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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Published in	Pattern recognition Vol. 108; p. 107561
Main Authors	Minh Dang, L., Min, Kyungbok, Wang, Hanxiang, Jalil Piran, Md, Hee Lee, Cheol, Moon, Hyeonjoon
Format	Journal Article
Language	English
Published	Elsevier Ltd 01.12.2020
Subjects	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
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Abstract	•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.
AbstractList	•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.
ArticleNumber	107561
Author	Minh Dang, L. Jalil Piran, Md Min, Kyungbok Hee Lee, Cheol Moon, Hyeonjoon Wang, Hanxiang
Author_xml	– sequence: 1 givenname: L. surname: Minh Dang fullname: Minh Dang, L. organization: Department of Computer Science and Engineering, Sejong University, Seoul 143-747, South Korea – sequence: 2 givenname: Kyungbok surname: Min fullname: Min, Kyungbok organization: Department of Computer Science and Engineering, Sejong University, Seoul 143-747, South Korea – sequence: 3 givenname: Hanxiang surname: Wang fullname: Wang, Hanxiang organization: Department of Computer Science and Engineering, Sejong University, Seoul 143-747, South Korea – sequence: 4 givenname: Md surname: Jalil Piran fullname: Jalil Piran, Md organization: Department of Computer Science and Engineering, Sejong University, Seoul 143-747, South Korea – sequence: 5 givenname: Cheol surname: Hee Lee fullname: Hee Lee, Cheol organization: Deep inspection, A-1314, Beopwon-ro 11 gil 25, Songpa-gu, Seoul 143-747, South Korea – sequence: 6 givenname: Hyeonjoon orcidid: 0000-0002-5576-8960 surname: Moon fullname: Moon, Hyeonjoon email: hmoon@sejong.ac.kr organization: Department of Computer Science and Engineering, Sejong University, Seoul 143-747, South Korea
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Snippet	•A comprehensive review of vision-based and sensor-based human activity recognition.•Summarize and discuss public datasets that are used in vision-based HAR...
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SubjectTerms	Action recognition Context-awareness Deep learning Human activity recognition Human-centric sensing Sensors Vision
Title	Sensor-based and vision-based human activity recognition: A comprehensive survey
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