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相关概念视频

Motor Unit Stimulation01:20

Motor Unit Stimulation

4.7K
When the neuron of a motor unit fires an action potential, it triggers a series of events, leading to a twitch contraction in the muscle fibers. The process of excitation-contraction coupling is crucial in relaying the action potential to the muscle fibers.
The latent period of contraction marks the onset of excitation-contraction coupling, when the action potential propagates across the sarcolemma, preparing the muscle fibers for contraction. As the fibers enter the contraction phase, the...
4.7K

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Construction and Application of a Tactile Somatosensory Comfort Model for Scrubbing Tasks.

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Motion Intention Recognition and DDPG-Based Adaptive Impedance Control for a Robotic Upper-Limb Exoskeleton.

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CNN-Based Modelling Reveals Temporal Brain Dynamics of Auditory Intensity Processing.

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Pathology-Informed Augmentation Improves Cross-Cohort IMU-to-vGRF Estimation Between Healthy Adults and Adults With Osteoarthritis.

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Effects of task-driven head orientations on gait and balance during walking in virtual reality.

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相关实验视频

Updated: May 5, 2026

An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces

Published on: March 10, 2011

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在动态肌电接口中增强和优化用户机器闭环协同适应.

Wei Li, Ping Shi, Sujiao Li

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |April 7, 2025
    PubMed
    概括
    此摘要是机器生成的。

    这项研究引入了肌电接口的新型协同适应策略,增强了身体残疾人的控制. 该系统的完成率为83.37%,提高了设备的可靠性和可用性.

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    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
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    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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    相关实验视频

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    An Experimental Platform to Study the Closed-loop Performance of Brain-machine Interfaces
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    A Human-machine-interface Integrating Low-cost Sensors with a Neuromuscular Electrical Stimulation System for Post-stroke Balance Rehabilitation
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    Real-Time Proxy-Control of Re-Parameterized Peripheral Signals using a Close-Loop Interface
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    科学领域:

    • 康复工程 康复工程 康复工程
    • 人与计算机的交互
    • 生物医学信号处理

    背景情况:

    • 表面电肌图 (sEMG) 和惯性测量单元 (IMU) 数据对于肌电控制至关重要.
    • 现有的肌电接口在未经训练的环境和大空间范围内面临挑战.
    • 用户机器协作是开发有效的协同适应接口的关键.

    研究的目的:

    • 开发和评估用于肌电接口的用户-机器闭环协同适应战略.
    • 在多样化和未经训练的环境中提高肌电控制的效率和可靠性.
    • 为身体残疾人提供增强的感官运动能力.

    主要方法:

    • 提出了一个集成sEMG和IMU数据的多式进步域对抗神经网络 (MPDANN).
    • 在混合现实环境中利用增强现实 (AR) 系统进行全息物体重新定位任务.
    • 实施了基于场景的动态不对称训练方案,以增量学习为持续的系统优化.

    主要成果:

    • MPDANN展示了有效的知识转移和多源域调整.
    • 参与者使用虚拟假肢执行全息物体操纵任务.
    • 在最后一天,MPDANN系统的平均完成率为83.37%±2.50%.

    结论:

    • 拟议的用户-机器闭环协同适应策略显著提高了肌电接口性能.
    • 这种新的方法可以实现跨场景识别,并提高用户对设备的可靠性.
    • 这些发现为设计用于增强感官运动能力的先进肌电接口提供了新的途径.