Evidence map›Paper›PMID 40551194›Full record

ArticleJournal of neuroengineering and rehabilitation2025

Individual muscle strengths in rehabilitation outcomes of distal radius fracture.

Lunjian Li, Xuanchi Liu, Lihai Zhang

Abstract read
In one paragraph

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 3 papers.

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0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

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2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Article
  2. Article
  3. Real-time biofeedback monitoring rehabilitation of distal radius fracture.Journal of neuroengineering and rehabilitation · 2025
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Lunjian LiDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, VIC, Australia.
Xuanchi LiuDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, VIC, Australia.
Lihai ZhangDepartment of Infrastructure Engineering, The University of Melbourne, Parkville, VIC, Australia. lihzhang@unimelb.edu.au.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundDistal radius fractures (DRFs) are common fracture types and elderly patients often struggle to achieve functional recovery, which could be overcome by precise rehabilitation. This study aims to develop an innovative approach for acquiring patient-specific musculoskeletal models to provide guidelines for therapists to tailor rehabilitation plans individually.

methodA wearable EMG detector (Myo armband) and a dynamometer (KDG grip strength tester, EH101) were used to collect EMG signals and grip forces from 20 volunteers at 0, 30, 50, 70, and 100 N, which were considered low-level gripping. The collected data was used to train neural networks to predict maximum grip force from low-level grip data only. Based on a novel scaling function, personalized models were scaled from a standard musculoskeletal model and were validated by comparing their results with experiments. Sequentially, the musculoskeletal forces of two volunteers with different muscle strengths (one strong in muscle strength and the other is weak, compared to baseline) were simulated under extension exercises to investigate the impact of individual muscle strengths on rehabilitation outcomes.

resultsThe trained model predicts the maximum grip force by EMG signals well. Based on the scaling function, the corresponding personalized musculoskeletal models can simulate grip forces that align well with experiment observations. The muscle loadings were also scaled proportionally to their scaling coefficients. However, the contact forces are not linear to the scaling coefficients. The healing outcome of weak individuals shows satisfactory improvement while that of strong individuals performs ordinarily.

conclusionThis study has successfully developed a convenient approach to detect the maximum grip strength of patients and verified the feasibility of scaling the musculoskeletal models. The non-linear relationship of contract forces to the scaling coefficients indicates the complexity of the musculoskeletal system. The healing outcomes from the case studies suggest that while adequate mechanical stimuli are beneficial, excessive or inappropriate stimuli can impede the healing process.

Indexed as

Hand StrengthMuscle StrengthRadius FracturesAdultAgedElectromyographyFemaleHumansMaleMiddle AgedMuscle, SkeletalMuscle Strength DynamometerNeural Networks, ComputerTreatment OutcomeWrist FracturesDistal radius fractureEMG signalsMusculoskeletal modelNeural networksPatient-specificRehabilitation outcomes

Identifiers

PMID40551194
PMCPMC12186376

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LicenceCC BY-NC-ND
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Registered trials

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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.