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系统审查自动的中风后步态分类系统.

Yiran Jiao1, Rylea Hart1, Stacey Reading1

  • 1Department of Exercise Sciences, Faculty of Science, University of Auckland, Auckland 1023, New Zealand.

Gait & posture
|February 17, 2024
PubMed
概括

数据驱动的步态分类系统在中风后显示出高精度,但通常存在方法上的缺陷. 未来的系统需要标准化开发,并专注于临床实用性,以获得更好的康复指导.

科学领域:

  • 生物医学工程 生物医学工程
  • 康复科学 康复科学 康复科学
  • 医疗保健中的机器学习

背景情况:

  • 对于中风康复的观察步态分析缺乏可靠性和准确性.
  • 数据驱动的步态分类提供了步态模式的自动量化和分类.
  • 之前的审查没有全面评估这些系统的发展和临床实用性.

研究的目的:

  • 系统地审查用于开发自动步态分类系统的方法.
  • 评估这些系统在治疗中风后行走障碍方面的潜在有效性.
  • 识别缺口,并为未来的系统开发提供建议.

主要方法:

  • 在PubMed,IEEE Xplore和Scopus数据库中进行系统的文献搜索.
  • 纳入和排除标准适用于2015年至2022年间发表的407项研究,确定了21项相关研究.
  • 提取有关开发方法,分类性能和临床实用性的数据.

主要成果:

  • 大多数系统报告了高分类准确度 (80%-100%).
  • 在各种研究中,在机器学习 (ML) 模型开发中发现了显著的方法错误.
  • 许多系统忽视了临床实用性,解释性和通用性等关键组件.
关键词:
步态分析 步态分析步行方式评估 步行方式评估半的步行方式机器学习是机器学习.

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结论:

  • 目前的数据驱动的步态分类系统有希望,但需要改进方法.
  • 未来的系统必须优先考虑临床意义,并采用标准化的ML开发实践.
  • 改进的系统可以更好地协助临床医生和治疗师指导中风康复.