ArticleSensors (Basel, Switzerland)2023
UCO Physical Rehabilitation: New Dataset and Study of Human Pose Estimation Methods on Physical Rehabilitation Exercises.
Article in Sensors (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers, 1 of them a synthesis that pooled it.
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Who cites it
10 citing papers in PubMed, 1 synthesis or guideline pooled it, 18 citations in OpenAlex.
- Perception, assessment, and coaching: a systematic review and taxonomy of computer vision-based physical rehabilitation techniques.Frontiers in rehabilitation sciences · 2026Pooled it
- An Adaptive IMU-Visual Multimodal Fusion System for Real-Time Exercise Recognition and Movement Quality Assessment.Sensors (Basel, Switzerland) · 2026Article
- Markerless On-Device Detection of Compensatory Movement Patterns in Upper-Limb Rehabilitation Exercises from Monocular RGB Video: A Validation Study in Healthy Adults.Sensors (Basel, Switzerland) · 2026Article
- Human Pose Estimation for Real-World Deployment: A Review of Methods, Systems, and Applications.Sensors (Basel, Switzerland) · 2026Review
- UCOPhyRehab++: A multi-modal and multi-view dataset for human rehabilitation analysis.Scientific data · 2026Article
- MobiPhysio: A 2D video dataset of physiotherapy exercises for AI-driven assessment and monitoring.Data in brief · 2026Article
- Efficient Thermal Pose Estimation: Balancing Accuracy and Edge Deployment for Smart Home Activity Recognition.Sensors (Basel, Switzerland) · 2026Article
- A real time action scoring system for movement analysis and feedback in physical therapy using human pose estimation.Scientific reports · 2025Article
- Automated Control of Rehabilitation Process in Physical Therapy Using a Novel Human Skeleton-Based Balanced Time Warping Algorithm.Sensors (Basel, Switzerland) · 2025Article
- LLM-FMS: A fine-grained dataset for functional movement screen action quality assessment.PloS one · 2025Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
Abstract
Physical rehabilitation plays a crucial role in restoring motor function following injuries or surgeries. However, the challenge of overcrowded waiting lists often hampers doctors' ability to monitor patients' recovery progress in person. Deep Learning methods offer a solution by enabling doctors to optimize their time with each patient and distinguish between those requiring specific attention and those making positive progress. Doctors use the flexion angle of limbs as a cue to assess a patient's mobility level during rehabilitation. From a Computer Vision perspective, this task can be framed as automatically estimating the pose of the target body limbs in an image. The objectives of this study can be summarized as follows: (i) evaluating and comparing multiple pose estimation methods; (ii) analyzing how the subject's position and camera viewpoint impact the estimation; and (iii) determining whether 3D estimation methods are necessary or if 2D estimation suffices for this purpose. To conduct this technical study, and due to the limited availability of public datasets related to physical rehabilitation exercises, we introduced a new dataset featuring 27 individuals performing eight diverse physical rehabilitation exercises focusing on various limbs and body positions. Each exercise was recorded using five RGB cameras capturing different viewpoints of the person. An infrared tracking system named OptiTrack was utilized to establish the ground truth positions of the joints in the limbs under study. The results, supported by statistical tests, show that not all state-of-the-art pose estimators perform equally in the presented situations (e.g., patient lying on the stretcher vs. standing). Statistical differences exist between camera viewpoints, with the frontal view being the most convenient. Additionally, the study concludes that 2D pose estimators are adequate for estimating joint angles given the selected camera viewpoints.
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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.