Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events

Sizhe Zhang; Shangqing Tu; Zhipeng Sui; Shuo Gao

doi:10.1109/FLEPS51544.2021.9469803

Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events

Sizhe Zhang
, Shangqing Tu
, Zhipeng Sui
, Shuo Gao^*

^*Corresponding author for this work

School of Instrumentation and Optoelectronic Engineering

Beihang University

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

Current commercial force touch panels can merely detect a single force touch's location and amplitude. However, in many applications, multiple force touch events can occur at the same time among different locations of the touch panel. To satisfy this need, in this article, a piezoelectric and machine learning-based technique is proposed. Here, the piezoelectric film-based touch panel is used to detect different force levels, while the machine learning algorithm is developed to interpret the locations and strengths of user applied multiple force touch events. High detection accuracy of 92.3% for location determination and 88.2% for force level recognition is achieved.

Original language	English
Title of host publication	FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems
Publisher	Institute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)	9781728191737
DOIs	https://doi.org/10.1109/FLEPS51544.2021.9469803
State	Published - 20 Jun 2021
Event	2021 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2021 - Virtual, Online Duration: 20 Jun 2021 → 23 Jun 2021

Publication series

Name	FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems

Conference

Conference	2021 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2021
City	Virtual, Online
Period	20/06/21 → 23/06/21

Keywords

Piezoelectric-based touch panel
force sensing
machine learning
multiple touch events

Access to Document

10.1109/FLEPS51544.2021.9469803

Cite this

Zhang, S., Tu, S., Sui, Z., & Gao, S. (2021). Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events. In FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems Article 9469803 (FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/FLEPS51544.2021.9469803

Zhang, Sizhe ; Tu, Shangqing ; Sui, Zhipeng et al. / Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events. FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems. Institute of Electrical and Electronics Engineers Inc., 2021. (FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems).

@inproceedings{f5792a13f5e542378dc1078ba8ee0fb6,

title = "Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events",

abstract = "Current commercial force touch panels can merely detect a single force touch's location and amplitude. However, in many applications, multiple force touch events can occur at the same time among different locations of the touch panel. To satisfy this need, in this article, a piezoelectric and machine learning-based technique is proposed. Here, the piezoelectric film-based touch panel is used to detect different force levels, while the machine learning algorithm is developed to interpret the locations and strengths of user applied multiple force touch events. High detection accuracy of 92.3\% for location determination and 88.2\% for force level recognition is achieved.",

keywords = "Piezoelectric-based touch panel, force sensing, machine learning, multiple touch events",

author = "Sizhe Zhang and Shangqing Tu and Zhipeng Sui and Shuo Gao",

note = "Publisher Copyright: {\textcopyright} 2021 IEEE.; 2021 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2021 ; Conference date: 20-06-2021 Through 23-06-2021",

year = "2021",

month = jun,

day = "20",

doi = "10.1109/FLEPS51544.2021.9469803",

language = "英语",

series = "FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

booktitle = "FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems",

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Zhang, S, Tu, S, Sui, Z & Gao, S 2021, Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events. in FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems., 9469803, FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems, Institute of Electrical and Electronics Engineers Inc., 2021 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2021, Virtual, Online, 20/06/21. https://doi.org/10.1109/FLEPS51544.2021.9469803

Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events. / Zhang, Sizhe; Tu, Shangqing; Sui, Zhipeng et al.
FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems. Institute of Electrical and Electronics Engineers Inc., 2021. 9469803 (FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events

AU - Zhang, Sizhe

AU - Tu, Shangqing

AU - Sui, Zhipeng

AU - Gao, Shuo

PY - 2021/6/20

Y1 - 2021/6/20

N2 - Current commercial force touch panels can merely detect a single force touch's location and amplitude. However, in many applications, multiple force touch events can occur at the same time among different locations of the touch panel. To satisfy this need, in this article, a piezoelectric and machine learning-based technique is proposed. Here, the piezoelectric film-based touch panel is used to detect different force levels, while the machine learning algorithm is developed to interpret the locations and strengths of user applied multiple force touch events. High detection accuracy of 92.3% for location determination and 88.2% for force level recognition is achieved.

AB - Current commercial force touch panels can merely detect a single force touch's location and amplitude. However, in many applications, multiple force touch events can occur at the same time among different locations of the touch panel. To satisfy this need, in this article, a piezoelectric and machine learning-based technique is proposed. Here, the piezoelectric film-based touch panel is used to detect different force levels, while the machine learning algorithm is developed to interpret the locations and strengths of user applied multiple force touch events. High detection accuracy of 92.3% for location determination and 88.2% for force level recognition is achieved.

KW - Piezoelectric-based touch panel

KW - force sensing

KW - machine learning

KW - multiple touch events

UR - https://www.scopus.com/pages/publications/85114123982

U2 - 10.1109/FLEPS51544.2021.9469803

DO - 10.1109/FLEPS51544.2021.9469803

M3 - 会议稿件

AN - SCOPUS:85114123982

T3 - FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems

BT - FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2021 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2021

Y2 - 20 June 2021 through 23 June 2021

ER -

Zhang S, Tu S, Sui Z, Gao S. Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events. In FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems. Institute of Electrical and Electronics Engineers Inc. 2021. 9469803. (FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems). doi: 10.1109/FLEPS51544.2021.9469803

Piezoelectric and Machine Learning-Based Technique for Classifying Force Levels and Locations of Multiple Force Touch Events

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