204775403
An Ensemble Learning Approach for Electrocardiogram Sensor Based Human Emotion Recognition
Recently, researchers in the area of biosensor based human emotion recognition have used different types of machine learning models for recognizing human emotions. However, most of them still lack the ability to recognize human emotions with higher classification accuracy incorporating a limited number of bio-sensors. In the domain of machine learning, ensemble learning methods have been successfully applied to solve different types of real-world machine learning problems which require improved classification accuracies. Emphasising on that, this research suggests an ensemble learning approach for developing a machine learning model that can recognize four major human emotions namely: anger; sadness; joy; and pleasure incorporating electrocardiogram (ECG) signals. As feature extraction methods, this analysis combines four ECG signal based techniques, namely: heart rate variability; empirical mode decomposition; with-in beat analysis; and frequency spectrum analysis. The first three feature extraction methods are well-known ECG based feature extraction techniques mentioned in the literature, and the fourth technique is a novel method proposed in this study. The machine learning procedure of this investigation evaluates the performance of a set of well-known ensemble learners for emotion classification and further improves the classification results using feature selection as a prior step to ensemble model training. Compared to the best performing single biosensor based model in the literature, the developed ensemble learner has the accuracy gain of 10.77%. Furthermore, the developed model outperforms most of the multiple biosensor based emotion recognition models with a significantly higher classification accuracy gain.
2848493
An intelligent system for facial emotion recognition
An intelligent emotion recognition system, interweaving psychological findings about emotion representation with analysis and evaluation of facial expressions has been generated and its performance has been investigated with experimental real data. Additionally, a fuzzy rule based system has been created for classifying facial expressions to the six archetypal emotion categories. The continuous 2-D emotion space was then examined and a pool of known and novel classification and clustering techniques have been applied to our data obtaining high rates in classification and clustering into quadrants of the emotion representation space.
27340194
Automatic Location of Facial Feature Points and Synthesis of Facial Sketches Using Direct Combined Model
Automatically locating multiple feature points (i.e., the shape) in a facial image and then synthesizing the corresponding facial sketch are highly challenging since facial images typically exhibit a wide range of poses, expressions, and scales, and have differing degrees of illumination and/or occlusion. When the facial sketches are to be synthesized in the unique sketching style of a particular artist, the problem becomes even more complex. To resolve these problems, this paper develops an automatic facial sketch synthesis system based on a novel direct combined model (DCM) algorithm. The proposed system executes three cascaded procedures, namely, (1) synthesis of the facial shape from the input texture information (i.e., the facial image); (2) synthesis of the exaggerated facial shape from the synthesized facial shape; and (3) synthesis of a sketch from the original input image and the synthesized exaggerated shape. Previous proposals for reconstructing facial shapes and synthesizing the corresponding facial sketches are heavily reliant on the quality of the texture reconstruction results, which, in turn, are highly sensitive to occlusion and lighting effects in the input image. However, the DCM approach proposed in this paper accurately reconstructs the facial shape and then produces lifelike synthesized facial sketches without the need to recover occluded feature points or to restore the texture information lost as a result of unfavorable lighting conditions. Moreover, the DCM approach is capable of synthesizing facial sketches from input images with a wide variety of facial poses, gaze directions, and facial expressions even when such images are not included within the original training data set.
12710581
Toward detecting emotions in spoken dialogs
The importance of automatically recognizing emotions from human speech has grown with the increasing role of spoken language interfaces in human-computer interaction applications. This paper explores the detection of domain-specific emotions using language and discourse information in conjunction with acoustic correlates of emotion in speech signals. The specific focus is on a case study of detecting negative and non-negative emotions using spoken language data obtained from a call center application. Most previous studies in emotion recognition have used only the acoustic information contained in speech. In this paper, a combination of three sources of information-acoustic, lexical, and discourse-is used for emotion recognition. To capture emotion information at the language level, an information-theoretic notion of emotional salience is introduced. Optimization of the acoustic correlates of emotion with respect to classification error was accomplished by investigating different feature sets obtained from feature selection, followed by principal component analysis. Experimental results on our call center data show that the best results are obtained when acoustic and language information are combined. Results show that combining all the information, rather than using only acoustic information, improves emotion classification by 40.7% for males and 36.4% for females (linear discriminant classifier used for acoustic information).
18005053
Evaluation of the affective valence of speech using pitch substructure
In order to study the relationship between emotion and intonation, a new technique is introduced for the extraction of the dominant pitches within speech utterances and the quasi-musical analysis of the multipitch structure. After the distribution of fundamental frequencies over the entire utterance has been obtained, the underlying pitch structure is determined using an unsupervised "cluster" (Gaussian mixtures) algorithm. The technique normally results in 3-6 pitch clusters per utterance that can then be evaluated in terms of their inherent dissonance, harmonic "tension", and "major or minor modality". Stronger dissonance and tension were found in utterances with negative affect, relative to utterances with positive affect. Most importantly, utterances that were evaluated as having positive or negative affect had significantly different modality values. Factor analysis showed that the measures involving multiple pitches were distinct from other acoustical measures, indicating that the pitch substructure is an independent factor contributing to the affective valence of speech prosody.
14130534
Adaptive virtual environments for neuropsychological assessment in serious games
While advances in military relevant simulations provide potential for increasing assessment of Soldier readiness to Return-to-Duty (e.g., following a blast injury), little has been done to develop these simulations into adaptive virtual environments (AVE) for improved neurocognitive and psychophysiological assessment. Adaptive assessments offer the potential for dynamically adapting the difficulty level specific to the patient's knowledge or ability. We present an iteration of the Virtual Reality for Cognitive Performance and Adaptive Treatment (VRCPAT) that proffers a framework for adapting scenarios in a game engine based upon the user's neurocognitive (task performance) and psychophysiological (e.g., heart rate, skin response, heart rate, and pupil diameter) states.
10705199
Usage of emotion recognition in military health care
Post Traumatic Stress Disorder (PTSD), depression and suicide are major psychiatric problem in both military and civilian situation. These mental diseases combine with emotion change. Recently, the technology of emotion recognition has been developed rapidly and highly. Therefore, we investigate if the emotion recognition by natural speaking voice could detect the emotion change which would occur when exposing mental stress. We used “Sensibility technology ST Emotion” (AGI Japan Inc.) for emotion voice analysis system. This system determines emotional elements as including anger, joy, sorrow, and calmness. It also measures feeling of excitement. Voice data were collected from the personnel of military medical corps participating in a special stressful mission. The voice data were divided into two groups depending on participating period. Some subject's feelings during experimental period were changed. There is a tendency that “joy” of long stay group (Group L) is lower than short stay group (Group S) and “sorrow” of Group L is higher than Group S. The result suggested that the techniques of emotion recognition may be used for screening of mental status in military situation. However, further development is necessary for practical use.
59427710
Biofeedback: a Player’s Anxiety as Input into a Video Game Environment
This paper introduces a hardware-software solution for biological feedback game development. Currently, game developers do not utilize any dynamic inputs that are based off of player’s biological reactions. The goal of this paper is to research how effectively and efficiently biofeedback can be implemented into a videogame. This process is focused on the hardware and software implementation. On the software side, the game dynamically generates events based on a player’s heart rate. We use a standard heart rate monitor that can be attached to the player’s thumb. Basing the software response the biofeedback yields an intimate player-game experience. Keywordsbiofeedback; video game; game interaction; hardware; software implementation.
15072858
Understanding the Contribution of Biometrics to Games User Research
Utilising biometric data has become an increasingly active area in the video games user research community, and a number of academic papers have been published introducing various biometric based analysis techniques in video games research. This paper aims to quantify the value of biometric methods as an addition to traditional observation-based user research methodologies, and their respective contributions to the production of formative feedback during the development of video games. Our results show that observation-based techniques can expose the majority of issues relating to usability, however the biometrics-based approach enabled researchers to discover latent issues in related to players’ feelings, immersion and gameplay experience and, in certain categories of issue, reveal up to 63% more issues than observation alone.
18966304
Toward Emotion Recognition in Car-Racing Drivers: A Biosignal Processing Approach
In this paper, we present a methodology and a wearable system for the evaluation of the emotional states of car-racing drivers. The proposed approach performs an assessment of the emotional states using facial electromyograms, electrocardiogram, respiration, and electrodermal activity. The system consists of the following: 1) the multisensorial wearable module; 2) the centralized computing module; and 3) the system's interface. The system has been preliminary validated by using data obtained from ten subjects in simulated racing conditions. The emotional classes identified are high stress, low stress, disappointment, and euphoria. Support vector machines (SVMs) and adaptive neuro-fuzzy inference system (ANFIS) have been used for the classification. The overall classification rates achieved by using tenfold cross validation are 79.3% and 76.7% for the SVM and the ANFIS, respectively.
25842790
Recognition of emotional distress in physically healthy primary care patients who perceive poor physical health.
This study examines the recognition and treatment of emotional distress in physically healthy primary care patients who perceive themselves to be in fair or poor physical health. Patients (N = 892) from three private primary care practices completed a mental health screening form prior to their medical visit which included an overall assessment of their physical health (1 = excellent, 2 = good, 3 = fair, 4 = poor). Following the visit, their physicians completed a questionnaire that included the same physical health assessment item. The study group, physically healthy patients who perceive poor physical health (HPPPH), included those patients who rated their physical health as 2 or 3 points more impaired than it was rated by their physician. HPPPH (N = 39) were significantly more likely than other patients (N = 853) to report a prior psychiatric hospitalization (p < 0.05), marital difficulties (p < 0.01), recent missed work due to a mental health problem (p < 0.001), and a range of anxiety, depressive, and psychosomatic symptoms. However, HPPPH were also significantly more likely than other patients to receive excellent emotional health ratings (p < 0.001) from their physicians and were less likely to receive mental health treatment (p < 0.05). Detection of emotional distress may be particularly difficult in physically healthy patients who have low physical health perceptions. Identification of pessimistic physical health perceptions may serve as an indicator for underlying emotional distress.
20131931
Emotion recognition from electrocardiogram signals using Hilbert Huang Transform
Equipping robots and computers with emotional intelligence is becoming important in Human-Computer Interaction (HCI). Bio-signal based methods are found to be reliable and accurate than conventional methods as they directly manifest the underlying activity of the Autonomous Nervous System (ANS). This paper focuses on recognizing six emotional states (happiness, sadness, fear, surprise, disgust and neutral) from Electrocardiogram (ECG) signals that were obtained from multiple subjects. The emotional data was collected by inducing emotions internally in the subject using audio visual clips. The normalized QRS derivative signal was obtained from captured emotional ECG data by means of a non-linear transform. Hilbert Huang Transform (HHT) based analysis was done to obtain the emotional features in low, high and total (low and high together) the frequency ranges. The classification results indicate that low frequency Intrinsic Mode Functions (IMF) contain more emotional information compared to the other frequency ranges. The performance of the system can be improved further by analyzing the information in the low frequency range.
621581
Emotion recognition based on physiological changes in music listening
Little attention has been paid so far to physiological signals for emotion recognition compared to audiovisual emotion channels such as facial expression or speech. This paper investigates the potential of physiological signals as reliable channels for emotion recognition. All essential stages of an automatic recognition system are discussed, from the recording of a physiological data set to a feature-based multiclass classification. In order to collect a physiological data set from multiple subjects over many weeks, we used a musical induction method that spontaneously leads subjects to real emotional states, without any deliberate laboratory setting. Four-channel biosensors were used to measure electromyogram, electrocardiogram, skin conductivity, and respiration changes. A wide range of physiological features from various analysis domains, including time/frequency, entropy, geometric analysis, subband spectra, multiscale entropy, etc., is proposed in order to find the best emotion-relevant features and to correlate them with emotional states. The best features extracted are specified in detail and their effectiveness is proven by classification results. Classification of four musical emotions (positive/high arousal, negative/high arousal, negative/low arousal, and positive/low arousal) is performed by using an extended linear discriminant analysis (pLDA). Furthermore, by exploiting a dichotomic property of the 2D emotion model, we develop a novel scheme of emotion-specific multilevel dichotomous classification (EMDC) and compare its performance with direct multiclass classification using the pLDA. An improved recognition accuracy of 95 percent and 70 percent for subject-dependent and subject-independent classification, respectively, is achieved by using the EMDC scheme.
7005255
Electrocardiogram-based emotion recognition system using empirical mode decomposition and discrete Fourier transform
Emotion recognition using physiological signals has gained momentum in the field of human computer-interaction. This work focuses on developing a user-independent emotion recognition system that would classify five emotions happiness, sadness, fear, surprise and disgust and neutral state. The various stages such as design of emotion elicitation protocol, data acquisition, pre-processing, feature extraction and classification are discussed. Emotional data were obtained from 30 undergraduate students by using emotional video clips. Power and entropy features were obtained in three ways - by decomposing and reconstructing the signal using empirical mode decomposition, by using a Hilbert-Huang transform and by applying a discrete Fourier transform to the intrinsic mode functions IMFs. Statistical analysis using analysis of variance indicates significant differences among the six emotional states p<0.001. Classification results indicate that applying the discrete Fourier transform instead of the Hilbert transform to the IMFs provides comparatively better accuracy for all the six classes with an overall accuracy of 52%. Although the accuracy is less, it reveals the possibility of developing a system that could identify the six emotional states in a user-independent manner using electrocardiogram signals. The accuracy of the system can be improved by investigating the power and entropy of the individual IMFs.
26190016
Neural Network Based Emotion Estimation Using Heart Rate Variability and Skin Resistance
In order to build a human-computer interface that is sensitive to a user's expressed emotion, we propose a neural network based emotion estimation algorithm using heart rate variability (HRV) and galvanic skin response (GSR). In this study, a video clip method was used to elicit basic emotions from subjects while electrocardiogram (ECG) and GSR signals were measured. These signals reflect the influence of emotion on the autonomic nervous system (ANS). The extracted features that are emotion-specific characteristics from those signals are applied to an artificial neural network in order to recognize emotions from new signal collections. Results show that the proposed method is able to accurately distinguish a user's emotion.
88491532
Emotion Recognition via Galvanic Skin Response: Comparison of Machine Learning Algorithms and Feature Extraction Methods
Emotions play a significant and powerful role in everyday life of human beings. Developing algorithms for computers to recognize emotional expression is widely studied area. In this study, emotion recognition from  Galvanic Skin Response signals was performed using time domain, wavelet and empirical mode decomposition based features. Valence and arousal have been categorized and relationship between physiological signals and arousal and valence has been studied using k-Nearest Neighbors, Decision Tree, Random Forest and Support Vector Machine algorithms. We have achieved 81.81% and 89.29% accuracy rate for arousal and valence respectively.
20851503
Emotion recognition from physiological signals using wireless sensors for presence technologies
In this article we describe a new approach to enhance presence technologies. First, we discuss the strong relationship between cognitive processes and emotions and how human physiology is uniquely affected when experiencing each emotion. Secondly, we introduce our prototype multimodal affective user interface. In the remainder of the paper we describe the emotion elicitation experiment we designed and conducted and the algorithms we implemented to analyse the physiological signals associated with emotions. These algorithms can then be used to recognise the affective states of users from physiological data collected via non-invasive technologies. The affective intelligent user interfaces we plan to create will adapt to user affect dynamically in the current context, thus providing enhanced social presence.
615964
Emotion recognition from physiological signals
Emotion recognition is one of the great challenges in human–human and human–computer interaction. Accurate emotion recognition would allow computers to recognize human emotions and therefore react accordingly. In this paper, an approach for emotion recognition based on physiological signals is proposed. Six basic emotions: joy, sadness, fear, disgust, neutrality and amusement are analysed using physiological signals. These emotions are induced through the presentation of International Affecting Picture System (IAPS) pictures to the subjects. The physiological signals of interest in this analysis are: electromyogram signal (EMG), respiratory volume (RV), skin temperature (SKT), skin conductance (SKC), blood volume pulse (BVP) and heart rate (HR). These are selected to extract characteristic parameters, which will be used for classifying the emotions. The SVM (support vector machine) technique is used for classifying these parameters. The experimental results show that the proposed methodology provides in general a recognition rate of 85% for different emotional states.
17431064
Physiological signals based human emotion Recognition: a review
Recent research in the field of Human Computer Interaction aims at recognizing the user's emotional state in order to provide a smooth interface between humans and computers. This would make life easier and can be used in vast applications involving areas such as education, medicine etc. Human emotions can be recognized by several approaches such as gesture, facial images, physiological signals and neuro imaging methods. Most of the researchers have developed user dependent emotion recognition system and achieved maximum classification rate. Very few researchers have tried to develop a user independent system and obtained lower classification rate. Efficient emotion stimulus method, larger data samples and intelligent signal processing techniques are essential for improving the classification rate of the user independent system. In this paper, we present a review on emotion recognition using physiological signals. The various theories on emotion, emotion recognition methodology and the current advancements in emotion research are discussed in subsequent topics. This would provide an insight on the current state of research and its challenges on emotion recognition using physiological signals, so that research can be advanced to obtain better recognition.
6869362
Frequency Band Analysis of Electrocardiogram (ECG) Signals for Human Emotional State Classification Using Discrete Wavelet Transform (DWT)
[Purpose] Intelligent emotion assessment systems have been highly successful in a variety of applications, such as e-learning, psychology, and psycho-physiology. This study aimed to assess five different human emotions (happiness, disgust, fear, sadness, and neutral) using heart rate variability (HRV) signals derived from an electrocardiogram (ECG). [Subjects] Twenty healthy university students (10 males and 10 females) with a mean age of 23 years participated in this experiment. [Methods] All five emotions were induced by audio-visual stimuli (video clips). ECG signals were acquired using 3 electrodes and were preprocessed using a Butterworth 3rd order filter to remove noise and baseline wander. The Pan-Tompkins algorithm was used to derive the HRV signals from ECG. Discrete wavelet transform (DWT) was used to extract statistical features from the HRV signals using four wavelet functions: Daubechies6 (db6), Daubechies7 (db7), Symmlet8 (sym8), and Coiflet5 (coif5). The k-nearest neighbor (KNN) and linear discriminant analysis (LDA) were used to map the statistical features into corresponding emotions. [Results] KNN provided the maximum average emotion classification rate compared to LDA for five emotions (sadness − 50.28%; happiness − 79.03%; fear − 77.78%; disgust − 88.69%; and neutral − 78.34%). [Conclusion] The results of this study indicate that HRV may be a reliable indicator of changes in the emotional state of subjects and provides an approach to the development of a real-time emotion assessment system with a higher reliability than other systems.
124454036
Analysis of affective ECG signals toward emotion recognition
Recently, as recognizing emotion has been one of the hallmarks of affective computing, more attention has been paid to physiological signals for emotion recognition. This paper presented an approach to emotion recognition using ElectroCardioGraphy (ECG) signals from multiple subjects. To collect reliable affective ECG data, we applied an arousal method by movie clips to make subjects experience specific emotions without external interference. Through precise location of P-QRS-T wave by continuous wavelet transform, an amount of ECG features was extracted sufficiently. Since feature selection is a combination optimization problem, Improved Binary Particle Swarm Optimization (IBPSO) based on neighborhood search was applied to search out effective features to improve classification results of emotion states with the help of fisher or K-Nearest Neighbor (KNN) classifier. In the experiment, it is shown that the approach is successful and the effective features got from ECG signals can express emotion states excellently.
13193058
Diurnal variation of the QT interval--influence of the autonomic nervous system.
To assess the influence of diurnal changes in the autonomic nervous system on the QT interval, 24 hour dynamic electrocardiographic monitoring was performed in six pacemaker dependent patients with normally innervated hearts, in six cardiac transplant patients with anatomically denervated hearts (but which respond to circulating catecholamines), and in nine diabetic patients with confirmed autonomic neuropathy. QT and RR intervals from hourly intervals were measured and Bazett's formula was used to correct QT intervals during sinus rhythm. All QT intervals were normalised by dividing by the mean QT for the 24 hours in each patient and were expressed as a percentage. There was pronounced diurnal variation of normalised QT in the patients with normally innervated hearts. QT intervals were longer during sleep than during waking hours (06.00 vs 18.00 h, 102.5% vs 97.8%). Diurnal variation was blunted in the transplant patients (101.3% vs 98.1%) and absent in the diabetic patients (100.0% vs 100.3%). In the normally innervated patients changes were most pronounced at the time of waking (06.00 vs 09.00 h, 102.5% vs 95.4%). There was no change in normalised QT in the transplant and diabetic patients at this time. There was no significant difference between normalised QT for the three groups during sleep, but this variable was shorter in innervated patients during waking hours (for example at 10.00 h, innervated 96.5%, transplant 100.7%, diabetic 100.7%). Diurnal changes of the QT interval may be pronounced in the innervated heart and are dependent on both variations in autonomic tone and concentrations of circulating catecholamines. These changes in repolarisation may be related to the reported diurnal pattern of ventricular arrhythmias.
17534173
Comparing features from ECG pattern and HRV analysis for emotion recognition system
We propose new features for emotion recognition from short ECG signals. The features represent the statistical distribution of dominant frequencies, calculated using spectrogram analysis of intrinsic mode function after applying the bivariate empirical mode decomposition to ECG. KNN was used to classify emotions in valence and arousal for a 3-class problem (low-medium-high). Using ECG from the Mahnob-HCI database, the average accuracies for valence and arousal were 55.8% and 59.7% respectively with 10-fold cross validation. The accuracies using features from standard Heart Rate Variability analysis were 42.6% and 47.7% for valence and arousal respectively for the 3-class problem. These features were also tested using subject-independent validation, achieving an accuracy of 59.2% for valence and 58.7% for arousal. The proposed features also showed better performance compared to features based on statistical distribution of instantaneous frequency, calculated using Hilbert transform of intrinsic mode function after applying standard empirical mode decomposition and bivariate empirical mode decomposition to ECG. We conclude that the proposed features offer a promising approach to emotion recognition based on short ECG signals. The proposed features could be potentially used also in applications in which it is important to detect quickly any changes in emotional state.
15670824
HRV Analysis: A Clinical and Diagnostic Tool in Chronic Obstructive Pulmonary Disease
This study's aim is to analyze heart rate dynamics in subjects with chronic obstructive pulmonary disease (COPD) by measures of heart rate variability (HRV). HRV is a simple and noninvasive measure of autonomic impulses. 38 adults were divided into two equal groups based on respiratory function: COPD and normal. HRV was monitored in the supine position for 30 minutes. After tests of normality, Kruskal-Wallis was used for the statistical analysis, with the level of significance set at P < 0.05. Principal component analysis identified two components representing 99.5% of total variance. Furthermore, it is suggested that the chaos forward parameter (CFP) which applies all three “chaotic globals” is the most influential, although others are statistically more significant. The COPD subjects exhibited a decrease in the CFP. COPD can be termed a dynamical condition, decreasing the chaotic response. The perceived benefits of such analysis include quantitative assessment and suitable pharmacological intervention in the respiratory condition, especially of other related dynamical diseases such as cardiac failure.
27947192
Stability of emotion experiences and their relations to traits of personality.
The article presents a theoretical framework for studying emotion-personality relations and an empirical study of the stability of 88 normal middle-class mothers' emotion experiences and their relations to personality during the 3 years after childbirth. Ss completed the Differential Emotions Scale (DES), Eysenck's Personality Questionnaire, Jackson's Personality Research Form, and Zuckerman's Sensation Seeking Scale. The DES demonstrated stability over 3 years. There was individual stability despite changes in group means during the postpartum period. Positive emotionality, as well as the discrete emotions of interest, enjoyment, and shyness, predicted Extraversion. Negative emotionality and the discrete negative emotions were significant predictors of Neuroticism. Positive emotionality was inversely related to Neuroticism. There were expectable correlations among specific emotions and primary traits of personality.
1262186
The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis
A new method for analysing nonlinear and non-stationary data has been developed. The key part of the method is the ‘empirical mode decomposition’ method with which any complicated data set can be decomposed into a finite and often small number of ‘intrinsic mode functions’ that admit well-behaved Hilbert transforms. This decomposition method is adaptive, and, therefore, highly efficient. Since the decomposition is based on the local characteristic time scale of the data, it is applicable to nonlinear and non-stationary processes. With the Hilbert transform, the ‘instrinic mode functions’ yield instantaneous frequencies as functions of time that give sharp identifications of imbedded structures. The final presentation of the results is an energy-frequency-time distribution, designated as the Hilbert spectrum. In this method, the main conceptual innovations are the introduction of ‘intrinsic mode functions’ based on local properties of the signal, which make the instantaneous frequency meaningful; and the introduction of the instantaneous frequencies for complicated data sets, which eliminate the need for spurious harmonics to represent nonlinear and non-stationary signals. Examples from the numerical results of the classical nonlinear equation systems and data representing natural phenomena are given to demonstrate the power of this new method. Classical nonlinear system data are especially interesting, for they serve to illustrate the roles played by the nonlinear and non-stationary effects in the energy-frequency-time distribution.
148574089
Comparison of Empirical Mode Decomposition and Wavelet Transform for Power Quality Assessment in FPGA
This paper focused on the execution of a new field-programmable gate array (FPGA) based method for a signal processing algorithm to detect power quality disturbance in real-time for a grid-connected PV system. The real-time implementation of a signal processing algorithm to detect disturbances is still under the premature stage. To demonstrate the grid and distribution generation disturbances scenario in real-time, a PV connected with the emulated grid is taken into consideration for experiment analysis, which is capable to replicate the grid’s behavior under normal operating conditions. Discrete Wavelet Transform (DWT), Empirical Mode Decomposition (EMD) and Hilbert transform (HT) are a novel signal analysis technique which decomposes the signals into a different frequency component and extract hidden features of the signal to detect the disturbances. In the noisy scenario, DWT performance is deteriorated compared to EMD. Hilbert transform is applied to 1st IMF of EMD to gain instantaneous amplitude and phase of the signal.
15055092
ECG Pattern Analysis for Emotion Detection
Emotion modeling and recognition has drawn extensive attention from disciplines such as psychology, cognitive science, and, lately, engineering. Although a significant amount of research has been done on behavioral modalities, less explored characteristics include the physiological signals. This work brings to the table the ECG signal and presents a thorough analysis of its psychological properties. The fact that this signal has been established as a biometric characteristic calls for subject-dependent emotion recognizers that capture the instantaneous variability of the signal from its homeostatic baseline. A solution based on the empirical mode decomposition is proposed for the detection of dynamically evolving emotion patterns on ECG. Classification features are based on the instantaneous frequency (Hilbert-Huang transform) and the local oscillation within every mode. Two experimental setups are presented for the elicitation of active arousal and passive arousal/valence. The results support the expectations for subject specificity, as well as demonstrating the feasibility of determining valence out of the ECG morphology (up to 89 percent for 44 subjects). In addition, this work differentiates for the first time between active and passive arousal, and advocates that there are higher chances of ECG reactivity to emotion when the induction method is active for the subject.
11926819
Combining ensemble methods and social network metrics for improving accuracy of OCSVM on intrusion detection in SCADA systems
Abstract Modern Supervisory Control and Data Acquisition (SCADA) systems used by the electric utility industry to monitor and control electric power generation, transmission and distribution are recognized today as critical components of the electric power delivery infrastructure. SCADA systems are large, complex and incorporate increasing numbers of widely distributed components. The presence of a real time intrusion detection mechanism, which can cope with different types of attacks, is of great importance in order to defend a system against cyber attacks. This defense mechanism must be distributed, cheap and above all accurate, since false positive alarms or mistakes regarding the origin of the intrusion mean severe costs for the system. Recently an integrated detection mechanism, namely IT-OCSVM, was proposed, which is distributed in a SCADA network as a part of a distributed intrusion detection system (DIDS), providing accurate data about the origin and the time of an intrusion. In this paper we also analyze the architecture of the integrated detection mechanism and we perform extensive simulations based on real cyber attacks in a small SCADA testbed in order to evaluate the performance of the proposed mechanism.
17464637
Applying an ensemble learning method for improving multi-label classification performance
In recent years, multi-label classification problem has become a controversial issue. In this kind of classification, each sample is associated with a set of class labels. Ensemble approaches are supervised learning algorithms in which an operator takes a number of learning algorithms, namely base-level algorithms and combines their outcomes to make an estimation. The simplest form of ensemble learning is to train the base-level algorithms on random subsets of data and then let them vote for the most popular classifications or average the predictions of the base-level algorithms. In this study, an ensemble learning method is proposed for improving multi-label classification evaluation criteria. We have compared our method with well-known base-level algorithms on some data sets. Experiment results show the proposed approach outperforms the base well-known classifiers for the multi-label classification problem.
8167216
Emotion recognition via random forest and galvanic skin response: Comparison of time based feature sets, window sizes and wavelet approaches
Emotions play a significant and powerful role in everyday life of human beings. Developing algorithms for computers to recognize emotional expression is a widely studied area. In this study, emotion recognition from Galvanic signals was performed using time domain and wavelet based features. Feature extraction has been done with various feature set attributes. Various length windows have been used for feature extraction. Various feature attribute sets have been implemented. Valence and arousal have been categorized and relationship between physiological signals and arousal and valence has been studied using Random Forest machine learning algorithm. We have achieved 71.53% and 71.04% accuracy rate for arousal and valence respectively by using only galvanic skin response signal. We have also showed that using convolution has positive affect on accuracy rate compared to non-overlapping window based feature extraction.
206716432
Real-Time Epileptic Seizure Detection from EEG Signals via Random Subspace Ensemble Learning
Real-time detection of seizure activities in epileptic patients is crucial and can help improve patients' quality of life. Accurate evaluation, pre-surgery assessments, seizure prevention, and emergency alerts for medical aid all depend on the rapid detection of the onset of seizures. A new method of feature selection and classification for rapid and precise epileptic seizure detection is discussed. In this solution, informative components of Electroencephalogram (EEG) data are extracted and an automatic method is presented using Infinite Independent Component Analysis (I-ICA) to select efficiently independent features. The feature space is divided into subspaces via random selection, and multi-channel Support Vector Machines (SVMs) are used to classify the subspaces, then, the result of each classifier is combined by majority voting to find the final output. To evaluate the solution, a benchmark clinical intracranial EEG (iEEG) of eight patients with temporal and extratemporal lobe epilepsy has been considered in a multi-tier cloud-computing architecture. Via the leave-one-out cross-validation, accuracy, sensitivity, specificity, and false positive and false negative ratios of the proposed method are 0.95, 0.96, 0.94, 0.06, and 0.04, respectively, which confirm the effectiveness of the proposed solution.
17557962
Affective space is bipolar.
Numerous previous studies found monopolar rather than bipolar dimensions of affect (defined as emotion represented in language), but may have included methodological biases against bipolarity. The present study of self-report data (N = 150) on 11 affect scales showed that response format and acquiescence response style significantly shifted correlations between hypothesized opposites away from showing bipolarity. When these biases were taken into account, pleasure was found to be the bipolar opposite of displeasure and arousal of sleepiness. In turn, pleasure-displeasure and degree of arousal formed a twodimensional bipolar space that accounted for almost all of the reliable variance in Thayer's four factors of activation plus a measure of depression. Dominance and submissiveness factors were also included in the study, but invalidity of the scales used precluded any conclusions regarding their bipolarity.
8703112
Emotion elicitation using films
Abstract Researchers interested in emotion have long struggled with the problem of how to elicit emotional responses in the laboratory. In this article, we summarise five years of work to develop a set of films that reliably elicit each of eight emotional states (amusement, anger, contentment, disgust, fear, neutral, sadness, and surprise). After evaluating over 250 films, we showed selected film clips to an ethnically diverse sample of 494 English-speaking subjects. We then chose the two best films for each of the eight target emotions based on the intensity and discreteness of subjects' responses to each film. We found that our set of 16 films successfully elicited amusement, anger, contentment. disgust, sadness, surprise, a relatively neutral state, and, to a lesser extent, fear. We compare this set of films with another set recently described by Philippot (1993), and indicate that detailed instructions for creating our set of film stimuli will be provided on request.
13900622
The use of fast Fourier transform for the estimation of power spectra: A method based on time averaging over short, modified periodograms
The use of the fast Fourier transform in power spectrum analysis is described. Principal advantages of this method are a reduction in the number of computations and in required core storage, and convenient application in nonstationarity tests. The method involves sectioning the record and averaging modified periodograms of the sections.
5139000
Feature Selection for Ensembles
The traditional motivation behind feature selection algorithms is to find the best subset of features for a task using one particular learning algonthm. Given the recent success of ensembles, however, we investigate the notion of ensemble feature selection in this paper. This task is harder than traditional feature selection in that one not only needs to find features germane to the learning task and learning algorithm, but one also needs to find a set of feature subsets that will promote disagreement among the ensemble's classifiers. In this paper, we present an ensemble feature selection approach that is based on genetic algorithms. Our algorithm shows improved performance over the popular and powerful ensemble approaches of AdaBoost and Bagging and demonstrates the utility of ensemble feature selection.
11833759
Improving software quality estimation by combining feature selection strategies with sampled ensemble learning
The efficiency (prediction accuracy) of a classification model is affected by the quality of training data. High dimensionality and class imbalance are two main problems that may cause low quality of training datasets, making data preprocessing a very important step for a classification problem. Feature (software metric) selection and data sampling are frequently used to overcome these problems. Feature selection (FS) is a process of selecting the most important attributes from the original dataset. Data sampling copes with class imbalance by adding/removing instances to/from training datasets. Another interesting method, called boosting (building multiple models, with each model tuned to work better on instances misclassifled by previous models), is found also effective for addressing the class imbalance problem. In this study, we investigate two types of FS approaches: individual FS and repetitive sampled FS. Following feature selection, models are built either using a plain learner or using a boosting algorithm, where random undersampling integrates with the AdaBoost algorithm. We focus on studying the impact of two FS methods (individual FS vs. repetitive sampled FS) and two model-building processes (boosting vs. plain learner) on software quality prediction. Six feature ranking techniques are examined in the experiment. The results demonstrate that the repetitive sampled FS generally has better performance than the individual FS technique when a plain learner is used for the subsequent learning process, and that boosting is more effective in improving classification performance than not using boosting.
10659969
Scikit-learn: Machine Learning in Python
Scikit-learn is a Python module integrating a wide range of state-of-the-art machine learning algorithms for medium-scale supervised and unsupervised problems. This package focuses on bringing machine learning to non-specialists using a general-purpose high-level language. Emphasis is put on ease of use, performance, documentation, and API consistency. It has minimal dependencies and is distributed under the simplified BSD license, encouraging its use in both academic and commercial settings. Source code, binaries, and documentation can be downloaded from http://scikit-learn.sourceforge.net.
9434024
Emotion Recognition through Physiological Signals for Human-Machine Communication
The ability to recognize emotion is one of the hallmarks of emotion intelligence. This paper proposed to recognize emotion using physiological signals obtained from multiple subjects. IAPS (International Affective Picture System) images were used to elicit target emotions. Five physiological signals: Blood volume pulse (BVP), Electromyography (EMG), Skin Conductance (SC), Skin Temperature (SKT) and Respiration (RESP) were selected to extract 30 features for recognition. Two pattern classification methods, Fisher discriminant and SVM method are used and compared for emotional state classification. The experimental results indicate that the proposed method provides very stable and successful emotional classification performance as 92% over six emotional states.
2137238
Emotion recognition system using short-term monitoring of physiological signals
A physiological signal-based emotion recognition system is reported. The system was developed to operate as a user-independent system, based on physiological signal databases obtained from multiple subjects. The input signals were electrocardiogram, skin temperature variation and electrodermal activity, all of which were acquired without much discomfort from the body surface, and can reflect the influence of emotion on the autonomic nervous system. The system consisted of preprocessing, feature extraction and pattern classification stages. Preprocessing and feature extraction methods were devised so that emotion-specific characteristics could be extracted from short-segment signals. Although the features were carefully extracted, their distribution formed a classification problem, with large overlap among clusters and large variance within clusters. A support vector machine was adopted as a pattern classifier to resolve this difficulty. Correct-classification ratios for 50 subjects were 78.4% and 61.8%, for the recognition of three and four categories, respectively.
14656871
An ultra-wearable, wireless, low power ECG monitoring system
Wearable electrocardiograph (ECG) monitoring systems today use electrodes that require skin preparation in advance, and require pastes or gels to make electrical contact to the skin. Moreover, they are not suitable for subjects at high levels of activity due to high noise spikes that can appear in the data. To address these problems, a new class of miniature, ultra low noise, capacitive sensor that does not require direct contact to the skin, and has comparable performance to gold standard ECG electrodes, has been developed. This paper presents a description and evaluation of a wireless version of a system based on these innovative ECG sensors. We use a wearable and ultra low power wireless sensor node called Eco. Experimental results show that the wireless interface will add minimal size and weight to the system while providing reliable, untethered operation.
14817580
Unobtrusive Sensing of Emotions (USE)
Emotions are acknowledged as a crucial element for artificial intelligence; this is, as is illustrated, no different for Ambient Intelligence (AmI). Unobtrusive Sensing of Emotions (USE) is introduced to enrich AmI with empathic abilities. USE coins the combination of speech and the electrocardiogram (ECG) as a powerful and unique combination to unravel people's emotions. In a controlled study, 40 people watched film scenes, in either an office or a home-like setting. It is shown that, when people's gender is taken into account, both heart rate variability (derived from the ECG) and the standard deviation of the fundamental frequency of speech indicate people's experienced valence and arousal, in parallel. As such, both measures validate each other. Thus, through USE reliable cues can be derived that indicate the emotional state of people, in particular when also people's environment is taken into account. Since all this is crucial for both AI and true AmI, this study provides a first significant leap forward in making AmI a success.
21614981
Development of a Wireless Capacitive Sensor for Ambulatory ECG Monitoring over Clothes
We present a wireless capacitive sensor for ambulatory ECG monitoring. The device is capable of detecting electrocardiographic potential through underwear. It is based on capacitive coupling involving the electrode, the underwear, and the skin. Validation of the system revealed the following: (1) The frequency response of the device with the underwear revealed that the gain of the detected signal was reduced, but distortion of the signal within the pass-band (from 6 to 45 Hz) was minimal even with the underwear. (2) The signal displayed a periodic waveform synchronized with the simultaneously recorded ECG, even when the underwear was inserted between the electrode and the skin. (3) Although the signal was seemed deteriorated slightly while walking, R-waves were recognized easily. Although there is still room for improvement in terms of its practical use, the proposed approach appears promising for application to ambulatory ECG monitoring.