Machine learning approaches to active stylus for capacitive touch screen panel applications

Hyoungsik Nam; Ki Hyuk Seol; Seungjun Park

doi:10.1002/sdtp.14015

Machine learning approaches to active stylus for capacitive touch screen panel applications

Hyoungsik Nam, Ki Hyuk Seol, Seungjun Park

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

1 Citation (Scopus)

Abstract

This paper introduces machine learning approaches on adding the stylus-touch to the capacitive touch screen technology. The proposed schemes can discriminate the stylus-touch from finger-touch as well as no-touch by means of classification algorithms using support vector machine and anomaly detection. The high frequency pulses are sent from a stylus to a touch screen and the receiver classifies the received sample sequences into three classes of no-touch, finger-touch, and stylus-touch. In addition, some possible applications of data transmission and user authentication are demonstrated.

Original language	English
Pages (from-to)	897-900
Number of pages	4
Journal	Digest of Technical Papers - SID International Symposium
Volume	51
Issue number	1
DOIs	https://doi.org/10.1002/sdtp.14015
Publication status	Published - 2020
Event	57th SID International Symposium, Seminar and Exhibition, Display Week, 2020 - Virtual, Online Duration: 3 Aug 2020 → 7 Aug 2020

Bibliographical note

Publisher Copyright:
© 2020 SID.

Keywords

Active stylus
Anomaly detection
Classification
Machine learning
Support vector machine
Touch screen

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10.1002/sdtp.14015

Cite this

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title = "Machine learning approaches to active stylus for capacitive touch screen panel applications",

abstract = "This paper introduces machine learning approaches on adding the stylus-touch to the capacitive touch screen technology. The proposed schemes can discriminate the stylus-touch from finger-touch as well as no-touch by means of classification algorithms using support vector machine and anomaly detection. The high frequency pulses are sent from a stylus to a touch screen and the receiver classifies the received sample sequences into three classes of no-touch, finger-touch, and stylus-touch. In addition, some possible applications of data transmission and user authentication are demonstrated.",

keywords = "Active stylus, Anomaly detection, Classification, Machine learning, Support vector machine, Touch screen",

author = "Hyoungsik Nam and Seol, {Ki Hyuk} and Seungjun Park",

note = "Publisher Copyright: {\textcopyright} 2020 SID.; 57th SID International Symposium, Seminar and Exhibition, Display Week, 2020 ; Conference date: 03-08-2020 Through 07-08-2020",

year = "2020",

doi = "10.1002/sdtp.14015",

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AU - Nam, Hyoungsik

AU - Seol, Ki Hyuk

AU - Park, Seungjun

PY - 2020

Y1 - 2020

N2 - This paper introduces machine learning approaches on adding the stylus-touch to the capacitive touch screen technology. The proposed schemes can discriminate the stylus-touch from finger-touch as well as no-touch by means of classification algorithms using support vector machine and anomaly detection. The high frequency pulses are sent from a stylus to a touch screen and the receiver classifies the received sample sequences into three classes of no-touch, finger-touch, and stylus-touch. In addition, some possible applications of data transmission and user authentication are demonstrated.

AB - This paper introduces machine learning approaches on adding the stylus-touch to the capacitive touch screen technology. The proposed schemes can discriminate the stylus-touch from finger-touch as well as no-touch by means of classification algorithms using support vector machine and anomaly detection. The high frequency pulses are sent from a stylus to a touch screen and the receiver classifies the received sample sequences into three classes of no-touch, finger-touch, and stylus-touch. In addition, some possible applications of data transmission and user authentication are demonstrated.

KW - Active stylus

KW - Anomaly detection

KW - Classification

KW - Machine learning

KW - Support vector machine

KW - Touch screen

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DO - 10.1002/sdtp.14015

M3 - Conference article

AN - SCOPUS:85094192023

SN - 0097-966X

VL - 51

SP - 897

EP - 900

JO - Digest of Technical Papers - SID International Symposium

JF - Digest of Technical Papers - SID International Symposium

IS - 1

T2 - 57th SID International Symposium, Seminar and Exhibition, Display Week, 2020

Y2 - 3 August 2020 through 7 August 2020

ER -

Machine learning approaches to active stylus for capacitive touch screen panel applications

Abstract

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Keywords

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