Real-Time Human Activity Recognition System Based on Capsule and LoRa
Real-Time Human Activity Recognition System Based on Capsule and LoRa
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DOI：
10.1109/jsen.2020.3004411
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发表时间：
2021-01-01
影响因子：
4.3
通讯作者：
Li, Juan
中科院分区：
文献类型：
--
作者：
Shi, Leixin;Xu, Hongji;Li, Juan
人类活动识别(HAR)已成为人工智能和模式识别领域的研究热点。但是，HAR系统在平台算法和无线接入技术方面还存在一些不足。一方面，卷积神经网络(CNN)和递归神经网络(RNN)等先进的分类框架已经被成功地用于HAR的分类任务，但这些框架只识别活动的特征数据，而忽略了特征之间的空间关系，从而可能导致错误识别。另一方面，现有的一些传输方式，如蓝牙和4G，在大范围、低功耗的情况下，很难实现实时传输。本文提出了一种基于胶囊和“远程”(LORA)的实时人体活动识别系统，为胶囊在人体行为识别中的应用开辟了先河。该框架并行封装了多个卷积层，解决了现有框架不能识别要素间空间关系的缺陷。同时，采用LORA组网技术替代现有的其他传输方式，实现了远距离传输和低功耗的结合。在福特汉姆大学无线传感器数据挖掘实验室收集的WISDM数据集上进行了实验，实验结果表明，所提出的胶囊框架获得了比CNN和RNN更高的分类结果，使得在智能监狱等特殊场景下基于智能传感器设备的实时HAR成为可能。
Human activity recognition (HAR) has become a research hotspot in the field of artificial intelligence and pattern recognition. However, the HAR system still has some deficiencies in the aspects of platform algorithms and wireless access technologies. On the one hand, some state-of-the-art frameworks such as convolutional neural network (CNN) and recurrent neural network (RNN) have been proven successfully in classification tasks of HAR, while those frameworks just identify the feature data of activity but ignore the spatial relationship among features, which may lead to incorrect recognition. On the other hand, some existing transmission modes, such as Bluetooth and 4G, are difficult to realize real-time transmission in the case of a large range and low-power consumption. In this paper, a real-time human activity recognition system based on capsule and "long range" (LoRa) is presented, which pioneers the application of capsule to HAR. The capsule framework encapsulates the multiple convolution layers in parallel to solve the defect that current frameworks cannot identify the spatial relationship among features. Simultaneously, the combination of long-distance transmission and low-power consumption is achieved by using LoRa networking technology instead of other existing transmission modes. The experiments are performed on the dataset WISDM that is collected by the Wireless Sensor Data Mining Lab in Fordham University, and the results demonstrate that the proposed capsule framework achieves a higher classification result than CNN and RNN, and the proposed system makes the real-time HAR based on intelligent sensor devices possible in some special scenarios such as smart prison.