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This paper proposes a method based on a planar array of electrostatic induction electrodes, which uses human body electrostatics to measure the height of hand movements. The human body is electrostatically charged for a variety of reasons. In the process of a hand movement, the change of a human body’s electric field is captured through the electrostatic sensors connected to the electrode array. A measurement algorithm for the height of hand movements is used to measure the height of hand movements after the direction of it has been obtained. Compared with the tridimensional array, the planar array has the advantages of less space and easy deployment; therefore, it is more widely used. In this paper, a human hand movement sensing system based on human body electrostatics was established to perform verification experiments. The results show that this method can measure the height of hand movements with good accuracy to meet the requirements of non-contact human-computer interactions.
Linyi Zhang; Xi Chen; Pengfei Li; Chuang Wang; Mengxuan Li. A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes. Sensors 2020, 20, 2943 .
AMA StyleLinyi Zhang, Xi Chen, Pengfei Li, Chuang Wang, Mengxuan Li. A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes. Sensors. 2020; 20 (10):2943.
Chicago/Turabian StyleLinyi Zhang; Xi Chen; Pengfei Li; Chuang Wang; Mengxuan Li. 2020. "A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes." Sensors 20, no. 10: 2943.
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.
Haoyu Wei; Pengfei Li; Kai Tang; Wei Wang; Xi Chen. Alternating Electric Field-Based Static Gesture-Recognition Technology. Sensors 2019, 19, 2375 .
AMA StyleHaoyu Wei, Pengfei Li, Kai Tang, Wei Wang, Xi Chen. Alternating Electric Field-Based Static Gesture-Recognition Technology. Sensors. 2019; 19 (10):2375.
Chicago/Turabian StyleHaoyu Wei; Pengfei Li; Kai Tang; Wei Wang; Xi Chen. 2019. "Alternating Electric Field-Based Static Gesture-Recognition Technology." Sensors 19, no. 10: 2375.
The authors wish to make the following corrections to this paper [...].
Kai Tang; Aijia Liu; Wei Wang; Pengfei Li; Xi Chen. Correction: Tang, K., et al., A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging. Sensors 2018, 18, 3050. Sensors 2019, 19, 1725 .
AMA StyleKai Tang, Aijia Liu, Wei Wang, Pengfei Li, Xi Chen. Correction: Tang, K., et al., A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging. Sensors 2018, 18, 3050. Sensors. 2019; 19 (7):1725.
Chicago/Turabian StyleKai Tang; Aijia Liu; Wei Wang; Pengfei Li; Xi Chen. 2019. "Correction: Tang, K., et al., A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging. Sensors 2018, 18, 3050." Sensors 19, no. 7: 1725.
In this paper, we propose a new fingerprint sensing technology based on electrostatic imaging, which can greatly improve fingerprint sensing distance. This can solve the problem of the existing capacitive fingerprint identification device being easy to damage due to limited detection distance and a protective coating that is too thin. The fingerprint recognition sensor can also be placed under a glass screen to meet the needs of the full screen design of the mobile phone. In this paper, the electric field distribution around the fingerprint is analyzed. The electrostatic imaging sensor design is carried out based on the electrostatic detection principle and MEMS (micro-electro-mechanical system) technology. The MEMS electrostatic imaging array, analog, and digital signal processing circuit structure are designed. Simulation and testing are carried out as well. According to the simulation and prototype test device test results, it is confirmed that our proposed electrostatic imaging-based fingerprint sensing technology can increase fingerprint recognition distance by 46% compared to the existing capacitive fingerprint sensing technology. A distance of more than 439 μm is reached.
Kai Tang; Aijia Liu; Wei Wang; Pengfei Li; Xi Chen. A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging. Sensors 2018, 18, 3050 .
AMA StyleKai Tang, Aijia Liu, Wei Wang, Pengfei Li, Xi Chen. A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging. Sensors. 2018; 18 (9):3050.
Chicago/Turabian StyleKai Tang; Aijia Liu; Wei Wang; Pengfei Li; Xi Chen. 2018. "A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging." Sensors 18, no. 9: 3050.
Accurate estimation of gait parameters is essential for obtaining quantitative information on motor deficits in Parkinson’s disease and other neurodegenerative diseases, which helps determine disease progression and therapeutic interventions. Due to the demand for high accuracy, unobtrusive measurement methods such as optical motion capture systems, foot pressure plates, and other systems have been commonly used in clinical environments. However, the high cost of existing lab-based methods greatly hinders their wider usage, especially in developing countries. In this study, we present a low-cost, noncontact, and an accurate temporal gait parameters estimation method by sensing and analyzing the electrostatic field generated from human foot stepping. The proposed method achieved an average 97% accuracy on gait phase detection and was further validated by comparison to the foot pressure system in 10 healthy subjects. Two results were compared using the Pearson coefficient r and obtained an excellent consistency (r = 0.99, p < 0.05). The repeatability of the purposed method was calculated between days by intraclass correlation coefficients (ICC), and showed good test-retest reliability (ICC = 0.87, p < 0.01). The proposed method could be an affordable and accurate tool to measure temporal gait parameters in hospital laboratories and in patients’ home environments.
Mengxuan Li; Pengfei Li; Shanshan Tian; Kai Tang; Xi Chen. Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method. Sensors 2018, 18, 1737 .
AMA StyleMengxuan Li, Pengfei Li, Shanshan Tian, Kai Tang, Xi Chen. Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method. Sensors. 2018; 18 (6):1737.
Chicago/Turabian StyleMengxuan Li; Pengfei Li; Shanshan Tian; Kai Tang; Xi Chen. 2018. "Estimation of Temporal Gait Parameters Using a Human Body Electrostatic Sensing-Based Method." Sensors 18, no. 6: 1737.
Non-contact human-computer interactions (HCI) based on hand gestures have been widely investigated. Here, we present a novel method to locate the real-time position of the hand using the electrostatics of the human body. This method has many advantages, including a delay of less than one millisecond, low cost, and does not require a camera or wearable devices. A formula is first created to sense array signals with five spherical electrodes. Next, a solving algorithm for the real-time measured hand position is introduced and solving equations for three-dimensional coordinates of hand position are obtained. A non-contact real-time hand position sensing system was established to perform verification experiments, and the principle error of the algorithm and the systematic noise were also analyzed. The results show that this novel technology can determine the dynamic parameters of hand movements with good robustness to meet the requirements of complicated HCI.
Kai Tang; Pengfei Li; Chuang Wang; Yifei Wang; Xi Chen. Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics. Sensors 2018, 18, 1677 .
AMA StyleKai Tang, Pengfei Li, Chuang Wang, Yifei Wang, Xi Chen. Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics. Sensors. 2018; 18 (6):1677.
Chicago/Turabian StyleKai Tang; Pengfei Li; Chuang Wang; Yifei Wang; Xi Chen. 2018. "Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics." Sensors 18, no. 6: 1677.