一个灵感来自生理学的元模式生成器,将意图和动力原始性联系起来,用于人类自愿运动
Miao Zhang1, Ronglei Sun2, Xinyue Zhang1
1Institute of Medical Equipment Science and Engineering, State Key Laboratory of Intelligent Manufacturing Equipment and Technology, School of Mechanical Science and Engineering, Huazhong University of Science and Technology, Wuhan, Hubei Province, 430074, China.
Medical & biological engineering & computing
|October 24, 2025
概括
这项研究引入了一种新的计算模型,用于下肢运动,通过脊柱整合弥合运动意图和关节运动. 该模型准确地预测了关节的角度,促进了神经肌肉骨的理解和假肢的控制.
科学领域:
- 神经科学是一个神经科学.
- 生物医学工程 生物医学工程
- 计算建模 计算建模
背景情况:
- 了解从运动意图到下肢关节运动的神经信号传输对于开发现实的运动生成模型至关重要.
- 现有的模型往往无法将脊柱整合输出与关节运动联系起来,或者完全绕过脊柱处理,缺乏生物相关性.
- 这种差距阻碍了神经假肢和康复机器人的进步.
研究的目的:
- 提出一个灵感来自生理学的计算模型,准确地表示下肢运动神经信号的层次传输.
- 为了应对将脊柱整合输出与关节运动脱的挑战.
- 为了在假肢和机器人中提供意图驱动的运动生成的生物可信基础.
主要方法:
- 开发了一种双层元模式生成器 (MPG),用于模拟脊髓整合,处理运动意图和联合反.
- MPG生成元模式信号,这些信号与关节特定的运动原体进行一对一映射.
- 实验验证涉及五种运动模式的八个受试者,分析了电肌图 (EMG) 信号和关节角度.
主要成果:
- 在元模式 (从EMG) 和运动原始 (从关节角度) 之间发现了一个明确的一对一对应.
- 该模型准确地预测了关节角度,与实验数据密切匹配,平均误差低于1.7%.
- 该模型成功地复制了从意图到联合执行的层次神经信号传输.
结论:
- 拟议的Meta-Pattern Generator (MPG) 模型有效地弥合了脊柱整合和下肢运动中的关节运动之间的差距.
- 这种以生理学为灵感的方法为神经肌肉骨建模提供了重要的见解.
- 该模型展示了推进下肢假肢和康复机器人的神经控制的潜力.
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