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Updated: Jul 15, 2025

Engineering Platform and Experimental Protocol for Design and Evaluation of a Neurally-controlled Powered Transfemoral Prosthesis
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通过PoseNet和人工智能赋予下肢疾病识别权力.

Hafeez Ur Rehman Siddiqui1, Adil Ali Saleem1, Muhammad Amjad Raza1

  • 1Institute of Computer Science, Khwaja Fareed University of Engineering and Information Technology, Abu Dhabi Road, Rahim Yar Khan 64200, Punjab, Pakistan.

Diagnostics (Basel, Switzerland)
|September 28, 2023
PubMed
概括

这项研究引入了一种使用PoseNet和机器学习的新方法,以从步态分析中分类膝盖,部和脚等下肢疾病. 人工神经网络实现了98.84%的准确性,提供了一个有前途的非侵入性诊断工具.

关键词:
人工神经网络的人工神经网络这是PoseNet的PoseNet.步态分析 步态分析下肢疾病 下肢疾病机器学习是机器学习.

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科学领域:

  • 生物力学 生物力学
  • 医疗成像医学成像
  • 机器学习 机器学习

背景情况:

  • 下肢疾病会影响运动和生活质量.
  • 准确的诊断对于有效的治疗计划至关重要.
  • 目前的诊断方法可能具有侵入性或缺乏详细的动力学信息.

研究的目的:

  • 开发和评估一种新的,非侵入性的方法来分类下肢疾病.
  • 为了利用步态分析和PoseNet功能来识别膝盖,部和脚的情况.
  • 为了比较各种机器学习算法的性能,用于此分类任务.

主要方法:

  • 使用视频数据和PoseNet算法进行步行分析,以提取关键关节运动.
  • 提取特征的标准化,用于输入机器学习模型.
  • 训练和测试随机森林,额外树分类器,多层感知器,人工神经网络 (ANN) 和卷积神经网络 (CNN),使用K折交叉验证对174名患者的数据集.

主要成果:

  • 该研究在分类下肢疾病方面取得了很高的准确性和精度.
  • 人工神经网络 (ANN) 显示了最高的分类准确率,为98.84%.
  • 提出的方法在区分各种下肢疾病方面被证明是有效的.

结论:

  • 开发的方法为诊断下肢疾病提供了一种非侵入性和高效的方法.
  • 基于PoseNet的步态分析与机器学习相结合,显示出提高诊断准确性的巨大潜力.
  • 这种方法可以帮助在膝盖,部和脚疾病的患者更好地规划治疗.