Wu, Y; Jiang, D; Liu, X; Bayford, R; Demosthenous, A; (2018) A Human-Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control. IEEE Transactions on Biomedical Circuits and Systems , 12 (6) pp. 1322-1333. 10.1109/TBCAS.2018.2878395.

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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. The ASIC operates from <formula><tex>$\pm$</tex></formula>1.65 V power supplies, occupies an area of 0.07 mm2, and has a minimum bio-impedance sensitivity of 12.7 m&#x03A9;p-p.

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