Biometric User Identification by Forearm EMG Analysis

Pleva, Matúš; Korecko, Stefan; Hládek, Daniel; Bours, Patrick; Skudal, Markus Hoff; Liao, Yuan-Fu

doi:10.1109/icce-taiwan55306.2022.9869268

Cited by 3 publications

(8 citation statements)

References 3 publications

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“…In air handwriting of an ID string 3D velocity, 3D acceleration, position, hand pose, and amplitude LM 100 ≥99.5 Pleva et al [166] investigated the use of Electromyography (EMG) sensor-based controllers over hand-based controllers in VR. The authors collected EMG and IMU data during an experiment with five subjects typing the word "password" while wearing a MYO.…”

Section: Biomechanicsmentioning

confidence: 99%

“…Pleva et al [166] investigated the use of Electromyography (EMG) sensor-based controllers over hand-based controllers in VR. The authors collected EMG and IMU data during an experiment with five subjects typing the word "password" while wearing a MYO.…”

Section: Electrical Bio-signalsmentioning

confidence: 99%

“…Pleva et al [166] investigated the use of Electromyography (EMG) sensor-based controllers over hand-based controllers in VR. The authors collected EMG and IMU data [155] 2022…”

Section: Electrical Bio-signalsmentioning

confidence: 99%

“…In recent years, research on bio-signals in XR has received a lot of attention. We selected papers that comprehensively include schemes and mechanisms that use biosignals methods for XR user authentication.Pleva et al[166] investigated the use of Electromyography (EMG) sensor-based controllers over hand-based controllers in VR. The authors collected EMG and IMU data during an experiment with five subjects typing the word "password" while wearing a MYO.…”

mentioning

confidence: 99%

“…Compared to physiological traits' data, bio-signals are more difficult to steal or forge continuity. In recent years, research on bio-signals in XR has received a lot We selected papers that comprehensively include schemes and mechanisms signals methods for XR user authentication.Pleva et al[166] investigated the use of Electromyography (EMG) s controllers over hand-based controllers in VR. The authors collected EMG an during an experiment with five subjects typing the word "password" whi MYO.…”

mentioning

confidence: 99%

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Recent Trends of Authentication Methods in Extended Reality: A Survey

Hallal,

Rhinelander,

Venkat

2024

ASI

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Extended Reality (XR) is increasingly gaining momentum in industries such as retail, health, and education. To protect users’ personal data, establishing a secure authentication system for XR devices becomes essential. Recently, the focus on authentication methods for XR devices has been limited. To further our understanding of this topic, we surveyed authentication schemes, particularly systems and methods deployed in XR settings. In this survey, we focused on reviewing and evaluating papers published during the last decade (between 2014 and 2023). We compared knowledge-based authentication, physical biometrics, behavioral biometrics, and multi-model methods in terms of accuracy, security, and usability. We also highlighted the benefits and drawbacks of those methods. These highlights will direct future Human–computer Interaction (HCI) and security research to develop secure, reliable, and practical authentication systems.

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Section: Biomechanicsmentioning

confidence: 99%

Section: Electrical Bio-signalsmentioning

confidence: 99%

“…Pleva et al [166] investigated the use of Electromyography (EMG) sensor-based controllers over hand-based controllers in VR. The authors collected EMG and IMU data [155] 2022…”

Section: Electrical Bio-signalsmentioning

confidence: 99%

mentioning

confidence: 99%

mentioning

confidence: 99%

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Recent Trends of Authentication Methods in Extended Reality: A Survey

Hallal,

Rhinelander,

Venkat

2024

ASI

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A driver authentication system integrated to stress-level determination for driving safety

Esener

2023

Soft Comput

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Multi-Day Analysis of Wrist Electromyogram-Based Biometrics for Authentication and Personal Identification

Pradhan

Lee

et al. 2023

IEEE Trans. Biom. Behav. Identity Sci.

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Recently, electromyogram (EMG) has been proposed for addressing some key limitations of current biometrics. Wrist-worn wearable sensors can provide a non-invasive method for acquiring EMG signals for gesture recognition or biometric applications. EMG signals contain individuals' information and can facilitate multi-length codes or passwords (for example, by performing a combination of hand gestures). However, current EMGbased biometric research has two critical limitations: small subject-pool for analysis and limited to single-session datasets. In this study, wrist EMG data were collected from 43 participants over three different days (Days 1, 8, and 29) while performing static hand/wrist gestures. Multi-day analysis involving training data and testing data from different days was employed to test the robustness of the EMG-based biometrics. The multi-day authentication resulted in a median equal error rate (EER) of 0.039 when the code is unknown, and an EER of 0.068 when the code is known to intruders. The multi-day identification achieved a median rank-5 accuracy of 93.0%. With intruders, a threshold-based identification resulted in a median rank-5 accuracy of 91.7% while intruders were denied access at a median rejection rate of 71.7%. These results demonstrated the potential of EMG-based biometrics in practical applications and bolster further research on EMG-based biometrics.

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Biometric User Identification by Forearm EMG Analysis

Cited by 3 publications

References 3 publications

Recent Trends of Authentication Methods in Extended Reality: A Survey

Recent Trends of Authentication Methods in Extended Reality: A Survey

A driver authentication system integrated to stress-level determination for driving safety

Multi-Day Analysis of Wrist Electromyogram-Based Biometrics for Authentication and Personal Identification

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