Next Article in Journal
Synchrotron Radiation Study of Gain, Noise, and Collection Efficiency of GaAs SAM-APDs with Staircase Structure
Next Article in Special Issue
Semi-Supervised Adversarial Learning Using LSTM for Human Activity Recognition
Previous Article in Journal
SiO2 Microsphere Array Coated by Ag Nanoparticles as Raman Enhancement Sensor with High Sensitivity and High Stability
Previous Article in Special Issue
A Novel Central Camera Calibration Method Recording Point-to-Point Distortion for Vision-Based Human Activity Recognition

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

German Research Centre for Artificial Intelligence (DFKI), 67663 Kaiserslautern, Germany
Author to whom correspondence should be addressed.
Academic Editors: Tanja Schultz, Hui Liu and Hugo Gamboa
Sensors 2022, 22(12), 4596;
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
Show Figures

Figure 1

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.

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.

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.

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

Back to TopTop