期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2021
卷号：12
期号：11
DOI：10.14569/IJACSA.2021.0121129
语种：English
出版社：Science and Information Society (SAI)
摘要：Internet of things (IOT) sensors, has received a lot of interest in recent years due to the rise of application demands in domains like ubiquitous and context-aware computing, activity surveillance, ambient assistive living and more specifically in Human activity recognition. The recent development in deep learning allows to extract high-level features automatically, and eliminates the reliance on traditional machine learning techniques, which depended heavily on hand crafted features. In this paper, we introduce a network that can identify a variety of everyday human actions that can be carried out in a smart home environment, by using raw signals generated from Internet of Thing’s motion sensors. We design our architecture basing on a combination of convolutional neural network (CNN) and Gated recurrent unit (GRU) layers. The CNN is first deployed to extract local and scale-invariance features, then the GRU layers are used to extract sequential temporal dependencies. We tested our model called (CNGRU) on three public datasets. It achieves an accuracy better or comparable to existing state of the art models.
关键词：IoT; deep learning; CNN; GRU; CNGRU; human activity recognition
