A Survey on Deep Learning for Human Activity Recognition

Fuqiang Gu, Mu-Huan Chung, M. Chignell, S. Valaee, Baoding Zhou, Xue Liu
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引用次数: 82

Abstract

Human activity recognition is a key to a lot of applications such as healthcare and smart home. In this study, we provide a comprehensive survey on recent advances and challenges in human activity recognition (HAR) with deep learning. Although there are many surveys on HAR, they focused mainly on the taxonomy of HAR and reviewed the state-of-the-art HAR systems implemented with conventional machine learning methods. Recently, several works have also been done on reviewing studies that use deep models for HAR, whereas these works cover few deep models and their variants. There is still a need for a comprehensive and in-depth survey on HAR with recently developed deep learning methods.
面向人类活动识别的深度学习研究综述
人类活动识别是医疗保健和智能家居等许多应用的关键。在本研究中,我们对深度学习在人类活动识别(HAR)方面的最新进展和挑战进行了全面调查。虽然有很多关于HAR的调查,但他们主要关注HAR的分类,并回顾了使用传统机器学习方法实现的最先进的HAR系统。最近,也有一些研究对使用深度模型进行HAR的研究进行了回顾,然而这些研究只涵盖了很少的深度模型及其变体。目前仍有必要利用最近发展的深度学习方法对HAR进行全面而深入的调查。
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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