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Open AccessArticle

Alternating Electric Field-Based Static Gesture-Recognition Technology

State Key Laboratory of Mechatronics Engineering and Control, Beijing Institute of Technology, Beijing 100081, China
Author to whom correspondence should be addressed.
Sensors 2019, 19(10), 2375;
Received: 21 April 2019 / Revised: 12 May 2019 / Accepted: 20 May 2019 / Published: 23 May 2019
(This article belongs to the Special Issue Social Robots and Sensors)
PDF [3782 KB, uploaded 23 May 2019]


Currently, gesture recognition based on electric-field detection technology has received extensive attention, which is mostly used to recognize the position and the movement of the hand, and rarely used for identification of specific gestures. A non-contact gesture-recognition technology based on the alternating electric-field detection scheme is proposed, which can recognize static gestures in different states and dynamic gestures. The influence of the hand on the detection system is analyzed from the principle of electric-field detection. A simulation model of the system is established to investigate the charge density on the hand surface and the potential change of the sensing electrodes. According to the simulation results, the system structure is improved, and the signal-processing circuit is designed to collect the signal of sensing electrodes. By collecting a large amount of data from different operators, the tree-model recognition algorithm is designed and a gesture-recognition experiment is implemented. The results show that the gesture-recognition correct rate is over 90%. With advantages of high response speed, low cost, small volume, and immunity to the surrounding environment, the system could be assembled on a robot that communicates with operators. View Full-Text
Keywords: human–computer interaction; gesture recognition; electric-field detection human–computer interaction; gesture recognition; electric-field detection

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Wei, H.; Li, P.; Tang, K.; Wang, W.; Chen, X. Alternating Electric Field-Based Static Gesture-Recognition Technology. Sensors 2019, 19, 2375.

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