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Deep Convolutional Neural Networks for Human Activity Recognition with Smartphone Sensors

机译：使用智能手机传感器进行人类活动识别的深度卷积神经网络

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Human activity recognition (HAR) using smartphone sensors utilize time-series, multivariate data to detect activities. Time-series data have inherent local dependency characteristics. Moreover, activities tend to be hierarchical and translation invariant in nature. Consequently, convolutional neural networks (convnet) exploit these characteristics, which make it appropriate in dealing with time-series sensor data. In this paper, we propose an architecture of convnets with sensor data gathered from smartphone sensors to recognize activities. Experiments show that increasing the number of convolutional layers increases performance, but the complexity of the derived features decreases with every additional layer. Moreover, preserving the information passed from layer to layer is more important, as opposed to blindly increasing the hyperparameters to improve performance. The convnet structure can also benefit from a wider filter size and lower pooling size setting. Lastly, we show that convnet outperforms all the other state-of-the-art techniques in HAR, especially SVM, which achieved the previous best result for the data set.
机译：使用智能手机传感器的人类活动识别（HAR）利用时间序列的多元数据来检测活动。时间序列数据具有固有的本地依赖性特征。而且，活动本质上是分层的，翻译不变。因此，卷积神经网络（convnet）利用了这些特性，使其适合处理时序传感器数据。在本文中，我们提出了一种卷积网络架构，该网络具有从智能手机传感器收集的传感器数据来识别活动。实验表明，增加卷积层数可以提高性能，但是派生特征的复杂性会随着每增加一层而降低。而且，与盲目的增加超参数以提高性能相比，保存从层传递到另一层的信息更为重要。卷积结构还可以受益于更大的过滤器大小和更低的池大小设置。最后，我们证明了convnet优于HAR中的所有其他最新技术，尤其是SVM，后者在数据集方面达到了以前最好的结果。

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