Evaluation of feature values of surface electromyograms for user authentication on mobile devices

Yamaba, Hisaaki; Kurogi, Akitoshi; Kubota, Shinichiro; Katayama, Tetsuro; Park, Mirang; Okazaki, Naonobu

doi:10.1007/s10015-016-0323-4

Cited by 22 publications

(9 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, no quantifiable results were concluded. In Shin et al (2017), Yamaba et al (2018b), Shioji et al (2019), and Yamaba et al (2017), the sEMG signals were used to classify the participants with various types of the classifiers, including artificial neural network (ANN), support vector machine (SVM), convolutional neural network (CNN), and Gaussian mixture model (GMM). However, only a small group of participants (5-11) was investigated, and the study protocol, which focused on participant classification and measured in classification accuracy, were not standard for verification or identification, making the results difficult to be compared with other biometric traits.…”

Section: Related Work and Our Contributionmentioning

confidence: 99%

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

Jiang

2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

Electrical biosignals are favored as biometric traits due to their hidden nature and allowing for liveness detection. This study explored the feasibility of surface electromyogram (sEMG), the electrical manifestation of muscle activities, as a biometric trait. The accurate gesture recognition from sEMG provided a unique advantage over two traditional electrical biosignal traits, electrocardiogram (ECG), and electroencephalogram (EEG), enabling users to customize their own gesture codes. The performance of 16 static wrist and hand gestures was systematically investigated in two identity management modes: verification and identification. The results showed that for a single fixed gesture, using only 0.8-second data, the averaged equal error rate (EER) for verification was 3.5%, and the averaged rank-1 for identification was 90.3%, both comparable to the reported performance of ECG and EEG. The function of customizing gesture code could further improve the verification performance to 1.1% EER. This work demonstrated the potential and effectiveness of sEMG as a biometric trait in user verification and identification, beneficial for the design of future biometric systems.

show abstract

Section: Related Work and Our Contributionmentioning

confidence: 99%

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

Jiang

2020

Front. Bioeng. Biotechnol.

View full text Add to dashboard Cite

show abstract

“…The Support Vector Machine (SVM) classifier was then used to verify the authenticity of the drawn pattern. In the studies of [25,26], sEMG signals generated from a list of gestures were used for mobile user authentication. A list of gestures is used as the password, which is combined with the features extracted from the sEMG signals to defend against shoulder-surfing attacks.…”

Section: Biometrics-based Mobile User Authenticationmentioning

confidence: 99%

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Dong

Zheng

2020

Applied Sciences

View full text Add to dashboard Cite

Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.

show abstract

“…Muscles can trigger a mechanical sensor, pressing a button or activating a foot pressure sensor for plantar biometric recognition. Works such as [12,22,23] presented authentication systems based on muscle-related biosignals.…”

Section: Sources Of Biosignalsmentioning

confidence: 99%

“…ere are authentication systems that require interaction from the user within periods of time. Examples of interactions are touching the device to collect the biosignal [14], a specific gesture [23], walking [12], or thinking about a previously defined theme [20].…”

Section: Actions From Users Continuous Authentication Mech-mentioning

confidence: 99%

A Continuous User Authentication System Based on Galvanic Coupling Communication for s-Health

Nakayama

Lenz

Banou

et al. 2019

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Smart health (s-health) is a vital topic and an essential research field today, supporting the real-time monitoring of user’s data by using sensors, either in direct or indirect contact with the human body. Real-time monitoring promotes changes in healthcare from a reactive to a proactive paradigm, contributing to early detection, prevention, and long-term management of health conditions. Under these new conditions, continuous user authentication plays a key role in protecting data and access control, once it focuses on keeping track of a user’s identity throughout the system operation. Traditional user authentication systems cannot fulfill the security requirements of s-health, because they are limited, prone to security breaches, and require the user to frequently authenticate by, e.g., a password or fingerprint. This interrupts the normal use of the system, being highly inconvenient and not user friendly. Also, data transmission in current authentication systems relies on wireless technologies, which are susceptible to eavesdropping during the pairing stage. Biological signals, e.g., electrocardiogram (ECG) and electroencephalogram (EEG), can offer continuous and seamless authentication bolstered by exclusive characteristics from each individual. However, it is necessary to redesign current authentication systems to encompass biometric traits and new communication technologies that can jointly protect data and provide continuous authentication. Hence, this article presents a novel biosignal authentication system, in which the photoplethysmogram (PPG) biosignal and a galvanic coupling (GC) channel lead to continuous, seamless, and secure user authentication. Furthermore, this article contributes to a clear organization of the state of the art on biosignal-based continuous user authentication systems, assisting research studies in this field. The evaluation of the system feasibility presents accuracy in keeping data integrity and up to 98.66% accuracy in the authentication process.

show abstract

Evaluation of feature values of surface electromyograms for user authentication on mobile devices

Cited by 22 publications

References 1 publication

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

Biometric From Surface Electromyogram (sEMG): Feasibility of User Verification and Identification Based on Gesture Recognition

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

A Continuous User Authentication System Based on Galvanic Coupling Communication for s-Health

Contact Info

Product

Resources

About