ArticleJournal of neuroengineering and rehabilitation2025
Real-time biofeedback monitoring rehabilitation of distal radius fracture.
Article in Journal of neuroengineering and rehabilitation, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
What it found
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Who cites it
2 citing papers in PubMed.
- Article
- Distal radius morphometry of volar curvature along with scaphoid and lunate facet inclinations and ulnar variance in the Anatolian population.Scientific reports · 2026Article
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Authors and funding
3 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundElderly patients often face challenges in recovering from distal radius fractures (DRFs), and inadequately guided rehabilitation may lead to delayed healing or secondary injury.
objectiveThis study aims to develop a system that monitors patients during rehabilitation exercises and provides real-time biofeedback to improve healing outcomes and minimize the risk of secondary injuries.
methodVolunteers performed a 10-second sequence including relaxation, flexion/extension, radial/ulnar deviation, and fisting. During these exercises, time series data of hand motions and corresponding electromyography (EMG) signals were collected. Musculoskeletal forces and healing outcomes were then computed to build a database for machine learning training and validation. Additionally, EMG signals and musculoskeletal forces were analysed to extract biomechanical insights into rehabilitation dynamics.
resultsVolunteers frequently performed unintended additional movements and exhibited distinct muscle activation patterns, even when instructed to perform specific target motions. Different exercises elicited varying mechanical stimuli, influencing cellular differentiation and ultimately impacting healing outcomes. Furthermore, machine learning predictions closely matched the simulation results.
conclusionIndividual variations in movement execution and muscle activation underscore the influence of personal movement patterns on rehabilitation outcomes. The simulation results highlight the importance of tailoring rehabilitation protocols to specific healing stages, as optimal exercise plans may vary across different phases of recovery. Furthermore, the strong concordance between simulation outcomes and machine learning predictions validates the prototype's potential to deliver real-time estimations of healing progress.
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Registered trials
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