Analysis of HD-sEMG Signals Using Channel Clustering Based on Time Domain Features For Functional Assessment with Ageing

Pattern unlock is a popular screen unlock scheme that protects the sensitive data and information stored in mobile devices from unauthorized access. However, it is also susceptible to various attacks, including guessing attacks, shoulder surfing attacks, smudge attacks, and side-channel attacks, which can achieve a high success rate in breaking the patterns. In this paper, we propose a new two-factor screen unlock scheme that incorporates surface electromyography (sEMG)-based biometrics with patterns for user authentication. sEMG signals are unique biometric traits suitable for person identification, which can greatly improve the security of pattern unlock. During a screen unlock session, sEMG signals are recorded when the user draws the pattern on the device screen. Time-domain features extracted from the recorded sEMG signals are then used as the input of a one-class classifier to identify the user is legitimate or not. We conducted an experiment involving 10 subjects to test the effectiveness of the proposed scheme. It is shown that the adopted time-domain sEMG features and one-class classifiers achieve good authentication performance in terms of the F 1 score and Half of Total Error Rate (HTER). The results demonstrate that the proposed scheme is a promising solution to enhance the security of pattern unlock.

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SVM based Classification Of sEMG Signals using Time Domain Features for the Applications towards Arm Exoskeletons

Proceedings of the Advances in Robotics 2019 ◽

10.1145/3352593.3352620 ◽

2019 ◽

Author(s):

Nagaswathi Amancherla ◽

Anish Turlapaty ◽

Balakrishna Gokaraju

Keyword(s):

Time Domain ◽

Semg Signals

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How young is your Muscle? A Machine Learning framework for motor functional assessment with ageing by NMF based analysis of HD-sEMG signal

10.1101/2020.02.12.946343 ◽

2020 ◽

Cited By ~ 1

Author(s):

Swati Banerjee ◽

Sofiane Boudaoud ◽

Kiyoka Kinugawa-Burron

Keyword(s):

Functional Assessment ◽

Muscle Activation ◽

Functional Abilities ◽

Semg Signal ◽

Learning Framework ◽

Sit To Stand ◽

Clustering Approach ◽

Clinical Tools ◽

Characteristic Features ◽

Semg Signals

AbstractObjectiveWith ageing, there are various changes in the autonomic nervous system and a simultaneous decline in the motor functional abilities of the human body. This study falls within the framework improvement of the clinical tools dedicated to the robust evaluation of motor function efficiency with ageing.MethodAnalysis of HD-sEMG signals recorded from 32 channels during Sit To Stand (STS) test are used for the functional assessment of body muscles. For this purpose, five primary characteristic features, iEMG, ARV, RMS, Skewness, Kurtosis, are employed for the study. A channel clustering approach is proposed based on the parameters using Non Negative Matrix Factorization (NMF).ResultsThe NMF based clustering of the HD-sEMG channels seems to be sensitive toward modifications of the muscle activation strategy with ageing during STS test.ConclusionThis manuscript provides a framework for the assessment of Motor Functional Age(MFA) of subjects having a range of chronological from 25 yrs to 75 yrs. The groups were made a decade apart and it was found that the MFA varies with the level of activeness of the muscle under study and a premature ageing is observed according to the change in activation pattern of the HD-sEMG grid.

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Human Gait Recognition Based on Multiple Feature Combination and Parameter Optimization Algorithms

Computational Intelligence and Neuroscience ◽

10.1155/2021/6693206 ◽

2021 ◽

Vol 2021 ◽

pp. 1-14

Author(s):

Farong Gao ◽

Taixing Tian ◽

Ting Yao ◽

Qizhong Zhang

Keyword(s):

Frequency Domain ◽

Time Domain ◽

Gait Recognition ◽

Fuzzy Entropy ◽

Human Gait ◽

Function Parameter ◽

Svm Classifier ◽

Feature Combination ◽

Multiple Feature ◽

Semg Signals

Accuracy is a key index of human gait recognition. In this paper, we propose an improved gait recognition algorithm, which combines multiple feature combination and artificial bee colony for optimizing the support vector machine (ABC-SVM). Firstly, considering the complexity characteristics of surface electromyography (sEMG) signals, four types of features are extracted from the denoised sEMG signals, including the time-domain features of integral of absolute value (IAV), variance (VAR), and number of zero-crossing (ZC) points, frequency-domain features of mean power frequency (MPF) and median frequency (MF), and wavelet features and fuzzy entropy features. Secondly, the classifiers of SVM, linear discriminant analysis (LDA), and extreme learning machine (ELM) are employed to recognize the gait with obtained features, including singe-class features, multiple combination features, and optimized features of dimension reduction by principal component analysis (PCA). Thirdly, the penalty coefficient and kernel function parameter of the SVM classifier are optimized by the ABC algorithm, and the influence of different features and classifiers on the recognition results is studied. Finally, the feature samples selected to construct the SVM classifier are trained and recognized. Results show that the classification performance of the ABC-SVM classifier is significantly better than that of the nonoptimized SVM classifier, and the average recognition rate is increased by 3.18%. In addition, the combined feature samples (time-domain, frequency-domain, wavelet, and fuzzy entropy features) not only improve the gait classification accuracy but also enhance the recognition stability.

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Identification of 7 Movements of the Human Hand Using sEMG - 360° on the Forearm

10.17488/rmib.42.3.3 ◽

2021 ◽

Author(s):

◽

A. Ibarra-Fuentes

Keyword(s):

Time Domain ◽

Index Finger ◽

Ring Finger ◽

Middle Finger ◽

Svm Classifier ◽

Human Hand ◽

Absolute Value ◽

The Time Domain ◽

Flexion And Extension ◽

Semg Signals

This document shows the identification of 7 gestures (movements) of the human hand from sEMG – 360° signals in the forearm. sEMG – 360° is the sEMG measurement through 8 channels every 45° making a total of 360°. When making a hand gesture, there will be 8 independents sEMG signals that will be used to identify the movement. The 7 gestures to identify are: relaxed hand (closed), open hand (fingers extended), flexion and extension of the little finger, the ring finger, the middle finger, the index finger and the thumb separately. 100 samples of each of the gesture were captured and 3 feature extractors were applied in the time domain (mean absolute value (MAV), root mean square value (RMS) and area vale under the curve (CUA)), then a vector support machine (SVM) classifier was applied to each extractor. The movements were identified and the percentage of accuracy in the identification was calculated for each extractor + SVM classifier. The calculation of the percentage of accuracy took into account the 8 channels for each gesture. 97.61 % accuracy was achieved in the identification of human hand gestures by applying sEMG – 360°.

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