论文标题
使用卷积神经网络工具的人类活动识别：最先进的审查，数据集，挑战和未来的前景
Human Activity Recognition Using Tools of Convolutional Neural Networks: A State of the Art Review, Data Sets, Challenges and Future Prospects
论文作者
论文摘要
人类活动识别（HAR）在人们的日常生活中起着重要作用，因为它具有从可穿戴或固定设备中学习有关人类活动的广泛高级信息的能力。研究界利用了基于深度学习和机器学习的许多方法进行了大量研究，以对人类的活动进行分类。这篇综述的主要目的是总结基于广泛的深神经网络体系结构的最新作品，即卷积神经网络（CNN）以识别人类活动。根据使用多模式传感设备，智能手机，雷达和视觉设备等输入设备的使用，将审查的系统聚集在四个类别中。这篇综述描述了每个审查系统的CNN体系结构的性能，优势，劣势和使用的使用超参数，并概述了可用的公共数据源。此外，还提出了与当前对基于CNN的HAR系统挑战的讨论。最后，这篇评论以一些潜在的未来方向结束，这对于希望为该领域做出贡献的研究人员提供了很大的帮助。
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.
