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Toward human activity recognition: a survey

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Abstract

Human activity recognition (HAR) is a complex and multifaceted problem. The research community has reported numerous approaches to perform HAR. Along with HAR approaches, various surveys have revealed HAR trends in various environments and applications. HAR is linked to a variety of technology-dependent daily life systems, such as human–computer interaction systems, security surveillance, video surveillance, healthcare surveillance, robotics, content-based information retrieval, and monitoring systems. Because of technological advancements, HAR trends change quickly and necessitate an up-to-date and broader perspective. This study offers an HAR taxonomy, which includes online/offline HAR, multimodal/unimodal HAR, handcrafted feature-based, and learning-based approaches. This study attempts to present the multidisciplinary nature of HAR, such as application areas, activity types, task complexities, benchmark datasets, and/methods. This research includes a comparative analysis of state-of-the-art HAR methods and a discussion of popular datasets. The selected studies have been categorized using taxonomy, and different attributes such as activity complexity, dataset size, and recognition rate have been used for their analysis. The comparative analysis of HAR approaches has also helped to highlight domain challenges and open research directions for HAR researchers to follow.

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Data availability

Data sharing is not applicable to this article as no datasets were produced or analyzed during the current study. However, this study is based on analyzing existing methods, and their sources are added to the manuscript.

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Acknowledgements

We acknowledge partial support from the National Center of Big Data and Cloud Computing (NCBC) and Higher Education Commission (HEC) of Pakistan for conducting this research.

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  1. Department of Computer Science, COMSATS University Islamabad, Lahore Campus, 1.5 KM Defence Road Off Raiwind Road, Lahore, Pakistan

    Gulshan Saleem & Usama Ijaz Bajwa

  2. Electronics and Power Engineering Department, Pakistan Navy Engineering College (PNEC), National University of Sciences and Technology (NUST), Habib Ibrahim Rehmatullah Road, Karachi, Pakistan

    Rana Hammad Raza

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Saleem, G., Bajwa, U.I. & Raza, R.H. Toward human activity recognition: a survey. Neural Comput & Applic 35, 4145–4182 (2023). https://doi.org/10.1007/s00521-022-07937-4

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