从模拟到现实:通过转移学习预测疲劳开始的扭矩
概括
与直接学习相比,转移学习通过将错误减少24.9%来改善肌肉疲劳扭矩预测. 这种方法提高了上肢运动的生物力学模型准确性.
科学领域:
- 生物力学 生物力学
- 人工智能的人工智能
- 人类运动科学科学 人类运动科学
背景情况:
- 肌肉疲劳显著影响上肢功能,但生物力学模型往往忽略了这一点.
- 在疲劳运动中准确预测关节扭矩对于理解和减轻功能限制至关重要.
研究的目的:
- 通过转移学习方法来提高疲劳运动期间肘部曲扭矩的预测准确度.
- 将转移学习模型的性能与直接学习模型和传统生物机械模拟进行比较.
主要方法:
- 开发了两个人工神经网络 (ANN):一个使用记录数据的直接学习,另一个使用转移学习 (在模拟数据上进行预训练,在记录数据上进行微调).
- 使用肌肉骨和肌肉疲劳模型对1701次模拟产生训练前数据.
- 收集了25名健康成年人在持续肘部曲期间的静态主体特征和动态肌肉激活/扭矩.
- 使用长短期内存 (LSTM) 网络架构进行扭矩预测,集成模拟和记录的数据集.
主要成果:
- 与直接学习模型 (8.28Nm) 相比,转移学习模型的平方根平均误差 (6.22Nm) 降低了24.9%.
- 两种ANN模型都超过了传统的肌肉骨模拟,这往往低于预测肘部屈曲扭矩.
- 转移学习表现出更好的稳定性和减少对生物力学模型假设的依赖.
结论:
- 从模拟到记录数据集的转移学习是改善扭矩预测在疲劳的上肢运动中的有效策略.
- 这种方法在现实条件下提高了生物力学预测的准确性和可靠性.
- 这些发现表明了开发更复杂和更适应的人类肌肉功能的模型的有希望的方向.
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