Individual muscle strengths in rehabilitation outcomes of distal radius fracture.

IF 5.2 2区 医学 Q1 ENGINEERING, BIOMEDICAL
Lunjian Li, Xuanchi Liu, Lihai Zhang
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引用次数: 0

Abstract

Background: Distal 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.

Method: A 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.

Results: The 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.

Conclusion: This 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.

个体肌力对桡骨远端骨折康复效果的影响。
背景:桡骨远端骨折(DRFs)是常见的骨折类型,老年患者往往难以实现功能恢复,这可以通过精确的康复来克服。本研究旨在开发一种获取患者特异性肌肉骨骼模型的创新方法,为治疗师量身定制康复计划提供指导。方法:采用可穿戴式肌电检测器(Myo臂带)和测功机(KDG握力测试仪EH101)采集20名志愿者在0、30、50、70和100 N时的肌电信号和握力。收集的数据用于训练神经网络,仅从低级握持数据预测最大握持力。基于一种新颖的缩放函数,从标准肌肉骨骼模型缩放个性化模型,并将其结果与实验结果进行比较验证。随后,在伸展运动中模拟两名不同肌肉力量的志愿者(与基线相比,一名肌肉力量强,另一名肌肉力量弱)的肌肉骨骼力,以研究个体肌肉力量对康复结果的影响。结果:训练后的模型能较好地预测肌电信号的最大握力。基于比例函数,相应的个性化肌肉骨骼模型可以模拟出与实验观察结果很好吻合的握力。肌肉负荷也按比例缩放其缩放系数。然而,接触力与标度系数并不是线性关系。虚弱个体的康复效果令人满意,而强壮个体的康复效果一般。结论:本研究成功开发了一种方便的方法来检测患者的最大握力,验证了肌肉骨骼模型缩放的可行性。收缩力与尺度系数的非线性关系表明了肌肉骨骼系统的复杂性。病例研究的愈合结果表明,虽然适当的机械刺激是有益的,但过度或不适当的刺激会阻碍愈合过程。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Journal of NeuroEngineering and Rehabilitation
Journal of NeuroEngineering and Rehabilitation 工程技术-工程:生物医学
CiteScore
9.60
自引率
3.90%
发文量
122
审稿时长
24 months
期刊介绍: Journal of NeuroEngineering and Rehabilitation considers manuscripts on all aspects of research that result from cross-fertilization of the fields of neuroscience, biomedical engineering, and physical medicine & rehabilitation.
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