HUMAN–COMPUTER INTERACTION MODELS AND THEIR ROLES IN THE DESIGN OF INTERACTIVE SYSTEMS

1990 ◽  
pp. 7-25
Author(s):  
Andy WHITEFIELD
Keyword(s):  
Human Computer Interaction ◽  
Interactive Systems ◽  
Interaction Models ◽  
Computer Interaction

scholarly journals Usability Engineering Methods for Interactive Intelligent Systems

AI Magazine ◽  
2009 ◽  
Vol 30 (4) ◽  
pp. 41 ◽  
Author(s):  
Aaron Spaulding ◽  
Julie Sage Weber
Keyword(s):  
Human Computer Interaction ◽  
Interaction Design ◽  
Intelligent Systems ◽  
Interactive Systems ◽  
Usability Engineering ◽  
Large Repertoire ◽  
Computer Interaction

The field of Human-Computer Interaction (HCI) offers designers and developers of interactive systems a large repertoire of methods for ensuring that their systems will be both usable and useful. This article offers a brief introduction to these methods, focusing on the ways in which they sometimes need to be adapted and extended to take into account the characteristic properties of systems that include some sort of AI. The discussion is organized around three types of activity: understanding users needs, interaction design, and evaluation. 

scholarly journals Interacting with personal fabrication devices

2018 ◽  
Vol 60 (2) ◽  
pp. 113-117
Author(s):  
Stefanie Mueller
Keyword(s):  
Human Computer Interaction ◽  
3D Model ◽  
Interaction Model ◽  
3D Models ◽  
Interactive Systems ◽  
3D Printer ◽  
3D Printers ◽  
Model Database ◽  
Domain I ◽  
Computer Interaction

AbstractPersonal fabrication tools, such as 3D printers, are on the way of enabling a future in which non-technical users will be able to create custom objects. While the hardware is now affordable and the number of people who own a 3D printer is increasing, only few create new 3D models. Most users download models from a 3D model database and after downloading fabricate them on their 3D printers. At most, users adjust a few parameters of the model, such as changing its color or browsing between predetermined shape options.I argue that personal fabrication has the potential for more: Instead of only consuming existing content, I envision a future in which non-technical users will create objects only trained experts can create today. While there are many open challenges for human-computer interaction, such as abstracting away the necessarydomainandmachineknowledge, I focus on improving theinteraction modelunderlying current personal fabrication devices.In this article, I illustrate why today’s interaction model for personal fabrication tools is not suitable for non-technical users. For this, I draw an analogy to the development of the user interface in personal computing and show how solutions developed by human-computer interaction researchers over the last decades can be applied to this new domain. I analyze the challenges when creating interactive systems for personal fabrication and describe six research prototypes I built to overcome these challenges. I discuss the limitations of these systems and conclude with an overview of recent advancements in personal fabrication that will allow us to go beyond what is possible today.

Multi-Facet Design of Interactive Systems through Visual Languages

2011 ◽  
pp. 174-204 ◽  
Author(s):  
Daniela Fogli ◽  
Andrea Marcante ◽  
Piero Mussio
Keyword(s):  
Human Computer Interaction ◽  
Visual Language ◽  
Interactive Systems ◽  
Interactive System ◽  
Domain Experts ◽  
System Usage ◽  
State Chart ◽  
Software Engineers ◽  
Specific Activities ◽  
Computer Interaction

In this chapter it is recognized that the knowledge relevant to the design of an interactive system is distributed among several stakeholders: domain experts, software engineers and Human-Computer Interaction experts. Hence, the design of an interactive system is a multi-facet activity requiring the collaboration of experts from these communities. Each community describes an interactive system through visual sentences of a Visual Language (VL). A first VL allows domain experts to reason on the system usage in their specific activities. A second VL, the State-Chart language, is used to specify the system behaviour for software engineers purposes. A communication gap exists among the two communities, in that domain experts do not understand software engineers jargon and vice versa. To overcome this gap, a third VL permits Human-Computer Interaction experts to translate the user view of the system embedded in their Visual Language into a specification in the software engineering Visual Language.

A Proposal to Model Interaction from the Analysis of Student - Pedagogic Conversational Agent Logs

2013 ◽  
pp. 148-162
Author(s):  
Diana Pérez-Marín ◽  
Ismael Pascual-Nieto
Keyword(s):  
Human Computer Interaction ◽  
Interactive Systems ◽  
Conversational Agent ◽  
Conversational Agents ◽  
Practical Application ◽  
Model Interaction ◽  
Multiple Personality ◽  
Evaluation Techniques ◽  
Computer Interaction

According to User-Centered Design, computer interactive systems should be implemented taking into account the users’ preferences. However, in some cases, it is not easy to apply conventional Human-Computer interaction evaluation techniques to identify the users’ needs and improve the user-system interaction. Therefore, this chapter proposes a procedure to model the interaction behaviour from the analysis of conversational agent dialog logs. A case study in which the procedure has been applied to model the behaviour of 20 children when interacting with multiple personality Pedagogic Conversational Agents is described as an illustrative sample of the goodness and practical application of the procedure.

Safety-critical human computer interaction

2019 ◽  
Vol 61 (1) ◽  
pp. 67-70 ◽  
Author(s):  
Simon Nestler
Keyword(s):  
Social Media ◽  
Human Computer Interaction ◽  
Mobile Computing ◽  
Mass Casualty ◽  
Interactive Systems ◽  
Mass Casualty Incidents ◽  
Research Results ◽  
Safety Critical ◽  
The Future ◽  
Computer Interaction

Abstract Dealing with usability issues of safety-critical interactive systems is essential for an efficient, effective and joyful use of these systems. This paper describes a prototypical safety-critical environment and discusses the HCI (human computer interaction) challenges of different interactive systems for safety-critical environments. We designed, developed and evaluated various interactive systems which solve different challenges in so-called mass casualty incidents (MCIs). In summary, we made contributions to three different areas of application: Mobile computing in safety-critical environments, simulation of safety-critical environments and social media in safety-critical environments. Finally, this paper gives further insights how all these research results can to be brought together in the future in order to be able to build usable interactive systems for safety-critical environments.

scholarly journals Enrichment of Human-Computer Interaction in Brain-Computer Interfaces via Virtual Environments

2017 ◽  
Vol 2017 ◽  
pp. 1-12 ◽  
Author(s):  
Alonso-Valerdi Luz María ◽  
Mercado-García Víctor Rodrigo
Keyword(s):  
Human Computer Interaction ◽  
Cognitive Processes ◽  
Interactive Systems ◽  
Memory Storage ◽  
Navigation Systems ◽  
Brain Computer Interfaces ◽  
Computer Interfaces ◽  
User Intentions ◽  
The Sixties ◽  
Computer Interaction

Tridimensional representations stimulate cognitive processes that are the core and foundation of human-computer interaction (HCI). Those cognitive processes take place while a user navigates and explores a virtual environment (VE) and are mainly related to spatial memory storage, attention, and perception. VEs have many distinctive features (e.g., involvement, immersion, and presence) that can significantly improve HCI in highly demanding and interactive systems such as brain-computer interfaces (BCI). BCI is as a nonmuscular communication channel that attempts to reestablish the interaction between an individual and his/her environment. Although BCI research started in the sixties, this technology is not efficient or reliable yet for everyone at any time. Over the past few years, researchers have argued that main BCI flaws could be associated with HCI issues. The evidence presented thus far shows that VEs can (1) set out working environmental conditions, (2) maximize the efficiency of BCI control panels, (3) implement navigation systems based not only on user intentions but also on user emotions, and (4) regulate user mental state to increase the differentiation between control and noncontrol modalities.

Design and Implementation of Human-Computer Interaction Systems Based on Transfer Support Vector Machine and EEG Signal for Depression Patients’ Emotion Recognition

2021 ◽  
Vol 11 (3) ◽  
pp. 948-954
Author(s):  
Xiang Chen ◽  
Lijun Xu ◽  
Ming Cao ◽  
Tinghua Zhang ◽  
Zhongan Shang ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Human Computer Interaction ◽  
Transfer Learning ◽  
Field Data ◽  
Interactive Systems ◽  
Classification Model ◽  
Support Vector ◽  
Target Domain ◽  
Emotional Changes ◽  
Computer Interaction

At present, the demand for intelligentization of human-computer interaction systems (HCIS) has become increasingly prominent. Being able to recognize the emotions of users of interactive systems is a distinguishing feature of intelligent interactive systems. The intelligent HCIS can analyze the emotional changes of patients with depression, complete the interaction with the patients in a more appropriate manner, and the recognition results can assist family members or medical personnel to make response measures based on the patient’s emotional changes. Based on this background, this paper proposes a sentiment recognition method based on transfer support vector machines (TSVM) and EEG signals. The ER (ER) results based on this method are applied to HCIS. Such a HCIS is mainly used for the interaction of patients with depression. When a new field related to a certain field appears, if the new field data is relabeled, the sample is expensive, and it is very wasteful to discard all the old field data. The main innovation of this research is that the introduced classification model is TSVM. TSVM is a transfer learning strategy based on SVM. Transfer learning aims to solve related but different target domain problems by using a large amount of labeled source domain data. Therefore, the transfer support vector machine based on the transfer mechanism can use the small labeled data of the target domain and a large amount of old data in the related domain to build a high-quality classification model for the target domain, which can effectively improve the accuracy of classification. Comparing the classification results with other classification models, it can be concluded that TSVM can effectively improve the accuracy of ER in patients with depression. The HCIS based on the classification model has higher accuracy and better stability.

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