| 王丽君,刘彦戎,王丽静.基于卷积长短时深度神经网络行为识别方法[J].电子测量与仪器学报,2020,34(9):160-166 |
| 基于卷积长短时深度神经网络行为识别方法 |
| CLDNN based human activity recognition method |
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| DOI： |
| 中文关键词: 人员行为识别 惯性数据 深度神经网络 |
| 英文关键词:human activity recognition inertial data deep neural network |
| 基金项目:陕西省教育厅专项科学研究(18JK1041)、物联网与智能技术科技创新团队建设项目(SSY18TD05)资助 |
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| 中文摘要: |
| 传统的人员行为识别中,通过人工特征对人员行为进行分类。 这些方法仅能够利用较浅层次的特征,其识别准确率有
限。 提出通过卷积长短时深度神经网络(convolutional long short-term deep neural networks, CLDNN)进行人员行为识别,并用新
的 GRU 门控单元代替传统的 LSTM 门控单元提高网络效率。 利用该网络结构,既可提取惯性数据中多层次特征,也可充分利
用时间序列相关性。 通过开源数据集的实验证明,该方法相比于传统的卷积网络和基于 LSTM 门控循环神经网络的识别准确
率分别提高了约 3%和 7%;用 GRU 门控单元代替 LSTM 单元后,所需的训练时间和前向的识别时间分别下降了 14%和 10%。 |
| 英文摘要: |
| Traditional machine learning based methods use hand-crafted features in the inertial data to achieve the task of human activity
recognition (HAR). However, as these features are normally without abstract high-level knowledge, the recognition rate is thus limited.
Deep learning based methods, on the other hand, can avoid the aforementioned disadvantage by learning high-level features through
labeled data. In this paper, the convolutional long short-term deep neural networks (CLDNN) is adopted for solving the HAR problem.
This network has the features of both the convolutional neural network (CNN) and the recurrent neural network (RNN), which can
extract features of different levels and can adopt the correlations in time sequences. Moreover, we use the GRU instead of LSTM as the
gated cell of the RNN, which can make the network lighter. Through experiments adopting open source data, we can show that our
method has 3% and 7% better recognition rate than CNN and RNN respectively, the training time and forward recognition time has
decreased by 14% and 10% respectively, if replace the LSTM with GRU cell. |
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