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Visualizing Motion Patterns in Acupuncture Manipulation
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基于皮肤行为的针点和非针点识别使用机器学习

Feifei Shi, Huansheng Ning, Ruoxiu Xiao

    IEEE journal of biomedical and health informatics
    |March 18, 2024
    PubMed
    概括

    本研究引入了一种机器学习方法,使用皮肤导电性自动识别针点 (AP) 和非针点. 这种方法提高了AP检测的准确性,有助于中国传统医学的临床实践.

    科学领域:

    • 生物医学工程 生物医学工程
    • 传统中国医药 传统中国医药
    • 机器学习 机器学习

    背景情况:

    • 针点 (AP) 检测目前严重依赖于手动定位,缺乏成熟的智能技术.
    • 自动AP识别对于推进传统中医的临床应用和研究至关重要.

    研究的目的:

    • 开发和评估一种基于皮肤导电性的机器学习模型,用于识别针点 (AP) 和非针点.
    • 提高临床环境中AP检测和定位的准确性和效率.

    主要方法:

    • 使用可穿戴传感器从五舒点和非针点收集皮肤导电数据,创建了12个AP类型的超过36000个样本的数据集.
    • 从时间,频率和非线性领域提取电气特征.
    • 应用和比较机器学习算法,包括SVM,RF,KNN,NB和XGBoost用于AP/非AP识别.

    主要成果:

    • XGBoost实现了最高的识别精度66.38%.
    • 提出了一种双向特征生成方法,以减轻AP类型和个体之间的差异.
    • 双向特征方法提高了识别精度7.17%.

    结论:

    • 该研究成功地展示了使用机器学习和皮肤导电性的针点和非针点的系统,自动识别.

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  • 这项研究有助于针点检测和传统中医理论的智能发展.
  • 这些发现支持将智能技术纳入临床实践,以更精确地识别针点.