scholarly journals Emotion Recognition Using Spectral Feature from Facial Electromygraphy Signals for Human-Machine Interface

2021 ◽  
Author(s):  
Jayendhra Shiva ◽  
Navaneethakrishna Makaram ◽  
P.A. Karthick ◽  
Ramakrishnan Swaminathan
Keyword(s):  
Emotion Recognition ◽  
Spectral Feature ◽  
Peak Frequency ◽  
Human Machine Interface ◽  
Support Vector ◽  
Svm Classifier ◽  
Facial Electromyography ◽  
Facial Emg ◽  
Machine Interface ◽  
Short Time

Recognition of the emotions demonstrated by human beings plays a crucial role in healthcare and human-machine interface. This paper reports an attempt to classify emotions using a spectral feature from facial electromyography (facial EMG) signals in the valence affective dimension. For this purpose, the facial EMG signals are obtained from the DEAP dataset. The signals are subjected to Short-Time Fourier Transform, and the peak frequency values are extracted from the signal in intervals of one second. Support vector machine (SVM) classifier is used for the classification of the features extracted. The extracted feature can classify the signals in the valence dimension with an accuracy of 61.37%. The proposed feature could be used as an added feature for emotion recognition, and this method of analysis could be extended to myoelectric control applications.

scholarly journals Eye-Tracking Analysis for Emotion Recognition

2020 ◽  
Vol 2020 ◽  
pp. 1-13
Author(s):  
Paweł Tarnowski ◽  
Marcin Kołodziej ◽  
Andrzej Majkowski ◽  
Remigiusz Jan Rak
Keyword(s):  
Support Vector Machine ◽  
Eye Movements ◽  
Eye Tracking ◽  
Emotion Recognition ◽  
Classification Accuracy ◽  
Pupil Diameter ◽  
Support Vector ◽  
Svm Classifier ◽  
High Arousal ◽  
Validation Method

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.

scholarly journals Personnel Emotion Recognition Model for Internet of Vehicles Security Monitoring

2021 ◽  
Author(s):  
Erkang Fu ◽  
Xi Li ◽  
Zhi Yao ◽  
Yuxin Ren ◽  
Yuanhao Wu ◽  
...  
Keyword(s):  
Emotion Recognition ◽  
Hot Spot ◽  
Development Trend ◽  
Support Vector ◽  
Svm Classifier ◽  
Expression Recognition ◽  
Low Carbon ◽  
Internet Of Vehicles ◽  
Recognition Model ◽  
Model Combining

Abstract In recent years, the Internet of vehicles (IOV) with intelligent networked automobiles as the terminal node has gradually become the development trend of the automotive industry and the research hot spot in related fields. This is due to its characteristics of intelligence, networking, low-carbon and energy saving. Real time emotion recognition for drivers and pedestrians in the community can be utilized to prevent fatigue driving and malicious collision, keep safety verification and pedestrian safety detection. This paper mainly studies the face emotion recognition model that can be utilized for IOV. Considering the fluctuation of image acquisition perspective and image quality in the application scene of IOV, the natural scene video similar to vehicle environment and its galvanic skin response (GSR) are utilized to make the testing set of emotion recognition. Then an expression recognition model combining codec and Support Vector Machine (SVM) classifier is proposed. Finally, emotion recognition testing is completed on the basis of Algorithm 1. The matching accuracy between the emotion recognition model and GSR is 82.01%. In the process of model testing, 189 effective videos are involved and 155 are correctly identified.

The Novel Indices of Short-Time Heart Rate Variability for Prediction of Cardiovascular and Cerebrovascular Events

2020 ◽  
Vol 10 (3) ◽  
pp. 769-774
Author(s):  
Shiliang Shao ◽  
Ting Wang ◽  
Chunhe Song ◽  
Yun Su ◽  
Xingchi Chen ◽  
...  
Keyword(s):  
Heart Rate ◽  
Heart Rate Variability ◽  
Nearest Neighbor ◽  
Singular Value ◽  
Support Vector ◽  
Svm Classifier ◽  
K Nearest Neighbor ◽  
Cerebrovascular Events ◽  
The Hilbert Transform ◽  
Short Time

In this paper, eight novel instantaneous indices of short-time heart rate variability (HRV) signals are proposed for prediction of cardiovascular and cerebrovascular events. The indices are based on Bubble Entropy (BE) and Singular Value Decompose (SVD). The process of indices calculation is as follows, firstly, the instantaneous amplitude (IA), instantaneous frequency (IF) and instantaneous phase (IP) of HRV signals are estimated by the Hilbert transform. Secondly, according to the HRV, IA, IP and IF, the BE and singular value (SV) is calculated, then eight novel indices are obtained, they are BEHRV, BEIA, BEIF, BEIP, SVHRV, SVIA, SVIF and SVIP. Last but not least, in order to evaluate the performance of the eight novel indices for prediction of cardiovascular and cerebrovascular events, the difference analysis of eight indices is carried out by t-test. According to the p value, seven of the eight indices BEHRV, BEIA, BEIF, BEIP, SVIA, SVIF and SVIP are thought to be the indices to discriminate the E group and N group. The K-nearest neighbor (KNN), support vector machine (SVM) and decision tree (DT) are applied on the seven novel indices. The results are that, seven novel indices are significantly different between the events and non-events groups, and the SVM classifier has the highest classification Acc and Spe for prediction of cardiovascular and cerebrovascular events, they are 88.31% and 90.19%, respectively.

scholarly journals High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan

Minerals ◽  
2020 ◽  
Vol 10 (11) ◽  
pp. 967
Author(s):  
Diana Krupnik ◽  
Shuhab D. Khan
Keyword(s):  
Spectral Feature ◽  
Short Wave ◽  
Support Vector ◽  
Svm Classifier ◽  
Northern Pakistan ◽  
Validation Data ◽  
Spectral Angle Mapper ◽  
Thin Sections ◽  
Mineral Mapping ◽  
Quartz Veins

The study of hand samples is a significant aspect of geoscience. This work showcases a technique for relatively quick and inexpensive mineral characterization, applied to a Cretaceous limestone formation and for sulfide-rich quartz vein samples from Northern Pakistan. Spectral feature parameters are derived from mineral mixtures of known abundance and are used for mineral mapping. Additionally, three well-known classification techniques—Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Neural Network—are compared. Point counting results from petrographic thin sections are used for validation the limestone samples, and QEMSCAN mineral maps for the sulfide samples. For classifying the carbonates, the SVM classifier produced results that are closest to the training set—with 84.4% accuracy and a kappa coefficient of 0.8. For classifying sulfides, SAM produced mineral abundances that were closest to the validation data, possibly due to the low reflectance of sulfides throughout the short-wave infrared spectrum with some differences in the overall spectral shape.

scholarly journals Classification of Relaxation and Concentration Mental States with EEG

Information ◽  
2021 ◽  
Vol 12 (5) ◽  
pp. 187
Author(s):  
Shingchern D. You
Keyword(s):  
Mental States ◽  
Support Vector ◽  
Svm Classifier ◽  
Eeg Signals ◽  
Consumer Market ◽  
Time Frequency ◽  
Average Accuracy ◽  
Spectral Components ◽  
Machine Interface

In this paper, we study the use of EEG (Electroencephalography) to classify between concentrated and relaxed mental states. In the literature, most EEG recording systems are expensive, medical-graded devices. The expensive devices limit the availability in a consumer market. The EEG signals are obtained from a toy-grade EEG device with one channel of output data. The experiments are conducted in two runs, with 7 and 10 subjects, respectively. Each subject is asked to silently recite a five-digit number backwards given by the tester. The recorded EEG signals are converted to time-frequency representations by the software accompanying the device. A simple average is used to aggregate multiple spectral components into EEG bands, such as α, β, and γ bands. The chosen classifiers are SVM (support vector machine) and multi-layer feedforward network trained individually for each subject. Experimental results show that features, with α+β+γ bands and bandwidth 4 Hz, the average accuracy over all subjects in both runs can reach more than 80% and some subjects up to 90+% with the SVM classifier. The results suggest that a brain machine interface could be implemented based on the mental states of the user even with the use of a cheap EEG device.

scholarly journals Optimal SVM with Features for MIR from Multi-Language

2020 ◽  
Vol 9 (8) ◽  
pp. 918-925
Keyword(s):  
Support Vector ◽  
Svm Classifier ◽  
Retrieval Model ◽  
Retrieval Rate ◽  
Kernel Support Vector Machine ◽  
Proposed Model ◽  
Retrieval Scheme ◽  
Optimal Kernel ◽  
Music Information ◽  
Short Time

Nowadays, the more attentiveness of humming scheme is MIR and query. Several existing works [1,3] are concentrated on the usage of Audio MIR and beat information which is computed by mechanical computer trial procedures. The design of music information retrieval is fundamentally working in search scheme. For a resourceful music search scheme, a few attributes measured to remove from the musical signal from dissimilar languages. For retrieval, model will consider optimal kernel Support Vector Machine (SVM) classifier, to produce a maximum signal retrieval rate in a short time. Here, entire analysis initially extracted some features from musical signal. Further, enhancing the retrieval level of proposed model Sequential Minimal Optimization (SMO) model utilized for SVM kernel function. In other words, the outcome demonstrates the work develop the consequences of the retrieval scheme. As of the consequences, the signal retrieval time has condensed by the highest precision of 97.3% through the optimal kernel SVM, which is edge over the contemporary effort.

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