A New Deep Anomaly Detection-Based Method for User Authentication Using Multichannel Surface EMG Signals of Hand Gestures

Li, Qingqing; Luo, Zhirui; Zheng, Jun

doi:10.1109/tim.2022.3164162

Cited by 28 publications

(8 citation statements)

References 55 publications

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“…The recognition of hand gestures using sEMG signals has garnered wide attention for various HCI applications including robot control [16], rehabilitation [17], sign language recognition [18], and user authentication [19]. The field of hand gesture recognition can be further subdivided into two categories: static gesture recognition and dynamic gesture recognition [20].…”

Section: B Related Workmentioning

confidence: 99%

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Tripathi¹,

Kumar²,

Prathosh³

et al. 2022

Preprint

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Airwriting Recognition is the task of identifying letters written in free space with finger movement. It is a dynamic gesture recognition with the vocabulary of gestures corresponding to letters in a given language. Electromyography (EMG) is a technique used to record electrical activity during muscle contraction and relaxation as a result of movement and is widely used for gesture recognition. Most of the current research in gesture recognition is focused on identifying static gestures. However, dynamic gestures are natural and userfriendly for being used as alternate input methods in Human-Computer Interaction applications. Airwriting recognition using EMG signals recorded from forearm muscles is therefore a viable solution. Since the user does not need to learn any new gestures and a large range of words can be formed by concatenating these letters, it is generalizable to a wider population. There has been limited work in recognition of airwriting using EMG signals and forms the core idea of the current work. The SurfMyoAiR dataset comprising of EMG signals recorded during writing English uppercase alphabets is constructed. Several different time-domain features to construct EMG envelope and two different timefrequency image representations: Short-Time Fourier Transform and Continuous Wavelet Transform were explored to form the input to a deep learning model for airwriting recognition. Several different deep learning architectures were exploited for this task. Additionally, the effect of various parameters such as signal length, window length and interpolation techniques on the recognition performance is comprehensively explored. The best-achieved accuracy was 78.50% and 62.19% in userdependent and independent scenarios respectively by using Short-Time Fourier Transform in conjunction with a 2D Convolutional Neural Network based classifier. Airwriting has great potential as a user-friendly modality to be used as an alternate input method in Human-Computer Interaction applications.

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Section: B Related Workmentioning

confidence: 99%

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

Tripathi¹,

Kumar²,

Prathosh³

et al. 2022

Preprint

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show abstract

“…Hand tracking methodologies can be broadly categorized into four types, as outlined in Table I: wearable-sensor-based [6], [7], Radio Frequency (RF)-based [8], [9], camera-based [10], [11], and acoustic-based solutions [12]. Wearable-sensorbased solutions leverage precise hand motion data acquisition, achieving optimal accuracy for gesture recognition.…”

Section: Introductionmentioning

confidence: 99%

V-Pen: An Acoustic-Based Fine-Grained Virtual Pen Input System Using Hand Tracking

Han,

Li,

et al. 2024

IEEE Trans. Instrum. Meas.

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Acoustic-based hand tracking technology leads to the next generation of the Human-Computer Interaction (HCI) mechanism. This approach uses embedded speakers and microphones on commercial devices to send and receive acoustic signals simultaneously, then the echo can be processed to obtain the hand's position. However, existing tracking approaches do not support multi-stroke input, as a result, the trajectory is incapable of character recognition with models trained by simple character images from databases such as MNIST and EMNIST. In this paper, V-Pen is proposed to estimate the status of the hand with the energy information acquired from the echo. Subsequently, Zadoff-Chu (ZC) sequences are used to obtain the initial position of the hand, and track the hand continuously with the change of phase for a smooth trajectory. While inputting the characters, V-Pen allows the user to input multiple strokes to get rid of redundant trajectory which affects the recognition. The results show that V-Pen achieves an average error of 4.3 mm for tracking and 94.8% recognition accuracy for 52 English letters, 10 numbers, and 20 Chinese characters.

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“…C OMPUTATIONAL musculoskeletal modelling aims to understand the mechanism of the nervous system to learn and adapts to physiological modifications, which is critical for various clinical applications, such as planning rehabilitative treatments, prostheses and robotics control, and designing assistive devices [1]- [3]. Physics-based musculoskeletal modelling methods could interpret transformation among neural excitation, muscle dynamics, and joint kinematics and kinetics using experimental measurements from markers and sensors [4], [5].…”

Section: Introductionmentioning

confidence: 99%

Boosting Personalized Musculoskeletal Modeling With Physics-Informed Knowledge Transfer

Zhang

Zhao

Bao³

et al. 2023

IEEE Trans. Instrum. Meas.

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A New Deep Anomaly Detection-Based Method for User Authentication Using Multichannel Surface EMG Signals of Hand Gestures

Cited by 28 publications

References 55 publications

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

SurfMyoAiR: A surface Electromyography based framework for Airwriting Recognition

V-Pen: An Acoustic-Based Fine-Grained Virtual Pen Input System Using Hand Tracking

Boosting Personalized Musculoskeletal Modeling With Physics-Informed Knowledge Transfer

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