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^*

^*此作品的通讯作者

仪器科学与光电工程学院

Beihang University

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

2 引用（Scopus）

摘要

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.

源语言	英语
主期刊名	FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems
出版商	Institute of Electrical and Electronics Engineers Inc.
ISBN（电子版）	9781728191737
DOI	https://doi.org/10.1109/FLEPS51544.2021.9469803
出版状态	已出版 - 20 6月 2021
活动	2021 IEEE International Conference on Flexible and Printable Sensors and Systems, FLEPS 2021 - Virtual, Online 期限: 20 6月 2021 → 23 6月 2021

出版系列

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

会议

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

访问文件

10.1109/FLEPS51544.2021.9469803

其它文件与链接

链接到 Scopus 的出版物

引用此

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. 在 FLEPS 2021 - IEEE International Conference on Flexible and Printable Sensors and Systems 文章 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 等. / 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",

address = "美国",

}

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. 在 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 等.
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).

科研成果: 书/报告/会议事项章节 › 会议稿件 › 同行评审

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. 在 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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