A Study of Personal Recognition Method Based on EMG Signal

Lu, Lijing; Mao, Jingna; Wang, Wuqi; Ding, Guangxin; Zhang, Zhiwei

doi:10.1109/tbcas.2020.3005148

Cited by 62 publications

(45 citation statements)

References 32 publications

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“…By comparison, most of the related studies conducted biometrics experiments using one gesture, and no study used more than six gestures. In addition, we showed 99.17% performance on twenty subjects, which was more subjects than in related works (except [12,16]). Figure 7 shows the 9th, 10th, and 11th gestures as performed by the 11th and 14th subjects, which showed the highest misclassification rates in a single biometrics test.…”

Section: Resultssupporting

confidence: 45%

“…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 collected while a subject pronounced five Japanese vowels, and noise was removed with a 5-500 Hz BPF and a 60 Hz NF; feature extraction and classification were performed using CNN. Lu [16] studied biometrics using EMG data from open hand gesture. After data conversion using discrete wavelet transform (DWT) and CWT, twenty-one people were classified as CNN-based siamese networks.…”

Section: Biometrics Using Emg Signalmentioning

confidence: 99%

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

Kim

Pan

2021

Applied Sciences

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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.

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Section: Resultssupporting

confidence: 45%

Section: Related Workmentioning

confidence: 99%

Section: Biometrics Using Emg Signalmentioning

confidence: 99%

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

Kim

Pan

2021

Applied Sciences

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“…Identification and recognition of study participants in a clinical trial-during the process of recruitment and during follow-up visits-is a growing issue [1]. Conventional methods for the recognition of participants in health facilities may include patient name, date of birth, government identity card with photo, and phone number [2][3][4][5]. However, these methods are not always reliable or accurate [5].…”

Section: Introductionmentioning

confidence: 99%

Use of Iris Scanning for Biometric Recognition of Healthy Adults Participating in an Ebola Vaccine Trial in the Democratic Republic of the Congo: Mixed Methods Study

Matuvanga¹,

Johnson²,

Larivière³

et al. 2021

J Med Internet Res

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Background A partnership between the University of Antwerp and the University of Kinshasa implemented the EBOVAC3 clinical trial with an Ebola vaccine regimen administered to health care provider participants in Tshuapa Province, Democratic Republic of the Congo. This randomized controlled trial was part of an Ebola outbreak preparedness initiative financed through Innovative Medicines Initiative-European Union. The EBOVAC3 clinical trial used iris scan technology to identify all health care provider participants enrolled in the vaccine trial, to ensure that the right participant received the right vaccine at the right visit. Objective We aimed to assess the acceptability, accuracy, and feasibility of iris scan technology as an identification method within a population of health care provider participants in a vaccine trial in a remote setting. Methods We used a mixed methods study. The acceptability was assessed prior to the trial through 12 focus group discussions (FGDs) and was assessed at enrollment. Feasibility and accuracy research was conducted using a longitudinal trial study design, where iris scanning was compared with the unique study ID card to identify health care provider participants at enrollment and at their follow-up visits. Results During the FGDs, health care provider participants were mainly concerned about the iris scan technology causing physical problems to their eyes or exposing them to spiritual problems through sorcery. However, 99% (85/86; 95% CI 97.1-100.0) of health care provider participants in the FGDs agreed to be identified by the iris scan. Also, at enrollment, 99.0% (692/699; 95% CI 98.2-99.7) of health care provider participants accepted to be identified by iris scan. Iris scan technology correctly identified 93.1% (636/683; 95% CI 91.2-95.0) of the participants returning for scheduled follow-up visits. The iris scanning operation lasted 2 minutes or less for 96.0% (656/683; 95% CI 94.6-97.5), and 1 attempt was enough to identify the majority of study participants (475/683, 69.5%; 95% CI 66.1-73.0). Conclusions Iris scans are highly acceptable as an identification tool in a clinical trial for health care provider participants in a remote setting. Its operationalization during the trial demonstrated a high level of accuracy that can reliably identify individuals. Iris scanning is found to be feasible in clinical trials but requires a trained operator to reduce the duration and the number of attempts to identify a participant. Trial Registration ClinicalTrials.gov NCT04186000; https://clinicaltrials.gov/ct2/show/NCT04186000

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“…However, since the device used in this study was a stand-alone type system and was not wearable, there were temporal and spatial limitations when using it for authentication [15]. Moreover, in another study, an individual used an EMG signal measurement module composed of eight channels, which performed personal authentication through convolutional neural network (CNN) analysis [16,17]. However, in these studies, the size of the device used was large [9], the measurement area was in the thigh, which was very inconvenient, and an additional stand-alone type of equipment was required for bio-signal measurement and processing [18].…”

Section: Introductionmentioning

confidence: 99%

Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

et al. 2021

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Personal authentication systems employing biometrics are attracting increasing attention owing to their relatively high security compared to existing authentication systems. In this study, a wearable electromyogram (EMG) system that can be worn on the forearm was developed to detect EMG signals and, subsequently, apply them for personal authentication. In previous studies, wet electrodes were attached to the skin for measuring biosignals. Wet electrodes contain adhesives and conductive gels, leading to problems such as skin rash and signal-quality deterioration in long-term measurements. The miniaturized wearable EMG system developed in this study comprised flexible dry electrodes attached to the watch strap, enabling EMG measurements without additional electrodes. In addition, for accurately classifying and applying the measured signal to the personal authentication system, an optimal algorithm for classifying the EMG signals based on a multi-class support vector machine (SVM) model was implemented. The model using cubic SVM achieved the highest personal authentication rate of 87.1%. We confirmed the possibility of implementing a wearable authentication system by measuring the EMG signal and artificial intelligence analysis algorithm presented in this study.

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A Study of Personal Recognition Method Based on EMG Signal

Cited by 62 publications

References 32 publications

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

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

Use of Iris Scanning for Biometric Recognition of Healthy Adults Participating in an Ebola Vaccine Trial in the Democratic Republic of the Congo: Mixed Methods Study

Development of Miniaturized Wearable Wristband Type Surface EMG Measurement System for Biometric Authentication

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