Biometric User Identification by Forearm EMG Analysis

Matus Pleva; Stefan Korecko; Daniel Hladek; Patrick Bours; Markus Hoff Skudal; Yuan Fu Liao

doi:10.1109/ICCE-Taiwan55306.2022.9869268

Biometric User Identification by Forearm EMG Analysis

Matus Pleva, Stefan Korecko, Daniel Hladek, Patrick Bours, Markus Hoff Skudal, Yuan Fu Liao

智能系統研究所

研究成果: Conference contribution › 同行評審

5 引文斯高帕斯（Scopus）

摘要

The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.

原文	English
主出版物標題	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
發行者	Institute of Electrical and Electronics Engineers Inc.
頁面	607-608
頁數	2
ISBN（電子）	9781665470506
DOIs	https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869268
出版狀態	Published - 2022
事件	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 - Taipei, 台灣持續時間: 6 7月 2022 → 8 7月 2022

出版系列

名字	Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Conference

Conference	2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022
國家/地區	台灣
城市	Taipei
期間	6/07/22 → 8/07/22

UN SDG

此研究成果有助於以下永續發展目標

存取文件

10.1109/ICCE-Taiwan55306.2022.9869268

其它文件與鏈接

Link to publication in Scopus

引用此

Pleva, M., Korecko, S., Hladek, D., Bours, P., Skudal, M. H., & Liao, Y. F. (2022). Biometric User Identification by Forearm EMG Analysis. 於 Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 (頁 607-608). (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869268

Pleva, Matus ; Korecko, Stefan ; Hladek, Daniel 等. / Biometric User Identification by Forearm EMG Analysis. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. 頁 607-608 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

@inproceedings{99cf6954f31944a9be814bd796b8662c,

title = "Biometric User Identification by Forearm EMG Analysis",

abstract = "The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.",

keywords = "EMG, Keystroke Dynamics, Typing Behavior, User Identification",

author = "Matus Pleva and Stefan Korecko and Daniel Hladek and Patrick Bours and Skudal, {Markus Hoff} and Liao, {Yuan Fu}",

note = "Publisher Copyright: {\textcopyright} 2022 IEEE.; 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022 ; Conference date: 06-07-2022 Through 08-07-2022",

year = "2022",

doi = "10.1109/ICCE-Taiwan55306.2022.9869268",

language = "English",

series = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",

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

pages = "607--608",

booktitle = "Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022",

address = "美國",

}

Pleva, M, Korecko, S, Hladek, D, Bours, P, Skudal, MH & Liao, YF 2022, Biometric User Identification by Forearm EMG Analysis. 於 Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Institute of Electrical and Electronics Engineers Inc., 頁 607-608, 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022, Taipei, 台灣, 6/07/22. https://doi.org/10.1109/ICCE-Taiwan55306.2022.9869268

Biometric User Identification by Forearm EMG Analysis. / Pleva, Matus; Korecko, Stefan; Hladek, Daniel 等.
Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc., 2022. p. 607-608 (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022).

研究成果: Conference contribution › 同行評審

TY - GEN

T1 - Biometric User Identification by Forearm EMG Analysis

AU - Pleva, Matus

AU - Korecko, Stefan

AU - Hladek, Daniel

AU - Bours, Patrick

AU - Skudal, Markus Hoff

AU - Liao, Yuan Fu

PY - 2022

Y1 - 2022

N2 - The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.

AB - The recent experience in the use of virtual reality (VR) technology has shown that users prefer Electromyography (EMG) sensor-based controllers over hand controllers. The results presented in this paper show the potential of EMG-based controllers, in particular the Myo armband, to identify a computer system user. In the first scenario, we train various classifiers with 25 keyboard typing movements for training and test with 75. The results with a 1-dimensional convolutional neural network indicate that we are able to identify the user with an accuracy of 93% by analyzing only the EMG data from the Myo armband. When we use 75 moves for training, accuracy increases to 96.45% after cross-validation.

KW - EMG

KW - Keystroke Dynamics

KW - Typing Behavior

KW - User Identification

UR - http://www.scopus.com/inward/record.url?scp=85138728255&partnerID=8YFLogxK

U2 - 10.1109/ICCE-Taiwan55306.2022.9869268

DO - 10.1109/ICCE-Taiwan55306.2022.9869268

M3 - Conference contribution

AN - SCOPUS:85138728255

T3 - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

SP - 607

EP - 608

BT - Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022

Y2 - 6 July 2022 through 8 July 2022

ER -

Pleva M, Korecko S, Hladek D, Bours P, Skudal MH, Liao YF. Biometric User Identification by Forearm EMG Analysis. 於 Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022. Institute of Electrical and Electronics Engineers Inc. 2022. p. 607-608. (Proceedings - 2022 IEEE International Conference on Consumer Electronics - Taiwan, ICCE-Taiwan 2022). doi: 10.1109/ICCE-Taiwan55306.2022.9869268

Biometric User Identification by Forearm EMG Analysis

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