Communication without words

Emotions are fundamental for human beings and play an important role in human cognition. Emotion is commonly associated with logical decision making, perception, human interaction, and to a certain extent, human intelligence itself. With the growing interest of the research community towards establishing some meaningful "emotional" interactions between humans and computers, the need for reliable and deployable solutions for the identification of human emotional states is required. Recent developments in using electroencephalography (EEG) for emotion recognition have garnered strong interest from the research community as the latest developments in consumer-grade wearable EEG solutions can provide a cheap, portable, and simple solution for identifying emotions. Since the last comprehensive review was conducted back from the years 2009 to 2016, this paper will update on the current progress of emotion recognition using EEG signals from 2016 to 2019. The focus on this state-of-the-art review focuses on the elements of emotion stimuli type and presentation approach, study size, EEG hardware, machine learning classifiers, and classification approach. From this state-of-the-art review, we suggest several future research opportunities including proposing a different approach in presenting the stimuli in the form of virtual reality (VR). To this end, an additional section devoted specifically to reviewing only VR studies within this research domain is presented as the motivation for this proposed new approach using VR as the stimuli presentation device. This review paper is intended to be useful for the research community working on emotion recognition using EEG signals as well as for those who are venturing into this field of research.

/pdf/eeg-based-emotion-recognition-a-state-of-the-art-review-of-c23xo1bpc6.pdf

EEG-Based Emotion Recognition: A State-of-the-Art Review of Current Trends and Opportunities.

Gesture recognition based on multilevel multimodal feature fusion

Gesture recognition technology enables machines to understand human gestures. The technology is considered as a key enabler for gaming and virtual reality applications. In this paper, we propose an effective, low-cost capacitive sensor device to recognize hand gestures. In particular, we designed a prototype of a wearable capacitive sensor unit to capture the capacitance values from the electrodes placed on finger phalanges. The sensor captures finger capacitance values. Each gesture has specific finger capacitance values. We applied a running median filter to the output of the sensor and extracted 15 features for gesture classification training and testing tasks. Subsequently, various analyses were performed to provide more insights into the sensing data. We applied and compared two machine learning algorithms: Error Correction Output Code Support Vector Machines (ECOC-SVM) and ${K}$ -Nearest Neighbour (KNN) classifiers. The training and testing recognition rates were observed for both intra-participant and inter-participant data sets. Further, we introduced a feature compression approach derived from correlation analysis to reduce the complexity of the machine learning algorithms. Using cross validation, we achieved a classification rate of approximately 99% for intra-participant data. We achieved a lower recognition rate of 97% (average cross validation testing) for compressed feature data set using both machine learning approaches. For the inter-participant data, the recognition rate was 99% (normalized feature data) using KNN and 97% using ECOC-SVM. The research findings show that our recognition system is competitive and has an immense potential for further study.

Multi-Features Capacitive Hand Gesture Recognition Sensor: A Machine Learning Approach

Ever since the development of digital devices, the recognition of human gestures has played an important role in many Human-Computer interface applications. Various wearable devices have been developed, and inertial sensors, magnetic sensors, gyro sensors, electromyography, force-sensitive resistors, and other types of sensors have been used to identify gestures. However, there are different drawbacks for each sensor, which affect the detection of gestures. In this paper, we present a new gesture recognition method using a Flexible Epidermal Tactile Sensor based on strain gauges to sense deformation. Such deformations are transduced to electric signals. By measuring the electric signals, the sensor can estimate the degree of deformation, including compression, tension, and twist, caused by movements of the wrist. The proposed sensor array was demonstrated to be capable of analyzing the eight motions of the wrist, and showed robustness, stability, and repeatability throughout a range of experiments aimed at testing the sensor array. We compared the performance of the prototype device with those of previous studies, under the same experimental conditions. The result shows our recognition method significantly outperformed existing methods.

https://www.mdpi.com/2072-666X/10/10/692/pdf

Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array.

Recently, computer form were smaller than before because of computing technique"s development and many wearable device are formed. So, computer"s cognition of human emotion has importantly considered , thus researches on analyzing the state of emotion are increasing. Human voice includes many information of human emotion. This paper proposes a discriminative feature vector selection for emotion classification based on speech. For this, we extract some feature vectors like Pitch, MFCC, LPC, LPCC from voice signals are divided into four emotion parts on happy, normal, sad, angry and compare a separability of the extracted feature vectors using Bhattacharyya distance. So more effective feature vectors are recommended for emotion classification.

https://www.koreascience.kr:443/article/JAKO201536553343095.pdf

음성신호기반의 감정분석을 위한 특징벡터 선택

The goal of the human interface is to recognize the user’s emotional state precisely. In the speech emotion recognition study, the most important issue is the effective parallel use of the extraction of proper speech features and an appropriate classification engine. Well defined speech databases are also needed to accurately recognize and analyze emotions from speech signals. In this work, we constructed a Korean emotional speech database for speech emotion analysis and proposed a feature combination that can improve emotion recognition performance using a recurrent neural network model. To investigate the acoustic features, which can reflect distinct momentary changes in emotional expression, we extracted F0, Mel-frequency cepstrum coefficients, spectral features, harmonic features, and others. Statistical analysis was performed to select an optimal combination of acoustic features that affect the emotion from speech. We used a recurrent neural network model to classify emotions from speech. The results show the proposed system has more accurate performance than previous studies.

https://www.mdpi.com/2076-3417/11/4/1890/pdf

A Study on a Speech Emotion Recognition System with Effective Acoustic Features Using Deep Learning Algorithms

People generally perceive other people’s emotions based on speech and facial expressions, so it can be helpful to use speech signals and facial images simultaneously. However, because the characteristics of speech and image data are different, combining the two inputs is still a challenging issue in the area of emotion-recognition research. In this paper, we propose a method to recognize emotions by synchronizing speech signals and image sequences. We design three deep networks. One of the networks is trained using image sequences, which focus on facial expression changes. Facial landmarks are also input to another network to reflect facial motion. The speech signals are first converted to acoustic features, which are used for the input of the other network, synchronizing the image sequence. These three networks are combined using a novel integration method to boost the performance of emotion recognition. A test comparing accuracy is conducted to verify the proposed method. The results demonstrated that the proposed method exhibits more accurate performance than previous studies.

/pdf/human-emotion-recognition-based-on-the-weighted-integration-23vx4rhcjo.pdf

Human emotion recognition based on the weighted integration method using image sequences and acoustic features

Recently, researches on analyzing relationship between the state of emotion and musical stimuli using EEG are increasing. These research shows that a selection of feature vectors is very important for the performance of EEG pattern classifiers. In this paper, we apply feature extraction methods, which were reviewed in the previous, to DEAP data for the emotion recognition. We limit to analysis features in time-domain for this research. To evaluate the feature vectors, the Relief algorithm and the Bhattacharyya distance are used. According to result, the power of signal is better for the emotion recognition than the other feature.

Sung-Woo Byun

Papers

음성신호기반의 감정분석을 위한 특징벡터 선택

Implementation of Hand Gesture Recognition Device Applicable to Smart Watch Based on Flexible Epidermal Tactile Sensor Array.

A Study on a Speech Emotion Recognition System with Effective Acoustic Features Using Deep Learning Algorithms

Human emotion recognition based on the weighted integration method using image sequences and acoustic features

Feature selection and comparison for the emotion recognition according to music listening