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/iotais.2018.8600826

Cited by 14 publications

(6 citation statements)

References 2 publications

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“…Shioji et al [41] proposed a novel approach to combine hand motion recognition and personal authentication concurrently, leveraging wrist EMG signals. The methodology involved a sequential process of measurement, preprocessing, feature extraction, and identification.…”

Section: Related Workmentioning

confidence: 99%

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

Ganiga,

S. N.,

Choi

et al. 2024

Sensors

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Personal identification is an important aspect of managing electronic health records (EHRs), ensuring secure access to patient information, and maintaining patient privacy. Traditionally, biometric, signature, username/password, photo identity, etc., are employed for user authentication. However, these methods can be prone to security breaches, identity theft, and user inconvenience. The security of personal information is of paramount importance, particularly in the context of EHR. To address this, our study leverages ResNet1D, a deep learning architecture, to analyze surface electromyography (sEMG) signals for robust identification purposes. The proposed ResNet1D-based personal identification approach using the sEMG signal can offer an alternative and potentially more secure method for personal identification in EHR systems. We collected a multi-session sEMG signal database from individuals, focusing on hand gestures. The ResNet1D model was trained using this database to learn discriminative features for both gesture and personal identification tasks. For personal identification, the model validated an individual’s identity by comparing captured features with their own stored templates in the healthcare EHR system, allowing secure access to sensitive medical information. Data were obtained in two channels when each of the 200 subjects performed 12 motions. There were three sessions, and each motion was repeated 10 times with time intervals of a day or longer between each session. Experiments were conducted on a dataset of 20 randomly sampled subjects out of 200 subjects in the database, achieving exceptional identification accuracy. The experiment was conducted separately for 5, 10, 15, and 20 subjects using the ResNet1D model of a deep neural network, achieving accuracy rates of 97%, 96%, 87%, and 82%, respectively. The proposed model can be integrated with healthcare EHR systems to enable secure and reliable personal identification and the safeguarding of patient information.

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Section: Related Workmentioning

confidence: 99%

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

Ganiga,

S. N.,

Choi

et al. 2024

Sensors

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“…For this purpose, the current study utilizes EMG signals collected from the wrist while performing hand gestures. A wrist electrode setup will facilitate the research and development of industry-grade wearable wristbands, which have previously been explored for gesture recognition [25] and biometric authentication applications [21,26]. For the scope of this paper, the multi-day biometric analysis was performed on the wrist-worn EMG over three sessions over the span of 30 days.…”

Section: A State-of-the-art In Emg-based Biometricsmentioning

confidence: 99%

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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“…Currently, research is being conducted on user identi cation using the characteristic that everyone shows different muscle development and activity levels 10 . Shioji et al 11 built a user identi cation system using EMG signals. Here, EMG data acquired from the wrist was used to identify motions and users by CNN.…”

Section: Related Workmentioning

confidence: 99%

“…compares user identi cation accuracy using CQT-based 2D spectrograms proposed here with popular existing methods. Shioji et al11 used a CNN to extract features and perform user identi cation on EMG signals, which are time series data. When using the CNN designed here, like Shioji et al's method, the accuracy was analyzed to be 82.1%.…”

mentioning

confidence: 99%

User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment

Kim

Choi

Pan

2023

Preprint

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User identification systems based on electromyogram (EMG) signals, generated inside the body in different signal patterns and exhibiting individual characteristics based on muscle development and activity, are being actively researched. However, nonlinear and abnormal signals constrain conventional user identification using EMG signals in improving accuracy by using the 1-D feature from each time and frequency domain. Therefore, multidimensional features containing time-frequency information extracted from EMG signals have attracted much attention to improving identification accuracy. We propose a user identification system using constant Q transform (CQT) based 2D features whose time-frequency resolution is customized according to EMG signals. The proposed user identification system comprises data preprocessing and normalization, CQT-based 2D image conversion, convolutional feature extraction, and classification by convolutional neural network. The experimental results showed that the accuracy of the proposed user identification system using CQT-based 2D spectrograms was 97.5%, an improvement of 15.4% and 2.1% compared to the accuracy of 1D features and short- time Fourier transform-based user identification, respectively.

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

Cited by 14 publications

References 2 publications

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

ResNet1D-Based Personal Identification with Multi-Session Surface Electromyography for Electronic Health Record Integration

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

User identification system based on 2D CQT spectrogram of EMG with adaptive frequency resolution adjustment

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