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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
An...
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一个初步的数据驱动的方法来分类膝盖不稳定性在特定主体的基于炼的游戏与坐运动.

Priyanka Ramasamy1, Poongavanam Palani2, Gunarajulu Renganathan3

  • 1Graduate School of Advanced Science and Engineering, Hiroshima University, Hiroshima 739-8527, Japan.

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概括

这项研究开发了一种游戏系统,用于跟踪坐期间的膝盖不稳定性,在检测问题时达到96%的准确性. 该系统使用多个传感器来提高下肢训练的安全性和有效性.

关键词:
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科学领域:

  • 生物力学 生物力学
  • 康复工程 康复工程 康复工程
  • 机器学习 机器学习

背景情况:

  • 下肢功能退化正在增加,影响核心力量和运动控制.
  • 不适当的式技术会导致膝盖的动态不稳定,减少训练的动力.
  • 需要练习系统来改善用户体验,并防止下肢训练期间受伤.

研究的目的:

  • 开发和验证基于游戏的运动跟踪系统,以实时检测坐期间膝盖的不稳定性.
  • 评估多式传感器融合在提高膝盖不稳定性分类准确性的有效性.
  • 调查长期短期记忆 (LSTM) 和支持矢量机 (SVM) 模型在识别膝盖不稳定事件中的性能.

主要方法:

  • 28名健康受试者参加了基于游戏的部训练.
  • 动态动力学特征采集使用基于深度摄像头的惯性测量单元 (IMU) 和Anima强板传感器.
  • 斯皮尔曼相关性被用于特征选择,LSTM和SVM模型被训练为膝盖不稳定的二元分类.

主要成果:

  • 使用LSTM和SVM模型,膝盖不稳定事件成功地以高准确度 (96%) 进行了分类.
  • 功能选择确定了膝盖不稳定的关键指标,包括膝盖震动,膝盖距离,腰深度,摆动速度和摆动面积.
  • 与单一模式方法相比,多式传感器方法显著提高了分类器的性能.

结论:

  • 拟议的系统有效地实时跟踪膝盖的不稳定性,使用多式联络传感器数据和机器学习.
  • 这种方法提高了下肢训练的安全性和有效性,特别是在游戏化康复环境中.
  • 这些发现支持使用集成传感器系统进行个性化和适应性物理治疗.