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• W2898323503 startingPage "1322" @default.
• W2898323503 abstract "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 high-performance 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 <sup xmlns:mml=http://www.w3.org/1998/Math/MathML xmlns:xlink=http://www.w3.org/1999/xlink>2</sup> ). The ASIC operates from ±1.65 V power supplies and has a minimum bio-impedance sensitivity of 12.7 mΩp-p." @default.
• W2898323503 created "2018-11-02" @default.
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• W2898323503 date "2018-12-01" @default.
• W2898323503 modified "2023-10-10" @default.
• W2898323503 title "A Human–Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control" @default.
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• W2898323503 doi "https://doi.org/10.1109/tbcas.2018.2878395" @default.
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