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人工智能驱动的平衡评估:对盲人和非盲人之间的比较研究,使用迷你BESTEST

Milagros Jaén-Vargas^1,2, Josué Pagán^3,4, Shiyang Li¹

¹Bioinstrumentation and Nanomedicine Laboratory, Center for Biomedical Technology (CTB), Universidad Politécnica de Madrid, Madrid, Spain.

PeerJ. Computer science

|March 26, 2025

概括

盲人个体是一个盲人个体.

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
人类运动科学科学人类运动科学

背景情况:

在全球范围内,有22亿人患有视力障碍,需要改进平衡评估和物理治疗.
前体系统对平衡至关重要,盲人经常需要专门的物理治疗.
像mini-BESTest这样的现有平衡测试存在局限性,包括潜在的物理治疗师偏见和对盲人群体缺乏应用.

研究的目的:

客观地评估盲人使用迷你BESTEST和惯性测量单位 (IMU) 的平衡.
在迷你BEST中确定最能区分盲人和有视力的特定活动.
开发机器学习模型,用于客观平衡评估和视力受损人群的远程康复.

主要方法:

利用OpenSense RT设备从29名盲人和有视力的参与者收集IMU数据.
开发了机器学习和深度学习模型来预测迷你BEST的最佳分数.
对惯性数据进行集群分析,并分析加速数据以获得性能见解.

主要成果:

单腿姿势被认为是盲人和有视力的人之间最有区别的活动.
分析表明,物理治疗师的评估和测试标准之间存在潜在的不一致.
机器学习模型在预测二进制分类的mini-BESTbest分数时获得了85.6%的F1分数.

结论:

与传统方法相比,IMU数据和机器学习提供了一种更客观的方法来评估盲人的平衡.
这些发现可以为为视力受损者量身定制的先进远程康复系统的开发提供信息.
客观平衡评估可以提高物理治疗结果和对视力障碍患者的患者护理.

关键词:

人工智能的人工智能是人工智能.资产负债表的平衡情况一个盲人,一个盲人.在IMU,IMU是IMU.开放式理智RT RT 开放式理智四季期是指四季期的时间.迷你BEST最好的最好的

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