基于BiLSTM的联合扭矩预测从机械表在同度收缩期间:概念验证研究的概念验证
概括
一个新的深度学习模型可以从机械图 (MMG) 信号预测在同位数收缩期间的脚关节扭矩. 这种工具在临床环境中对例行肌肉强度评估充满希望.
科学领域:
- 生物力学 生物力学
- 生物医学工程 生物医学工程
- 机器学习 机器学习
背景情况:
- 准确量化肌肉力量在临床实践中至关重要.
- 目前用于评估肌肉力量的方法往往缺乏日常使用的实用性.
- 需要易于使用的工具来测量在同度收缩期间的关节扭矩.
研究的目的:
- 开发和评估一个深度学习模型,用于预测脚脚下曲扭矩.
- 为了利用在同位数收缩期间记录的时间序列机械图 (MMG) 信号.
- 评估模型的性能与手动肌肉测试程序相比.
主要方法:
- 设计了四个基于堆叠双向长期短期记忆和密集层的深度学习架构.
- 模型变化包括不同的单位数量 (32-512) 和学率 (0.0-0.8).
- 在10个主体的数据集上使用离开一个主体的交叉验证来评估性能.
主要成果:
- 最优的深度学习模型解释了10个数据集中的9个数据集中70%以上的差异.
- 性能通过减少误差指标 (例如,根-平均平方误差) 和测量和预测扭矩之间的斜率接近1.0来评估.
- 结果表明该模型能够以合理的准确度预测关节扭矩.
结论:
- 拟议的深度学习模型证明了在持续的同度收缩过程中量化平均关节扭矩的可行性.
- 这种方法在临床环境中为例行肌肉力量评估提供了潜在的实用工具.
- 进一步验证可能会提高其对不同人群的临床效用.
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