Analysing human feelings by Affective Computing - survey
01 Aug 2016-pp 1-6
TL;DR: This paper mainly emphasizes on basics of computing feelings while they are in session, such as machine-based fact finding, smart over-seeing, perceptual connection, and so on.
Abstract: Affective computing is one of the active research topic getting a lot of research attention recently. This increase in research interest is driven by many areas that are being worked on such as machine-based fact finding, smart over-seeing, perceptual connection, and so on. Identifying or deducing the feelings while they are on a particular task has a multidisciplinary domain involvement. This paper mainly emphasizes on basics of computing feelings while they are in session.
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TL;DR: In this paper, the contribution of these fields along with their theories, concepts, models, and implications in affective computing is explained in detail, along with some existing affective databases are also presented in this work.
17 citations
01 Nov 2017
TL;DR: The development and an initial evaluation of the AGaR – a serious game with virtual reality and natural interaction is presented, both to aid patients to execute repetitive exercises and to aid physiotherapists to follow the rehabilitation process.
Abstract: Games can make training procedures more engaging for patients. Considering the complexity of the process for upper limb function rehabilitation, this paper presents the development and an initial evaluation of the AGaR – a serious game with virtual reality and natural interaction, both to aid patients to execute repetitive exercises and to aid physiotherapists to follow the rehabilitation process. Additionally, we obtain and analyze data about patients' engagement as a differential in relation to others games developed for similar goals. In this game, the patient has to associate two different images with complementary meanings, using a movement sensor to drag the image to the target. To evaluate the game, an initial experiment was conducted with patients. The results show that, within the rounds played by the participants of the experiment, the number of wrong associations made by them varies according to patient, with no standard found. The engagement tends to increase during use of the game, throughout the rounds.
15 citations
Cites methods from "Analysing human feelings by Affecti..."
...Usually this area uses methods to recognize the six basic human emotions (fear, anger, happiness, surprise, disgust and sadness)[8]....
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TL;DR: The formula of the emotional prediction accuracy of the MEC server, which first collects data from emotion sensors and then computes the emotion of each user, is given and the optimal solution is given in closed form.
Abstract: This paper considers a mobile edge computing (MEC) system, where the MEC server first collects data from emotion sensors and then computes the emotion of each user. We give the formula of the emotional prediction accuracy. In order to improve the energy efficiency of the system, we propose resources allocation algorithms. We aim to minimize the total energy consumption of the MEC server and sensors by jointly optimizing the computing resources allocation and the data transmitting time. The formulated problem is a non-convex problem, which is very difficult to solve in general. However, we transform it into convex problems and apply convex optimization techniques to address it. The optimal solution is given in closed form. Simulation results show that the total energy consumption of our system can be effectively reduced by the proposed scheme compared with the benchmark.
11 citations
Cites background from "Analysing human feelings by Affecti..."
...Linear Discriminant Classifiers (LDC) makes classification based on the value obtained from the linear grouping of the feature values [13]....
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TL;DR: Zhang et al. as discussed by the authors discussed the concept of hybrid human-artificial intelligence (H-AI) and proposed a holistic architecture of H-AI in social computing, which consists of three layers: object layer, intelligent processing layer and application layer.
Abstract: With the convergence of modern computing technology and social sciences, both theoretical research and practical applications of social computing have been extended to new domains. In particular, social computing was significantly influenced by the recent advances of artificial intelligence (AI). However, the conventional technologies of AI have various drawbacks in dealing with complicated and dynamic problems. Such deficiency can be rectified by hybrid human-artificial intelligence (H-AI), which integrates both human intelligence and AI into one unity, forming a new enhanced intelligence. H-AI in dealing with social problems shows some advantages over the conventional AI. This article firstly reviews the latest research progresses of AI in social computing. Secondly, it summarizes typical challenges AI faces in social computing, which motivate the necessity to introduce H-AI to tackle social-oriented problems. Finally, we discuss the concept of H-AI and propose a holistic architecture of H-AI in social computing, which consists of three layers: object layer, intelligent processing layer, and application layer. The proposed architecture shows that H-AI has significant advantages over AI in solving social problems.
7 citations
01 Jan 2018
TL;DR: This chapter emphasizes on retrieving user emotions from keyboard and mouse using different parameters, which can be user keyboard typing style, mouse movements, and some physiological sensors used.
Abstract: This chapter emphasizes on retrieving user emotions from keyboard and mouse using different parameters. These parameters can be user keyboard typing style, mouse movements, and some physiological sensors are used. This field of retrieving emotions from machines comes under the field of affective computing.
4 citations
References
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TL;DR: This work introduces, analyzes and demonstrates a recursive hierarchical generalization of the widely used hidden Markov models, which is motivated by the complex multi-scale structure which appears in many natural sequences, particularly in language, handwriting and speech.
Abstract: We introduce, analyze and demonstrate a recursive hierarchical generalization of the widely used hidden Markov models, which we name Hierarchical Hidden Markov Models (HHMM) Our model is motivated by the complex multi-scale structure which appears in many natural sequences, particularly in language, handwriting and speech We seek a systematic unsupervised approach to the modeling of such structures By extending the standard Baum-Welch (forward-backward) algorithm, we derive an efficient procedure for estimating the model parameters from unlabeled data We then use the trained model for automatic hierarchical parsing of observation sequences We describe two applications of our model and its parameter estimation procedure In the first application we show how to construct hierarchical models of natural English text In these models different levels of the hierarchy correspond to structures on different length scales in the text In the second application we demonstrate how HHMMs can be used to automatically identify repeated strokes that represent combination of letters in cursive handwriting
1,050 citations
"Analysing human feelings by Affecti..." refers background in this paper
...In its place, the series of outputs reliant on the states are visible [20] ....
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Journal Article•
TL;DR: An experimental prototype of the affective e-Learning model was built to help improveStudents’ learning experience by customizing learning material delivery based on students’ emotional state and indicated the superiority of emotion aware over non-emotion-aware with a performance increase of 91%.
Abstract: Using emotion detection technologies from biophysical signals, this study explored how emotion evolves during learning process and how emotion feedback could be used to improve learning experiences. This article also described a cutting-edge pervasive e-Learning platform used in a Shanghai online college and proposed an affective e-Learning model, which combined learners’ emotions with the Shanghai e-Learning platform. The study was guided by Russell’s circumplex model of affect and Kort’s learning spiral model. The results about emotion recognition from physiological signals achieved a best-case accuracy (86.3%) for four types of learning emotions. And results from emotion revolution study showed that engagement and confusion were the most important and frequently occurred emotions in learning, which is consistent with the findings from AutoTutor project. No evidence from this study validated Kort’s learning spiral model. An experimental prototype of the affective e-Learning model was built to help improve students’ learning experience by customizing learning material delivery based on students’ emotional state. Experiments indicated the superiority of emotion aware over non-emotion-aware with a performance increase of 91%.
387 citations
"Analysing human feelings by Affecti..." refers background in this paper
...Psychological being healthy, services like recommendations, usage suggestions, can help the user based on current feelings by computing applications when working out a particular user's state of feelings [5]....
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01 Jan 2009
TL;DR: A novel learning method, Support Vector Machine (SVM), is applied on different data which have two or multi class, and the comparative results using different kernel functions for all data samples are shown.
Abstract: Classification is one of the most important tasks for different application such as text categorization, tone recognition, image classification, micro-array gene expression, proteins structure predictions, data Classification etc. Most of the existing supervised classification methods are based on traditional statistics, which can provide ideal results when sample size is tending to infinity. However, only finite samples can be acquired in practice. In this paper, a novel learning method, Support Vector Machine (SVM), is applied on different data (Diabetes data, Heart Data, Satellite Data and Shuttle data) which have two or multi class. SVM, a powerful machine method developed from statistical learning and has made significant achievement in some field. Introduced in the early 90’s, they led to an explosion of interest in machine learning. The foundations of SVM have been developed by Vapnik and are gaining popularity in field of machine learning due to many attractive features and promising empirical performance. SVM method does not suffer the limitations of data dimensionality and limited samples [1] & [2]. In our experiment, the support vectors, which are critical for classification, are obtained by learning from the training samples. In this paper we have shown the comparative results using different kernel functions for all data samples.
332 citations
"Analysing human feelings by Affecti..." refers background in this paper
...SVM -is a form of (usually binary) linear c1assifier which selects in which of the two (or more) likely classes, each input may fall into [17]....
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TL;DR: The authors can show that neutral faces perceived to possess various personality traits contain objective resemblance to emotional expression, and support the idea that trait inferences are in part the result of an overgeneralization of emotion recognition systems.
Abstract: People make trait inferences based on facial appearance despite little evidence that these inferences accurately reflect personality. The authors tested the hypothesis that these inferences are driven in part by structural resemblance to emotional expressions. The authors first had participants judge emotionally neutral faces on a set of trait dimensions. The authors then submitted the face images to a Bayesian network classifier trained to detect emotional expressions. By using a classifier, the authors can show that neutral faces perceived to possess various personality traits contain objective resemblance to emotional expression. In general, neutral faces that are perceived to have positive valence resemble happiness, faces that are perceived to have negative valence resemble disgust and fear, and faces that are perceived to be threatening resemble anger. These results support the idea that trait inferences are in part the result of an overgeneralization of emotion recognition systems. Under this hypothesis, emotion recognition systems, which typically extract accurate information about a person's emotional state, are engaged during the perception of neutral faces that bear subtle resemblance to emotional expressions. These emotions could then be misattributed as traits.
244 citations
"Analysing human feelings by Affecti..." refers background in this paper
...NicuSebe in an interview, [8] is the analysis of a person's face while they are using a certain product (he stated ice cream as an case)....
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