计算机科学
数据科学
背景(考古学)
活动识别
数据收集
人工智能
优势和劣势
地理
心理学
数学
社会心理学
统计
考古
作者
L. Minh Dang,Kyungbok Min,Hanxiang Wang,Md. Jalil Piran,Cheol Hee Lee,Hyeonjoon Moon
标识
DOI:10.1016/j.patcog.2020.107561
摘要
Human activity recognition (HAR) technology that analyzes data acquired from various types of sensing devices, including vision sensors and embedded sensors, has motivated the development of various context-aware applications in emerging domains, e.g., the Internet of Things (IoT) and healthcare. Even though a considerable number of HAR surveys and review articles have been conducted previously, the major/overall HAR subject has been ignored, and these studies only focus on particular HAR topics. Therefore, a comprehensive review paper that covers major subjects in HAR is imperative. This survey analyzes the latest state-of-the-art research in HAR in recent years, introduces a classification of HAR methodologies, and shows advantages and weaknesses for methods in each category. Specifically, HAR methods are classified into two main groups, which are sensor-based HAR and vision-based HAR, based on the generated data type. After that, each group is divided into subgroups that perform different procedures, including the data collection, pre-processing methods, feature engineering, and the training process. Moreover, an extensive review regarding the utilization of deep learning in HAR is also conducted. Finally, this paper discusses various challenges in the current HAR topic and offers suggestions for future research.
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