使用卷积神经网络工具的人类活动识别：最新评论，数据集，挑战和未来展望
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects
摘要：
人类活动识别（HAR）在人们的日常生活中起着重要的作用，因为它能够从可穿戴或固定设备中学习有关人类活动的广泛的高级信息。人们对HAR进行了大量研究，研究界也利用了许多基于深度学习和机器学习的方法来对人类活动进行分类。本综述的主要目标是总结最近基于各种深度神经网络架构的工作，即用于人类活动识别的卷积神经网络（CNN）。根据多模式传感设备、智能手机、雷达和视觉设备等输入设备的使用，所审查的系统被分为四类。本文介绍了性能，优势，劣势，和所使用的超参数的CNN架构的每个审查系统与可用的公共数据源的概述。此外，讨论了当前基于CNN的HAR系统面临的挑战。最后，本文总结了一些潜在的未来方向，这将是很大的帮助，为研究人员谁愿意在这一领域作出贡献。
英文摘要：
Human Activity Recognition (HAR) plays a significant role in the everyday life of people because of its ability to learn extensive high-level information about human activity from wearable or stationary devices. A substantial amount of research has been conducted on HAR and numerous approaches based on deep learning and machine learning have been exploited by the research community to classify human activities. The main goal of this review is to summarize recent works based on a wide range of deep neural networks architecture, namely convolutional neural networks (CNNs) for human activity recognition. The reviewed systems are clustered into four categories depending on the use of input devices like multimodal sensing devices, smartphones, radar, and vision devices. This review describes the performances, strengths, weaknesses, and the used hyperparameters of CNN architectures for each reviewed system with an overview of available public data sources. In addition, a discussion with the current challenges to CNN-based HAR systems is presented. Finally, this review is concluded with some potential future directions that would be of great assistance for the researchers who would like to contribute to this field.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
