基于特征融合的人体运动识别
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复旦大学

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Human Activity recognition based on features fusion
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Fudan University

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    摘要:

    目的:随着人工智能技术的发展,人体健康监护的智能交互正在逐步实现。智能手机已经广泛普及,可利用智能手机采集信息,但从大量信息中提取有效的信息成分需要有效可行的分析方法。通过研究人体运动识别方法,能够为日常安全防护、身体状况评估以及其它生物医学研究提供支持。方法:通过手机中内置的传感器采集人体常见的运动的信号,采用卷积神经网络与自回归模型相结合的特征提取方法,对人体运动进行辨识。结果:利用该种方法分别对实验采集的数据、两类公共数据集UCI HAR和WISDM进行了测试,均取得90%以上的识别正确率。结论:所提出的模型消除了目前常用的模式识别需要手工设计特征值的缺陷,自回归模型的引入,有效减少了大规模堆积卷积层的计算量。此外,还论证了该方法的高效性与可行性。

    Abstract:

    Purpose: With the development of artificial intelligence technology, the intelligent interaction of human health monitoring is gradually being realized. Smartphones have become widely available and can be used to collect information, but extracting valid information components from large amounts of information requires an effective and versatile analysis method. Methods: The common motion signals of the human body were collected by the built-in sensors in the mobile phone, and the feature extraction method combined with the convolutional neural network and the autoregressive model was used to identify the human motion. Results: Using this method, the experimental data and two types of public data sets UCI HAR and WISDM were tested respectively, and the recognition accuracy rate was more than 90%. Conclusion: The proposed model eliminates the defect that the commonly used pattern recognition requires manual design of eigenvalues. The introduction of autoregressive models effectively reduces the computational complexity of large-scale stacked convolutional layers. In addition, the efficiency and feasibility of the method are also demonstrated.

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历史
  • 收稿日期:2018-12-14
  • 最后修改日期:2019-02-15
  • 录用日期:2019-02-18
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