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Human activity recognition with smartphone sensors using deep learning neural networks

机译：使用深度学习神经网络的智能手机传感器进行人类活动识别

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Human activities are inherently translation invariant and hierarchical. Human activity recognition (HAR), a field that has garnered a lot of attention in recent years due to its high demand in various application domains, makes use of time-series sensor data to infer activities. In this paper, a deep convolutional neural network (convnet) is proposed to perform efficient and effective HAR using smartphone sensors by exploiting the inherent characteristics of activities and 1D time-series signals, at the same time providing a way to automatically and data-adaptively extract robust features from raw data. Experiments show that convnets indeed derive relevant and more complex features with every additional layer, although difference of feature complexity level decreases with every additional layer. A wider time span of temporal local correlation can be exploited (1 x 9-1 x 14) and a low pooling size (1 x 2-1 x 3) is shown to be beneficial. Convnets also achieved an almost perfect classification on moving activities, especially very similar ones which were previously perceived to be very difficult to classify. Lastly, convnets outperform other state-of-the-art data mining techniques in HAR for the benchmark dataset collected from 30 volunteer subjects, achieving an overall performance of 94.79% on the test set with raw sensor data, and 95.75% with additional information of temporal fast Fourier transform of the HAR data set. (C) 2016 Published by Elsevier Ltd.
机译：人类活动本质上是翻译不变的和层次的。近年来，由于人类活动识别（HAR）在各个应用领域中的高需求而引起了广泛关注，该领域利用时间序列传感器数据来推断活动。本文提出了一种深度卷积神经网络（convnet），该方法可通过利用活动和一维时间序列信号的固有特征，使用智能手机传感器执行高效有效的HAR，同时提供一种自动和自适应数据的方式从原始数据中提取强大的功能。实验表明，卷积确实在每个附加层上都派生了相关且更复杂的特征，尽管特征复杂度级别的差异随附加层而减小。可以利用更宽的时间局部相关时间跨度（1 x 9-1 x 14），并且显示较小的合并大小（1 x 2-1 x 3）是有益的。 Convnets还对移动活动实现了几乎完美的分类，尤其是以前认为很难分类的非常相似的分类。最后，对于30个志愿者受试者收集的基准数据集，convnet优于HAR的其他最新数据挖掘技术，在原始传感器数据的测试集上，convnet的总体表现为94.79％，在其他信息的基础上，其效果为95.75％。 HAR数据集的时间快速傅里叶变换。 （C）2016由Elsevier Ltd.出版

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