神经运动:开源平台,具有神经机械和深度网络模块,用于在自愿运动期间生成表面EMG信号
Shihan Ma1,2, Irene Mendez Guerra1, Arnault Hubert Caillet1
1Department of Bioengineering, Imperial College London, London, United Kingdom.
PLoS computational biology
|July 3, 2024
概括
NeuroMotion是一个新的开源模拟平台,用于生成电肌图 (EMG) 信号. 该工具通过为自愿运动提供准确,计算高效的EMG合成来增强神经机械研究.
科学领域:
- 生物力学和神经科学 生物力学和神经科学
- 人类运动的计算建模.
背景情况:
- 神经机械研究需要精确的电肌图 (EMG) 信号模拟.
- 现有的EMG模拟模型往往缺乏准确性,多功能性或是计算密集型.
研究的目的:
- 介绍NeuroMotion,一个开源的计算平台,用于合成全频 EMG 信号.
- 克服当前EMG模拟模型在准确性,条件和计算负载方面的局限性.
主要方法:
- 开发了一个模块化平台,集成上肢肌肉骨模型 (OpenSim API),用于发动机单元动作潜能 (MUAP) 生成的深度神经网络 (BioMime) 和发动机单元池模型.
- 通过模拟EMG信号进行自愿的手,手腕和前臂运动来证明平台的功能.
主要成果:
- 在不同的生理条件和运动下展示了动态的MUAP波形变化.
- 验证了使用合成EMG数据来增强联合角度回归的实验数据的使用,使用在合成数据上训练的脊回归器从实验数据中预测联合角度.
结论:
- 在神经机械研究中,NeuroMotion为EMG信号合成提供了一种多功能和高效的工具.
- 该平台促进了因果关系的研究,算法代和非可测量的参数的验证,赋予了人类电生理学的未来研究的力量.
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