Sensor-based and vision-based human activity recognition: A comprehensive survey

•A comprehensive review of vision-based and sensor-based human activity recognition.•Summarize and discuss public datasets that are used in vision-based HAR and sensor-based HAR.•Categorize and analyze standard data processing, and feature engineering processes used in HAR.•Categorize and analyze ma...

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Vydané v:Pattern recognition Ročník 108; s. 107561
Hlavní autori: Minh Dang, L., Min, Kyungbok, Wang, Hanxiang, Jalil Piran, Md, Hee Lee, Cheol, Moon, Hyeonjoon
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Elsevier Ltd 01.12.2020
Predmet:
Action recognition
Context-awareness
Deep learning
Human activity recognition
Human-centric sensing
Sensors
Vision
Action recognition
Deep learning
Vision
Context-awareness
Sensors
Human activity recognition
Human-centric sensing
ISSN:0031-3203, 1873-5142
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Popis
Shrnutí:•A comprehensive review of vision-based and sensor-based human activity recognition.•Summarize and discuss public datasets that are used in vision-based HAR and sensor-based HAR.•Categorize and analyze standard data processing, and feature engineering processes used in HAR.•Categorize and analyze machine learning techniques for HAR and focus on current deep learning research in HAR.•Discuss challenges and show future directions for HAR. 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.
ISSN:0031-3203
1873-5142
DOI:10.1016/j.patcog.2020.107561