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相关概念视频

Imaging Biological Samples with Optical Microscopy01:18

Imaging Biological Samples with Optical Microscopy

Optical microscopy uses optic principles to provide detailed images of samples. Antonie van Leeuwenhoek designed the first compound optical microscope in the 17th century to visualize blood cells, bacteria, and yeast cells. In 1830, Joseph Jackson Lister created an essentially modern light microscope. The 20th century saw the development of microscopes with enhanced magnification and resolution.
In optical microscopy, the specimen to be viewed is placed on a glass slide and clipped on the stage...
Electron Microscope Tomography and Single-particle Reconstruction01:07

Electron Microscope Tomography and Single-particle Reconstruction

Transmission electron microscopy (TEM) can be used to determine the 3D structure of biological samples with the help of techniques such as electron microscope tomography and single-particle reconstruction. While single-particle reconstruction can examine macromolecules and macromolecular complexes in vitro conditions only, tomography permits the study of cell components or small cells in vivo.
Electron Tomography
Electron tomography can be performed either in TEM or STEM (scanning transmission...

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基于物理的贝叶斯融合后处理器,用于使用表面电肌图进行增强的步态相识别

Rami Mobarak, Alessandro Mengarelli, Rami N Khushaba

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

    这项研究引入了基于物理的贝叶斯融合 (PI-BF) 系统,以改善下肢假肢的肌电控制. PI-BF提高了步态识别的准确性和稳定性,确保了更安全,更可靠的辅助设备性能.

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    科学领域:

    • 生物医学工程
    • 机器人技术
    • 康复技术

    背景情况:

    • 肌电模式识别系统对于控制下肢假肢和外骨至关重要.
    • 肌电控制中的信号变化可能导致不安全,不自然的步态转变.

    研究的目的:

    • 开发和验证一个新的物理信息化贝叶斯融合 (PI-BF) 后处理器.
    • 提高下肢辅助器件的安全性,可靠性和自然进展.

    主要方法:

    • 从表面电肌图 (sEMG) 信号中提取时间域 (TD) 和时间依赖功率谱描述器 (TD-PSD) 的特征.
    • 使用SVM,ANN,KNN和CNN-LSTM模型进行分类.
    • 将PI-BF后处理器应用于分类器输出,将其与贝叶斯融合和没有后处理进行比较.

    主要成果:

    • 在使用SVM的SIAT-LLMD数据集中,PI-BF的分类精度提高了5.5%,达到85%.
    • 降低过渡检测差异 (TDD) 至0.1±59.8毫秒,并提高了5%的输出稳定性 (INS指数).
    • 实现了90%左右的实时步行阶段识别准确度.

    结论:

    • PI-BF有效地抑制不稳定的过渡,并促进肌电控制中的自然步行进展.
    • 拟议的PI-BF为改善下肢辅助器件的性能提供了一种实用而低复杂的解决方案.
    • PI-BF提高了肌电控制系统的安全性,可靠性和实时性.