Human presence and motion detection through electrostatic sensing

Human motion detection is essential in a wide range of applications. In view of the advantages of non-contact sensing, high sensitivity, fast response, simple installation and low cost, electrostatic sensors in different sizes and arrangements have been developed for human motion detection recently. This paper evaluates for the first time the performance of electrostatic sensors for human presence and motion detection. Experimental tests were conducted to obtain and analyse sensor signals of typical walking and stepping motions. The effects of sensor location, ambient conditions and the subject under test on the sensor output are quantified through systematic experimental testing.

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Publication Details

Journal
Measurement Sensors
Published
2024-12-30
DOI
https://doi.org/10.1016/j.measen.2024.101705
Citations
1
Primary Topic
Context-Aware Activity Recognition Systems
Type
article
Field-Weighted Citation Impact
0.25
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article

Human presence and motion detection through electrostatic sensing

Fabio Passaniti, Enrico Alessi, Yong Yan, Luca Gubellini et al.
1 citations
Measurement Sensors
Context-Aware Activity Recognition Systems
0.25
article

Human presence and motion detection through electrostatic sensing

Fabio Passaniti, Enrico Alessi, Yong Yan, Luca Gubellini, Lijuan Wang, Chih-Yen Chen
article en
1 citations

Abstract

Human motion detection is essential in a wide range of applications. In view of the advantages of non-contact sensing, high sensitivity, fast response, simple installation and low cost, electrostatic sensors in different sizes and arrangements have been developed for human motion detection recently. This paper evaluates for the first time the performance of electrostatic sensors for human presence and motion detection. Experimental tests were conducted to obtain and analyse sensor signals of typical walking and stepping motions. The effects of sensor location, ambient conditions and the subject under test on the sensor output are quantified through systematic experimental testing.

Measurement SensorsVol. 38
University of Kent (GB), STMicroelectronics (Italy) (IT)
Openalex Percentile: Top 43%
Context-Aware Activity Recognition Systems
0.25
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