scholarly journals Design of User-Customized Negative Emotion Classifier Based on Feature Selection Using Physiological Signal Sensors

Sensors ◽  
2018 ◽  
Vol 18 (12) ◽  
pp. 4253 ◽  
Author(s):  
JeeEun Lee ◽  
Sun K. Yoo
Keyword(s):  
Feature Selection ◽  
Negative Emotion ◽  
Electrodermal Activity ◽  
Cognitive Status ◽  
Physiological Signals ◽  
Self Assessment ◽  
Physiological Signal ◽  
Distribution Shape ◽  
The Difference ◽  
The Individual

First, the Likert scale and self-assessment manikin are used to provide emotion analogies, but they have limits for reflecting subjective factors. To solve this problem, we use physiological signals that show objective responses from cognitive status. The physiological signals used are electrocardiogram, skin temperature, and electrodermal activity (EDA). Second, the degree of emotion felt, and the related physiological signals, vary according to the individual. KLD calculates the difference in probability distribution shape patterns between two classes. Therefore, it is possible to analyze the relationship between physiological signals and emotion. As the result, features from EDA are important for distinguishing negative emotion in all subjects. In addition, the proposed feature selection algorithm showed an average accuracy of 92.5% and made it possible to improve the accuracy of negative emotion recognition.

User Experience Evaluation in Virtual Reality based on Subjective Feelings and Physiological Signals

2019 ◽  
Vol 63 (6) ◽  
pp. 60413-1-60413-11
Author(s):  
Yunfang Niu ◽  
Danli Wang ◽  
Ziwei Wang ◽  
Fan Sun ◽  
Kang Yue ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Positive Emotions ◽  
Electrodermal Activity ◽  
Self Report ◽  
Physiological Signals ◽  
Physiological Data ◽  
Self Assessment ◽  
Emotion Experience ◽  
Eye Fatigue ◽  
Electrocardiogram Ecg

Abstract At present, the research on emotion in the virtual environment is limited to the subjective materials, and there are very few studies based on objective physiological signals. In this article, the authors conducted a user experiment to study the user emotion experience of virtual reality (VR) by comparing subjective feelings and physiological data in VR and two-dimensional display (2D) environments. First, they analyzed the data of self-report questionnaires, including Self-assessment Manikin (SAM), Positive And Negative Affect Schedule (PANAS) and Simulator Sickness Questionnaire (SSQ). The result indicated that VR causes a higher level of arousal than 2D, and easily evokes positive emotions. Both 2D and VR environments are prone to eye fatigue, but VR is more likely to cause symptoms of dizziness and vertigo. Second, they compared the differences of electrocardiogram (ECG), skin temperature (SKT) and electrodermal activity (EDA) signals in two circumstances. Through mathematical analysis, all three signals had significant differences. Participants in the VR environment had a higher degree of excitement, and the mood fluctuations are more frequent and more intense. In addition, the authors used different machine learning models for emotion detection, and compared the accuracies on VR and 2D datasets. The accuracies of all algorithms in the VR environment are higher than that of 2D, which corroborated that the volunteers in the VR environment have more obvious skin electrical signals, and had a stronger sense of immersion. This article effectively compensated for the inadequacies of existing work. The authors first used objective physiological signals for experience evaluation and used different types of subjective materials to make contrast. They hope their study can provide helpful guidance for the engineering reality of virtual reality.

scholarly journals Evaluating physiological signal salience for estimating metabolic energy cost from wearable sensors

2019 ◽  
Vol 126 (3) ◽  
pp. 717-729 ◽  
Author(s):  
Kimberly A. Ingraham ◽  
Daniel P. Ferris ◽  
C. David Remy
Keyword(s):  
Heart Rate ◽  
Steady State ◽  
Indirect Calorimetry ◽  
Energy Cost ◽  
Electrodermal Activity ◽  
Wearable Sensors ◽  
Metabolic Energy ◽  
Physiological Signals ◽  
Physiological Signal ◽  
Signal Salience

Body-in-the-loop optimization algorithms have the capability to automatically tune the parameters of robotic prostheses and exoskeletons to minimize the metabolic energy expenditure of the user. However, current body-in-the-loop algorithms rely on indirect calorimetry to obtain measurements of energy cost, which are noisy, sparsely sampled, time-delayed, and require wearing a respiratory mask. To improve these algorithms, the goal of this work is to predict a user’s steady-state energy cost quickly and accurately using physiological signals obtained from portable, wearable sensors. In this paper, we quantified physiological signal salience to discover which signals, or groups of signals, have the best predictive capability when estimating metabolic energy cost. We collected data from 10 healthy individuals performing 6 activities (walking, incline walking, backward walking, running, cycling, and stair climbing) at various speeds or intensities. Subjects wore a suite of physiological sensors that measured breath frequency and volume, limb accelerations, lower limb EMG, heart rate, electrodermal activity, skin temperature, and oxygen saturation; indirect calorimetry was used to establish the ‘ground truth’ energy cost for each activity. Evaluating Pearson’s correlation coefficients and single and multiple linear regression models with cross validation (leave-one- subject-out and leave-one- task-out), we found that 1) filtering the accelerations and EMG signals improved their predictive power, 2) global signals (e.g., heart rate, electrodermal activity) were more sensitive to unknown subjects than tasks, while local signals (e.g., accelerations) were more sensitive to unknown tasks than subjects, and 3) good predictive performance was obtained combining a small number of signals (4–5) from multiple sensor modalities. NEW & NOTEWORTHY In this paper, we systematically compare a large set of physiological signals collected from portable sensors and determine which sensor signals contain the most salient information for predicting steady-state metabolic energy cost, robust to unknown subjects or tasks. This information, together with the comprehensive data set that is published in conjunction with this paper, will enable researchers and clinicians across many fields to develop novel algorithms to predict energy cost from wearable sensors.

scholarly journals H σχέση των συναισθημάτων με την εστιαζόμενη στον εαυτό προσοχή και την ρύθμιση της συμπεριφοράς

2020 ◽  
Vol 23 (1) ◽  
pp. 37
Author(s):  
Γεωργία Παναγιώτου ◽  
Δημήτρης Αγοραστός
Keyword(s):  
Negative Emotion ◽  
Subjective Evaluation ◽  
Self Regulation ◽  
Evaluation Process ◽  
Current Theory ◽  
Current Status ◽  
Focused Attention ◽  
New Findings ◽  
The Difference ◽  
The Individual

The present study has two aims: First it summarizes current theory and research on the association between self-consciousness/self-focused attention and different aspects of emotions in both typical and clinical populations. Second, it presents some new findings which address this association. As described in the literature, in the process of achieving one’s goals the individual compares oneself with standards and regulates one’s behavior, making behavioral adjustments or changing the goal in order to minimize the difference between one’s current status and one’s goals. During this self-evaluation process, which is an inherent part of self-regulation, attention is focused on the self and can be related to either positive or negative emotions depending on the subjective evaluation regarding the likelihood of achieving one’s goals. In psychopathology this process seems to dysfunction, so that the individual is trapped in a vicious cycle of negative emotion and increased self-focused attention/selfconsciousness. Research suggests that specific emotions, or their dimensions are associated with increased self-focused attention but it remains unclear which these dimensions are and how are related to psychopathology. Τhe findings we present attempt to separate the effects of the basic affective dimensions of valence, arousal and dominance on increased self-focused attention.

scholarly journals Deep Neural Networks and Transfer Learning on a Multivariate Physiological Signal Dataset

2021 ◽  
Vol 8 (3) ◽  
pp. 35
Author(s):  
Andrea Bizzego ◽  
Giulio Gabrieli ◽  
Gianluca Esposito
Keyword(s):  
Neural Networks ◽  
Transfer Learning ◽  
Deep Neural Networks ◽  
Electrodermal Activity ◽  
Physiological Signals ◽  
Support Vector ◽  
Specific Measure ◽  
Physiological Signal ◽  
Different Types ◽  
Acceleration Signals

While Deep Neural Networks (DNNs) and Transfer Learning (TL) have greatly contributed to several medical and clinical disciplines, the application to multivariate physiological datasets is still limited. Current examples mainly focus on one physiological signal and can only utilise applications that are customised for that specific measure, thus it limits the possibility of transferring the trained DNN to other domains. In this study, we composed a dataset (n=813) of six different types of physiological signals (Electrocardiogram, Electrodermal activity, Electromyogram, Photoplethysmogram, Respiration and Acceleration). Signals were collected from 232 subjects using four different acquisition devices. We used a DNN to classify the type of physiological signal and to demonstrate how the TL approach allows the exploitation of the efficiency of DNNs in other domains. After the DNN was trained to optimally classify the type of signal, the features that were automatically extracted by the DNN were used to classify the type of device used for the acquisition using a Support Vector Machine. The dataset, the code and the trained parameters of the DNN are made publicly available to encourage the adoption of DNN and TL in applications with multivariate physiological signals.

User Experience Evaluation in Virtual Reality based on Subjective Feelings and Physiological Signals

2020 ◽  
Vol 2020 (13) ◽  
pp. 60413-1-60413-11
Author(s):  
Yunfang Niu ◽  
Danli Wang ◽  
Ziwei Wang ◽  
Fan Sun ◽  
Kang Yue ◽  
...  
Keyword(s):  
Virtual Reality ◽  
Positive Emotions ◽  
Electrodermal Activity ◽  
Self Report ◽  
Physiological Signals ◽  
Physiological Data ◽  
Self Assessment ◽  
Emotion Experience ◽  
Eye Fatigue ◽  
Electrocardiogram Ecg

At present, the research on emotion in the virtual environment is limited to the subjective materials, and there are very few studies based on objective physiological signals. In this article, the authors conducted a user experiment to study the user emotion experience of virtual reality (VR) by comparing subjective feelings and physiological data in VR and two-dimensional display (2D) environments. First, they analyzed the data of self-report questionnaires, including Self-assessment Manikin (SAM), Positive And Negative Affect Schedule (PANAS) and Simulator Sickness Questionnaire (SSQ). The result indicated that VR causes a higher level of arousal than 2D, and easily evokes positive emotions. Both 2D and VR environments are prone to eye fatigue, but VR is more likely to cause symptoms of dizziness and vertigo. Second, they compared the differences of electrocardiogram (ECG), skin temperature (SKT) and electrodermal activity (EDA) signals in two circumstances. Through mathematical analysis, all three signals had significant differences. Participants in the VR environment had a higher degree of excitement, and the mood fluctuations are more frequent and more intense. In addition, the authors used different machine learning models for emotion detection, and compared the accuracies on VR and 2D datasets. The accuracies of all algorithms in the VR environment are higher than that of 2D, which corroborated that the volunteers in the VR environment have more obvious skin electrical signals, and had a stronger sense of immersion. This article effectively compensated for the inadequacies of existing work. The authors first used objective physiological signals for experience evaluation and used different types of subjective materials to make contrast. They hope their study can provide helpful guidance for the engineering reality of virtual reality.

scholarly journals USING THE VIDEO ANALYSIS IN AEROBIC GYMNASTICS

2014 ◽  
Vol 5 (2) ◽  
pp. 779-783
Author(s):  
Salvatore Napolitano
Keyword(s):  
Video Analysis ◽  
High Speed ◽  
Female Athletes ◽  
Control Group ◽  
Self Assessment ◽  
Training Methodology ◽  
Training Activities ◽  
The Difference ◽  
The Individual ◽  
Experimental Group

The Aerobic Gymnastics is a complex sport and the movements are performed continuously, intensely at high speed with the musical accompaniment. One can directly assess the overall performance to the naked eye, but is not able to assess the individual elements of movement and technical aspects. The video analysis indirectly, through the ability to stop and review the various stages of movement several times, facilitates the evaluation. The aim of this study is to verify whether the use of video analysis in daily training activities can facilitate the evaluations of the coaches. 4 female athletes will be evaluated using the tabs in the Code of Points with annotations for deductions (0.10 slight error, mean error 0:20, 0:50 fault) and after 30 sessions in two different ways. The athletes are divided into two groups: 1) Control which continues to be evaluated with the traditional forms, 2) Experimental which is evaluated through the use of two cameras, which apply the points of repelle on specific anatomical points. At the end all the athletes will be traditionally evaluated and one will compare the assessments to highlight the difference between the two groups. The athletes in the experimental group improved at 0.30 and 0.40 compared to initial assessments made without the video analysis and compared to the control group. The experimental group compared with the control group has a better final evaluations in the matter of execution and cleanliness of the gesture. Probably the rapidity of correction of the act requested by the coach after watching the video and the subsequent execution of the athlete support proper execution. This new training methodology may be also tested on athletes, in order to allow a self-assessment through the measurement of the movie and the subsequent correction of performance so that it can better understand the errors committed and implicitly suggest the correction. The simultaneous use of video analysis by athletes and coaches during the training could further improve the result.

scholarly journals Emotion recognition techniques using physiological signals and video games –Systematic review–

2017 ◽  
Vol 26 (46) ◽  
Author(s):  
Mauro Callejas-Cuervo ◽  
Laura Alejandra Martínez-Tejada ◽  
Andrea Catherine Alarcón-Aldana
Keyword(s):  
Systematic Review ◽  
Video Games ◽  
Emotion Recognition ◽  
Emotional Reactions ◽  
Physiological Signals ◽  
Multimedia Tools ◽  
Physiological Signal ◽  
Important Approach ◽  
The Individual ◽  
Education Psychology

Emotion recognition systems from physiological signals are innovative techniques that allow studying the behavior and reaction of an individual when exposed to information that may evoke emotional reactions through multimedia tools, for example, video games. This type of approach is used to identify the behavior of an individual in different fields, such as medicine, education, psychology, etc., in order to assess the effect that the content has on the individual that is interacting with it. This article shows a systematic review of articles that report studies on emotion recognition with physiological signals and video games, between January 2010 and April 2016. We searched in eight databases, and found 15 articles that met the selection criteria. With this systematic review, we found that the use of video games as emotion stimulation tools has become an innovative field of study, due to their potential to involve stories and multimedia tools that can interact directly with the person in fields like rehabilitation. We detected clear examples where video games and physiological signal measurement became an important approach in rehabilitation processes, for example, in Posttraumatic Stress Disorder (PTSD) treatments.

Empirical Study On Attributes Leading To Satisfaction Among Bank Customer

2019 ◽  
Vol 118 (7) ◽  
pp. 101-110
Author(s):  
Ms.U.Sakthi Veeralakshmi ◽  
Dr.G. Venkatesan
Keyword(s):  
Service Quality ◽  
Behavioral Intention ◽  
Banking Sector ◽  
Positive Impact ◽  
Quality Performance ◽  
Public And Private ◽  
The Difference ◽  
The Individual ◽  
Private Banking ◽  
Service Gap

This research aims at measuring the service quality in public and private banking sector and identifying its relationship to customer satisfaction and behavioral intention. The study was conducted among 500 bank customers by using revised SERVQUAL instrument with 26 items. Behavioral intention of the customers was measured by using the behavioral intention battery. The researcher has used a seven point likert scaling to measure the expected and perceived service quality (performance) and the behavioral intention of the customer. The instrument was selected as the most reliable device to measure the difference-score conceptualization. It is used to evaluate service gap between expectation and perception of service quality. Modifications are made on the SERVQUAL instrument to make it specific to the Banking sector. Questions were added to the instrument like Seating space for waiting (Tangibility), Parking space in the Bank (Tangibility), Variety of products / schemes available (Tangibility), Banks sincere steps to handling Grievances of the customers (Responsiveness). The findings of the study revealed that the customer’s perception (performance) is lower than expectation of the service quality rendered by banks. Responsiveness and Assurance SQ dimensions were the most important dimensions in service quality scored less SQ gap. The study concluded that the individual service quality dimensions have a positive impact on Overall Satisfaction.

Smiling won’t make you feel better: Response-focused emotion regulation strategies have little impact on cognitive, behavioural, physiological, and subjective outcomes

2020 ◽  
Author(s):  
Nancy Bahl ◽  
Allison Ouimet
Keyword(s):  
Emotion Regulation ◽  
Negative Emotion ◽  
Electrodermal Activity ◽  
Adaptive Strategy ◽  
Expressive Suppression ◽  
Memory Accuracy ◽  
Cognitive Behavioural ◽  
Regulation Strategies ◽  
The Common ◽  
Subjective Outcomes

Background and Objectives. Response-focused emotion regulation (RF-ER) strategies may alter people’s evoked emotions, influencing psychophysiology, memory accuracy, and affect. Researchers have found that participants engaging in expressive suppression (ES; a RF-ER strategy) experience increased sympathetic nervous system arousal, affect (i.e., higher subjective anxiety and negative emotion), and lowered memory accuracy. It is unclear, however, whether all RF-ER strategies exert maladaptive effects. Expressive dissonance (ED; displaying an expression opposite from how one feels) is a RF-ER strategy, and thus likely considered “maladaptive”. As outlined by the facial feedback hypothesis, however, smiling may increase positive emotion, suggesting it may be an adaptive strategy. We compared the effects of ED and ES to a control condition on psychophysiology, memory accuracy, and affect, to assess whether ED is an adaptive RF-ER strategy, relative to ES. Methods. We randomly assigned 144 female participants to engage in ED, ES, or to naturally observe, while viewing negative and arousing images. We recorded electrodermal activity and self-reported affect throughout the experiment and participants completed memory tasks. Results. There were no differences between groups across outcomes. Conclusion. Engaging in ES or ED may not lead to negative or positive impacts, shedding doubt on the common conclusion that specific strategies are categorically adaptive or maladaptive.

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