Classifying simulated gait impairments using privacy-preserving explainable artificial intelligence and mobile phone videos.PLOS digital health · 2025Article
- Diffusion models enable zero-shot pose estimation for lower-limb prosthetic users.PLOS digital health · 2025Article
- Wearable system for the measurement of gait cycle kinematic and kinetic signals.Wearable technologies · 2025Article
- Validation and application of computer vision algorithms for video-based tremor analysis.NPJ digital medicine · 2024Article
- Detecting the symptoms of Parkinson's disease with non-standard video.Journal of neuroengineering and rehabilitation · 2024Article
- Clinical gait analysis using video-based pose estimation: Multiple perspectives, clinical populations, and measuring change.PLOS digital health · 2024Article
- Accuracy of Video-Based Gait Analysis Using Pose Estimation During Treadmill Walking Versus Overground Walking in Persons After Stroke.Physical therapy · 2024Article
- Automated Gait Analysis Based on a Marker-Free Pose Estimation Model.Sensors (Basel, Switzerland) · 2023Article
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Authors and funding
4 authors.
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Abstract
Despite the common focus of gait in rehabilitation, there are few tools that allow quantitatively characterizing gait in the clinic. We recently described an algorithm, trained on a large dataset from our clinical gait analysis laboratory, which produces accurate cycle-by-cycle estimates of spatiotemporal gait parameters including step timing and walking velocity. Here, we demonstrate this system generalizes well to clinical care with a validation study on prosthetic users seen in therapy and outpatient clinics. Specifically, estimated walking velocity was similar to annotated 10-m walking velocities, and cadence and foot contact times closely mirrored our wearable sensor measurements. Additionally, we found that a 2D keypoint detector pretrained on largely able-bodied individuals struggles to localize prosthetic joints, particularly for those individuals with more proximal or bilateral amputations, but after training a prosthetic-specific joint detector video-based gait analysis also works on these individuals. Further work is required to validate the other outputs from our algorithm including sagittal plane joint angles and step length. Code for the gait transformer and the trained weights are available at https://github.com/peabody124/GaitTransformer .
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