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Article

The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey

German Research Centre for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
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Author to whom correspondence should be addressed.
Academic Editors: Tanja Schultz, Hui Liu and Hugo Gamboa
Sensors 2022, 22(12), 4596; https://doi.org/10.3390/s22124596
Received: 13 May 2022 / Revised: 13 June 2022 / Accepted: 16 June 2022 / Published: 17 June 2022
(This article belongs to the Special Issue Sensors for Human Activity Recognition)
Human activity recognition (HAR) has become an intensive research topic in the past decade because of the pervasive user scenarios and the overwhelming development of advanced algorithms and novel sensing approaches. Previous HAR-related sensing surveys were primarily focused on either a specific branch such as wearable sensing and video-based sensing or a full-stack presentation of both sensing and data processing techniques, resulting in weak focus on HAR-related sensing techniques. This work tries to present a thorough, in-depth survey on the state-of-the-art sensing modalities in HAR tasks to supply a solid understanding of the variant sensing principles for younger researchers of the community. First, we categorized the HAR-related sensing modalities into five classes: mechanical kinematic sensing, field-based sensing, wave-based sensing, physiological sensing, and hybrid/others. Specific sensing modalities are then presented in each category, and a thorough description of the sensing tricks and the latest related works were given. We also discussed the strengths and weaknesses of each modality across the categorization so that newcomers could have a better overview of the characteristics of each sensing modality for HAR tasks and choose the proper approaches for their specific application. Finally, we summarized the presented sensing techniques with a comparison concerning selected performance metrics and proposed a few outlooks on the future sensing techniques used for HAR tasks. View Full-Text
Keywords: human activity recognition; sensing technique human activity recognition; sensing technique
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MDPI and ACS Style

Bian, S.; Liu, M.; Zhou, B.; Lukowicz, P. The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey. Sensors 2022, 22, 4596. https://doi.org/10.3390/s22124596

AMA Style

Bian S, Liu M, Zhou B, Lukowicz P. The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey. Sensors. 2022; 22(12):4596. https://doi.org/10.3390/s22124596

Chicago/Turabian Style

Bian, Sizhen, Mengxi Liu, Bo Zhou, and Paul Lukowicz. 2022. "The State-of-the-Art Sensing Techniques in Human Activity Recognition: A Survey" Sensors 22, no. 12: 4596. https://doi.org/10.3390/s22124596

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