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相关概念视频

Ankle Joint01:10

Ankle Joint

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The ankle is formed by the talocrural joint (crural = leg). It consists of the articulations between the talus bone of the foot and the distal ends of the tibia and fibula of the leg. The superior aspect of the talus bone is square-shaped and has three areas of articulation. The top of the talus articulates with the inferior tibia. This is the portion of the ankle joint that carries the body weight between the leg and foot. The sides of the talus are firmly held in position by the articulations...
1.4K

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使用人工神经网络和多模式IMU数据进行脚动力学估计.

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    此摘要是机器生成的。

    本研究介绍了KEEN,这是一个使用人工神经网络 (ANN) 和最小惯性测量单元 (IMU) 进行实时脚运动的框架. 即使是单个IMU也可以提供临床上可接受的估计,为成本效益高,实际的伤害预防和康复铺平道路.

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    科学领域:

    • 生物力学和生物医学工程
    • 医疗保健中的人工智能
    • 可穿戴式传感器技术

    背景情况:

    • 惯性测量单元 (IMU) 提供便携式关节动力学监测,但存在精度限制,实时处理挑战和复杂的校准需求.
    • 现有的方法通常需要精确的传感器到分段校准,这阻碍了IMU基于运动分析的广泛临床和日常应用.

    研究的目的:

    • 推出KEEN (KinEmatics估计网络),这是一个创新的框架,利用轻量级人工神经网络 (ANN) 来实现实时,无校准的多平面脚运动预测.
    • 评估最小IMU配置和各种ANN模型的有效性,以准确跟踪脚运动.

    主要方法:

    • 开发和评估了五个ANN算法,包括长短期记忆 (LSTM) 和卷积神经网络 (CNN) 模型,使用四个IMU的42个输入.
    • 在学科内部和学科间的任务中评估模型性能,以确定概括能力.
    • 研究了在微控制器上部署CNN模型的可行性,以使用单个脚跟安装IMU进行实时动力学估计.

    主要成果:

    • 一个单脚部安装的IMU,当通过CNN模型处理时,提供了临床上可接受的脚动力学估计 (RMSE:4.13° ±0.55°).
    • LSTM网络在主题内部任务中表现出色 (RMSE:1.88° ±0.02°),而CNN和CNN-LSTM模型表现出优异的主题间概括性.
    • 在一个单一IMU的微控制器上实时部署CNN产生了有希望的结果 (RMSE:3.34° ±0.48°),证明了其实际适用性.

    结论:

    • KEEN框架有效地利用ANN和最小IMU来实现准确的实时脚动力学,克服校准障碍.
    • 最小的IMU配置,特别是带有CNN的单脚IMU,显示出具有成本效益和实际临床应用的巨大潜力.
    • 这种方法为足受伤的早期预防和康复提供了可行的解决方案,提高了可访问性和可用性.