同时预测多个未测量的肌肉激活通过协同效应外推
1Human-Centric Design Research Lab, Department of Mechanical Engineering, Texas Tech University, Lubbock, TX 79409.
Journal of biomechanical engineering
|December 24, 2024
概括
这项研究使用肌肉协同推断和EMG驱动模型预测了多个未测量的肌肉激活. 该方法准确地重建深层肌肉活动,以改善关节动力学和康复策略.
科学领域:
- 生物力学 生物力学
- 神经康复疗法 神经康复疗法
- 生物医学工程 生物医学工程
背景情况:
- 估计肌肉力量对于关节动力学和康复至关重要,特别是对于神经系统疾病.
- 表面电肌图 (EMG) 可以捕捉表面肌肉活动,但深层肌肉仍然难以测量.
- 目前的方法通常需要侵入性技术来进行深层肌肉评估,从而限制了实时应用.
研究的目的:
- 开发一种全面的方法,同时预测上肢多个未测量的肌肉激活.
- 用肌肉协同推断和EMG驱动的建模来扩展以前的单个肌肉预测方法.
- 改善肌肉功能的非侵入性评估,以加强生物力学分析和康复.
主要方法:
- 利用非负矩阵因子化将表面EMG数据分解为肌肉协同组件.
- 通过将EMG驱动模型预测与反向动力学之间的差异最小化,重建了未测量的深层肌肉的激活.
- 采用了经过验证的EMG驱动的建模框架,与肌肉协同推断集成.
主要成果:
- 成功地预测了多个未测量的肌肉激活,并且具有很高的准确性.
- 在各种场景中,在预测和实验测量EMG信号之间证明了90%以上的相关性.
- 使用实验收集的肌肉激活数据验证了方法.
结论:
- 拟议的方法提供了一种非侵入性和准确的方法,用于估计上肢深部肌肉激活.
- 这种技术在促进生物力学研究和个性化康复策略方面具有重大潜力.
- 能够更全面地估计肌肉力量,这对于理解和治疗肌肉功能受损至关重要.
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