A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices
A Deep Learning Approach to on-Node Sensor Data Analytics for Mobile or Wearable Devices
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DOI：
10.1109/jbhi.2016.2633287
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发表时间：
2017-01-01
影响因子：
7.7
通讯作者：
Yang, Guang-Zhong
中科院分区：
文献类型：
--
作者：
Ravi, Daniele;Wong, Charence;Yang, Guang-Zhong
近年来，可穿戴设备的日益普及意味着，现在可以连续捕获各种生理和功能数据，用于运动、健康和医疗保健等领域的应用。这种丰富的信息需要有效的分类和分析方法，而深度学习是一种很有前途的大规模数据分析技术。虽然深度学习在利用高性能计算平台的实现中取得了成功，但其在低功耗可穿戴设备上的应用受到资源限制。在本文中，我们提出了一种深度学习方法，该方法将从惯性传感器数据中学习到的特征与来自一组浅层特征的补充信息相结合，以实现准确和实时的活动分类。这种组合方法的设计旨在克服典型深度学习框架中存在的一些限制，其中需要节点上的计算。为了优化所提出的方法进行实时节点上计算，在数据传递到深度学习框架之前进行了谱域预处理。我们提出的深度学习方法的分类准确性使用实验室和现实世界的活动数据集对最先进的方法进行了评估。我们的结果表明，该方法在不同的人类活动数据集上是有效的，优于其他方法，包括我们组合管道中使用的两种方法。我们还证明了该方法的计算时间符合智能手机和可穿戴传感器平台上实时节点处理的约束。
The increasing popularity of wearable devices in recent years means that a diverse range of physiological and functional data can now be captured continuously for applications in sports, wellbeing, and healthcare. This wealth of information requires efficient methods of classification and analysis where deep learning is a promising technique for large-scale data analytics. While deep learning has been successful in implementations that utilize high-performance computing platforms, its use on low-power wearable devices is limited by resource constraints. In this paper, we propose a deep learning methodology, which combines features learned from inertial sensor data together with complementary information from a set of shallow features to enable accurate and real-time activity classification. The design of this combined method aims to overcome some of the limitations present in a typical deep learning frame work where on-node computation is required. To optimize the proposed method for real-time on-node computation, spectral domain preprocessing is used before the data are passed onto the deep learning framework. The classification accuracy of our proposed deep learning approach is evaluated against state-of-the-art methods using both laboratory and real world activity datasets. Our results show the validity of the approach on different human activity datasets, outperforming other methods, including the two methods used within our combined pipeline. We also demonstrate that the computation times for the proposed method are consistent with the constraints of real-time on-node processing on smartphones and a wearable sensor platform.