神经增强运动到EMG:通过使用Seq2Seq方法从肌肉骨模型改进模拟的肌肉活动
Tatsuya Teramae1, Takamitsu Matsubara1,2, Tomoyuki Noda1
1Department of Brain Robot Interface, Computational Neuroscience Laboratories, Advanced Telecommunications Research Institute International, Kyoto, Japan.
Frontiers in bioengineering and biotechnology
|August 11, 2025
概括
这项研究引入了一个新的框架,以改善肌肉活动估计,以便更好地康复和体育科学应用. 神经增强运动到EMG (NEM2E) 框架完善了模拟的肌肉活动,提高了运动分析的准确性.
科学领域:
- 生物力学 生物力学
- 计算神经科学是一种神经科学.
- 康复工程 康复工程 康复工程
背景情况:
- 电肌图 (EMG) 对于运动功能评估至关重要,但由于耗时的程序而受到限制.
- 像OpenSim这样的肌肉骨模拟模型越来越多地被使用,但准确的肌肉活动估计仍然是一个挑战.
- 优化肌肉骨模型的现有方法在估计准确度上显示出有限的改进.
研究的目的:
- 为了解决模拟肌肉活动估计中的时空扭曲.
- 引入神经增强运动到EMG (NEM2E) 框架,以改进肌肉活动预测.
- 在肌肉骨模型中提高肌肉活动估计的准确性.
主要方法:
- 开发了空间时间扭曲改进网络 (STDR-Net),使用具有注意力机制的序列对序列模型.
- 将STDR-Net集成到神经增强运动到EMG (NEM2E) 框架中,以减轻时空扭曲.
- 验证了NEM2E公共步行和跑步运动数据集的框架.
主要成果:
- NEM2E框架显著提高了模拟肌肉活动估计的准确性.
- 在跑步数据集中的所有五个肌肉中都实现了增强估计.
- 在行走数据集中,五个肌肉中的两个肌肉的准确性得到了改善.
结论:
- 通过解决时空扭曲,NEM2E框架有效地改进了OpenSim生成的肌肉活动估计.
- 拟议的STDR-Net在提高肌肉活动分析的精度方面表现有前途.
- 这种方法有可能在康复,体育科学和机器人技术中推进个性化应用.
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