eprintid: 10061153
rev_number: 24
eprint_status: archive
userid: 608
dir: disk0/10/06/11/53
datestamp: 2018-11-09 15:50:05
lastmod: 2021-09-25 23:11:59
status_changed: 2019-02-01 12:57:23
type: article
metadata_visibility: show
creators_name: Wu, Y
creators_name: Jiang, D
creators_name: Liu, X
creators_name: Bayford, R
creators_name: Demosthenous, A
title: A Human-Machine Interface Using Electrical Impedance Tomography for Hand Prosthesis Control
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F46
keywords: Voltage measurement
,
Electrodes
,
Prosthetics
,
Tomography
,
Current measurement
,
Muscles
,
Impedance,

Current driver
,
electrical impedance tomography
,
human machine interface
,
hand prosthesis control
,
instrumentation amplifier
note: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
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.
date: 2018-12
date_type: published
official_url: https://doi.org/10.1109/TBCAS.2018.2878395
oa_status: green
full_text_type: other
language: eng
primo: open
primo_central: open_green
article_type_text: Article
verified: verified_manual
elements_id: 1600417
doi: 10.1109/TBCAS.2018.2878395
lyricists_name: Bayford, Richard
lyricists_name: Demosthenous, Andreas
lyricists_name: Jiang, Dai
lyricists_name: Liu, Xiao
lyricists_name: Wu, Yu
lyricists_id: RBAYF91
lyricists_id: ACDEM08
lyricists_id: DJIAN68
lyricists_id: XLIUX30
lyricists_id: YWUXX19
actors_name: Bracey, Alan
actors_id: ABBRA90
actors_role: owner
full_text_status: public
publication: IEEE Transactions on Biomedical Circuits and Systems
volume: 12
number: 6
pagerange: 1322-1333
issn: 1932-4545
citation:        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 <https://doi.org/10.1109/TBCAS.2018.2878395>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/10061153/1/08510921.pdf