ArticleIEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society2020
A Deep Learning Framework for Assessing Physical Rehabilitation Exercises.
Article in IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 1 of them a synthesis that pooled it.
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Who cites it
50 citing papers in PubMed, 1 synthesis or guideline pooled it, 224 citations in OpenAlex.
- Machine Learning-Based Computer Vision for Depth Camera-Based Physiotherapy Movement Assessment: A Systematic Review.Sensors (Basel, Switzerland) · 2025Pooled it
- A Sensor-Augmented Telerehabilitation System for Knee Osteoarthritis: A Randomized Controlled Trial of Neuromuscular, Functional, and Psychosocial Outcomes.Sensors (Basel, Switzerland) · 2025Trial
- Intelligent Wearable Rehabilitation System Based on Edge Computing.Sensors (Basel, Switzerland) · 2026Article
- A hybrid AI vision model for single-camera tracking of rehabilitation exercises: a proof of concept in clinical settings.Scientific reports · 2026Article
- A biomechanical-constrained temporal learning framework for lightweight skeleton-based exercise recognition.Scientific reports · 2026Article
- Integrating visual transformers with biomechanical constraints for action quality assessment in competitive sports.Scientific reports · 2026Article
- Evaluation of Smartphone Camera Positioning on Artificial Intelligence Pose Estimation Accuracy for Exercise Detection: Observational Study.JMIR mHealth and uHealth · 2026Observational
- A Compensatory-Topology and Relay-Gating Graph Network for multimodal stroke rehabilitation assessment.Frontiers in physiology · 2026Article
- Generalizable CNN-based detection of form and speed errors in physiotherapy using multimodal activity images.Scientific reports · 2025Article
- Spike train analysis in rehabilitation movement classification using deep learning approach.Scientific reports · 2025Article
- Musculoskeletal Digital Therapeutics and Digital Health Rehabilitation: A Global Paradigm Shift in Orthopedic Care.Journal of clinical medicine · 2025Review
- Towards an RGB camera-based live repetition counter using auto correlation with action recognition for home rehabilitation.Scientific reports · 2025Article
- Closing the data gap: leveraging pretrained neural networks for robotic surgical assessment on limited clinical data.Journal of robotic surgery · 2025Article
- Multiscale SPD manifold learning for rehabilitation exercise evaluation.Scientific reports · 2025Article
- Smartphone-Based Markerless Motion Capture for Accessible Rehabilitation: A Computer Vision Study.Sensors (Basel, Switzerland) · 2025Article
- Frame topology fusion-based hierarchical graph convolution for automatic assessment of physical rehabilitation exercises.Scientific reports · 2025Article
- A Knee Rehabilitation Exercises Dataset for Postural Assessment using Wearable Devices.Scientific data · 2025Article
- A novel method for assessing cycling movement status: an exploratory study integrating deep learning and signal processing technologies.BMC medical informatics and decision making · 2025Article
- Advancing healthcare practice and education via data sharing: demonstrating the utility of open data by training an artificial intelligence model to assess cardiopulmonary resuscitation skills.Advances in health sciences education : theory and practice · 2025Article
- Feasibility of YOLOX computer model-based assessment of knee function compared with manual assessment for people with severe knee osteoarthritis.BMC medical informatics and decision making · 2025Article
Corrections and comments
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Authors and funding
3 authors at 1 institution in 1 country.
Funding
Abstract
Computer-aided assessment of physical rehabilitation entails evaluation of patient performance in completing prescribed rehabilitation exercises, based on processing movement data captured with a sensory system. Despite the essential role of rehabilitation assessment toward improved patient outcomes and reduced healthcare costs, existing approaches lack versatility, robustness, and practical relevance. In this paper, we propose a deep learning-based framework for automated assessment of the quality of physical rehabilitation exercises. The main components of the framework are metrics for quantifying movement performance, scoring functions for mapping the performance metrics into numerical scores of movement quality, and deep neural network models for generating quality scores of input movements via supervised learning. The proposed performance metric is defined based on the log-likelihood of a Gaussian mixture model, and encodes low-dimensional data representation obtained with a deep autoencoder network. The proposed deep spatio-temporal neural network arranges data into temporal pyramids, and exploits the spatial characteristics of human movements by using sub-networks to process joint displacements of individual body parts. The presented framework is validated using a dataset of ten rehabilitation exercises. The significance of this work is that it is the first that implements deep neural networks for assessment of rehabilitation performance.
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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.