对抗性反控制肌肉长度的变化,以有效地稳定不自愿的姿势
Masami Iwamoto1, Noritoshi Atsumi1, Daichi Kato1
1Human Science Research-Domain, Toyota Central R&D Labs., Inc., 41-1, Yokomichi, Nagakute, Aichi 480-1192, Japan.
Biomimetics (Basel, Switzerland)
|October 25, 2024
概括
这项研究表明,脊椎动物的非自愿姿势稳定是通过肌肉长度反来实现的,而不是肌肉分类. 这一发现是开发舒适的人形控制系统和座椅设计的关键.
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.
- 机器人技术 机器人技术 机器人技术
背景情况:
- 脊椎动物通过协调肌肉激活来表现非自愿的姿势稳定.
- 许多肌肉为稳定较少的关节做出贡献的精确机制仍然不完全理解.
研究的目的:
- 为了研究潜在的计算机制非自愿的姿势稳定.
- 确定最有效的控制策略,以在引力下保持中性身体姿势 (NBP).
主要方法:
- 开发了一个详细的计算人体模型,包括949个肌肉动作线和22个关节.
- 用两种反控制模型应用演员关键强化学习 (ACRL):肌肉长度变化 (FCM-ML) 和关节角度差异.
- 六种控制方法的比较,包括ACRL与正常化高斯网络 (ACRL-NGN) 和深度决定性政策梯度.
主要成果:
- 与FCM-ML相结合的ACRL-NGN证明了最有效的非自愿NBP稳定.
- 这种最佳方法利用了基于肌肉长度变化的对抗性反控制,避免协同模式或肌肉分类 (屈曲器,延伸器等). ) 的情况.
结论:
- 脊椎动物肌肉对姿势的控制很可能在没有预定义的肌肉类别的情况下进行特定的关节动作.
- 肌肉被非自愿地控制,以达到NBP,在重力下最舒适的姿势.
- 配备FCM-ML模型的ACRL-NGN是人形肌肉控制和舒适的座椅设计的有希望的方法.
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