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Updated: May 21, 2025

Biomechanical Changes Related to Low Back Pain: An Innovative Tool for Movement Pattern Assessment and Treatment Evaluation in Rehabilitation
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基于机器学习的计算机视觉用于基于深度摄像头的物理治疗运动评估:系统性审查

Yafeng Zhou1,2, Fadilla 'Atyka Nor Rashid1, Marizuana Mat Daud3

  • 1Center for Artificial Intelligence Technology (CAIT), Faculty of Information Science and Technology, Universiti Kebangsaan Malaysia, Bangi 43600, Selangor, Malaysia.

Sensors (Basel, Switzerland)
|March 17, 2025
PubMed
概括
此摘要是机器生成的。

使用深度摄像头的机器学习计算机视觉显示了物理治疗运动评估的前景. 需要进一步的研究来解决现实世界的验证和用于临床使用的算法概括.

关键词:
计算机视觉 计算机视觉深度摄像机的深度摄像机机器学习是机器学习.物理治疗运动评估物理治疗运动评估系统性审查 系统性审查

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科学领域:

  • 康复技术 康复技术 康复技术
  • 医疗成像医学成像
  • 医疗保健中的人工智能

背景情况:

  • 物理治疗运动评估传统上依赖于手动观察.
  • 机器学习 (ML) 和计算机视觉 (CV) 提供客观,定量运动分析.
  • 深度摄像机提供丰富的3D数据,用于增强运动跟踪.

研究的目的:

  • 系统地审查基于ML的CV在物理治疗运动评估中的最新进展 (2020-2024年).
  • 确定实施场景,数据收集/处理方法和所使用的算法.
  • 突出关键的挑战和未来的研究方向.

主要方法:

  • 按照PRISMA指南进行系统的文献审查.
  • 在Web of Science,Scopus,PubMed和ADS进行的搜索.
  • 对18项精选的研究进行分析,重点关注物理治疗运动评估中的ML/CV.

主要成果:

  • 确定了当地 (50%),临床 (33.4%) 和远程 (22.3%) 的实施场景.
  • Kinect系列深度摄像机 (65.4%) 是普遍存在的; RGB-D (55.6%) 和骨架数据 (27.8%) 是常见的处理方法.
  • 算法包括传统的ML (44.4%) 和深度学习 (41.7%).

结论:

  • 基于ML的CV系统证明了物理治疗运动评估的有效性.
  • 挑战包括有限的现实世界验证,数据集多样性和算法概括.
  • 未来的研究应该专注于临床验证和改善算法通用性,以便在实践中应用.