Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming

Yang, Zhongliang; Chen, Yumiao

doi:10.3389/fnins.2016.00445

Cited by 14 publications

(17 citation statements)

References 65 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…There are three types of features that can be extracted from sEMG signals: time domain features, frequency domain features, and time-frequency domain features [28]. In our study, we concentrated on using features extracted from the time domain, which are widely used in studies and practices due to their low computational complexity compared with frequency domain and time-frequency domain features and performance in low-noise environments [29][30][31][32]. We selected the following 11 time domain features in this study.…”

Section: Semg Feature Extractionmentioning

confidence: 99%

See 1 more Smart Citation

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Dong

Zheng

2020

Applied Sciences

View full text Add to dashboard Cite

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.

show abstract

Section: Semg Feature Extractionmentioning

confidence: 99%

“…The Root Mean Square (RMS) is another popular feature for EMG pattern recognition [30,32], which is related to constant force and non-fatiguing contraction. RMS is similar to the standard deviation of the EMG signal, as the mean of the signal is close to zero.…”

Section: High-order Temporal Moment (Tm)mentioning

confidence: 99%

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Dong

Zheng

2020

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In our previous study (Yang and Chen, 2016 ), we proposed an sEMG-based method using two analysis windows and GEP for the recognition of 11 basic one-stroke shapes from sketching in conceptual design. The average recognition rate for the 11 basic one-stroke shapes achieved by the GEP classifier was more than 96%.…”

Section: Introductionmentioning

confidence: 99%

A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity

Chen

Yang

2017

Front. Neurosci.

Self Cite

View full text Add to dashboard Cite

Recently, several researchers have considered the problem of reconstruction of handwriting and other meaningful arm and hand movements from surface electromyography (sEMG). Although much progress has been made, several practical limitations may still affect the clinical applicability of sEMG-based techniques. In this paper, a novel three-step hybrid model of coordinate state transition, sEMG feature extraction and gene expression programming (GEP) prediction is proposed for reconstructing drawing traces of 12 basic one-stroke shapes from multichannel surface electromyography. Using a specially designed coordinate data acquisition system, we recorded the coordinate data of drawing traces collected in accordance with the time series while 7-channel EMG signals were recorded. As a widely-used time domain feature, Root Mean Square (RMS) was extracted with the analysis window. The preliminary reconstruction models can be established by GEP. Then, the original drawing traces can be approximated by a constructed prediction model. Applying the three-step hybrid model, we were able to convert seven channels of EMG activity recorded from the arm muscles into smooth reconstructions of drawing traces. The hybrid model can yield a mean accuracy of 74% in within-group design (one set of prediction models for all shapes) and 86% in between-group design (one separate set of prediction models for each shape), averaged for the reconstructed x and y coordinates. It can be concluded that it is feasible for the proposed three-step hybrid model to improve the reconstruction ability of drawing traces from sEMG.

show abstract

“…Electromyography (EMG) and force sensors are two of the widely used control methods to control those exoskeletons based on the user’s motion intention [5,6,7,8,9,10,11,12]. However, for the seriously amputated and paralyzed people who cannot generate sufficient muscle signals or movements, they are not able to provide the completed EMG and force signals, which will affect the estimation of motion intention.…”

Section: Introductionmentioning

confidence: 99%

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

Tang

Sun

Zhang

et al. 2016

Sensors

Self Cite

View full text Add to dashboard Cite

To recognize the user’s motion intention, brain-machine interfaces (BMI) usually decode movements from cortical activity to control exoskeletons and neuroprostheses for daily activities. The aim of this paper is to investigate whether self-induced variations of the electroencephalogram (EEG) can be useful as control signals for an upper-limb exoskeleton developed by us. A BMI based on event-related desynchronization/synchronization (ERD/ERS) is proposed. In the decoder-training phase, we investigate the offline classification performance of left versus right hand and left hand versus both feet by using motor execution (ME) or motor imagery (MI). The results indicate that the accuracies of ME sessions are higher than those of MI sessions, and left hand versus both feet paradigm achieves a better classification performance, which would be used in the online-control phase. In the online-control phase, the trained decoder is tested in two scenarios (wearing or without wearing the exoskeleton). The MI and ME sessions wearing the exoskeleton achieve mean classification accuracy of 84.29% ± 2.11% and 87.37% ± 3.06%, respectively. The present study demonstrates that the proposed BMI is effective to control the upper-limb exoskeleton, and provides a practical method by non-invasive EEG signal associated with human natural behavior for clinical applications.

show abstract

Surface EMG-based Sketching Recognition Using Two Analysis Windows and Gene Expression Programming

Cited by 14 publications

References 65 publications

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

Enhancing the Security of Pattern Unlock with Surface EMG-Based Biometrics

A Novel Hybrid Model for Drawing Trace Reconstruction from Multichannel Surface Electromyographic Activity

A Brain-Machine Interface Based on ERD/ERS for an Upper-Limb Exoskeleton Control

Contact Info

Product

Resources

About