基于特征融合的人体运动识别
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Human Activity Recognition Based on Features Fusion
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    摘要:

    目的 基于手机内置传感器所获得人体运动信号,建立人体运动识别模型,为身体状况评估、特殊人群监护以及其他生物医学研究提供支持。方法 使用手机内置传感器采集运动信号,并结合公共数据集UCI HAR和WISDM作为实验数据。采用卷积神经网络与自回归模型相结合的特征提取方式,建立人体运动识别模型。结果 模型在自采集数据、UCI HAR和WISDM中均取得90%以上的识别正确率。结论 引入自回归模型,可以避免手工设计特征值的缺陷,并有效减少大规模堆积卷积层的计算量。研究结果证明,基于特征融合的方法可以有效识别人体运动。

    Abstract:

    Objective To establish a human activity recognition (HAR)model based on human activity signals obtained by built-in sensors of the mobile phone, so as to support daily physical state assessment, special population monitoring and other biomedical researches. Methods The mobile signal was collected using the mobile phone built-in sensor, and the public data set UCI HAR and WISDM were used as experimental data. The HAR model was established by using the feature extraction method combined with convolutional neural network and autoregressive model. Results The models all achieved more than 90% recognition accuracy in the self-collected dataset, UCI HAR and WISDM. Conclusions The introduction of autoregressive model can avoid the manual design eigenvalues and effectively reduce the computational complexity of large-scale stacked convolutional layers. The research findings prove that the method based on feature fusion can effectively recognize human activity.

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连西静,崔升.基于特征融合的人体运动识别[J].医用生物力学,2019,34(6):644-649

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  • 收稿日期:2018-12-14
  • 最后修改日期:2019-02-10
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  • 在线发布日期: 2019-12-25
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