Towards a High Accuracy Wearable Hand Gesture Recognition System Using EIT

Wu, Yu; Jiang, Dai; Duan, Jinming; Liu, Xiao; Bayford, Richard; Demosthenous, Andreas

doi:10.1109/iscas.2018.8351296

Cited by 26 publications

(24 citation statements)

References 10 publications

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“…An Arduino-Nano microcontroller is placed on the station for prosthesis motion control. The previous study [26] showed that groups of five hand gestures give optimal performance, and it also suggested a round robin sub-grouping method to increase the total number of gestures recognizable but did not provide an intuitive way for robin rotation.…”

Section: Hand Prosthesis Motion Controlmentioning

confidence: 97%

“…Originally developed as an imaging technique, EIT has recently been used for HMI related applications [24], [25]. Our first prototype reported in [26] overcame some of the design challenges listed above with hardware improvements over the AD5933 (a commercial bio-impedance analyzer chip using a two-electrode measurement scheme) based systems in [24], [25]. However, the overall system was cumbersome and power demanding.…”

Section: Background and Related Workmentioning

confidence: 99%

“…There is a design trade-off between input range, noise performance and power consumption. Based on resistive mesh simulation and comparing the specification of the IA in [26], the input range was designed to be 100 mVp-p; the biasing current was set to 25 µA, R1 = 2 kΩ and R2 = 20 kΩ. With the gain of the IA set to 10 V/V, its simulated input-referred noise was 10 µVrms over the bandwidth 25 kHz to 1 MHz.…”

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confidence: 99%

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A Human–Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

Jiang

Liu

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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This paper presents a human-machine interface that establishes a link between the user and a hand prosthesis. It successfully uses electrical impedance tomography, a conventional bio-impedance imaging technique, using an array of electrodes contained in a wristband on the user's forearm. Using a highperformance analog front-end application specific integrated circuit (ASIC) the user's forearm inner bio-impedance redistribution is accurately assessed. These bio-signatures are strongly related to hand motions and using artificial neural networks, they can be learned so as to recognize the user's intention in real-time for prosthesis operation. In this work, eleven hand motions are designed for prosthesis operation with a gesture switching enabled sub-grouping method. Experiments with five subjects show that the system can achieve 98.5% accuracy with a grouping of three gestures and an accuracy of 94.4% with two sets of five gestures. The ASIC comprises a current driver with common-mode reduction capability and a current feedback instrumentation amplifier (that occupy an area of 0.07 mm 2). The ASIC operates from ±1.65 V power supplies and has a minimum bio-impedance sensitivity of 12.7 mΩp-p.

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Section: Hand Prosthesis Motion Controlmentioning

confidence: 97%

Section: Background and Related Workmentioning

confidence: 99%

mentioning

confidence: 99%

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A Human–Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

Jiang

Liu

et al. 2018

IEEE Trans. Biomed. Circuits Syst.

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“…In computer systems, these color components are stored as an integer value ranging from 0 to 255. However this space color is very luma-dependent and detection of a certain range of color may be unreliable under different lighting conditions [28]. Normalized RGB space color takes this into account and forms a chromatic color space, which removes luminance and can be defined by the formula in Equation 1 [29].…”

Section: • Skin Segmentationmentioning

confidence: 99%

A Gesture-based Recognition System for Augmented Reality

Kasinathan

Fajrillah

2019

Int. J. Adv. Sci. Eng. Inf. Technol.

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With the geometrical improvement in Information Technology, current conventional input devices are becoming increasingly obsolete and lacking. Experts in Human Computer Interaction (HCI) are convinced that input devices remain the bottleneck of information acquisition specifically in when using Augmented Reality (AR) technology. Current input mechanisms are unable to compete with this trend towards naturalness and expressivity which allows users to perform natural gestures or operations and convert them as input. Hence, a more natural and intuitive input device is imperative, specifically gestural inputs that have been widely perceived by HCI experts as the next big input device. To address this gap, this project is set to develop a prototype of hand gesture recognition system based on computer vision in modeling basic human-computer interactions. The main motivation in this work is a technology that requires no outfitting of additional equipment whatsoever by the users. The gesture-based had recognition system was implemented using the Rapid Application Development (RAD) methodology and was evaluated in terms of its usability and performance through five levels of testing, which are unit testing, integration testing, system testing, recognition accuracy testing, and user acceptance testing. The test results of unit, integration, system testing as well as user acceptance testing produced favorable results. In conclusion, current conventional input devices will continue to bottleneck this advancement in technology; therefore, a better alternative input technique should be looked into, in particularly, gesture-based input technique which offers user a more natural and intuitive control.

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“…The system, as shown in Fig. 1, consists of a fully integrated EIT system using commercial ICs for high accuracy hand-gesture detection [2]. It captures the bio-impedance data and sends it to the ANN for feature learning and classification.…”

Section: Introductionmentioning

confidence: 99%

Live Demonstration: A Wearable EIT System for Hand Prosthesis Motion Controls

Jiang

Bayford

et al. 2018

2018 IEEE International Symposium on Circuits and Systems (ISCAS)

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A wearable electrical impedance tomography system for hand prosthesis motion control is demonstrated. The system captures the user's hand motion by measuring the impedance alterations caused by muscle and bone movement inside the forearm. These impedance data are sent to an artificial neural network for motion classification which is then used to manipulate a hand prosthesis. During the live demonstration, a sensor band is put on a volunteers' forearm for data acquisition. After signal processing, hand gestures learnt by the neural network can be recognized and the same hand motion can be recreated through the hand prosthesis in real-time. I.

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Towards a High Accuracy Wearable Hand Gesture Recognition System Using EIT

Cited by 26 publications

References 10 publications

A Human–Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

A Human–Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control

A Gesture-based Recognition System for Augmented Reality

Live Demonstration: A Wearable EIT System for Hand Prosthesis Motion Controls

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