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Sensor-Based Human Activity and Behavior Computing

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Vision, Sensing and Analytics: Integrative Approaches

Part of the book series: Intelligent Systems Reference Library ((ISRL,volume 207))

Abstract

A major goal of human activity and behavior recognition (HAR) is to recognize activities and behaviors from a series of action data for different subjects under different environmental conditions. The use of wearables, smart devices, and vision-based systems enables the collection of human action and behavior data for greater health benefits, rehabilitation, elderly care, and monitoring. This chapter overviews the state-of-the-art in activity and behavior recognition based on sensor data. First, we provided a general architecture of HAR describing primary data sources and pre-processing techniques. Then, we describe robust feature extraction and selection strategies with a comparative analysis of statistical and deep learning-based models. Additionally, we provide information about more than 100 benchmark datasets and repositories in this domain. The paper concludes with a discussion of major issues and challenges, highlighting open issues and scopes that need to be approached in prospective research.

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Notes

  1. 1.

    http://ps.ewi.utwente.nl/Datasets.php.

  2. 2.

    http://hasc.jp/.

  3. 3.

    http://www.cs.ucr.edu/~eamonn/time_series_data/.

  4. 4.

    https://www.physionet.org/physiobank.

  5. 5.

    http://crawdad.org/.

  6. 6.

    Cooking Activity Challenge with International Conference on Activity and Behavior Computing (ABC), 2020 https://abc-research.github.io/cook2020.

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Authors and Affiliations

  1. Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh

    Anindya Das Antar, Masud Ahmed & Md Atiqur Rahman Ahad

  2. Department of CSE, University of Michigan, Ann Arbor, MI, USA

    Anindya Das Antar

  3. Department of Intelligent Media, Osaka University, Suita, Japan

    Md Atiqur Rahman Ahad

  4. Department of Information Systems, University of Maryland Baltimore County, Baltimore, MD, USA

    Masud Ahmed

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  1. Anindya Das Antar
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  2. Masud Ahmed
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  3. Md Atiqur Rahman Ahad
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Correspondence to Md Atiqur Rahman Ahad .

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Editors and Affiliations

  1. Professor, Department of Electrical and Electronic Engineering, University of Dhaka, Dhaka, Bangladesh

    Md Atiqur Rahman Ahad

  2. Solutions Architect for Greenfield and Startups, Amazon Web Services, USA, Visiting Professor, Graduate School of Regional Innovation, Mie University, Tsu, Japan

    Atsushi Inoue

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Antar, A.D., Ahmed, M., Ahad, M.A.R. (2021). Sensor-Based Human Activity and Behavior Computing. In: Ahad, M.A.R., Inoue, A. (eds) Vision, Sensing and Analytics: Integrative Approaches. Intelligent Systems Reference Library, vol 207. Springer, Cham. https://doi.org/10.1007/978-3-030-75490-7_6

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