Analysis of Facial EMG Signal for Emotion Recognition Using Wavelet Packet Transform and SVM

Kehri, Vikram; Ingle, Rahul; Patil, Sangram; Awale, R. N.

doi:10.1007/978-981-13-0923-6_21

Cited by 28 publications

(14 citation statements)

References 13 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In the case of brain activity, signals from the central nervous system are recorded using electroencephalography (EEG) [ 12 , 14 , 15 ], as well as medical magnetic resonance imaging (fMRI) [ 18 ]. Other physiological signals used include: electrocardiography (ECG) [ 19 – 21 ], electromyography (EMG) [ 22 ], electrodermal activity (EDA) [ 19 , 20 , 23 ], heart rate [ 24 – 26 ], respiration rate and depth [ 24 , 27 ], and arterial pressure [ 24 ]. Eye-tracking [ 28 – 32 ] and pupil width [ 33 – 36 ] are also used to recognize emotions.…”

Section: Introductionmentioning

confidence: 99%

Eye-Tracking Analysis for Emotion Recognition

Tarnowski

Kołodziej

Majkowski

et al. 2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

This article reports the results of the study related to emotion recognition by using eye-tracking. Emotions were evoked by presenting a dynamic movie material in the form of 21 video fragments. Eye-tracking signals recorded from 30 participants were used to calculate 18 features associated with eye movements (fixations and saccades) and pupil diameter. To ensure that the features were related to emotions, we investigated the influence of luminance and the dynamics of the presented movies. Three classes of emotions were considered: high arousal and low valence, low arousal and moderate valence, and high arousal and high valence. A maximum of 80% classification accuracy was obtained using the support vector machine (SVM) classifier and leave-one-subject-out validation method.

show abstract

Section: Introductionmentioning

confidence: 99%

Eye-Tracking Analysis for Emotion Recognition

Tarnowski

Kołodziej

Majkowski

et al. 2020

Computational Intelligence and Neuroscience

View full text Add to dashboard Cite

show abstract

“…Feature extraction performs a key role by converting huge datasets into the précised meaning. There are various techniques with numerous complexity in time and frequency domain, which shows different FEMG characteristics [5]. For feature extraction, we implemented a WT method that generates wavelet coefficients [29][30][31][32][33][34][35][36].…”

Section: Feature Extractionmentioning

confidence: 99%

“…EMG signals are a noninvasive technique of understanding muscle activity [5]. Due to the nonlinear and random nature of FEMG signals, mathematical tools such as DFT and FFT are failed to provide specific information.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Analysis of Wavelet-Based FEMG Signal Denoising with Threshold Functions and Facial Expression Classification Using SVM and LSSVM

2020

IJE

View full text Add to dashboard Cite

“…In general, p(F |K i ) represents the contingent probability of the wrist motion, and it can be defined as below 4 :…”

Section: B Training and Classificationmentioning

confidence: 99%

“…Recently, motion capture technology (MCT) is very widely used in many areas involving 3D digital animation, sports and exercise research, medical applications, and neuroscience research, etc. [1] [2] [3] [4]. The motion capture covers position tracking of human, feature extraction, pattern and recognition [5].…”

Section: Introductionmentioning

confidence: 99%

Wrist Motion Recognition by Using Electromyographic Signals

Luo

Yang

Liu

et al. 2019

2019 IEEE 4th International Conference on Advanced Robotics and Mechatronics (ICARM)

View full text Add to dashboard Cite

Wrist motion classification is a very common research topic in scientific study. However, wrist motion recognition of the surgeon is often neglected in the robot-assisted surgery or surgical training. Therefore, the objective is to develop a classification method to recognize wrist motion of the surgeon. In order to do that, we present a linear discriminant analysis (LDA) algorithm involving surface electromyography (sEMG) signals to evaluate the motions in this paper. Firstly, sEMG signals are collected by using a MYO armband which can be worn on the forearm of a subject. Root-mean-square (RMS) and waveform length (WL) feature are extracted from the sEMG signals and then those features are regarded as input of the LDA to train the classifier. As a result, we can obtain a classifier to recognize four kinds of wrist motions. Classification experiment is performed by two subjects. The experimental results have been demonstrated by using the proposed approach and it is shown that the accuracy of wrist motion by using RMS feature is higher than that of by using WL feature.

show abstract

Analysis of Facial EMG Signal for Emotion Recognition Using Wavelet Packet Transform and SVM

Cited by 28 publications

References 13 publications

Eye-Tracking Analysis for Emotion Recognition

Eye-Tracking Analysis for Emotion Recognition

A Comparative Analysis of Wavelet-Based FEMG Signal Denoising with Threshold Functions and Facial Expression Classification Using SVM and LSSVM

Wrist Motion Recognition by Using Electromyographic Signals

Contact Info

Product

Resources

About