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Chen Xi
State Key Laboratory of Mechatronics Engineering and Control, Beijing Institute of Technology, Beijing 100081, China

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Journal article
Published: 22 May 2020 in Sensors
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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.

ACS Style

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 Style

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 (10):2943.

Chicago/Turabian Style

Linyi 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.

Journal article
Published: 03 June 2019 in Sensors
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Hemiparesis is one of the common sequelae of neurological diseases such as strokes, which can significantly change the gait behavior of patients and restrict their activities in daily life. The results of gait characteristic analysis can provide a reference for disease diagnosis and rehabilitation; however, gait correlation as a gait characteristic is less utilized currently. In this study, a new non-contact electrostatic field sensing method was used to obtain the electrostatic gait signals of hemiplegic patients and healthy control subjects, and an improved Detrended Cross-Correlation Analysis cross-correlation coefficient method was proposed to analyze the obtained electrostatic gait signals. The results show that the improved method can better obtain the dynamic changes of the scaling index under the multi-scale structure, which makes up for the shortcomings of the traditional Detrended Cross-Correlation Analysis cross-correlation coefficient method when calculating the electrostatic gait signal of the same kind of subjects, such as random and incomplete similarity in the trend of the scaling index spectrum change. At the same time, it can effectively quantify the correlation of electrostatic gait signals in subjects. The proposed method has the potential to be a powerful tool for extracting the gait correlation features and identifying the electrostatic gait of hemiplegic patients.

ACS Style

Shanshan Tian; Mengxuan Li; Yifei Wang; Xi Chen. Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients. Sensors 2019, 19, 2529 .

AMA Style

Shanshan Tian, Mengxuan Li, Yifei Wang, Xi Chen. Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients. Sensors. 2019; 19 (11):2529.

Chicago/Turabian Style

Shanshan Tian; Mengxuan Li; Yifei Wang; Xi Chen. 2019. "Application of an Improved Correlation Method in Electrostatic Gait Recognition of Hemiparetic Patients." Sensors 19, no. 11: 2529.

Journal article
Published: 23 May 2019 in Sensors
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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.

ACS Style

Haoyu Wei; Pengfei Li; Kai Tang; Wei Wang; Xi Chen. Alternating Electric Field-Based Static Gesture-Recognition Technology. Sensors 2019, 19, 2375 .

AMA Style

Haoyu Wei, Pengfei Li, Kai Tang, Wei Wang, Xi Chen. Alternating Electric Field-Based Static Gesture-Recognition Technology. Sensors. 2019; 19 (10):2375.

Chicago/Turabian Style

Haoyu Wei; Pengfei Li; Kai Tang; Wei Wang; Xi Chen. 2019. "Alternating Electric Field-Based Static Gesture-Recognition Technology." Sensors 19, no. 10: 2375.

Conference paper
Published: 28 April 2019 in Communications in Computer and Information Science
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Automatic grasp planning of robotic hand is always a difficult problem in robotics. Although researchers have developed underactuated hands with less degrees of freedom, automatic grasp planning is still a difficult problem because of the huge number of possible hand configurations. But humans simplify this problem by applying several usual grasp starting postures on most of grasp tasks. In this paper, a method for grasping planning is presented which can use a set of heuristic rules to limit the possibilities of hand configurations. By modeling the object as a set of primitive shapes and applying appropriate grasp starting postures on it, many grasp samples can be generated. And this is the basic content of the heuristic rules. Then, given grasp samples in finite number, a Simulink and Adams co-simulator is built to simulate these discrete grasp samples. The main purpose of the co-simulator is to simulate the dynamic behavior of robotic hand when grasping and measure the grasp quality using force closure and grasp measurement of largest-minimum resisted wrench. Last, a grasp library with force closure grasps is built where all items of grasp are sorted by the function value of grasp measurement. With the library, it’s easy to plan a grasp for certain object by just selecting an appropriate grasp from the library.

ACS Style

Wei Wang; Qiangbing Zhang; Zeyuan Sun; Xi Chen; Zhihong Jiang. Automatic Operating Method of Robot Dexterous Multi-fingered Hand Using Heuristic Rules. Communications in Computer and Information Science 2019, 420 -431.

AMA Style

Wei Wang, Qiangbing Zhang, Zeyuan Sun, Xi Chen, Zhihong Jiang. Automatic Operating Method of Robot Dexterous Multi-fingered Hand Using Heuristic Rules. Communications in Computer and Information Science. 2019; ():420-431.

Chicago/Turabian Style

Wei Wang; Qiangbing Zhang; Zeyuan Sun; Xi Chen; Zhihong Jiang. 2019. "Automatic Operating Method of Robot Dexterous Multi-fingered Hand Using Heuristic Rules." Communications in Computer and Information Science , no. : 420-431.

Journal article
Published: 11 April 2019 in Sensors
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Walking is a basic requirement for participating in daily activities. Neurological diseases such as stroke can significantly affect one’s gait and thereby restrict one’s activities that are a part of daily living. Previous studies have demonstrated that gait temporal parameters are useful for characterizing post-stroke hemiparetic gait. However, no previous studies have investigated the symmetry, regularity and stability of post-stroke hemiparetic gaits. In this study, the dynamic time warping (DTW) algorithm, sample entropy method and empirical mode decomposition-based stability index were utilized to obtain the three aforementioned types of gait features, respectively. Studies were conducted with 15 healthy control subjects and 15 post-stroke survivors. Experimental results revealed that the proposed features could significantly differentiate hemiparetic patients from healthy control subjects by a Mann–Whitney test (with a p-value of less than 0.05). Finally, four representative classifiers were utilized in order to evaluate the possible capabilities of these features to distinguish patients with hemiparetic gaits from the healthy control subjects. The maximum area under the curve values were shown to be 0.94 by the k-nearest-neighbor (kNN) classifier. These promising results have illustrated that the proposed features have considerable potential to promote the future design of automatic gait analysis systems for clinical practice.

ACS Style

Mengxuan Li; Shanshan Tian; Linlin Sun; Xi Chen. Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method. Sensors 2019, 19, 1737 .

AMA Style

Mengxuan Li, Shanshan Tian, Linlin Sun, Xi Chen. Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method. Sensors. 2019; 19 (7):1737.

Chicago/Turabian Style

Mengxuan Li; Shanshan Tian; Linlin Sun; Xi Chen. 2019. "Gait Analysis for Post-Stroke Hemiparetic Patient by Multi-Features Fusion Method." Sensors 19, no. 7: 1737.

Correction
Published: 10 April 2019 in Sensors
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The authors wish to make the following corrections to this paper [...].

ACS Style

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 Style

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 (7):1725.

Chicago/Turabian Style

Kai 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.

Journal article
Published: 12 November 2018 in Energies
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Negative corona discharge occurs widely in high voltage transmission lines and other “high voltage” uses, which can cause strong electromagnetic interference (EMI). In this research, the pulse current of multi-needle negative corona discharge and its electromagnetic (EM) radiation characteristics were studied and compared with that of single-needle negative corona discharge. A dipole radiation model was established to analyze the EM radiation characteristics of the negative corona discharge. The results show that the Trichel pulse discharge process of one discharge needle in multi-needle discharge structure will inhibit the discharge of the other discharge needles. It is only when the voltage reaches a certain threshold will the current and EM radiation fields of multi-needle discharge structure with a significant increasing of amolitude. The frequency of EM radiation of negative corona discharge is not affected by the number of needles, but is only related to ambient air pressure. This research provides a basis for detecting corona discharge sources in different conditions.

ACS Style

Chuang Wang; Xi Chen; Jiting Ouyang; Tie Li; Jialu Fu. Pulse Current of Multi-Needle Negative Corona Discharge and Its Electromagnetic Radiation Characteristics. Energies 2018, 11, 3120 .

AMA Style

Chuang Wang, Xi Chen, Jiting Ouyang, Tie Li, Jialu Fu. Pulse Current of Multi-Needle Negative Corona Discharge and Its Electromagnetic Radiation Characteristics. Energies. 2018; 11 (11):3120.

Chicago/Turabian Style

Chuang Wang; Xi Chen; Jiting Ouyang; Tie Li; Jialu Fu. 2018. "Pulse Current of Multi-Needle Negative Corona Discharge and Its Electromagnetic Radiation Characteristics." Energies 11, no. 11: 3120.

Journal article
Published: 12 September 2018 in Sensors
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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.

ACS Style

Kai Tang; Aijia Liu; Wei Wang; Pengfei Li; Xi Chen. A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging. Sensors 2018, 18, 3050 .

AMA Style

Kai 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 Style

Kai Tang; Aijia Liu; Wei Wang; Pengfei Li; Xi Chen. 2018. "A Novel Fingerprint Sensing Technology Based on Electrostatic Imaging." Sensors 18, no. 9: 3050.

Journal article
Published: 28 May 2018 in Sensors
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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.

ACS Style

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 Style

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 (6):1737.

Chicago/Turabian Style

Mengxuan 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.

Journal article
Published: 23 May 2018 in Sensors
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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.

ACS Style

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 Style

Kai 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 Style

Kai 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.

Conference paper
Published: 01 January 2018 in ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS II: Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics (AMME 2018)
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When the robot travels on the surface of different media, the uncertainty of the medium will seriously affect the autonomous action of the robot. In this paper, the distribution characteristics of multiple electrostatic charges on the surface of materials are detected, so as to improve the accuracy of the existing electrostatic signal material identification methods, which is of great significance to help the robot optimize the control algorithm. In this paper, based on the electrostatic signal material identification method proposed by predecessors, the multi-channel detection circuit is used to obtain the electrostatic charge distribution at different positions of the material surface, the weights are introduced into the eigenvalue matrix, and the weight distribution is optimized by the evolutionary algorithm, which makes the eigenvalue matrix more accurately reflect the surface charge distribution characteristics of the material. The matrix is used as the input of the k-Nearest Neighbor (kNN)classification algorithm to classify the dielectric materials. The experimental results show that the proposed method can significantly improve the recognition rate of the existing electrostatic signal material recognition methods.

ACS Style

Kai Liu; Xi Chen; Jingnan Li. Material identification based on electrostatic sensing technology. ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS II: Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics (AMME 2018) 2018, 1955, 020013 .

AMA Style

Kai Liu, Xi Chen, Jingnan Li. Material identification based on electrostatic sensing technology. ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS II: Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics (AMME 2018). 2018; 1955 (1):020013.

Chicago/Turabian Style

Kai Liu; Xi Chen; Jingnan Li. 2018. "Material identification based on electrostatic sensing technology." ADVANCES IN MATERIALS, MACHINERY, ELECTRONICS II: Proceedings of the 2nd International Conference on Advances in Materials, Machinery, Electronics (AMME 2018) 1955, no. 1: 020013.

Journal article
Published: 11 August 2015 in Journal of Electrostatics
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This paper proposes a technique for three dimensional non-contact sensing of hand motion through electrostatic field. In order to determine hand direction and speed in three-dimensions, the technique exploits the geometrical relationship among six spherical electrodes organized in two planes, and the induced current on the electrode due to hand movement. This system consists of an electrode array, current-sensing circuits, a digital signal processing module with virtual instrument technology, and LabVIEW for visualization. The experimental results demonstrate that by analyzing the currents induced in the electrodes, the angle and speed of hand motion can be inferred with high precision, and the system's effectiveness enable for accurate recognition of hand motion and human–computer interaction.

ACS Style

Kai Tang; Xi Chen; Wei Zheng; Qingwei Han; Pengfei Li. A non-contact technique using electrostatics to sense three-dimensional hand motion for human computer interaction. Journal of Electrostatics 2015, 77, 101 -109.

AMA Style

Kai Tang, Xi Chen, Wei Zheng, Qingwei Han, Pengfei Li. A non-contact technique using electrostatics to sense three-dimensional hand motion for human computer interaction. Journal of Electrostatics. 2015; 77 ():101-109.

Chicago/Turabian Style

Kai Tang; Xi Chen; Wei Zheng; Qingwei Han; Pengfei Li. 2015. "A non-contact technique using electrostatics to sense three-dimensional hand motion for human computer interaction." Journal of Electrostatics 77, no. : 101-109.