ArticleJournal of neuroengineering and rehabilitation2025
Achieving precision assessment of functional clinical scores for upper extremity using IMU-Based wearable devices and deep learning methods.
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 9 papers.
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Who cites it
9 citing papers in PubMed.
- Contribution of artificial intelligence research to the development of inclusive applications for people with disabilities: A scoping review (2015-2025).PLOS digital health · 2026Article
- A wearable sensor-based kinematic dataset collected under standardized rehabilitation tasks from 120 post-stroke patients.Scientific data · 2026Article
- Feature-Level Fusion of Surface Electromyography and Mechanomyography Signals for MVC-Normalized Shoulder Abduction Force-Level Classification in Healthy Adults.Sensors (Basel, Switzerland) · 2026Article
- Quantifying Upper Limb Movement During Naturalistic Driving: A Clinically Informed Ecological Approach.Sensors (Basel, Switzerland) · 2026Article
- White Matter Structural Biomarkers Derived from Diffusion Tensor Imaging to Estimate Upper-Extremity Motor Function in Stroke Using Machine Learning.Brain topography · 2026Article
- A Compensatory-Topology and Relay-Gating Graph Network for multimodal stroke rehabilitation assessment.Frontiers in physiology · 2026Article
- Automated EMG-Based Classification of Upper Extremity Motor Impairment Levels in Subacute Stroke.Sensors (Basel, Switzerland) · 2025Article
- Estimation of Fugl-Meyer Assessment Upper-Extremity Sub-Scores Using a Mixup-Augmented LSTM Autoencoder and Wearable Sensor Data.Sensors (Basel, Switzerland) · 2025Article
- A narrative review of AI monitoring in postoperative pain management and functional rehabilitation for spinal cord injury.Frontiers in neurologyReview
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Stroke is a serious cerebrovascular disease, and rehabilitation following the acute phase is particularly crucial. Not all rehabilitation outcomes are favorable, highlighting the necessity for personalized rehabilitation. Precision assessment is essential for tailored rehabilitation interventions. Wearable inertial measurement units (IMUs) and deep learning approaches have been effectively employed for motor function prediction. This study aims to use machine learning techniques and data collected from IMUs to assess the Fugl-Meyer upper extremity subscale for post-stroke patients with motor dysfunction. IMUs signals from 120 patients were collected during a clinical trial. These signals were fed into a gated recurrent unit network to complete the scoring of individual actions, which were then aggregated to obtain the total score. Simultaneously, on the basis of the internal correlation between the Fugl-Meyer assessment and the Brunnstrom scale, Brunnstrom stage prediction models of the arm and hand were established via the random forest and extremely randomized trees algorithm. The experimental results show that the proposed models can score Fugl-Meyer items with a high accuracy of 92.66%. The R
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Registered trials
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