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Finger displacement sensing: FEM simulation and model prediction of a three-layer electrode design

Finger displacement sensing: FEM simulation and model prediction of a three-layer electrode design
Finger displacement sensing: FEM simulation and model prediction of a three-layer electrode design

There is a growing and significant interest in home-based therapy or telerehabilitation for physiological disabilities, for example, as a result of stroke. These technologies allow more flexibility in implementing rehabilitation sessions and offer the potential to reduce the economic burden of outpatient appointments and reduce the reliance on healthcare systems. However, extra effort needs to be made to make such systems effective. This paper investigates the feasibility of a home-based device, which is capable of detecting minute movements of patients' fingers in regular training and testing sessions, addressing ease of use, motivation of practice, as well as feedback and guidance on performance. Toward this aim, the measuring techniques that are compatible with these targets were investigated. Based on a customizable three-layer electrodes design for use with an MGC3030 motion sensor IC, a finite-element method simulation in COMSOL Multiphysics and a nonlinear regression analysis using MATLAB were carried out. Four nonlinear equations were introduced to describe the motion of the index and middle fingers in the electrical field (E-field) generated. The form of the prediction models agrees with the hypothesis based on the quasi-static E-field sensing theory. In addition, these prediction models fit well with the relationship between finger distance and the voltage signals detected. With the prediction model, the targeted system is capable of detecting combined movements of two fingers at a resolution of 0.94 mm in a portable smart device for robust hand rehabilitation.

COMSOL simulation, contactless finger movement detection, electrical field (E-field) sensing, Electrodes, hand rehabilitation, Indexes, Mathematical model, Medical treatment, nonlinear regression analysis., Predictive models, Sensors, Training
0018-9456
Hu, Nan
580a7979-65b9-42e3-895d-27604338e836
Chappell, Paul H.
2d2ec52b-e5d0-4c36-ac20-0a86589a880e
Harris, Nick R.
237cfdbd-86e4-4025-869c-c85136f14dfd
Hu, Nan
580a7979-65b9-42e3-895d-27604338e836
Chappell, Paul H.
2d2ec52b-e5d0-4c36-ac20-0a86589a880e
Harris, Nick R.
237cfdbd-86e4-4025-869c-c85136f14dfd

Hu, Nan, Chappell, Paul H. and Harris, Nick R. (2018) Finger displacement sensing: FEM simulation and model prediction of a three-layer electrode design. IEEE Transactions on Instrumentation and Measurement. (doi:10.1109/TIM.2018.2884545).

Record type: Article

Abstract

There is a growing and significant interest in home-based therapy or telerehabilitation for physiological disabilities, for example, as a result of stroke. These technologies allow more flexibility in implementing rehabilitation sessions and offer the potential to reduce the economic burden of outpatient appointments and reduce the reliance on healthcare systems. However, extra effort needs to be made to make such systems effective. This paper investigates the feasibility of a home-based device, which is capable of detecting minute movements of patients' fingers in regular training and testing sessions, addressing ease of use, motivation of practice, as well as feedback and guidance on performance. Toward this aim, the measuring techniques that are compatible with these targets were investigated. Based on a customizable three-layer electrodes design for use with an MGC3030 motion sensor IC, a finite-element method simulation in COMSOL Multiphysics and a nonlinear regression analysis using MATLAB were carried out. Four nonlinear equations were introduced to describe the motion of the index and middle fingers in the electrical field (E-field) generated. The form of the prediction models agrees with the hypothesis based on the quasi-static E-field sensing theory. In addition, these prediction models fit well with the relationship between finger distance and the voltage signals detected. With the prediction model, the targeted system is capable of detecting combined movements of two fingers at a resolution of 0.94 mm in a portable smart device for robust hand rehabilitation.

Full text not available from this repository.

More information

Accepted/In Press date: 8 November 2018
e-pub ahead of print date: 21 December 2018
Keywords: COMSOL simulation, contactless finger movement detection, electrical field (E-field) sensing, Electrodes, hand rehabilitation, Indexes, Mathematical model, Medical treatment, nonlinear regression analysis., Predictive models, Sensors, Training

Identifiers

Local EPrints ID: 429237
URI: https://eprints.soton.ac.uk/id/eprint/429237
ISSN: 0018-9456
PURE UUID: e1beb6aa-5c4b-49cb-8403-2f0ca17407d6
ORCID for Nan Hu: ORCID iD orcid.org/0000-0002-8436-3575
ORCID for Nick R. Harris: ORCID iD orcid.org/0000-0003-4122-2219

Catalogue record

Date deposited: 22 Mar 2019 17:30
Last modified: 23 Mar 2019 01:37

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