An EMG-Based Personal Identification Method Using Continuous Wavelet Transform and Convolutional Neural Networks

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

doi:10.1109/biocas.2019.8919230

Cited by 13 publications

(10 citation statements)

References 9 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: 47%

“…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%

“…Subsequently, the time-frequency features were extracted to classify individuals through artificial neural networks (ANNs), SVMs, and KNNs. Lu [12] studied biometrics using EMG signals for open hand gestures in twenty-one people. EMG signals were converted into CWT for each channel to classify individuals through CNN.…”

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: 47%

Section: Related Workmentioning

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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“…As a new domain in personal identification, biometrics with living body features, such as electromyography (EMG) signals, have been developed. 31,32 This domain is in the experimental stage and is expected to be developed in the future. 31,32 In recent years, the progress of artificial intelligence has been remarkable.…”

Section: Emg Signal Informationmentioning

confidence: 99%

“…31,32 This domain is in the experimental stage and is expected to be developed in the future. 31,32 In recent years, the progress of artificial intelligence has been remarkable. The authors believe that it can contribute to the progress of all eight domains described above, in the future.…”

Section: Emg Signal Informationmentioning

confidence: 99%

Personal identification systems for persons with dementia: a systematic review

Matsuda

Yoshimura

2021

Psychogeriatrics

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People with dementia can become missing and unable to be identified due to their cognitive decline and may not have belongings, including personally identifiable information. The aim of this systematic review was to summarise and discuss the current status of personal identification systems for missing and unidentified persons with dementia. An electronic systematic literature search was conducted by using PubMed, Web of Science, Cochrane Library, and CINAHL. By performing an electronic literature search, 128 texts were extracted. However, only three eligible texts were included in this review, and these were divided on the basis of the analysis target as follows: medical and human community support systems and digital systems. It is important to develop medical and social care systems and to construct and advance social personal identification systems, including medical and human community support systems and digital systems, for persons with dementia. However, this systematic review clarified that there have only been a few reports about the development and construction of social systems for missing and unidentified persons with dementia. People who interact with persons with dementia should consider this problem, which is increasing each year, and strive to build a social system that can be applied to personal identification as soon as possible.

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

Esener

2023

Soft Comput

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An EMG-Based Personal Identification Method Using Continuous Wavelet Transform and Convolutional Neural Networks

Cited by 13 publications

References 9 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

Personal identification systems for persons with dementia: a systematic review

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

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