scholarly journals Análise da Experiência do Usuário (UX) de Narrativa Transmídia através de Mouse-Tracking

2021 ◽  
Vol 22 (50) ◽  
Author(s):  
Rita de Cássia Romeiro Paulino ◽  
Marcos César Da Rocha Seruffo ◽  
Marina Lisboa Empinotti ◽  
Kennedy Edson Silva de Souza ◽  
Ana Carla Pimenta
Keyword(s):  
Artificial Intelligence ◽  
User Experience ◽  
Evaluation Tool ◽  
Mouse Tracking ◽  
Santa Catarina

Neste artigo apresentamos um método de avaliação da experiência do usuário a partir de métricas de rastreamento de mouse (mouse-tracking). Para identificar as interações no site transmídia De barrio somos utilizamos um sistema quantitativo de rastreamento de interações intitulado Artificial Intelligence and Mouse Tracking-Based user Experience Evaluation Tool (AIMT-UXT). Recorremos à entrevista estruturada para obter dados para confrontar resultados analíticos das interações. Este mapeamento foi feito com alunos de Jornalismo da Universidade Federal de Santa Catarina, que navegaram no site e participaram da pesquisa. Como resultado, observamos maior interesse nos audiovisuais.

scholarly journals User Experience Evaluation Using Mouse Tracking and Artificial Intelligence

IEEE Access ◽  
2019 ◽  
Vol 7 ◽  
pp. 96506-96515 ◽  
Author(s):  
Kennedy E. S. Souza ◽  
Marcos C. R. Seruffo ◽  
Harold D. De Mello ◽  
Daniel Da S. Souza ◽  
Marley M. B. R. Vellasco
Keyword(s):  
Artificial Intelligence ◽  
User Experience ◽  
Mouse Tracking

An Evaluation Framework for User Experience Using Eye Tracking, Mouse Tracking, Keyboard Input, and Artificial Intelligence: A Case Study

2021 ◽  
pp. 1-15
Author(s):  
Kennedy Edson Silva de Souza ◽  
Igor Leonardo de Aviz ◽  
Harold Dias de Mello ◽  
Karla Figueiredo ◽  
Marley Maria Bernardes Rebuzzi Vellasco ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Eye Tracking ◽  
User Experience ◽  
Evaluation Framework ◽  
Mouse Tracking ◽  
Keyboard Input

Artificial Intelligence in Predicting Cardiac Arrest: A Scoping Review (Preprint)

2021 ◽  
Author(s):  
Asma Alamgir ◽  
Osama Mousa 2nd ◽  
Zubair Shah 3rd
Keyword(s):  
Artificial Intelligence ◽  
At Risk ◽  
Cardiac Arrest ◽  
Scoping Review ◽  
Pediatric Population ◽  
Data Extraction ◽  
Warning System ◽  
Reference List ◽  
Evaluation Tool ◽  
Patients At Risk

BACKGROUND Cardiac arrest is a life-threatening cessation of heart activity. Early prediction of cardiac arrest is important as it provides an opportunity to take the necessary measures to prevent or intervene during the onset. Artificial intelligence technologies and big data have been increasingly used to enhance the ability to predict and prepare for the patients at risk. OBJECTIVE This study aims to explore the use of AI technology in predicting cardiac arrest as reported in the literature. METHODS Scoping review was conducted in line with guidelines of PRISMA Extension for Scoping Review (PRISMA-ScR). Scopus, Science Direct, Embase, IEEE, and Google Scholar were searched to identify relevant studies. Backward reference list checking of included studies was also conducted. The study selection and data extraction were conducted independently by two reviewers. Data extracted from the included studies were synthesized narratively. RESULTS Out of 697 citations retrieved, 41 studies were included in the review, and 6 were added after backward citation checking. The included studies reported the use of AI in the prediction of cardiac arrest. We were able to classify the approach taken by the studies in three different categories - 26 studies predicted cardiac arrest by analyzing specific parameters or variables of the patients while 16 studies developed an AI-based warning system. The rest of the 5 studies focused on distinguishing high-risk cardiac arrest patients from patients, not at risk. 2 studies focused on the pediatric population, and the rest focused on adults (n=45). The majority of the studies used datasets with a size of less than 10,000 (n=32). Machine learning models were the most prominent branch of AI used in the prediction of cardiac arrest in the studies (n=38) and the most used algorithm belonged to the neural network (n=23). K-Fold cross-validation was the most used algorithm evaluation tool reported in the studies (n=24). CONCLUSIONS : AI is extensively being used to predict cardiac arrest in different patient settings. Technology is expected to play an integral role in changing cardiac medicine for the better. There is a need for more reviews to learn the obstacles of implementing AI technologies in the clinical setting. Moreover, research focusing on how to best provide clinicians support to understand, adapt and implement the technology in their practice is also required.

Investigating the Impact of Design Thinking, Content Strategy, and Artificial Intelligence: A “Streams” Approach for Technical Communication and User Experience

2021 ◽  
pp. 004728162110419
Author(s):  
Gustav Verhulsdonck ◽  
Tharon Howard ◽  
Jason Tham
Keyword(s):  
Artificial Intelligence ◽  
User Experience ◽  
Technical Communication ◽  
Design Thinking ◽  
Professional Communication ◽  
Ux Design ◽  
Content Strategy ◽  
The Impact ◽  
Technical And Professional Communication ◽  
User Centered

Technical and professional communication (TPC) and user experience (UX) design are often seen as intertwined due to being user-centered. Yet, as widening industry positions combine TPC and UX, new streams enrich our understanding. This article looks at three such streams, namely, design thinking, content strategy, and artificial intelligence to uncover specific industry practices, skills, and ways to advocate for users. These streams foster a multistage user-centered methodology focused on a continuous designing process, strategic ways for developing content across different platforms and channels, and for developing in smart contexts where agentive products act for users. In this article, we synthesize these developments and draw out how these impact TPC.

OP138 Stakeholders’ Involvement When Developing A mHealth Assessment Tool

2019 ◽  
Vol 35 (S1) ◽  
pp. 31-32
Author(s):  
Elisa Puigdomenech Puig ◽  
Elisa Poses Ferrer ◽  
Lina Masana ◽  
Mireia Espallargues
Keyword(s):  
Focus Groups ◽  
User Experience ◽  
Delphi Technique ◽  
Assessment Tool ◽  
Clinical Effectiveness ◽  
Assessment Tools ◽  
Evaluation Tool ◽  
Privacy And Security ◽  
Safety Issues ◽  
Health Apps

IntroductionDue to the specific characteristics and challenges of mobile health (mHealth) technologies there is a need to have assessment tools based on their particularities to be used by health technology assessment (HTA) agencies and evaluation experts. In the development of a comprehensive and practical evaluation tool for the evaluation of mHealth solutions we aimed to include the views and opinions of key stakeholders: health professionals, developers, hospital managers, HTA agencies, patients and general public.MethodsFocus groups and an online modification of the Delphi technique are being used to discuss and agree on domains and criteria to be included in the mHealth assessment tool. Domains and criteria used for health apps evaluation were drawn from a literature review on the topic. The initial list includes 95 criteria grouped into the following domains: purpose of the app, privacy and security, clinical effectiveness, content of the intervention, user experience and usability, interoperability, expenses, impact on the organization, and legal and ethical aspects. Data coming from focus groups is currently being analyzed from a thematic and content analysis perspective.ResultsFocus groups with professionals have showed that the most important domains to be considered when evaluating health apps are those related with security, user experience, and clinical effectiveness. Some criteria were considered to be mandatory (mainly regarding safety issues), on which a first step assessment should indicate whether the app ‘pass or fails’ for the subsequent throughout assessment. Focus groups with patients will provide insight on critical aspects related to the choice, use and adherence to a health app.ConclusionsInsights from main stakeholders on the design of the tool for mHealth assessment are relevant and complementary between them. Next steps include (i) the agreement of criteria by using an online modification of the Delphi Technique and (ii) piloting of the tool.

scholarly journals Reports on the 2017 AAAI Spring Symposium Series

AI Magazine ◽  
2017 ◽  
Vol 38 (4) ◽  
pp. 99-106
Author(s):  
Jeannette Bohg ◽  
Xavier Boix ◽  
Nancy Chang ◽  
Elizabeth F. Churchill ◽  
Vivian Chu ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Machine Learning ◽  
Computer Science ◽  
User Experience ◽  
Object Perception ◽  
Learning Systems ◽  
Language Understanding ◽  
Social Good ◽  
Symposium Series ◽  
The Social

The Association for the Advancement of Artificial Intelligence, in cooperation with Stanford University's Department of Computer Science, presented the 2017 Spring Symposium Series, held Monday through Wednesday, March 27–29, 2017 on the campus of Stanford University. The eight symposia held were Artificial Intelligence for the Social Good (SS-17-01); Computational Construction Grammar and Natural Language Understanding (SS-17-02); Computational Context: Why It's Important, What It Means, and Can It Be Computed? (SS-17-03); Designing the User Experience of Machine Learning Systems (SS-17-04); Interactive Multisensory Object Perception for Embodied Agents (SS-17-05); Learning from Observation of Humans (SS-17-06); Science of Intelligence: Computational Principles of Natural and Artificial Intelligence (SS-17-07); and Wellbeing AI: From Machine Learning to Subjectivity Oriented Computing (SS-17-08). This report, compiled from organizers of the symposia, summarizes the research that took place.

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