Human activity recognition has gained importance in recent years due to its applications in various fields such as health, security and surveillance, entertainment, and intelligent environments. A significant amount of work has been done on human activity recognition and researchers have leveraged different approaches, such as wearable, object-tagged, and device-free, to recognize human activities. In this article, we present a comprehensive survey of the work conducted over the 10-year period of 2010–2019 in various areas of human activity recognition with main focus on device-free solutions. The device-free approach is becoming very popular due to the fact that the subject is not required to carry anything. Instead, the environment is tagged with devices to capture the required information. We propose a new taxonomy for categorizing the research work conducted in the field of activity recognition and divide the existing literature into three sub-areas: action-based, motion-based, and interaction-based. We further divide these areas into ten different sub-topics and present the latest research works in these sub-topics. Unlike previous surveys which focus only on one type of activities, to the best of our knowledge, we cover all the sub-areas in activity recognition and provide a comparison of the latest research work in these sub-areas. Specifically, we discuss the key attributes and design approaches for the work presented. Then we provide extensive analysis based on 10 important metrics, to present a comprehensive overview of the state-of-the-art techniques and trends in different sub-areas of device-free human activity recognition. In the end, we discuss open research issues and propose future research directions in the field of human activity recognition.

近年来，由于人类活动识别在健康，安全和监视，娱乐和智能环境等各个领域中的应用，因此变得越来越重要。在人类活动识别方面已经进行了大量工作，研究人员利用可穿戴，带有标签的对象以及无设备等不同方法来识别人类活动。在本文中，我们将对2010-2019年这十年间在人类活动识别各个领域中开展的工作进行全面调查，主要侧重于无设备解决方案。由于不需要受试者携带任何东西的事实，无设备方法变得非常流行。而是用设备标记环境以捕获所需的信息。操作为主，基于运动和相互作用为基础的。我们将这些领域进一步划分为十个不同的子主题，并介绍这些子主题中的最新研究成果。据我们所知，与以往的调查仅关注一种类型的活动不同，我们涵盖了活动识别中的所有子领域，并提供了这些子领域中最新研究工作的比较。具体来说，我们讨论了所介绍工作的关键属性和设计方法。然后，我们基于10个重要指标进行了广泛的分析，以全面概述无设备人类活动识别的不同子领域的最新技术和趋势。最后，我们讨论开放的研究问题，并提出人类活动识别领域的未来研究方向。