Deep Learning for Heterogeneous Human Activity Recognition in Complex IoT Applications
Deep Learning for Heterogeneous Human Activity Recognition in Complex IoT Applications
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DOI：
10.1109/jiot.2020.3038416
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发表时间：
2022-04-15
影响因子：
10.6
通讯作者：
Ryan, Michael
中科院分区：
文献类型：
--
作者：
Abdel-Basset, Mohamed;Hawash, Hossam;Ryan, Michael
随着无线传感技术的不断改进，物联网（IoT）的概念已经被广泛采用，并且由于其在环境辅助生活，智能医疗和智能家居等场景中的广泛应用而变得无处不在。在这方面，人类活动识别（HAR）是智能系统对人类行为进行持续监视的重要组成部分。由于智能手机在每个人的生活中无处不在的影响，智能手机惯性传感器被用作本研究的案例研究。大多数传统的方法把HAR作为一个时间序列分类问题，然而，识别的准确性降低异构传感器。在这篇文章中，我们研究了将感觉异质HAR（HHAR）数据编码为三通道图像表示（即，RGB），因此将HHAR任务视为图像分类问题。由于目前的卷积网络模型在物联网环境中部署时计算量很大，因此我们提出了一种轻量级模型图像编码HHAR，称为多尺度图像编码HHAR（MS-IE-HHAR）。该模型采用了一个层次多尺度提取（HME）模块，其次是一个改进的spatialwise和channelwise注意（ISCA）模块，形成模型的主要架构。HME模块由一组剩余连接的洗牌群卷积（SG-Conv）形成，以从不同的感受野提取和学习图像表示，同时减少网络参数的数量。ISCA模块结合了一个轻量级的spatialwise注意（SwA）块和一个改进的channelwise注意（CwA）模块，使网络能够对空间相关性以及信道相互依赖性信息给予指导性的关注。最后，两个广泛可用的HHAR公共数据集（即，HHAR UCI和MHEALTH）被用来评估所提出的模型的性能，准确率分别超过98%和99%，证明了模型的优越性，从异构数据源建模HAR。
With continued improvements in wireless sensing technology, the notion of the Internet of Things (IoT) has been widely adopted and has become pervasive owing to its broad applications in scenarios such as ambient assisted living, smart healthcare, and smart homes. In that regard, human activity recognition (HAR) is a vital element of intelligent systems to undertake persistent surveillance of human behavior. Due to the omnipresent impact of smartphones in each person's life, smartphone inertial sensors are used as a case study for this research. Most of the conventional approaches regard HAR as a time-series classification problem; yet, the accuracy of recognition degrades for heterogeneous sensors. In this article, we investigate encoding sensory heterogeneous HAR (HHAR) data into three-channel image representation (i.e., RGB), hence treat the HHAR task as an image classification problem. Since present convolutional network models are computationally heavy when deployed in the IoT environment, we propose a lightweight model image encoded HHAR, called multiscale image-encoded HHAR (MS-IE-HHAR). The model employs a hierarchical multiscale extraction (HME) module followed by an improved spatialwise and channelwise attention (ISCA) module to form the main architecture of the model. The HME module is formed by a group of residually connected shuffle group convolutions (SG-Conv) to extract and learn image representations from different receptive fields while reducing the number of network parameters. The ISCA module combines a lightweight spatialwise attention (SwA) block and an improved channelwise attention (CwA) module to enable the network to pay instructive attention to spatial correlations as well as channel interdependency information. Finally, two widely available HHAR public data sets (i.e., HHAR UCI, and MHEALTH) were used to evaluate the performance of the proposed models with accuracy over 98% and 99%, respectively, demonstrating the model superiority for modeling HAR from heterogeneous data sources.