Personal Authentication and Hand Motion Recognition Based on Wrist EMG Analysis by a Convolutional Neural Network

Shioji, Ryohei; Ito, Shin‐ichi; Ito, M.; Fukumi, Minoru

doi:10.1109/scis-isis.2018.00184

Cited by 14 publications

(15 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

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

“…Most existing works using EMG signals have been conducted on gesture recognition and biometrics from hand muscles measured while performing hand gestures [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18]. These works include studies using EMG signals of the lower body when a subject is walking [14] and those using EMG signals of the mouth muscles when talking [15,18].…”

Section: Related Workmentioning

confidence: 99%

“…EMG signals were converted into CWT for each channel to classify individuals through CNN. Shioji [13] studied gesture recognition and biometrics using three gestures. Noise was removed from the signal using a 20 Hz HPF and a 60 Hz NF; feature extraction and classification were performed using CNN.…”

Section: Biometrics Using Emg Signalmentioning

confidence: 99%

Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

Kim

Pan

2021

Applied Sciences

View full text Add to dashboard Cite

Electromyogram (EMG) signals cannot be forged and have the advantage of being able to change the registered data as they are characterized by the waveform, which varies depending on the gesture. In this paper, a two-step biometrics method was proposed using EMG signals based on a convolutional neural network–long short-term memory (CNN-LSTM) network. After preprocessing of the EMG signals, the time domain features and LSTM network were used to examine whether the gesture matched, and single biometrics was performed if the gesture matched. In single biometrics, EMG signals were converted into a two-dimensional spectrogram, and training and classification were performed through the CNN-LSTM network. Data fusion of the gesture recognition and single biometrics was performed in the form of an AND. The experiment used Ninapro EMG signal data as the proposed two-step biometrics method, and the results showed 83.91% gesture recognition performance and 99.17% single biometrics performance. In addition, the false acceptance rate (FAR) was observed to have been reduced by 64.7% through data fusion.

show abstract

“…In addition, Myo armband is one of the most popular devices for EMGrelated research because of the portability and efficient data transmitting mechanism [25] [26] [27]. Comparing with the existing work [28] [29], EmgAuth does not need extra training set and is resilient to the positions of EMG sensors, and does not need users to do extra actions.…”

Section: B Emg-based Applicationsmentioning

confidence: 99%

EmgAuth: An EMG-based Smartphone Unlocking System Using Siamese Network

Fan

et al. 2020

2020 IEEE International Conference on Pervasive Computing and Communications (PerCom)

View full text Add to dashboard Cite

Screen lock is a critical security feature for smartphones to prevent unauthorized access. Although various screen unlocking technologies including fingerprint and facial recognition have been widely adopted, they still have some limitations. For example, fingerprints can be stolen by special material stickers and facial recognition systems can be cheated by 3Dprinted head models. In this paper, we propose EmgAuth, a novel electromyography(EMG)-based smartphone unlocking system based on the Siamese network. EmgAuth leverages the Myo armband to collect the EMG data of smartphone users and enables users to unlock their smartphones when picking up and watching their smartphones. In particular, when training the Siamese network, we design a special data augmentation technique to make the system resilient to the rotation of the armband. We conduct experiments including 40 participants and the evaluation results show that EmgAuth can effectively authenticate users with an average true acceptance rate of 91.81% while keeping the average false acceptance rate of 7.43%. In addition, we also demonstrate that EmgAuth can work well for smartphones with different sizes and at different locations, and is applicable for users with different postures. EmgAuth bears great promise to serve as a good supplement for existing screen unlocking systems to improve the safety of smartphones.

show abstract

Personal Authentication and Hand Motion Recognition Based on Wrist EMG Analysis by a Convolutional Neural Network

Cited by 14 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

Two-Step Biometrics Using Electromyogram Signal Based on Convolutional Neural Network-Long Short-Term Memory Networks

EmgAuth: An EMG-based Smartphone Unlocking System Using Siamese Network

Contact Info

Product

Resources

About