Special Issue "Socio-Cognitive and Affective Computing"

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 (28 February 2018)

Printed Edition Available!
A printed edition of this Special Issue is available here.

Special Issue Editors

Guest Editor
Prof. Dr. Antonio Fernández-Caballero

Universidad de Castilla-La Mancha, Ciudad Real, Spain
Website | E-Mail
Interests: image processing; cognitive vision; robot vision; multisensor fusion; multimodal interfaces; ambient intelligence
Guest Editor
Dr. Pascual González

Departamento de Sistemas Informáticos, Escuela Superior de Ingeniería Informática, Universidad de Castilla-La Mancha, Campus Universitario s/n, 02071 Albacete, Spain
Website | E-Mail
Interests: virtual reality; human–computer interaction; context-aware computing; tele-rehabilitation
Guest Editor
Dr. María Teresa López

Departamento de Sistemas Informáticos, Escuela Superior de Ingeniería Informática, Universidad de Castilla-La Mancha, Campus Universitario s/n, 02071 Albacete, Spain
Website | E-Mail
Interests: computer vision; emotion detection
Guest Editor
Dr. Elena Navarro

Departamento de Sistemas Informáticos, Escuela Superior de Ingeniería Informática, Universidad de Castilla-La Mancha, Campus Universitario s/n, 02071 Albacete, Spain
Website | E-Mail
Interests: virtual and augmented reality; ubiquitous and pervasive computing; context-aware computing

Special Issue Information

Dear Colleagues,

Social cognition focuses on how people process, store, and apply information about other people and social situations. It focuses on the role that cognitive processes play in our social interactions. On the other hand, the term cognitive computing is generally used to refer to new hardware and/or software that mimics the functioning of the human brain and helps to improve human decision-making. In this sense, it is a type of computing with the goal of discovering more accurate models of how the human brain/mind senses, reasons, and responds to stimuli.

Thus, Socio-Cognitive Computing should be understood as a set of theoretical interdisciplinary frameworks, methodologies, methods and hardware/software tools to model how the human brain mediates social interactions. In addition, Affective Computing is the study and development of systems and devices that can recognize, interpret, process, and simulate human affects, a fundamental aspect of socio-cognitive neuroscience. It is an interdisciplinary field spanning computer science, electrical engineering, psychology, and cognitive science.

Moreover, Physiological Computing is a category of technology in which electrophysiological data recorded directly from human activity are used to interface with a computing device. This technology becomes even more relevant when computing can be integrated pervasively in everyday life environments. Thus, Socio-Cognitive and Affective Computing systems should be able to adapt their behavior according to the Physiological Computing paradigm.

This Special Issue on “Socio-Cognitive and Affective Computing” aims at integrating these various albeit complementary fields. Proposals from researchers who use signals from the brain and/or body to infer people’s intentions and psychological state in smart computing systems are welcome. Designing this kind of systems requires combining knowledge and methods of ubiquitous and pervasive computing, as well as physiological data measurement and processing, with those of socio-cognitive and affective computing. Papers with a special focus on multidisciplinary approaches and multimodality are especially welcome.

Topics of interest include (but are not restricted to):

  • Socio-Cognitive Computing

  • Affective Computing

  • Sentient Computing

  • Social Interaction

  • Virtual and Augmented Reality

  • Emotional Robots

  • Ubiquitous and Pervasive Computing

  • Mobile Computing

  • Context Aware Computing

  • Ambient Intelligence

  • Ambient Assisted Living

  • Physiological Computing

  • Brain–Computer Interfaces

  • Biofeedback and Neurofeedback Systems

  • Eye Movements, Gaze Monitoring and Eye Blink Activity

  • Wearable Systems

  • Applications and Case Studies

Prof. Dr. Antonio Fernández-Caballero
Dr. María Teresa López
Dr. Pascual González
Dr. Elena Navarro
Guest Editors

Manuscript Submission Information

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Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1500 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Published Papers (14 papers)

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Editorial

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Open AccessEditorial Special Issue on Socio-Cognitive and Affective Computing
Appl. Sci. 2018, 8(8), 1371; https://doi.org/10.3390/app8081371
Received: 2 August 2018 / Accepted: 14 August 2018 / Published: 15 August 2018
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Abstract
Social cognition focuses on how people process, store, and apply information about other people and social situations. [...] Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available

Research

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Open AccessArticle Evaluating Human–Automation Etiquette Strategies to Mitigate User Frustration and Improve Learning in Affect-Aware Tutoring
Appl. Sci. 2018, 8(6), 895; https://doi.org/10.3390/app8060895
Received: 3 March 2018 / Revised: 24 April 2018 / Accepted: 18 May 2018 / Published: 30 May 2018
Cited by 1 | PDF Full-text (2105 KB) | HTML Full-text | XML Full-text
Abstract
Human–automation etiquette applies human–human etiquette conventions to human–computer interaction (HCI). The research described in this paper investigates how to mitigate user frustration and support student learning through changes in the style in which a computer tutor interacts with a learner. Frustration can significantly [...] Read more.
Human–automation etiquette applies human–human etiquette conventions to human–computer interaction (HCI). The research described in this paper investigates how to mitigate user frustration and support student learning through changes in the style in which a computer tutor interacts with a learner. Frustration can significantly impact the quality of learning in tutoring. This study examined an approach to mitigate frustration through the use of different etiquette strategies to change the amount of imposition feedback placed on the learner. An experiment was conducted to explore how varying the interaction style of system feedback impacted aspects of the learning process. System feedback was varied through different etiquette strategies. Participants solved mathematics problems under different frustration conditions with feedback given in different etiquette styles. Changing etiquette strategies from one math problem to the next led to changes in motivation, confidence satisfaction, and performance. The most effective etiquette strategies changed depending on if the user was frustrated or not. This work aims to provide mechanisms to support the promotion of individualized learning in the context of high level math instruction by basing affect-aware adaptive tutoring system design on varying etiquette strategies. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Proposing Enhanced Feature Engineering and a Selection Model for Machine Learning Processes
Appl. Sci. 2018, 8(4), 646; https://doi.org/10.3390/app8040646
Received: 6 March 2018 / Revised: 10 April 2018 / Accepted: 10 April 2018 / Published: 20 April 2018
Cited by 2 | PDF Full-text (9373 KB) | HTML Full-text | XML Full-text
Abstract
Machine Learning (ML) requires a certain number of features (i.e., attributes) to train the model. One of the main challenges is to determine the right number and the type of such features out of the given dataset’s attributes. It is not uncommon for [...] Read more.
Machine Learning (ML) requires a certain number of features (i.e., attributes) to train the model. One of the main challenges is to determine the right number and the type of such features out of the given dataset’s attributes. It is not uncommon for the ML process to use dataset of available features without computing the predictive value of each. Such an approach makes the process vulnerable to overfit, predictive errors, bias, and poor generalization. Each feature in the dataset has either a unique predictive value, redundant, or irrelevant value. However, the key to better accuracy and fitting for ML is to identify the optimum set (i.e., grouping) of the right feature set with the finest matching of the feature’s value. This paper proposes a novel approach to enhance the Feature Engineering and Selection (eFES) Optimization process in ML. eFES is built using a unique scheme to regulate error bounds and parallelize the addition and removal of a feature during training. eFES also invents local gain (LG) and global gain (GG) functions using 3D visualizing techniques to assist the feature grouping function (FGF). FGF scores and optimizes the participating feature, so the ML process can evolve into deciding which features to accept or reject for improved generalization of the model. To support the proposed model, this paper presents mathematical models, illustrations, algorithms, and experimental results. Miscellaneous datasets are used to validate the model building process in Python, C#, and R languages. Results show the promising state of eFES as compared to the traditional feature selection process. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Path Planning Strategy for Vehicle Navigation Based on User Habits
Appl. Sci. 2018, 8(3), 407; https://doi.org/10.3390/app8030407
Received: 6 February 2018 / Revised: 6 March 2018 / Accepted: 6 March 2018 / Published: 9 March 2018
Cited by 2 | PDF Full-text (4821 KB) | HTML Full-text | XML Full-text
Abstract
Vehicle navigation is widely used in path planning of self driving travel, and it plays an increasing important role in people's daily trips. Therefore, path planning algorithms have attracted substantial attention. However, most path planning methods are based on public data, aiming at [...] Read more.
Vehicle navigation is widely used in path planning of self driving travel, and it plays an increasing important role in people's daily trips. Therefore, path planning algorithms have attracted substantial attention. However, most path planning methods are based on public data, aiming at different driver groups rather than a specific user. Hence, this study proposes a personalized path decision algorithm that is based on user habits. First, the categories of driving characteristics are obtained through the investigation of public users, and the clustering results corresponding to the category space are obtained by log fuzzy C-means clustering algorithm (LFCM) based on the driving information contained in the log trajectories. Then, the road performance personalized quantization algorithm evaluation is proposed to evaluate roads from the user’s field of vision. Finally, adaptive ant colony algorithm is improved and used to validate the path planning based on the road performance personalized values. Results show that the algorithm can meet the personalized requirements of the user path selection in the path decision. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Applicability of Emotion Recognition and Induction Methods to Study the Behavior of Programmers
Appl. Sci. 2018, 8(3), 323; https://doi.org/10.3390/app8030323
Received: 22 December 2017 / Revised: 7 February 2018 / Accepted: 24 February 2018 / Published: 26 February 2018
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Abstract
Recent studies in the field of software engineering have shown that positive emotions can increase and negative emotions decrease the productivity of programmers. In the field of affective computing, many methods and tools to recognize the emotions of computer users were proposed. However, [...] Read more.
Recent studies in the field of software engineering have shown that positive emotions can increase and negative emotions decrease the productivity of programmers. In the field of affective computing, many methods and tools to recognize the emotions of computer users were proposed. However, it has not been verified yet which of them can be used to monitor the emotional states of software developers. The paper describes a study carried out on a group of 35 participants to determine which of these methods can be used during programming. During the study, data from multiple sensors that are commonly used in methods of emotional recognition were collected. The participants were extensively questioned about the sensors’ invasiveness during programming. This allowed us to determine which of them are applicable in the work of programmers. In addition, it was verified which methods are suitable for use in the work environment and which are only suitable in the laboratory. Moreover, three methods for inducing negative emotions have been proposed, and their effectiveness has been verified. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Predicting Human Behaviour with Recurrent Neural Networks
Appl. Sci. 2018, 8(2), 305; https://doi.org/10.3390/app8020305
Received: 15 December 2017 / Revised: 25 January 2018 / Accepted: 9 February 2018 / Published: 20 February 2018
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Abstract
As the average age of the urban population increases, cities must adapt to improve the quality of life of their citizens. The City4Age H2020 project is working on the early detection of the risks related to mild cognitive impairment and frailty and on [...] Read more.
As the average age of the urban population increases, cities must adapt to improve the quality of life of their citizens. The City4Age H2020 project is working on the early detection of the risks related to mild cognitive impairment and frailty and on providing meaningful interventions that prevent these risks. As part of the risk detection process, we have developed a multilevel conceptual model that describes the user behaviour using actions, activities, and intra- and inter-activity behaviour. Using this conceptual model, we have created a deep learning architecture based on long short-term memory networks (LSTMs) that models the inter-activity behaviour. The presented architecture offers a probabilistic model that allows us to predict the user’s next actions and to identify anomalous user behaviours. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Uncertainty Flow Facilitates Zero-Shot Multi-Label Learning in Affective Facial Analysis
Appl. Sci. 2018, 8(2), 300; https://doi.org/10.3390/app8020300
Received: 21 December 2017 / Revised: 12 February 2018 / Accepted: 13 February 2018 / Published: 19 February 2018
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Abstract
Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on [...] Read more.
Featured Application: The proposed Uncertainty Flow framework may benefit the facial analysis with its promised elevation in discriminability in multi-label affective classification tasks. Moreover, this framework also allows the efficient model training and between tasks knowledge transfer. The applications that rely heavily on continuous prediction on emotional valance, e.g., to monitor prisoners’ emotional stability in jail, can be directly benefited from our framework.

Abstract: To lower the single-label dependency on affective facial analysis, it urges the fruition of multi-label affective learning. The impediment to practical implementation of existing multi-label algorithms pertains to scarcity of scalable multi-label training datasets. To resolve this, an inductive transfer learning based framework, i.e.,Uncertainty Flow, is put forward in this research to allow knowledge transfer from a single labelled emotion recognition task to a multi-label affective recognition task. I.e., the model uncertainty—which can be quantified in Uncertainty Flow—is distilled from a single-label learning task. The distilled model uncertainty ensures the later efficient zero-shot multi-label affective learning. On the theoretical perspective, within our proposed Uncertainty Flow framework, the feasibility of applying weakly informative priors, e.g., uniform and Cauchy prior, is fully explored in this research. More importantly, based on the derived weight uncertainty, three sets of prediction related uncertainty indexes, i.e., soft-max uncertainty, pure uncertainty and uncertainty plus are proposed to produce reliable and accurate multi-label predictions. Validated on our manual annotated evaluation dataset, i.e., the multi-label annotated FER2013, our proposed Uncertainty Flow in multi-label facial expression analysis exhibited superiority to conventional multi-label learning algorithms and multi-label compatible neural networks. The success of our proposed Uncertainty Flow provides a glimpse of future in continuous, uncertain, and multi-label affective computing. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Towards New Mappings between Emotion Representation Models
Appl. Sci. 2018, 8(2), 274; https://doi.org/10.3390/app8020274
Received: 24 November 2017 / Revised: 10 January 2018 / Accepted: 9 February 2018 / Published: 12 February 2018
Cited by 1 | PDF Full-text (254 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
There are several models for representing emotions in affect-aware applications, and available emotion recognition solutions provide results using diverse emotion models. As multimodal fusion is beneficial in terms of both accuracy and reliability of emotion recognition, one of the challenges is mapping between [...] Read more.
There are several models for representing emotions in affect-aware applications, and available emotion recognition solutions provide results using diverse emotion models. As multimodal fusion is beneficial in terms of both accuracy and reliability of emotion recognition, one of the challenges is mapping between the models of affect representation. This paper addresses this issue by: proposing a procedure to elaborate new mappings, recommending a set of metrics for evaluation of the mapping accuracy, and delivering new mapping matrices for estimating the dimensions of a Pleasure-Arousal-Dominance model from Ekman’s six basic emotions. The results are based on an analysis using three datasets that were constructed based on affect-annotated lexicons. The new mappings were obtained with linear regression learning methods. The proposed mappings showed better results on the datasets in comparison with the state-of-the-art matrix. The procedure, as well as the proposed metrics, might be used, not only in evaluation of the mappings between representation models, but also in comparison of emotion recognition and annotation results. Moreover, the datasets are published along with the paper and new mappings might be created and evaluated using the proposed methods. The study results might be interesting for both researchers and developers, who aim to extend their software solutions with affect recognition techniques. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle A Parallel Approach for Frequent Subgraph Mining in a Single Large Graph Using Spark
Appl. Sci. 2018, 8(2), 230; https://doi.org/10.3390/app8020230
Received: 3 January 2018 / Revised: 28 January 2018 / Accepted: 31 January 2018 / Published: 2 February 2018
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Abstract
Frequent subgraph mining (FSM) plays an important role in graph mining, attracting a great deal of attention in many areas, such as bioinformatics, web data mining and social networks. In this paper, we propose SSiGraM (Spark based Single Graph [...] Read more.
Frequent subgraph mining (FSM) plays an important role in graph mining, attracting a great deal of attention in many areas, such as bioinformatics, web data mining and social networks. In this paper, we propose SSiGraM (Spark based Single Graph Mining), a Spark based parallel frequent subgraph mining algorithm in a single large graph. Aiming to approach the two computational challenges of FSM, we conduct the subgraph extension and support evaluation parallel across all the distributed cluster worker nodes. In addition, we also employ a heuristic search strategy and three novel optimizations: load balancing, pre-search pruning and top-down pruning in the support evaluation process, which significantly improve the performance. Extensive experiments with four different real-world datasets demonstrate that the proposed algorithm outperforms the existing GraMi (Graph Mining) algorithm by an order of magnitude for all datasets and can work with a lower support threshold. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessFeature PaperArticle Estimation of Mental Distress from Photoplethysmography
Appl. Sci. 2018, 8(1), 69; https://doi.org/10.3390/app8010069
Received: 11 November 2017 / Revised: 20 December 2017 / Accepted: 3 January 2018 / Published: 5 January 2018
Cited by 2 | PDF Full-text (1666 KB) | HTML Full-text | XML Full-text | Supplementary Files
Abstract
This paper introduces the design of a new wearable photoplethysmography (PPG) sensor and its assessment for mental distress estimation. In our design, a PPG sensor obtains blood volume information by means of an optical plethysmogram technique. A number of temporal, morphological and frequency [...] Read more.
This paper introduces the design of a new wearable photoplethysmography (PPG) sensor and its assessment for mental distress estimation. In our design, a PPG sensor obtains blood volume information by means of an optical plethysmogram technique. A number of temporal, morphological and frequency markers are computed using time intervals between adjacent normal cardiac cycles to characterize pulse rate variability (PRV). In order to test the efficiency of the developed wearable for classifying distress versus calmness, the well-known International Affective Picture System has been used to induce different levels of arousal in forty-five healthy participants. The obtained results have shown that temporal features present a single discriminant power between emotional states of calm and stress, ranging from 67 to 72%. Moreover, a discriminant tree-based model is used to assess the possible underlying relationship among parameters. In this case, the combination of temporal parameters reaches 82.35% accuracy. Considering the low difficulty of metrics and methods used in this work, the algorithms are prepared to be embedded into a micro-controller device to work in real-time and in a long-term fashion. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Large Scale Community Detection Using a Small World Model
Appl. Sci. 2017, 7(11), 1173; https://doi.org/10.3390/app7111173
Received: 27 September 2017 / Revised: 1 November 2017 / Accepted: 2 November 2017 / Published: 15 November 2017
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Abstract
In a social network, small or large communities within the network play a major role in deciding the functionalities of the network. Despite of diverse definitions, communities in the network may be defined as the group of nodes that are more densely connected [...] Read more.
In a social network, small or large communities within the network play a major role in deciding the functionalities of the network. Despite of diverse definitions, communities in the network may be defined as the group of nodes that are more densely connected as compared to nodes outside the group. Revealing such hidden communities is one of the challenging research problems. A real world social network follows small world phenomena, which indicates that any two social entities can be reachable in a small number of steps. In this paper, nodes are mapped into communities based on the random walk in the network. However, uncovering communities in large-scale networks is a challenging task due to its unprecedented growth in the size of social networks. A good number of community detection algorithms based on random walk exist in literature. In addition, when large-scale social networks are being considered, these algorithms are observed to take considerably longer time. In this work, with an objective to improve the efficiency of algorithms, parallel programming framework like Map-Reduce has been considered for uncovering the hidden communities in social network. The proposed approach has been compared with some standard existing community detection algorithms for both synthetic and real-world datasets in order to examine its performance, and it is observed that the proposed algorithm is more efficient than the existing ones. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Effects of Viewing Displays from Different Distances on Human Visual System
Appl. Sci. 2017, 7(11), 1153; https://doi.org/10.3390/app7111153
Received: 3 October 2017 / Revised: 1 November 2017 / Accepted: 6 November 2017 / Published: 9 November 2017
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Abstract
The current stereoscopic 3D displays have several human-factor issues including visual-fatigue symptoms such as eyestrain, headache, fatigue, nausea, and malaise. The viewing time and viewing distance are factors that considerably affect the visual fatigue associated with 3D displays. Hence, this study analyzes the [...] Read more.
The current stereoscopic 3D displays have several human-factor issues including visual-fatigue symptoms such as eyestrain, headache, fatigue, nausea, and malaise. The viewing time and viewing distance are factors that considerably affect the visual fatigue associated with 3D displays. Hence, this study analyzes the effects of display type (2D vs. 3D) and viewing distance on visual fatigue during a 60-min viewing session based on electroencephalogram (EEG) relative beta power, and alpha/beta power ratio. In this study, twenty male participants watched four videos. The EEGs were recorded at two occipital lobes (O1 and O2) of each participant in the pre-session (3 min), post-session (3 min), and during a 60-min viewing session. The results showed that the decrease in relative beta power of the EEG and the increase in the alpha/beta ratio from the start until the end of the viewing session were significantly higher when watching the 3D display. When the viewing distance was increased from 1.95 m to 3.90 m, the visual fatigue was decreased in the case of the 3D-display, whereas the fatigue was increased in the case of the 2D-display. Moreover, there was approximately the same level of visual fatigue when watching videos in 2D or 3D from a long viewing distance (3.90 m). Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Open AccessArticle Modelling the Interruption on HCI Using BDI Agents with the Fuzzy Perceptions Approach: An Interactive Museum Case Study in Mexico
Appl. Sci. 2017, 7(8), 832; https://doi.org/10.3390/app7080832
Received: 27 July 2017 / Revised: 9 August 2017 / Accepted: 9 August 2017 / Published: 13 August 2017
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Abstract
Technological advancements have revolutionized the proliferation and availability of information to users, which has created more complex and intensive interactions between users and systems. The learning process of users is essential in the construction of new knowledge when pursuing improvements in user experience. [...] Read more.
Technological advancements have revolutionized the proliferation and availability of information to users, which has created more complex and intensive interactions between users and systems. The learning process of users is essential in the construction of new knowledge when pursuing improvements in user experience. In this paper, the interruption factor is considered in relation to interaction quality due to human–computer interaction (HCI) being seen to affect the learning process. We present the results obtained from 500 users in an interactive museum in Tijuana, Mexico as a case study. We model the HCI of an interactive exhibition using belief–desire–intention (BDI) agents; we adapted the BDI architecture using the Type-2 fuzzy inference system to add perceptual human-like capabilities to agents, in order to describe the interaction and interruption factor on user experience. The resulting model allows us to describe content adaptation through the creation of a personalized interaction environment. We conclude that managing interruptions can enhance the HCI, producing a positive learning process that influences user experience. A better interaction may be achieved if we offer the right kind of content, taking the interruptions experienced into consideration. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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Other

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Open AccessConcept Paper A User-Centred Well-Being Home for the Elderly
Appl. Sci. 2018, 8(6), 850; https://doi.org/10.3390/app8060850
Received: 13 March 2018 / Revised: 17 May 2018 / Accepted: 18 May 2018 / Published: 23 May 2018
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Abstract
Every single instant a person generates a large amount of information that somehow is lost. This information can assume a large diversity of means, such as an oral word, a sneeze, an increase in heartbeat or even facial expressions. We present a model [...] Read more.
Every single instant a person generates a large amount of information that somehow is lost. This information can assume a large diversity of means, such as an oral word, a sneeze, an increase in heartbeat or even facial expressions. We present a model which promotes the well-being of the elderly in their homes. The general idea behind the model is that every single experience may mean something, and therefore may be recorded, measured and even have adequate responses. There is no device that provides a more natural interaction than a human body and every one of us, sends and receives useful information, which sometimes gets lost. Trends show that the future will be filled with pervasive IoT devices, present in most aspects of human life’s. In this we focus on which aspects are more important for the well-being of a person and which devices, technologies and interactions may be used to collect data directly from users and measure their physiological and emotional responses. Even though not all the technologies presented in this article are yet mainstream, they have been evolving very rapidly and evidence makes us believe that the efficiency of this approach will be closely related to their advances. Full article
(This article belongs to the Special Issue Socio-Cognitive and Affective Computing) Printed Edition available
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