High force sensing accuracy in piezoelectric based interactive displays by artificial neural networks

Shuo Gao; Jifang Duan; Zhicheng Wei; Arokia Nathan

doi:10.1002/sdtp.12443

High force sensing accuracy in piezoelectric based interactive displays by artificial neural networks

Shuo Gao
, Jifang Duan^*
, Zhicheng Wei
, Arokia Nathan

^*Corresponding author for this work

School of Instrumentation and Optoelectronic Engineering

University College London
Hebei Normal University
University of Cambridge

Research output: Contribution to journal › Conference article › peer-review

2 Scopus citations

Abstract

Over panel stress non-uniformity strongly limits the detection accuracy of piezo based force sensing in interactive displays. In this work, nested artificial neural networks based technique is presented to address the issue of stress non-uniformity. High detection accuracy in terms of touch position and force amplitude is demonstrated by the proposed technique.

Original language	English
Pages (from-to)	1893-1894
Number of pages	2
Journal	Digest of Technical Papers - SID International Symposium
Volume	49
Issue number	1
DOIs	https://doi.org/10.1002/sdtp.12443
State	Published - 2018
Event	SID Symposium, Seminar, and Exhibition 2018, Display Week 2018 - Los Angeles, United States Duration: 20 May 2018 → 25 May 2018

Keywords

Artificial neural networks
Force sensing
Interactive display
Piezoelectric touch panel

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10.1002/sdtp.12443

Cite this

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title = "High force sensing accuracy in piezoelectric based interactive displays by artificial neural networks",

abstract = "Over panel stress non-uniformity strongly limits the detection accuracy of piezo based force sensing in interactive displays. In this work, nested artificial neural networks based technique is presented to address the issue of stress non-uniformity. High detection accuracy in terms of touch position and force amplitude is demonstrated by the proposed technique.",

keywords = "Artificial neural networks, Force sensing, Interactive display, Piezoelectric touch panel",

author = "Shuo Gao and Jifang Duan and Zhicheng Wei and Arokia Nathan",

note = "Publisher Copyright: {\textcopyright} 2018, Blackwell Publishing Ltd. All rights reserved.; SID Symposium, Seminar, and Exhibition 2018, Display Week 2018 ; Conference date: 20-05-2018 Through 25-05-2018",

year = "2018",

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AU - Gao, Shuo

AU - Duan, Jifang

AU - Wei, Zhicheng

AU - Nathan, Arokia

PY - 2018

Y1 - 2018

N2 - Over panel stress non-uniformity strongly limits the detection accuracy of piezo based force sensing in interactive displays. In this work, nested artificial neural networks based technique is presented to address the issue of stress non-uniformity. High detection accuracy in terms of touch position and force amplitude is demonstrated by the proposed technique.

AB - Over panel stress non-uniformity strongly limits the detection accuracy of piezo based force sensing in interactive displays. In this work, nested artificial neural networks based technique is presented to address the issue of stress non-uniformity. High detection accuracy in terms of touch position and force amplitude is demonstrated by the proposed technique.

KW - Artificial neural networks

KW - Force sensing

KW - Interactive display

KW - Piezoelectric touch panel

UR - https://www.scopus.com/pages/publications/85054030815

U2 - 10.1002/sdtp.12443

DO - 10.1002/sdtp.12443

M3 - 会议文章

AN - SCOPUS:85054030815

SN - 0097-966X

VL - 49

SP - 1893

EP - 1894

JO - Digest of Technical Papers - SID International Symposium

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IS - 1

T2 - SID Symposium, Seminar, and Exhibition 2018, Display Week 2018

Y2 - 20 May 2018 through 25 May 2018

ER -

High force sensing accuracy in piezoelectric based interactive displays by artificial neural networks

Abstract

Keywords

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