scholarly journals The uulmMAC Database—A Multimodal Affective Corpus for Affective Computing in Human-Computer Interaction

Sensors ◽  
2020 ◽  
Vol 20 (8) ◽  
pp. 2308 ◽  
Author(s):  
Dilana Hazer-Rau ◽  
Sascha Meudt ◽  
Andreas Daucher ◽  
Jennifer Spohrs ◽  
Holger Hoffmann ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
Affective Computing ◽  
Signal Quality ◽  
Multimodal Data ◽  
Machine Learning Applications ◽  
The Stability ◽  
The University ◽  
Physiological Reactions ◽  
Multimodal Data Analysis ◽  
Computer Interaction

In this paper, we present a multimodal dataset for affective computing research acquired in a human-computer interaction (HCI) setting. An experimental mobile and interactive scenario was designed and implemented based on a gamified generic paradigm for the induction of dialog-based HCI relevant emotional and cognitive load states. It consists of six experimental sequences, inducing Interest, Overload, Normal, Easy, Underload, and Frustration. Each sequence is followed by subjective feedbacks to validate the induction, a respiration baseline to level off the physiological reactions, and a summary of results. Further, prior to the experiment, three questionnaires related to emotion regulation (ERQ), emotional control (TEIQue-SF), and personality traits (TIPI) were collected from each subject to evaluate the stability of the induction paradigm. Based on this HCI scenario, the University of Ulm Multimodal Affective Corpus (uulmMAC), consisting of two homogenous samples of 60 participants and 100 recording sessions was generated. We recorded 16 sensor modalities including 4 × video, 3 × audio, and 7 × biophysiological, depth, and pose streams. Further, additional labels and annotations were also collected. After recording, all data were post-processed and checked for technical and signal quality, resulting in the final uulmMAC dataset of 57 subjects and 95 recording sessions. The evaluation of the reported subjective feedbacks shows significant differences between the sequences, well consistent with the induced states, and the analysis of the questionnaires shows stable results. In summary, our uulmMAC database is a valuable contribution for the field of affective computing and multimodal data analysis: Acquired in a mobile interactive scenario close to real HCI, it consists of a large number of subjects and allows transtemporal investigations. Validated via subjective feedbacks and checked for quality issues, it can be used for affective computing and machine learning applications.

30 years at the University of Maryland's human-computer interaction lab (HCIL)

interactions ◽  
2013 ◽  
Vol 20 (5) ◽  
pp. 50-57 ◽  
Author(s):  
Ben Shneiderman ◽  
Kent Norman ◽  
Catherine Plaisant ◽  
Benjamin B. Bederson ◽  
Allison Druin ◽  
...  
Keyword(s):  
Human Computer Interaction ◽  
The University ◽  
Computer Interaction

Multimodel Expressions

2006 ◽  
pp. 110-131
Author(s):  
Lesley Axelrod ◽  
Kate Hone
Keyword(s):  
Human Computer Interaction ◽  
Affective Computing ◽  
Emotional Design ◽  
Ethical Considerations ◽  
Emotional Recognition ◽  
Innovative Technologies ◽  
User Centred Design ◽  
Recognition Algorithms ◽  
Multimedia Technologies ◽  
Computer Interaction

In a culture which places increasing emphasis on happiness and wellbeing, multimedia technologies include emotional design to improve commercial edge. This chapter explores affective computing and illustrates how innovative technologies are capable of emotional recognition and display. Research in this domain has emphasised solving the technical difficulties involved, through the design of ever more complex recognition algorithms. But fundamental questions about the use of such technology remain neglected. Can it really improve human-computer interaction? For which types of application is it suitable? How is it best implemented? What ethical considerations are there? We review this field and discuss the need for user-centred design. We describe and give evidence from a study that explores some of the user issues in affective computing.

scholarly journals Psychological Aspects in lifelike synthetic agents: Towards to the Personality Markup Language (A Brief Survey)

RENOTE ◽  
2009 ◽  
Vol 7 (3) ◽  
pp. 390-400
Author(s):  
Maria Augusta Silveira Netto Nunes
Keyword(s):  
Human Computer Interaction ◽  
Affective Computing ◽  
Psychological Aspects ◽  
Markup Language ◽  
Starting Point ◽  
Synthetic Agents ◽  
Computer Interaction

This paper describes how human psychological aspects have been used in lifelike synthetic agents in order to provide believability during the human-computer interaction. We describe a brief survey of applications where Affective Computing Scientists have applied psychological aspects, like Emotion and Personality. Based on those aspects we describe the effort done by Affective Computing scientists in order to create a Markup Language to express and standardize Emotions. Because they have not yet concentrated their effort on Personality, here, we propose a starting point to create a Markup Language to express Personality.

scholarly journals “Excavating AI” Re-excavated: Debunking a Fallacious Account of the JAFFE Dataset

2021 ◽  
Author(s):  
Michael J Lyons
Keyword(s):  
Machine Learning ◽  
Facial Expression ◽  
Human Computer Interaction ◽  
Present Article ◽  
Face Perception ◽  
Affective Computing ◽  
The Creation ◽  
Computer Interaction ◽  
Training Sets

Twenty-five years ago, my colleagues Miyuki Kamachi and Jiro Gyoba and I designed and photographed JAFFE, a set of facial expression images intended for use in a study of face perception. In 2019, without seeking permission or informing us, Kate Crawford and Trevor Paglen exhibited JAFFE in two widely publicized art shows. In addition, they published a nonfactual account of the images in the essay “Excavating AI: The Politics of Images in Machine Learning Training Sets.” The present article recounts the creation of the JAFFE dataset and unravels each of Crawford and Paglen’s fallacious statements. I also discuss JAFFE more broadly in connection with research on facial expression, affective computing, and human-computer interaction.

Human-Computer Interaction in Education

2011 ◽  
pp. 60-69
Author(s):  
Gary A. Berg
Keyword(s):  
Los Angeles ◽  
Human Computer Interaction ◽  
Evolutionary Process ◽  
Point Of View ◽  
University Of California ◽  
Educational Environments ◽  
The Subject ◽  
Research In Education ◽  
The University ◽  
Computer Interaction

I come to the subject of this book from a very different path than most of those thinking about the use of computers in educational environments. My formal education focused originally on literature and film studies, and film production at the University of California at Berkeley, San Francisco State University, and the University of California at Los Angeles. I became professionally involved in educational administration through the backdoor of continuing education focused first on the entertainment industry, and then more broadly. It was after this combined experience of studying film and television and working in adult education that I began research in education and earned a doctorate in the field of higher education from Claremont Graduate University, with a special emphasis on distance learning. I hope that the different point of view I have developed from my eclectic background gives me the ability to make something of a unique contribution to this evolving new field. What follows is an attempt to spark a discussion that will lead to answers to the question of what are the most effective techniques for the design of computer learning environments. This is not a how-to book—we are too early in the evolutionary process of the medium to give such specific guidance. Rather, my intention is to offer some theories to elevate the thinking bout computers in education. Because the subject is interdisciplinary, combining science with the humanities, the theoretical discussion draws from abroad range of disciplines: psychology, educational theory, film criticism, and computer science. The book looks at the notion of computer as medium and what such an idea might mean for education. I suggest that the understanding of computers as a medium may be a key to re-envisioning educational technology. Oren (1995) argues that understanding computers as a medium means enlarging human-computer interaction (HCI) research to include issues such as the psychology of media, evolution of genre and form, and the societal implications of media, all of which are discussed here. Computers began to be used in educational environments much later than film, and I would have to agree with others who claim that the use of computers instructionally is still quite unsophisticated.

scholarly journals Feature extraction and classification methods of facial expression: a survey

2021 ◽  
Vol 2 (1) ◽  
pp. 26-32
Author(s):  
Moe Moe Htay
Keyword(s):  
Feature Extraction ◽  
Facial Expression ◽  
Human Computer Interaction ◽  
Affective Computing ◽  
Recognition System ◽  
Expression Recognition ◽  
Care Assessment ◽  
Main Components ◽  
Fatigue Detection ◽  
Computer Interaction

Facial Expression is a significant role in affective computing and one of the non-verbal communication for human computer interaction. Automatic recognition of human affects has become more challenging and interesting problem in recent years. Facial Expression is the significant features to recognize the human emotion in human daily life. Facial Expression Recognition System (FERS) can be developed for the application of human affect analysis, health care assessment, distance learning, driver fatigue detection and human computer interaction. Basically, there are three main components to recognize the human facial expression. They are face or face’s components detection, feature extraction of face image, classification of expression. The study proposed the methods of feature extraction and classification for FER.

scholarly journals Towards Hybrid Model for Human-Computer Interaction in Latvian

2020 ◽  
Author(s):  
Inguna Skadiņa ◽  
Didzis Goško
Keyword(s):  
Deep Learning ◽  
Human Computer Interaction ◽  
Hybrid Approach ◽  
Training Data ◽  
Student Service ◽  
Knowledge Based ◽  
Learning Techniques ◽  
Data Driven Approach ◽  
The University ◽  
Computer Interaction

Human-computer interaction, especially in form of dialogue systems and chatbots, has become extremely popular during the last decade. The dominant approach in the recent development of practical virtual assistants is the application of deep learning techniques. However, in case of less resourced language (or domain), the application of deep learning could be very complicated due to the lack of necessary training data. In this paper, we discuss possibility to apply hybrid approach to dialogue modelling by combining data-driven approach with the knowledge-based approach. Our hypothesis is that by combining different agents (general domain chatbot, frequently asked questions module and goal oriented virtual assistant) into single virtual assistant we can facilitate adequacy and fluency of the conversation. We investigate suitability of different widely used techniques in less resourced settings. We demonstrate feasibility of our approach for morphologically rich less resourced language Latvian through initial virtual assistant prototype for the student service of the University of Latvia.

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