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通过使用端到端机器学习方法使用中风后的原始动力轨迹数据来估计上肢功能.

Wanyi Qing, Changjie Pan, Jianing Zhang

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    机器学习模型使用原始运动数据准确预测中风损伤. 这种方法对追踪运动功能和远程康复有希望.

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    科学领域:

    • 神经科学是一个神经科学.
    • 康复医学 康复医学 康复医学
    • 生物医学工程 生物医学工程

    背景情况:

    • 脑卒中显著影响运动功能,需要可靠的评估工具.
    • 当前的自动评估方法往往忽略了原始的动力学数据.
    • 准确预测运动障碍对于有效康复至关重要.

    研究的目的:

    • 为了研究原始动力学轨迹对中风损伤的预测能力.
    • 为了比较不同的机器学习模型来预测Fugl-Meyer上肢评估 (FMA-UE) 评分.
    • 探索轨迹数据在远程康复中的潜力.

    主要方法:

    • 收集了21名慢性中风患者在完成任务时的干部和上肢的运动数据.
    • 利用机器学习模型,包括基于变压器的网络,残余神经网络 (ResNet) 和支持向量回归 (SVR).
    • 集成的轨迹数据来预测FMA-UE得分.

    主要成果:

    • 一个基于变压器的模型在预测FMA-UE得分方面表现出了卓越的表现.
    • 前进的任务产生了最高的预测准确性 (R2=0.905±0.028).
    • 预测的FMA-UE得分与实际的患者评估有很强的相关性.

    结论:

    • 原始的动力轨迹数据可以有效地预测中风后的运动损伤水平.
    • 变压器模型显示了自动化中风评估的巨大潜力.
    • 这种方法为远程监测和远程康复提供了一种可行的方法.