Ontology highlight
ABSTRACT: Background
Elderly patients often face challenges in recovering from distal radius fractures (DRFs), and inadequately guided rehabilitation may lead to delayed healing or secondary injury.Objective
This 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.Method
Volunteers 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. Ad
SUBMITTER: Li L
PROVIDER: S-EPMC12512404 | biostudies-literature | 2025 Oct
REPOSITORIES: biostudies-literature