Department of Computer Science, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan.
Department of Software Engineering, National University of Computer and Emerging Sciences, Chiniot-Faisalabad Campus, Chiniot 35400, Pakistan.
Sensors (Basel). 2022 Aug 27;22(17):6463. doi: 10.3390/s22176463.
Nowadays, Human Activity Recognition (HAR) is being widely used in a variety of domains, and vision and sensor-based data enable cutting-edge technologies to detect, recognize, and monitor human activities. Several reviews and surveys on HAR have already been published, but due to the constantly growing literature, the status of HAR literature needed to be updated. Hence, this review aims to provide insights on the current state of the literature on HAR published since 2018. The ninety-five articles reviewed in this study are classified to highlight application areas, data sources, techniques, and open research challenges in HAR. The majority of existing research appears to have concentrated on daily living activities, followed by user activities based on individual and group-based activities. However, there is little literature on detecting real-time activities such as suspicious activity, surveillance, and healthcare. A major portion of existing studies has used Closed-Circuit Television (CCTV) videos and Mobile Sensors data. Convolutional Neural Network (CNN), Long short-term memory (LSTM), and Support Vector Machine (SVM) are the most prominent techniques in the literature reviewed that are being utilized for the task of HAR. Lastly, the limitations and open challenges that needed to be addressed are discussed.
如今，人类活动识别（HAR）在各种领域得到了广泛的应用，基于视觉和传感器的数据使先进技术能够检测、识别和监控人类活动。已经有关于 HAR 的几篇综述和调查，但由于文献不断增长，HAR 文献的现状需要更新。因此，本综述旨在提供自 2018 年以来发表的 HAR 文献的最新情况。本研究回顾了 95 篇文章，对其进行分类，以突出 HAR 中的应用领域、数据源、技术和开放研究挑战。现有研究大多集中在日常生活活动上，其次是基于个人和群体活动的用户活动。然而，关于实时活动（如可疑活动、监控和医疗保健）的文献很少。现有的大部分研究都使用了闭路电视（CCTV）视频和移动传感器数据。卷积神经网络（CNN）、长短期记忆（LSTM）和支持向量机（SVM）是文献中用于 HAR 任务的最突出技术。最后，讨论了需要解决的局限性和开放性挑战。
