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相关概念视频

Steps in the Modeling Process01:14

Steps in the Modeling Process

187
Albert Bandura's theory of observational learning identifies four critical processes: attention, retention, motor reproduction, and reinforcement or motivation.
Attention is the first necessary component for observational learning. It involves focusing on what the model is doing and saying. For example, if you decide to take a drawing class to enhance your skills, you need to pay close attention to the instructor's words and hand movements. The characteristics of the model significantly...
187

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相关实验视频

Updated: Jun 9, 2025

A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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基于强化学习的EMG驱动肌肉骨模型个性化的高效框架.

Joseph Berman, I-Chieh Lee, Jie Yin

    IEEE transactions on neural systems and rehabilitation engineering : a publication of the IEEE Engineering in Medicine and Biology Society
    |October 22, 2024
    PubMed
    概括

    这项研究引入了一个快速的AI框架,以个性化电肌图 (EMG) 驱动的肌肉骨模型 (MMs) 来进行假肢控制. 新方法显著加快了模型个性化,提高了假肢手和手腕运动的准确性.

    科学领域:

    • 生物医学工程 生物医学工程
    • 机器人技术 机器人技术 机器人技术
    • 神经科学是一个神经科学.

    背景情况:

    • 电肌图 (EMG) 驱动的肌肉骨模型 (MMs) 对于先进的假肢控制至关重要.
    • 个性化这些模型对于准确和直观的假肢功能至关重要.
    • 当前的个性化方法可能是耗时和计算密集的.

    研究的目的:

    • 开发一种新的,快速的框架,以使用EMG信号来个性化上肢肌肉骨模型.
    • 为了提高手和手腕运动估计的准确性,用于假肢应用.
    • 将拟议框架的效率和有效性与现有方法进行比较.

    主要方法:

    • 用一个通用的上肢肌肉骨模型作为基线.
    • 采用基于人工神经网络的策略,使用强化学习 (RL) 训练参数微调.
    • 将基于RL的框架与基线模型和模拟化 (SA) 进行了比较,以优化.
    • 对非残疾人进行线下评估,对包括截肢者在内的人类进行线上评估.

    主要成果:

    • 与通用MM相比,个性化的MM在线和线下测试中显著减少了运动估计错误.
    • 基于RL的框架在不到一秒的时间内实现了模型优化,大大超过了SA (超过13分钟).

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    Last Updated: Jun 9, 2025

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    Published on: November 6, 2015

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    Subject-specific Musculoskeletal Model for Studying Bone Strain During Dynamic Motion
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  • 个性化模型的动力学估计准确度与较慢的方法相似.

    • 结论:

    • 开发的基于RL的个性化框架为EMG驱动的肌肉骨模型提供了实用且高效的解决方案.
    • 这种方法可以显著有利于上肢假肢和其他辅助设备的日常使用.
    • 快速个性化MMs是提高假肢性能和用户体验的关键.