research-article

Sensor-Based Activity Recognition

Authors:

Zhiwen YuAuthors Info & Claims

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews, Volume 42, Issue 6

Pages 790 - 808

https://doi.org/10.1109/TSMCC.2012.2198883

Published: 01 November 2012 Publication History

Abstract

Research on sensor-based activity recognition has, recently, made significant progress and is attracting growing attention in a number of disciplines and application domains. However, there is a lack of high-level overview on this topic that can inform related communities of the research state of the art. In this paper, we present a comprehensive survey to examine the development and current status of various aspects of sensor-based activity recognition. We first discuss the general rationale and distinctions of vision-based and sensor-based activity recognition. Then, we review the major approaches and methods associated with sensor-based activity monitoring, modeling, and recognition from which strengths and weaknesses of those approaches are highlighted. We make a primary distinction in this paper between data-driven and knowledge-driven approaches, and use this distinction to structure our survey. We also discuss some promising directions for future research.

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Ye NZhang LCheng DBu CSun SWu HSong A(2025)Dynamic instance-aware layer-bit-select network on human activity recognition using wearable sensorsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109260137:PBOnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.engappai.2024.109260
Li ZLiu HHuan ZLiang J(2024)Active contrastive coding reducing label effort for sensor-based human activity recognitionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23480446:2(3987-3999)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-234804
Takeda NLegaspi RNishimura YIkeda KMinamikawa APlötz TChernova S(2024)Sensor event sequence prediction for proactive smart homeJournal of Ambient Intelligence and Smart Environments10.3233/AIS-23042916:3(275-308)Online publication date: 24-Sep-2024
https://dl.acm.org/doi/10.3233/AIS-230429
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cover image IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews

IEEE Transactions on Systems, Man, and Cybernetics, Part C: Applications and Reviews Volume 42, Issue 6

November 2012

1070 pages

ISSN:1094-6977

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IEEE Press

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Published: 01 November 2012

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Ye NZhang LCheng DBu CSun SWu HSong A(2025)Dynamic instance-aware layer-bit-select network on human activity recognition using wearable sensorsEngineering Applications of Artificial Intelligence10.1016/j.engappai.2024.109260137:PBOnline publication date: 7-Jan-2025
https://dl.acm.org/doi/10.1016/j.engappai.2024.109260
Li ZLiu HHuan ZLiang J(2024)Active contrastive coding reducing label effort for sensor-based human activity recognitionJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23480446:2(3987-3999)Online publication date: 14-Feb-2024
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Takeda NLegaspi RNishimura YIkeda KMinamikawa APlötz TChernova S(2024)Sensor event sequence prediction for proactive smart homeJournal of Ambient Intelligence and Smart Environments10.3233/AIS-23042916:3(275-308)Online publication date: 24-Sep-2024
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