A non-contact technique using electrostatics to sense three-dimensional hand motion for human computer interaction

Tang, Kai; Chen, Xi; Zheng, Wei; Han, Qingwei; Li, Pengfei

doi:10.1016/j.elstat.2015.07.006

Cited by 8 publications

(6 citation statements)

References 15 publications

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“…For gesture recognition, now the general recognition methods are mainly based on visual image analysis, but they are easy to be affected by light and occlusion. TENG can use electrostatic signal detection technology to sense the change of electrostatic field around the object so as to detect and recognize the target. , It has the advantages of a small working blind area, simple system deployment, noncontact, and so on. However, a hybrid sensor for recognizing both voice and gesture sensor has never been realized by TENG technique.…”

Section: Introductionmentioning

confidence: 99%

Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

Luo

Yang

et al. 2023

ACS Appl. Mater. Interfaces

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With the development of science and technology, human− machine interaction has brought great benefits to the society. Here, we design a voice and gesture signal translator (VGST), which can translate natural actions into electrical signals and realize efficient communication in human−machine interface. By spraying silk protein on the copper of the device, the VGST can achieve improved output and a wide frequency response of 20−2000 Hz with a high sensitivity of 167 mV/dB, and the resolution of frequency detection can reach 0.1 Hz. By designing its internal structure, its resonant frequency and output voltage can be adjusted. The VGST can be used as a high-fidelity platform to effectively recover recorded music and can also be combined with machine learning algorithms to realize the function of speech recognition with a high accuracy rate of 97%. It also has good antinoise performance to recognize speech correctly even in noisy environments. Meanwhile, in gesture recognition, the triboelectric translator is able to recognize simple hand gestures and to judge the distance between hand and the VGST based on the principle of electrostatic induction. This work demonstrates that triboelectric nanogenerator (TENG) technology can have great application prospects and significant advantages in human−machine interaction and high-fidelity platforms.

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Section: Introductionmentioning

confidence: 99%

Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

Luo

Yang

et al. 2023

ACS Appl. Mater. Interfaces

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“…In 2007, Professor Takiguchi of Japan proposed to use electrodes to detect electrostatic signals generated by human walking, which has the characteristics of a low false alarm rate, small dead area, and simple system design and deployment compared to other human detection technologies [18,19]. Kai Tang used a tridimensional array of electrodes to measure the direction [20] and speed of hand movements and proposed a real-time hand position sensing technology based on human body electrostatics for human-computer interactions [21]. Both of these methods need a tridimensional array of electrodes, which cannot adapt to scenarios where the placement or operating space is narrow, but the height of a hand movement is needed.…”

Section: Introductionmentioning

confidence: 99%

A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes

Zhang

Chen

et al. 2020

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.

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“…The disturbed electric field will lead to an electrostatic induction on the electrode in space, thus hand movement can be verified by detecting the electrostatic induction signal on the electrode. Detecting the hand position using the human body’s own electrostatic field is a method for recognizing human hand movements under non-contact conditions that is not influenced by environmental factors such as light [ 9 ]. Therefore, the study of real-time hand position sensing technology based on human body electrostatics can obtain hand movement trajectories via electrostatics detection using a non-contact method, avoiding the disadvantages of being visually sensitive to light and complicated by background interference, preventing a poor user experience.…”

Section: Introductionmentioning

confidence: 99%

“…In 2015, we conducted research on sensing the direction and velocity parameters of human hand movements with an electrostatic electrode array. Theoretical and experimental studies confirmed that the direction and velocity parameters of the hand movement can be obtained with the human body’s electrostatic information and can be applied to simple interactive games [ 9 ].…”

Section: Introductionmentioning

confidence: 99%

Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics

Tang

Wang

et al. 2018

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.

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A non-contact technique using electrostatics to sense three-dimensional hand motion for human computer interaction

Cited by 8 publications

References 15 publications

Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

Human–Machine Interaction via Dual Modes of Voice and Gesture Enabled by Triboelectric Nanogenerator and Machine Learning

A Method for Measuring the Height of Hand Movements Based on a Planar Array of Electrostatic Induction Electrodes

Real-Time Hand Position Sensing Technology Based on Human Body Electrostatics

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