ArticleSensors (Basel, Switzerland)2023
Design and Validation of Vision-Based Exercise Biofeedback for Tele-Rehabilitation.
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.
- Advanced Home-Based Shoulder Rehabilitation: A Systematic Review of Remote Monitoring Devices and Their Therapeutic Efficacy.Sensors (Basel, Switzerland) · 2024Pooled it
- An Adaptive IMU-Visual Multimodal Fusion System for Real-Time Exercise Recognition and Movement Quality Assessment.Sensors (Basel, Switzerland) · 2026Article
- [Current applications and prospects of artificial intelligence in burn diagnosis and treatment].Zhongguo xiu fu chong jian wai ke za zhi = Zhongguo xiufu chongjian waike zazhi = Chinese journal of reparative and reconstructive surgery · 2026Review
- Spatio-temporal graph attention network for rehabilitation movement classification.Medical & biological engineering & computing · 2026Article
- Article
- Generalizable CNN-based detection of form and speed errors in physiotherapy using multimodal activity images.Scientific reports · 2025Article
- Enhancing automated lower limb rehabilitation exercise task recognition through multi-sensor data fusion in tele-rehabilitation.Biomedical engineering online · 2024Article
- Joint angle estimation during shoulder abduction exercise using contactless technology.Biomedical engineering online · 2024Article
- Recent Advances in Sensor Technology for Healthcare and Biomedical Applications (Volume II).Sensors (Basel, Switzerland) · 2023Article
- Technological advances in lower-limb tele-rehabilitation: A review of literature.Journal of rehabilitation and assistive technologies engineeringReview
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Tele-rehabilitation has the potential to considerably change the way patients are monitored from their homes during the care process, by providing equitable access without the need to travel to rehab centers or shoulder the high cost of personal in-home services. Developing a tele-rehab platform with the capability of automating exercise guidance is likely to have a significant impact on rehabilitation outcomes. In this paper, a new vision-based biofeedback system is designed and validated to identify the quality of performed exercises. This new system will help patients to refine their movements to get the most out of their plan of care. An open dataset was used, which consisted of data from 30 participants performing nine different exercises. Each exercise was labeled as "Correctly" or "Incorrectly" executed by five clinicians. We used a pre-trained 3D Convolution Neural Network (3D-CNN) to design our biofeedback system. The proposed system achieved average accuracy values of 90.57% ± 9.17% and 83.78% ± 7.63% using 10-Fold and Leave-One-Subject-Out (LOSO) cross validation, respectively. In addition, we obtained average F1-scores of 71.78% ± 5.68% using 10-Fold and 60.64% ± 21.3% using LOSO validation. The proposed 3D-CNN was able to classify the rehabilitation videos and feedback on the quality of exercises to help users modify their movement patterns.
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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.