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A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Computing and Artificial Intelligence".

Deadline for manuscript submissions: closed (15 July 2020) | Viewed by 66898

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Special Issue Editors

Prof. Dr. Oresti Banos

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Guest Editor

Research Center for Information and Communication Technologies, University of Granada, 18014 Granada, Spain
Interests: wearable, ubiquitous, and mobile computing; artificial intelligence; data mining; digital health
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Luis A. Castro

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Guest Editor

Sonora Institute of Technology (ITSON), 85000 Ciudad Obregon, Mexico
Interests: human–computer interaction; ubiquitous and mobile computing; mobile sensing; context awareness; behaviour and context sensing
Special Issues, Collections and Topics in MDPI journals

Prof. Dr. Claudia Villalonga

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Guest Editor

Universidad Internacional de La Rioja, Logroño, Spain
Interests: ontologies; semantics; context awareness; machine learning; artificial intelligence

Special Issue Information

Dear Colleagues,

Emotions play a very important role in how we think and behave. As such, the emotions we feel every day can compel us to act and influence the decisions and plans we make about our lives. Being able to measure, analyze, and better comprehend how or why our emotions may change is thus of much relevance to understand human behavior and its consequences. Despite the great efforts made in the past in the study of human emotions, it is only now with the advent of wearable, mobile, and ubiquitous technologies that we can aim at sensing and recognizing emotions, continuously and in the wild. This Special Issue aims at bringing together the latest experiences, findings, and developments regarding ubiquitous sensing, modelling, and recognition of human emotions.

Original, high-quality contributions from both academia and industry are sought. Manuscripts submitted for review should not have been published elsewhere or be under review by other journals or peer-reviewed conferences.

Topics of interest include, but are not limited to:

Wearable, mobile, and ubiquitous emotion recognition systems
Algorithms and features for the recognition of emotional states from face, speech, body gestures, and physiological measures
Methods for multi-modal recognition of individual and group emotion
Benchmarking, datasets, and simulation tools that have been applied to study and/or support emotion recognition
Applications of emotion recognition including education, health care, entertainment, vehicle operation, social agents, and ambient intelligence

Prof. Dr. Oresti Banos
Prof. Dr. Luis A. Castro
Prof. Dr. Claudia Villalonga
Guest Editors

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Keywords

emotion recognition
multi-modal sensing
wearable, mobile and ubiquitous computing
affective computing

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Published Papers (10 papers)

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Editorial

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3 pages, 160 KiB

Open AccessEditorial

Ubiquitous Technologies for Emotion Recognition

by Oresti Banos, Luis A. Castro and Claudia Villalonga

Appl. Sci. 2021, 11(15), 7019; https://doi.org/10.3390/app11157019 - 29 Jul 2021

Viewed by 1703

Abstract

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

Research

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17 pages, 2449 KiB

Open AccessArticle

Driver Stress State Evaluation by Means of Thermal Imaging: A Supervised Machine Learning Approach Based on ECG Signal

by Daniela Cardone, David Perpetuini, Chiara Filippini, Edoardo Spadolini, Lorenza Mancini, Antonio Maria Chiarelli and Arcangelo Merla

Appl. Sci. 2020, 10(16), 5673; https://doi.org/10.3390/app10165673 - 15 Aug 2020

Cited by 56 | Viewed by 5522

Abstract

Traffic accidents determine a large number of injuries, sometimes fatal, every year. Among other factors affecting a driver’s performance, an important role is played by stress which can decrease decision-making capabilities and situational awareness. In this perspective, it would be beneficial to develop a non-invasive driver stress monitoring system able to recognize the driver’s altered state. In this study, a contactless procedure for drivers’ stress state assessment by means of thermal infrared imaging was investigated. Thermal imaging was acquired during an experiment on a driving simulator, and thermal features of stress were investigated with comparison to a gold-standard metric (i.e., the stress index, SI) extracted from contact electrocardiography (ECG). A data-driven multivariate machine learning approach based on a non-linear support vector regression (SVR) was employed to estimate the SI through thermal features extracted from facial regions of interest (i.e., nose tip, nostrils, glabella). The predicted SI showed a good correlation with the real SI (r = 0.61, p = ~0). A two-level classification of the stress state (STRESS, SI ≥ 150, versus NO STRESS, SI < 150) was then performed based on the predicted SI. The ROC analysis showed a good classification performance with an AUC of 0.80, a sensitivity of 77%, and a specificity of 78%. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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15 pages, 2388 KiB

Open AccessArticle

Cost-Effective CNNs for Real-Time Micro-Expression Recognition

by Reda Belaiche, Yu Liu, Cyrille Migniot, Dominique Ginhac and Fan Yang

Appl. Sci. 2020, 10(14), 4959; https://doi.org/10.3390/app10144959 - 19 Jul 2020

Cited by 17 | Viewed by 3320

Abstract

Micro-Expression (ME) recognition is a hot topic in computer vision as it presents a gateway to capture and understand daily human emotions. It is nonetheless a challenging problem due to ME typically being transient (lasting less than 200 ms) and subtle. Recent advances in machine learning enable new and effective methods to be adopted for solving diverse computer vision tasks. In particular, the use of deep learning techniques on large datasets outperforms classical approaches based on classical machine learning which rely on hand-crafted features. Even though available datasets for spontaneous ME are scarce and much smaller, using off-the-shelf Convolutional Neural Networks (CNNs) still demonstrates satisfactory classification results. However, these networks are intense in terms of memory consumption and computational resources. This poses great challenges when deploying CNN-based solutions in many applications, such as driver monitoring and comprehension recognition in virtual classrooms, which demand fast and accurate recognition. As these networks were initially designed for tasks of different domains, they are over-parameterized and need to be optimized for ME recognition. In this paper, we propose a new network based on the well-known ResNet18 which we optimized for ME classification in two ways. Firstly, we reduced the depth of the network by removing residual layers. Secondly, we introduced a more compact representation of optical flow used as input to the network. We present extensive experiments and demonstrate that the proposed network obtains accuracies comparable to the state-of-the-art methods while significantly reducing the necessary memory space. Our best classification accuracy was 60.17% on the challenging composite dataset containing five objectives classes. Our method takes only 24.6 ms for classifying a ME video clip (less than the occurrence time of the shortest ME which lasts 40 ms). Our CNN design is suitable for real-time embedded applications with limited memory and computing resources. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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18 pages, 897 KiB

Open AccessArticle

Call Redistribution for a Call Center Based on Speech Emotion Recognition

by Milana Bojanić, Vlado Delić and Alexey Karpov

Appl. Sci. 2020, 10(13), 4653; https://doi.org/10.3390/app10134653 - 6 Jul 2020

Cited by 48 | Viewed by 5522

Abstract

Call center operators communicate with callers in different emotional states (anger, anxiety, fear, stress, joy, etc.). Sometimes a number of calls coming in a short period of time have to be answered and processed. In the moments when all call center operators are busy, the system puts that call on hold, regardless of its urgency. This research aims to improve the functionality of call centers by recognition of call urgency and redistribution of calls in a queue. It could be beneficial for call centers giving health care support for elderly people and emergency call centers. The proposed recognition of call urgency and consequent call ranking and redistribution is based on emotion recognition in speech, giving greater priority to calls featuring emotions such as fear, anger and sadness, and less priority to calls featuring neutral speech and happiness. Experimental results, obtained in a simulated call center, show a significant reduction in waiting time for calls estimated as more urgent, especially the calls featuring the emotions of fear and anger. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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19 pages, 7433 KiB

Open AccessArticle

Driver Facial Expression Analysis Using LFA-CRNN-Based Feature Extraction for Health-Risk Decisions

by Chang-Min Kim, Ellen J. Hong, Kyungyong Chung and Roy C. Park

Appl. Sci. 2020, 10(8), 2956; https://doi.org/10.3390/app10082956 - 24 Apr 2020

Cited by 22 | Viewed by 4141

Abstract

As people communicate with each other, they use gestures and facial expressions as a means to convey and understand emotional state. Non-verbal means of communication are essential to understanding, based on external clues to a person’s emotional state. Recently, active studies have been conducted on the lifecare service of analyzing users’ facial expressions. Yet, rather than a service necessary for everyday life, the service is currently provided only for health care centers or certain medical institutions. It is necessary to conduct studies to prevent accidents that suddenly occur in everyday life and to cope with emergencies. Thus, we propose facial expression analysis using line-segment feature analysis-convolutional recurrent neural network (LFA-CRNN) feature extraction for health-risk assessments of drivers. The purpose of such an analysis is to manage and monitor patients with chronic diseases who are rapidly increasing in number. To prevent automobile accidents and to respond to emergency situations due to acute diseases, we propose a service that monitors a driver’s facial expressions to assess health risks and alert the driver to risk-related matters while driving. To identify health risks, deep learning technology is used to recognize expressions of pain and to determine if a person is in pain while driving. Since the amount of input-image data is large, analyzing facial expressions accurately is difficult for a process with limited resources while providing the service on a real-time basis. Accordingly, a line-segment feature analysis algorithm is proposed to reduce the amount of data, and the LFA-CRNN model was designed for this purpose. Through this model, the severity of a driver’s pain is classified into one of nine types. The LFA-CRNN model consists of one convolution layer that is reshaped and delivered into two bidirectional gated recurrent unit layers. Finally, biometric data are classified through softmax. In addition, to evaluate the performance of LFA-CRNN, the performance was compared through the CRNN and AlexNet Models based on the University of Northern British Columbia and McMaster University (UNBC-McMaster) database. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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25 pages, 4749 KiB

Open AccessArticle

EEG-Based Emotion Recognition Using Logistic Regression with Gaussian Kernel and Laplacian Prior and Investigation of Critical Frequency Bands

by Chao Pan, Cheng Shi, Honglang Mu, Jie Li and Xinbo Gao

Appl. Sci. 2020, 10(5), 1619; https://doi.org/10.3390/app10051619 - 29 Feb 2020

Cited by 52 | Viewed by 6820

Abstract

Emotion plays a nuclear part in human attention, decision-making, and communication. Electroencephalogram (EEG)-based emotion recognition has developed a lot due to the application of Brain-Computer Interface (BCI) and its effectiveness compared to body expressions and other physiological signals. Despite significant progress in affective computing, emotion recognition is still an unexplored problem. This paper introduced Logistic Regression (LR) with Gaussian kernel and Laplacian prior for EEG-based emotion recognition. The Gaussian kernel enhances the EEG data separability in the transformed space. The Laplacian prior promotes the sparsity of learned LR regressors to avoid over-specification. The LR regressors are optimized using the logistic regression via variable splitting and augmented Lagrangian (LORSAL) algorithm. For simplicity, the introduced method is noted as LORSAL. Experiments were conducted on the dataset for emotion analysis using EEG, physiological and video signals (DEAP). Various spectral features and features by combining electrodes (power spectral density (PSD), differential entropy (DE), differential asymmetry (DASM), rational asymmetry (RASM), and differential caudality (DCAU)) were extracted from different frequency bands (Delta, Theta, Alpha, Beta, Gamma, and Total) with EEG signals. The Naive Bayes (NB), support vector machine (SVM), linear LR with L1-regularization (LR_L1), linear LR with L2-regularization (LR_L2) were used for comparison in the binary emotion classification for valence and arousal. LORSAL obtained the best classification accuracies (77.17% and 77.03% for valence and arousal, respectively) on the DE features extracted from total frequency bands. This paper also investigates the critical frequency bands in emotion recognition. The experimental results showed the superiority of Gamma and Beta bands in classifying emotions. It was presented that DE was the most informative and DASM and DCAU had lower computational complexity with relatively ideal accuracies. An analysis of LORSAL and the recently deep learning (DL) methods is included in the discussion. Conclusions and future work are presented in the final section. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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23 pages, 886 KiB

Open AccessArticle

Deep Learning for EEG-Based Preference Classification in Neuromarketing

by Mashael Aldayel, Mourad Ykhlef and Abeer Al-Nafjan

Appl. Sci. 2020, 10(4), 1525; https://doi.org/10.3390/app10041525 - 24 Feb 2020

Cited by 130 | Viewed by 17847

Abstract

The traditional marketing methodologies (e.g., television commercials and newspaper advertisements) may be unsuccessful at selling products because they do not robustly stimulate the consumers to purchase a particular product. Such conventional marketing methods attempt to determine the attitude of the consumers toward a product, which may not represent the real behavior at the point of purchase. It is likely that the marketers misunderstand the consumer behavior because the predicted attitude does not always reflect the real purchasing behaviors of the consumers. This research study was aimed at bridging the gap between traditional market research, which relies on explicit consumer responses, and neuromarketing research, which reflects the implicit consumer responses. The EEG-based preference recognition in neuromarketing was extensively reviewed. Another gap in neuromarketing research is the lack of extensive data-mining approaches for the prediction and classification of the consumer preferences. Therefore, in this work, a deep-learning approach is adopted to detect the consumer preferences by using EEG signals from the DEAP dataset by considering the power spectral density and valence features. The results demonstrated that, although the proposed deep-learning exhibits a higher accuracy, recall, and precision compared with the k-nearest neighbor and support vector machine algorithms, random forest reaches similar results to deep learning on the same dataset. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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15 pages, 856 KiB

Open AccessArticle

Application of Texture Descriptors to Facial Emotion Recognition in Infants

by Ana Martínez, Francisco A. Pujol and Higinio Mora

Appl. Sci. 2020, 10(3), 1115; https://doi.org/10.3390/app10031115 - 7 Feb 2020

Cited by 14 | Viewed by 6589

Abstract

The recognition of facial emotions is an important issue in computer vision and artificial intelligence due to its important academic and commercial potential. If we focus on the health sector, the ability to detect and control patients’ emotions, mainly pain, is a fundamental objective within any medical service. Nowadays, the evaluation of pain in patients depends mainly on the continuous monitoring of the medical staff when the patient is unable to express verbally his/her experience of pain, as is the case of patients under sedation or babies. Therefore, it is necessary to provide alternative methods for its evaluation and detection. Facial expressions can be considered as a valid indicator of a person’s degree of pain. Consequently, this paper presents a monitoring system for babies that uses an automatic pain detection system by means of image analysis. This system could be accessed through wearable or mobile devices. To do this, this paper makes use of three different texture descriptors for pain detection: Local Binary Patterns, Local Ternary Patterns, and Radon Barcodes. These descriptors are used together with Support Vector Machines (SVM) for their classification. The experimental results show that the proposed features give a very promising classification accuracy of around 95% for the Infant COPE database, which proves the validity of the proposed method. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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16 pages, 11744 KiB

Open AccessArticle

Detection of Emotion Using Multi-Block Deep Learning in a Self-Management Interview App

by Dong Hoon Shin, Kyungyong Chung and Roy C. Park

Appl. Sci. 2019, 9(22), 4830; https://doi.org/10.3390/app9224830 - 11 Nov 2019

Cited by 15 | Viewed by 4732

Abstract

Recently, domestic universities have constructed and operated online mock interview systems for students’ preparation for employment. Students can have a mock interview anywhere and at any time through the online mock interview system, and can improve any problems during the interviews via images stored in real time. For such practice, it is necessary to analyze the emotional state of the student based on the situation, and to provide coaching through accurate analysis of the interview. In this paper, we propose detection of user emotions using multi-block deep learning in a self-management interview application. Unlike the basic structure for learning about whole-face images, the multi-block deep learning method helps the user learn after sampling the core facial areas (eyes, nose, mouth, etc.), which are important factors for emotion analysis from face detection. Through the multi-block process, sampling is carried out using multiple AdaBoost learning. For optimal block image screening and verification, similarity measurement is also performed during this process. A performance evaluation of the proposed model compares the proposed system with AlexNet, which has mainly been used for facial recognition in the past. As comparison items, the recognition rate and extraction time of the specific area are compared. The extraction time of the specific area decreased by 2.61%, and the recognition rate increased by 3.75%, indicating that the proposed facial recognition method is excellent. It is expected to provide good-quality, customized interview education for job seekers by establishing a systematic interview system using the proposed deep learning method. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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Review

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23 pages, 1762 KiB

Open AccessReview

Thermal Infrared Imaging-Based Affective Computing and Its Application to Facilitate Human Robot Interaction: A Review

by Chiara Filippini, David Perpetuini, Daniela Cardone, Antonio Maria Chiarelli and Arcangelo Merla

Appl. Sci. 2020, 10(8), 2924; https://doi.org/10.3390/app10082924 - 23 Apr 2020

Cited by 80 | Viewed by 9097

Abstract

Over recent years, robots are increasingly being employed in several aspects of modern society. Among others, social robots have the potential to benefit education, healthcare, and tourism. To achieve this purpose, robots should be able to engage humans, recognize users’ emotions, and to some extent properly react and "behave" in a natural interaction. Most robotics applications primarily use visual information for emotion recognition, which is often based on facial expressions. However, the display of emotional states through facial expression is inherently a voluntary controlled process that is typical of human–human interaction. In fact, humans have not yet learned to use this channel when communicating with a robotic technology. Hence, there is an urgent need to exploit emotion information channels not directly controlled by humans, such as those that can be ascribed to physiological modulations. Thermal infrared imaging-based affective computing has the potential to be the solution to such an issue. It is a validated technology that allows the non-obtrusive monitoring of physiological parameters and from which it might be possible to infer affective states. This review is aimed to outline the advantages and the current research challenges of thermal imaging-based affective computing for human–robot interaction. Full article

(This article belongs to the Special Issue Ubiquitous Technologies for Emotion Recognition)

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