Emotional States Detection Approaches Based on Physiological Signals for Healthcare Applications: A Review

2019 ◽  
pp. 47-74
Author(s):  
Diana Patricia Tobón Vallejo ◽  
Abdulmotaleb El Saddik
Keyword(s):  
Physiological Signals ◽  
Emotional States ◽  
Healthcare Applications

scholarly journals Predicting Stress in Teens from Wearable Device Data Using Machine Learning Methods

2020 ◽  
Author(s):  
Claire W. Jin ◽  
Ame Osotsi ◽  
Zita Oravecz
Keyword(s):  
High Stress ◽  
Well Being ◽  
Physiological Signals ◽  
Support Vector ◽  
Stressful Event ◽  
Emotional States ◽  
Machine Model ◽  
Daily Lives ◽  
Physiological Indicators ◽  
Physiological Markers

AbstractStress management is a pervasive issue in the modern high schooler’s life. Despite many efforts to support adolescents’ mental well-being, teenagers often fail to recognize signs of high stress and anxiety until their emotions have escalated. Being able to identify early signs of these intense emotional states and predict their onset using physiological signals collected passively in real-time could help teenagers improve their awareness of their emotional well-being and take a more proactive approach to managing their emotions. To evaluate the potential of this approach, we collected data from high schoolers with Empatica E4 wearable health monitors (wristband) while they were living their daily lives. The data consisted of stressful event reports and physiological markers over the course of 4 weeks. We developed a random forest model and a support vector machine model and systematically assessed their performance in terms of predicting the onset of stress events and identifying physiological signals of stress. The models showed strong performance in terms of these measures and provided insights on physiological indicators of adolescent stress.

scholarly journals Emotional State Recognition from Peripheral Physiological Signals Using Fused Nonlinear Features and Team-Collaboration Identification Strategy

Entropy ◽  
2020 ◽  
Vol 22 (5) ◽  
pp. 511 ◽  
Author(s):  
Lizheng Pan ◽  
Zeming Yin ◽  
Shigang She ◽  
Aiguo Song
Keyword(s):  
Decision Making ◽  
Emotion Recognition ◽  
Physiological Signals ◽  
Support Vector ◽  
Emotional States ◽  
Multiple Perspectives ◽  
Team Collaboration ◽  
Advantages And Disadvantages ◽  
Nonlinear Features ◽  
Identification Strategy

Emotion recognition realizing human inner perception has a very important application prospect in human-computer interaction. In order to improve the accuracy of emotion recognition, a novel method combining fused nonlinear features and team-collaboration identification strategy was proposed for emotion recognition using physiological signals. Four nonlinear features, namely approximate entropy (ApEn), sample entropy (SaEn), fuzzy entropy (FuEn) and wavelet packet entropy (WpEn) are employed to reflect emotional states deeply with each type of physiological signal. Then the features of different physiological signals are fused to represent the emotional states from multiple perspectives. Each classifier has its own advantages and disadvantages. In order to make full use of the advantages of other classifiers and avoid the limitation of single classifier, the team-collaboration model is built and the team-collaboration decision-making mechanism is designed according to the proposed team-collaboration identification strategy which is based on the fusion of support vector machine (SVM), decision tree (DT) and extreme learning machine (ELM). Through analysis, SVM is selected as the main classifier with DT and ELM as auxiliary classifiers. According to the designed decision-making mechanism, the proposed team-collaboration identification strategy can effectively employ different classification methods to make decision based on the characteristics of the samples through SVM classification. For samples which are easy to be identified by SVM, SVM directly determines the identification results, whereas SVM-DT-ELM collaboratively determines the identification results, which can effectively utilize the characteristics of each classifier and improve the classification accuracy. The effectiveness and universality of the proposed method are verified by Augsburg database and database for emotion analysis using physiological (DEAP) signals. The experimental results uniformly indicated that the proposed method combining fused nonlinear features and team-collaboration identification strategy presents better performance than the existing methods.

scholarly journals Assessment of Emotional States Through Physiological Signals and Its Application in Music Therapy for Disabled People

IEEE Access ◽  
2020 ◽  
Vol 8 ◽  
pp. 127659-127671
Author(s):  
Andrea Valenzuela Ramirez ◽  
Gemma Hornero ◽  
Daniel Royo ◽  
Angel Aguilar ◽  
Oscar Casas
Keyword(s):  
Music Therapy ◽  
Physiological Signals ◽  
Emotional States ◽  
Disabled People

scholarly journals Impact of Physiological Signals Acquisition in the Emotional Support Provided in Learning Scenarios

Sensors ◽  
2019 ◽  
Vol 19 (20) ◽  
pp. 4520 ◽  
Author(s):  
Uria-Rivas ◽  
Rodriguez-Sanchez ◽  
Santos ◽  
Vaquero ◽  
Boticario
Keyword(s):  
Emotional Support ◽  
Ambient Intelligence ◽  
Affective Computing ◽  
Emotional State ◽  
Signal To Noise Ratio ◽  
Physiological Signals ◽  
Emotional States ◽  
Skin Response ◽  
Physiological Sensors ◽  
Rule Based Approach

Physiological sensors can be used to detect changes in the emotional state of users with affective computing. This has lately been applied in the educational domain, aimed to better support learners during the learning process. For this purpose, we have developed the AICARP (Ambient Intelligence Context-aware Affective Recommender Platform) infrastructure, which detects changes in the emotional state of the user and provides personalized multisensorial support to help manage the emotional state by taking advantage of ambient intelligence features. We have developed a third version of this infrastructure, AICARP.V3, which addresses several problems detected in the data acquisition stage of the second version, (i.e., intrusion of the pulse sensor, poor resolution and low signal to noise ratio in the galvanic skin response sensor and slow response time of the temperature sensor) and extends the capabilities to integrate new actuators. This improved incorporates a new acquisition platform (shield) called PhyAS (Physiological Acquisition Shield), which reduces the number of control units to only one, and supports both gathering physiological signals with better precision and delivering multisensory feedback with more flexibility, by means of new actuators that can be added/discarded on top of just that single shield. The improvements in the quality of the acquired signals allow better recognition of the emotional states. Thereof, AICARP.V3 gives a more accurate personalized emotional support to the user, based on a rule-based approach that triggers multisensorial feedback, if necessary. This represents progress in solving an open problem: develop systems that perform as effectively as a human expert in a complex task such as the recognition of emotional states.

scholarly journals Flexible and Highly Sensitive Pressure Sensors Based on Microstructured Carbon Nanowalls Electrodes

Nanomaterials ◽  
2019 ◽  
Vol 9 (4) ◽  
pp. 496 ◽  
Author(s):  
Xi Zhou ◽  
Yongna Zhang ◽  
Jun Yang ◽  
Jialu Li ◽  
Shi Luo ◽  
...  
Keyword(s):  
Pressure Sensor ◽  
High Performance ◽  
Limit Of Detection ◽  
Pressure Sensors ◽  
High Sensitivity ◽  
Physiological Signals ◽  
Healthcare Applications ◽  
Highly Sensitive ◽  
Carbon Nanowalls ◽  
Pressure Regime

Wearable pressure sensors have attracted widespread attention in recent years because of their great potential in human healthcare applications such as physiological signals monitoring. A desirable pressure sensor should possess the advantages of high sensitivity, a simple manufacturing process, and good stability. Here, we present a highly sensitive, simply fabricated wearable resistive pressure sensor based on three-dimensional microstructured carbon nanowalls (CNWs) embedded in a polydimethylsiloxane (PDMS) substrate. The method of using unpolished silicon wafers as templates provides an easy approach to fabricate the irregular microstructure of CNWs/PDMS electrodes, which plays a significant role in increasing the sensitivity and stability of resistive pressure sensors. The sensitivity of the CNWs/PDMS pressure sensor with irregular microstructures is as high as 6.64 kPa−1 in the low-pressure regime, and remains fairly high (0.15 kPa−1) in the high-pressure regime (~10 kPa). Both the relatively short response time of ~30 ms and good reproducibility over 1000 cycles of pressure loading and unloading tests illustrate the high performance of the proposed device. Our pressure sensor exhibits a superior minimal limit of detection of 0.6 Pa, which shows promising potential in detecting human physiological signals such as heart rate. Moreover, it can be turned into an 8 × 8 pixels array to map spatial pressure distribution and realize array sensing imaging.

scholarly journals Deep learning for healthcare applications based on physiological signals: A review

2018 ◽  
Vol 161 ◽  
pp. 1-13 ◽  
Author(s):  
Oliver Faust ◽  
Yuki Hagiwara ◽  
Tan Jen Hong ◽  
Oh Shu Lih ◽  
U Rajendra Acharya
Keyword(s):  
Deep Learning ◽  
Physiological Signals ◽  
Healthcare Applications

scholarly journals Estimation of Organizational Competitiveness by a Hybrid of One-Dimensional Convolutional Neural Networks and Self-Organizing Maps Using Physiological Signals for Emotional Analysis of Employees

Sensors ◽  
2021 ◽  
Vol 21 (11) ◽  
pp. 3760
Author(s):  
Saad Awadh Alanazi ◽  
Madallah Alruwaili ◽  
Fahad Ahmad ◽  
Alaa Alaerjan ◽  
Nasser Alshammari
Keyword(s):  
Emotional Intelligence ◽  
Physiological Signals ◽  
Classification Model ◽  
Emotional States ◽  
Self Organizing Map ◽  
Self Organizing Maps ◽  
One Dimensional ◽  
Significant Difference ◽  
Skin Response ◽  
Self Organizing

The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

scholarly journals A Review of Emotion Recognition Based on EEG using DEAP Dataset

2021 ◽  
pp. 298-303
Author(s):  
Rama Chaudhary ◽  
Ram Avtar Jaswal
Keyword(s):  
Facial Expression ◽  
Emotion Recognition ◽  
Facial Expressions ◽  
Physiological Signals ◽  
Emotional States ◽  
Modern Time ◽  
Human Machine Interaction ◽  
Electrocardiogram Ecg ◽  
Electroencephalogram Eeg ◽  
Machine Interaction

In modern time, the human-machine interaction technology has been developed so much for recognizing human emotional states depending on physiological signals. The emotional states of human can be recognized by using facial expressions, but sometimes it doesn’t give accurate results. For example, if we detect the accuracy of facial expression of sad person, then it will not give fully satisfied result because sad expression also include frustration, irritation, anger, etc. therefore, it will not be possible to determine the particular expression. Therefore, emotion recognition using Electroencephalogram (EEG), Electrocardiogram (ECG) has gained so much attraction because these are based on brain and heart signals respectively. So, after analyzing all the factors, it is decided to recognize emotional states based on EEG using DEAP Dataset. So that, the better accuracy can be achieved.

STATISTICAL PREDICTION OF EMOTIONAL STATES BY PHYSIOLOGICAL SIGNALS WITH MANOVA AND MACHINE LEARNING

2012 ◽  
Vol 26 (04) ◽  
pp. 1250008 ◽  
Author(s):  
TUNG-HUNG CHUEH ◽  
TAI-BEEN CHEN ◽  
HENRY HORNG-SHING LU ◽  
SHAN-SHAN JU ◽  
TEH-HO TAO ◽  
...  
Keyword(s):  
Machine Learning ◽  
Logistic Model ◽  
Nearest Neighbor ◽  
Statistical Technique ◽  
Physiological Signals ◽  
Statistical Prediction ◽  
Emotional States ◽  
K Nearest Neighbor ◽  
Learning Methods ◽  
Machine Learning Methods

For the importance of communication between human and machine interface, it would be valuable to develop an implement which has the ability to recognize emotional states. In this paper, we proposed an approach which can deal with the daily dependence and personal dependence in the data of multiple subjects and samples. 30 features were extracted from the physiological signals of subject for three states of emotion. The physiological signals measured were: electrocardiogram (ECG), skin temperature (SKT) and galvanic skin response (GSR). After removing the daily dependence and personal dependence by the statistical technique of MANOVA, six machine learning methods including Bayesian network learning, naive Bayesian classification, SVM, decision tree of C4.5, Logistic model and K-nearest-neighbor (KNN) were implemented to differentiate the emotional states. The results showed that Logistic model gives the best classification accuracy and the statistical technique of MANOVA can significantly improve the performance of all six machine learning methods in emotion recognition system.

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