| Title | Biometric User Identification by Forearm EMG Analysis |
| Publication Type | Conference Paper |
| Year of Publication | 2022 |
| Authors | Pleva, Matus, Korecko, Stefan, Hladek, Daniel, Bours, Patrick, Skudal, Markus Hoff, Liao, Yuan-Fu |
| Conference Name | 2022 IEEE International Conference on Consumer Electronics - Taiwan |
| Keywords | Electric potential, electromyography, EMG, Human Behavior, Keyboards, keystroke analysis, keystroke dynamics, Metrics, Production, pubcrawl, Sensors, Training, Typing Behavior, user identification, virtual reality |
| 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. |
| Notes | ISSN: 2575-8284 |
| DOI | 10.1109/ICCE-Taiwan55306.2022.9869268 |
| Citation Key | pleva_biometric_2022 |
Biometric User Identification by Forearm EMG Analysis