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SpikerShield Heart and Brain sensor.

SpikerShield Heart and Brain sensor.

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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 learn...

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... subject only needs to have a few plasters on their hand in order to record the ECG signals, which makes it not too disturbing to the subject as well. Figure 1 shows an image of the wearable sensor used. ...

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... Non-physiological signals are easy to collect and closely related to our daily life. Another is to identify emotions through physiological signals, such as Electroencephalogram (EEG) [8], Electromyogram (EMG) [9], Electrocardiogram (ECG) [10] and Galvanic Skin Response (GSR) [11]. Among them, the EEG signals are spontaneously electric activity of the neurons in human brain that can reflect the truthful and plentiful emotional information within the individuals. ...
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italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Emotion analysis has been employed in many fields such as human-computer interaction, rehabilitation, and neuroscience. But most emotion analysis methods mainly focus on healthy controls or depression patients. This paper aims to classify the emotional expressions in individuals with hearing impairment based on EEG signals and facial expressions. Two kinds of signals were collected simultaneously when the subjects watched affective video clips, and we labeled the video clips with discrete emotional states (fear, happiness, calmness, and sadness). We extracted the differential entropy (DE) features based on EEG signals and converted DE features into EEG topographic maps (ETM). Next, the ETM and facial expressions were fused by the multichannel fusion method. Finally, a deep learning classifier CBAM_ResNet34 combined Residual Network (ResNet) and Convolutional Block Attention Module (CBAM) was used for subject-dependent emotion classification. The results show that the average classification accuracy of four emotions recognition after multimodal fusion achieves 78.32%, which is higher than 67.90% for facial expressions and 69.43% for EEG signals. Moreover, visualization by the Gradient-weighted Class Activation Mapping (Grad-CAM) of ETM showed that the prefrontal, temporal and occipital lobes were the brain regions closely related to emotional changes in individuals with hearing impairment.</i
... In [19], the authors explained a machine learning method to recognize four major types of human emotions which are anger, sadness, joy, and pleasure. The authors incorporated electrocardiogram (ECG) signals to recognize emotions. ...
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Emotion is the most important component of being human, and very essential for everyday activities, such as the interaction between people, decision making, and learning. In order to adapt to the COVID-19 pandemic situation, most of the academic institutions relied on online video conferencing platforms to continue educational activities. Due to low bandwidth in many developing countries, educational activities are being mostly carried out through audio interaction. Recognizing an emotion from audio interaction is important when video interaction is limited or unavailable. The literature has documented several studies on detection of emotion in Bangla text and audio speech data. In this paper, ensemble machine learning methods are used to improve the performance of emotion detection from speech data extracted from audio data. The ensemble learning system consists of several base classifiers, each of which is trained with both spontaneous emotional speech and acted emotional speech data. Several trials with different ensemble learning methods are compared to show how these methods can yield an improvement over traditional machine learning method. The experimental results show the accuracy of ensemble learning methods; 84.37% accuracy was achieved using the ensemble learning with bootstrap aggregation and voting method.
... However, older studies focusing on linear and quadratic discriminant analysis remain relevant, achieving suitable accuracy for their respective classifications [5], [22]. Furthermore, adapted ML classifiers and combinations of ML classifiers forming ensembles have demonstrated potential for binary classifications in emotion detection [23], [24]. In comparison to other studies, [19] achieved the highest accuracy for multiple emotion detection from ECG data and reported setting the new state of the art for ECG emotion detection. ...
... Certain windows may include multiple emotive annotations; hence to identify the most pertinent emotion, the mean of all annotation values per window is calculated and rounded to the nearest annotation (1-4) using Euclidean distance. Alternative approaches [24] omit these windows and the neighbouring segments to prevent confusion from mixed emotions. ...
... The full model comparison is shown in Figure 1. In contrast with the state of the art [19], [24] the performance achieved is much lower for ECG and PPG; however, this work focuses on the variance between the signals for affective analysis rather than achieving high classification accuracy. Analysing the ROC curves from RF demonstrates the true and false positive rates per signal for each affective state, see Figure 4. On average, ECG demonstrates increased capabilities for affective classification by achieving a higher ROC area than PPG, varying with a range of 0.02-0.10. ...
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Advances in wearable technology have significantly increased the sensitivity and accuracy of devices for recording physiological signals. Commercial off-the-shelf wearable devices can gather large quantities of physiological data un-obtrusively. This enables momentary assessments of human physiology, which provide valuable insights into an individual’s health and psychological state. Leveraging these insights provides significant benefits for human-to-computer interaction and personalised health care. This work contributes an analysis of variance occurring in features representative of affective states extracted from electrocardiograms and photoplethysmography; subsequently identifies the cardiac measures most descriptive of affective states from both signals and provides insights into signal and emotion-specific cardiac measures; finally baseline performance for automated affective state detection from physiological signals is established.
... We underline that the HRV indices in Table 1, even the non-linear ones, are derived from the series of IBI, that is, only from the duration of the beat, neglecting other information obtainable from the ECG signal. Alternative features can be extracted using other ECG signal-based techniques [25]. ...
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Automatically recognizing negative emotions, such as anger or stress, and also positive ones, such as euphoria, can contribute to improving well-being. In real-life, emotion recognition is a difficult task since many of the technologies used for this purpose in both laboratory and clinic environments, such as electroencephalography (EEG) and electrocardiography (ECG), cannot realistically be used. Photoplethysmography (PPG) is a non-invasive technology that can be easily integrated into wearable sensors. This paper focuses on the comparison between PPG and ECG concerning their efficacy in detecting the psychophysical and affective states of the subjects. It has been confirmed that the levels of accuracy in the recognition of affective variables obtained by PPG technology are comparable to those achievable with the more traditional ECG technology. Moreover, the affective psychological condition of the participants (anxiety and mood levels) may influence the psychophysiological responses recorded during the experimental tests.
... Based on these features different emotions were detected using several machine learning classifiers. Authors [16], have extracted four different features from the ECG signal, i.e., heart rate variability (HRV), with-in beat (WIB), frequency spectrum, and signal decomposition-based features. For evaluating the performance of the obtained features ensemble classifiers are used. ...
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Human dependence on computers is increasing day by day, thus human interaction with computers must be more dynamic and contextual rather than static or generalized. The development of such devices requires knowledge of the emotional state of the user interacting with it. Thus, for this purpose an emotion recognition system is required. Physiological signals namely, the electrocardiogram (ECG) and electroencephalogram (EEG) are being studied here for emotion recognition. This paper proposes novel entropy-based features in the Fourier-Bessel domain instead of the Fourier domain where frequency resolution is twice as compared to the later. Also, to represent such non-stationary signals, Fourier-Bessel series expansion (FBSE) is used. It has non-stationary basis functions which makes it more suitable than the Fourier representation. EEG and ECG signals are decomposed into narrow-band modes, using FBSE based empirical wavelet transform (FBSE-EWT). The proposed entropies of each mode are computed to form the feature vector which are further used to develop a machine learning models. The proposed emotion detection algorithm is evaluated using publicly available DREAMER dataset. K-nearest neighbors (KNN) classifier provides accuracies of 97.84%, 97.91%, and 97.86% for arousal, valence, and dominance classes, respectively. Finally, this paper concludes that the obtained entropy features are suitable for emotion recognition from given physiological signals.
... As a result of the development of unsupervised DNN algorithms, researchers have become more interested in identifying hidden ill state signals in a 12-lead ECG. It has been demonstrated that utilising a 12-lead ECG, it is possible to identify hyperkalemia, cardiac failure, hypoglycemia, [68] and even changes in mental states [69].Mayo Clinic Rochester researchers Attia et al. [70] investigated whether an AIenabled 12-lead ECG in normal sinus rhythm might be used to detect prior or impending AF events. They used 0.65 million ECGs in a 7:1:2 ratio to train, validate, and test the AI algorithm and discovered that an AI-enabled The accuracy of an ECG recorded during normal sinus rhythm as a screening test for AF was 79. 4 percent. ...
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... For ECG signals, there are large number of researches focusing on different types of feature extraction methods. Few such methods include heart rate variability (HRV), empirical mode decomposition (EMD) with-in beat analysis (WIB), FFT analysis, and various methods of wavelet transformations [23]. For GSR, as the skin conductance is mainly related with arousal level, thus useful information related with its amplitude and frequency is analyzed in time and frequency domains by applying various techniques and extracting some statistical parameters as: median, mean, standard deviation, minimum, maximum, as well as ratio of minimum and maximum [24]. ...
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Detection of mental states like stress/anxiety, mediation is a widely researched topic and is important for ensuring overall well-being of an individual. Several approaches have been reported in the literature for prediction or assessment of mental states. Recently, with advances in sensor technology, various physiological signals are being used by researchers for detecting mental states. In the present study, we have used a light weight deep convolutional neural network (CNN) for creating a mental state prediction model. The proposed detection model is created using publicly available WESAD dataset. The dataset contains electrocardiogram (ECG), galvanic skin response (GSR), skin temperature and electromyogram (EMG) signals recorded using a wearable device. Results show that for binary classification of stress vs no-stress condition our results are comparable with that reported in state-of-the-art machine learning/deep learning-based approaches. However, for three class classification of baseline vs stress vs amusement states, our model gives an accuracy of 90% which is much higher compared to that reported in the literature. In addition, we have also tried to classify various binary states like stress vs baseline,stress vs amusement and stress vs meditation conditions. The f1 score obtained for these classes are 0.96, 0.87 and 0.91, respectively, which are much higher than that reported in state-of-the-art literature using same dataset. Proposed light weight CNN-based mental state classification model is computationally less complex compared to other deep networks used by the researchers. Thus, it can be used for monitoring mental state successfully in real-life scenarios.
... This proposed model is called the Discrete Emotion Model.The proposed study aims to identify discrete emotions through ECG signals. A study by Dissanayake et al. [15] recognized 6 emotional classes through HRV features obtained from ECG signal. An accuracy of 80% was achieved using Extra Tree Classifier with feature selection. ...
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Smart wearables have played an integral part in our day to day life. From recording ECG signals to analysing body fat composition, the smart wearables can do it all. The smart devices encompass various sensors which can be employed to derive meaningful information regarding the user's physical and psychological conditions. Our approach focuses on employing such sensors to identify and obtain the variations in the mood of a user at a given instance through the use of supervised machine learning techniques. The study examines the performance of various supervised learning models such as Decision Trees, Random Forests, XGBoost, LightGBM on the dataset. With our proposed model, we obtained a high recognition rate of 92.5% using XGBoost and LightGBM for 9 different emotion classes. By utilizing this, we aim to improvise and suggest methods to aid emotion recognition for better mental health analysis and mood monitoring.
... Rattanyu with his co-worker (2010) proposed works on emotion recognition for robots working in the living area. Dissanayake et al. (2019) work on a machine learning program for 4 main emotions (Anger, sadness, pleasure, and joy). ...
Chapter
Human emotions like neutral, sad, happy, and others reveal the state of the mind of a person. This information is useful and thus finds its technical, interpersonal, and societal applications in various areas like surveillance, suicide prevention, marketing and strategy, entertainment, etc. The espousal of data science peaked the scientific interest in the detection of human emotions in the late 2000s and early 2010s. The recognition of human emotions is exigent, and it requires accurate inspection of physiological responses and/or facial expressions. Advances in the areas of bio-physiology and neuroscience have introduced numerous new tools for the detection of human emotions. However, many of these tools have certain shortcomings that make their usage limited. Therefore, researchers are continuously working towards new techniques and technologies to find better solutions than the existing ones for the detection of human emotions. This chapter deals with various tools and techniques that are being used for the recognition of human emotions.
... Emotion recognition using ECG signal is done in [39]. ECG signal features such as WIB mean, standard deviation, median, etc., are calculated, and various time and frequency domain parameters have been calculated using EMD. ...
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Electroencephalogram (EEG) signals are the recording of brain electrical activity, commonly used for emotion recognition. Different EEG rhythms carry different neural dynamics. EEG rhythms are separated using tunable Q-factor wavelet transform (TQWT). Several features like mean, standard deviation, information potential are extracted from the TQWT-based EEG rhythms. Machine learning classifiers are used to differentiate various emotional states automatically. The authors have validated the proposed model using a publicly available database. Obtained classification accuracy of 92.9% proves the candidature of the proposed method for emotion identification.