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相关概念视频

Classification of Skeletal Muscle Fibers01:48

Classification of Skeletal Muscle Fibers

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Skeletal muscles continuously produce ATP to provide the energy that enables muscle contractions. Skeletal muscle fibers can be categorized into three types based on differences in their contraction speed and how they produce ATP, as well as physical differences related to these factors. Most human muscles contain all three muscle fiber types, albeit in varying proportions.
Slow-Twitch Muscle Fibers
Slow oxidative, muscle fibers appear red due to large numbers of capillaries and high levels of...
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Classification of Skeletal Muscle Relaxants01:28

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Skeletal muscle relaxants are a group of drugs that can reduce muscle stiffness and induce temporary paralysis to relieve pain. These agents can act centrally to reduce muscle tone or spasms in painful conditions such as multiple sclerosis (MS), amyotrophic lateral sclerosis (ALS), or spinal injuries; they are called antispasmodics or spasmolytics.
Peripherally acting skeletal muscle relaxants interfere with the neurotransmission at the neuromuscular end plate to induce paralysis during...
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A Structured Rehabilitation Protocol for Improved Multifunctional Prosthetic Control: A Case Study
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对上肢假肢的sEMG分类器进行再培训的方法

Tom Donnelly1,2, Elena Seminati1,2,3, Benjamin Metcalfe1,2,4

  • 1Centre for Accountable, Responsible, and Transparent AI (ART-AI), Department of Computer Science, University of Bath, Bath, United Kingdom.

Frontiers in neurorobotics
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概括
此摘要是机器生成的。

机器学习用于假肢控制,随着时间的推移而退化. 本研究引入了三种改造训练方法以提高准确性,信号噪声比和最近邻近方法显示了肌电假肢的最佳性能.

关键词:
手的手势识别手势识别会议间的再培训.机器学习是机器学习.肌电假肢 肌电假肢表面电力学图 (surface electromyography) 是一种表面电力学图.

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科学领域:

  • 生物医学工程 生物医学工程
  • 康复技术 康复技术 康复技术
  • 机器学习在假肢方面

背景情况:

  • 肌电上肢假肢的高放弃率 (44%) 对生活质量产生负面影响,并增加受伤风险.
  • 在假肢中,传统的信号处理不如机器学习模式识别对运动意图的强度.
  • 表面电肌图 (sEMG) 信号的非静止性和每日变化降低了机器学习分类的准确性,需要重新培训或调整.

研究的目的:

  • 评估用于肌电假肢的机器学习分类器再培训的三个不同的范式.
  • 为了比较信心得分的有效性,最近邻居窗口评估,以及一种新的信号噪声比 (SNR) 方法来减轻准确性损失.

主要方法:

  • 该研究评估了基于机器学习的肌电假肢控制的三种再培训范式.
  • 评估的范式包括信心评分,最近邻窗评估和基于SNR的新方法.
  • 通过使用NinaPro 6数据集在5天内10次会话间的间歇准确度来衡量有效性.

主要成果:

  • 与没有再培训相比,所有评估的再培训模式都显示出更好的准确性.
  • 最近邻窗口评估和基于SNR的方法比信心评分方法的平均准确性提高了5%.
  • 这些发现突出了适应性再培训策略的潜力,以提高假肢控制可靠性.

结论:

  • 重新培训对于保持机器学习分类器在肌电假肢中的性能至关重要.
  • 基于SNR和近邻方法的准确性比信心得分更高.
  • 这些先进的再培训技术可以帮助减少假肢放弃和改善用户的结果.