Hybrid deep learning framework for human activity recognition

Q4 Mathematics
S. Pushpalatha, Shrishail Math
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引用次数: 1

Abstract

The aim of the recognition in the human activity is to recognize the actions of the individuals using a set of observations and their environmental conditions. Since last two decades, the research on this Human Activity Recognition (HAR) has captured the attention of several computer science communities because of the strength to provide support to different applications and the connection to different fields of study such as, human-computer interaction, healthcare, monitoring, entertainment and education. There are many existing methods like deep learning which have been used to develop to recognize the different activities of the human, but couldn’t identify the sudden change of the activities in the human. This paper presents a method using the deep learning methods which can recognize the specific identities and identify a change from one activity to another for the applications of the healthcare. In this method, a deep convolutional neural network is built using which the features are extracted for the collection of the data from the sensors. After which the Gated Recurrent Unit (GRU) captures the long-tern dependency between the different actions which helps to improve the identification rate of the HAR. From the CNN and GRU, a model of wearable sensor can be proposed which can identify the changes of the activities and can accurately recognize these activities. Experiment have been conducted using open-source University of California (UCI) HAR dataset which composed of six different activity such as lying, standing, sitting, walking downstairs, walking upstairs and walking. The CNN-based model achieves a detection accuracy of 95.99% whereas the CNN-GRU model achieves a detection accuracy of 96.79% which is better than most existing HAR methods.
人类活动识别的混合深度学习框架
在人类活动中,识别的目的是通过一系列观察和环境条件来识别个体的行为。在过去的二十年里,对这种人类活动识别(HAR)的研究已经引起了几个计算机科学界的关注,因为它能够为不同的应用提供支持,并与不同的研究领域(如人机交互、医疗保健、监测、娱乐和教育)建立联系。现有的许多方法,如深度学习,都是用来识别人的不同活动,但不能识别人的活动的突然变化。本文提出了一种使用深度学习方法的方法,该方法可以识别特定身份并识别从一个活动到另一个活动的变化,用于医疗保健应用。在该方法中,建立了一个深度卷积神经网络,利用该网络提取传感器数据的特征。之后,门控循环单元(GRU)捕获不同动作之间的长期依赖关系,这有助于提高HAR的识别率。从CNN和GRU可以提出一种可穿戴传感器模型,该模型可以识别活动的变化,并能准确地识别这些活动。实验使用开源的加州大学(UCI) HAR数据集进行,该数据集由躺着、站着、坐着、下楼、上楼和走路等六种不同的活动组成。基于cnn的模型的检测准确率为95.99%,而CNN-GRU模型的检测准确率为96.79%,优于现有的大多数HAR方法。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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