InnoHAR: A Deep Neural Network for Complex Human Activity Recognition
InnoHAR: A Deep Neural Network for Complex Human Activity Recognition
复制标题
InnoHAR：用于复杂人类活动识别的深度神经网络
DOI：
10.1109/access.2018.2890675
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发表时间：
2019-01-01
期刊：
影响因子：
3.9
通讯作者：
Duan, Shihong
中科院分区：
文献类型：
--
作者：
Xu, Cheng;Chai, Duo;Duan, Shihong
基于传感器网络的人类活动识别是普适计算和体域网络领域的一个重要研究方向。现有的研究多采用统计机器学习的方法来人工提取和构造不同运动的特征。然而，面对增长极快、没有明显规律的波形数据，传统的特征工程方法变得越来越无能为力。随着深度学习技术的发展，我们不需要人工提取特征，可以提高复杂人类活动识别问题的性能。通过借鉴深度神经网络在图像识别方面的经验，提出了一种基于初始神经网络和递归神经网络相结合的深度学习模型(InnoHAR)。该模型端到端地输入多路传感器的波形数据。通过使用各种基于核的卷积层，由类似初始的模块来提取多维特征。结合GRU，实现了对时间序列特征的建模，充分利用数据特征完成分类任务。通过在三个应用最广泛的公共HAR数据集上的实验验证，与现有方法相比，本文提出的方法具有一致的优越性能和良好的泛化性能。
Human activity recognition (HAR) based on sensor networks is an important research direction in the fields of pervasive computing and body area network. Existing researches often use statistical machine learning methods to manually extract and construct features of different motions. However, in the face of extremely fast-growing waveform data with no obvious laws, the traditional feature engineering methods are becoming more and more incapable. With the development of deep learning technology, we do not need to manually extract features and can improve the performance in complex human activity recognition problems. By migrating deep neural network experience in image recognition, we propose a deep learning model (InnoHAR) based on the combination of inception neural network and recurrent neural network. The model inputs the waveform data of multi-channel sensors end-to-end. Multi-dimensional features are extracted by inception-like modules by using various kernel-based convolution layers. Combined with GRU, modeling for time series features is realized, making full use of data characteristics to complete classification tasks. Through experimental verification on three most widely used public HAR datasets, our proposed method shows consistent superior performance and has good generalization performance, when compared with the state-of-the-art.