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相关概念视频

Functional Classification of Joints01:09

Functional Classification of Joints

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Functional Classification of Joints
The functional classification of joints is determined by the amount of mobility between the adjacent bones. Joints are functionally classified as a synarthrosis or immobile joint, an amphiarthrosis or slightly moveable joint, or as a diarthrosis, a freely moveable joint. Fibrous and cartilaginous joints can be functionally classified as either synarthroses  or amphiarthroses, whereas all synovial joints are classified as diarthroses.
Synarthrosis
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Methods of Obtaining Topography01:25

Methods of Obtaining Topography

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Topography involves measuring and mapping land elevations, natural features, and artificial structures to create accurate representations of the terrain. Topographic surveying relies on traditional and modern methods, each with distinct advantages and limitations.Traditional Surveying Methods:Transit stadia surveys and plane table surveys were widely used traditional surveying methods. These techniques relied on instruments like theodolites and stadia rods for measuring distances and angles,...
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End Point Prediction: Gran Plot01:07

End Point Prediction: Gran Plot

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A Gran plot is used to predict the equivalence volume or endpoint of a potentiometric or acid-base titration without reaching the endpoint. Typically, titration data is collected as a function of the titrant's volume up to a point less than the equivalence volume and then transformed into a linear format. The straight line is extended to the x-axis, indicating the necessary titrant volume to achieve the equivalence point.
For potentiometric titration, the Gran plot is created by plotting...
342

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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine
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Visualization Method for Proprioceptive Drift on a 2D Plane Using Support Vector Machine

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在复杂的地形中预测脚位的概率融合方法.

Jingfeng Xiong, Chuheng Chen, Yuanwen Zhang

    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。

    这项研究引入了一种新的概率融合方法,用于预测复杂地形中的脚位,帮助下肢残疾人. 该方法可以在挑战性环境 (如坡道和楼梯) 中实现准确和快速的预测.

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    相关实验视频

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    科学领域:

    • 生物力学和机器人技术
    • 辅助技术 辅助技术 辅助技术
    • 机器学习用于人类运动分析

    背景情况:

    • 现有的足位预测方法仅限于平地行走.
    • 复杂的地形 (坡道,楼梯,障碍物) 对当前的行走辅助技术构成重大挑战.
    • 准确的足位预测对于提高下肢残疾人日常生活中的移动性和安全性至关重要.

    研究的目的:

    • 为复杂的地形开发和验证一个强大的脚位预测方法.
    • 改善在不同环境中为下肢残疾人提供步行辅助系统.
    • 为了在不均和多样化的地面表面上实现更自然和更安全的航行.

    主要方法:

    • 一种概率融合方法,结合了深度学习和环境约束.
    • 在增强数据上训练一个深度学习模型,以预测初步的脚位概率分布.
    • 使用环境信息和人类步行限制计算可行的着陆区域.
    • 将概率分布与可行面积融合在一起,用于在发现脱落后的最终脚位预测.

    主要成果:

    • 实现了8.19 ± 1.20厘米的根平均平方误差 (RMSE),占平均步伐长度的不到8%.
    • 在实验中证明了95.11 ± 3.09%的着陆可行的区域精度 (LFAA).
    • 与现有的复杂地形研究相比,提出的方法显示出更快,更准确的预测.

    结论:

    • 概率融合方法有效地预测了复杂地形中的脚位.
    • 这一进步对改善下肢残疾人的辅助设备具有重大潜力.
    • 该方法提供了一个更可靠的解决方案,用于在具有挑战性的环境中提供现实世界的步行辅助.