Comparative study of user experience evaluation techniques based on mouse and gaze tracking

Aviz, Igor L.; Souza, Kennedy Edson Silva de; Ribeiro, Elison; Mello, Harold de; Seruffo, Marcos César da Rocha

doi:10.1145/3323503.3360623

Cited by 8 publications

(4 citation statements)

References 9 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Em relação ao rastreamento de movimentos do cursor do mouse e dos olhos, o estudo de caso desenvolvido por Aviz et al [1] comparou dados de rastreamento do mouse com as interações de olhar do usuário, a fim de determinar se o movimento dos olhos seguem o cursor do mouse de uma página web. Essa combinação de rastreamento entre olhos e mouse possibilitou apontar que usuários com hábitos de navegações dinâmicas tendem a seguir o mouse com seu olhar, enquanto os usuários com hábitos estáticos mantêm o mouse parado e usam seu olhar para escanear a tela.…”

Section: Trabalhos Relacionadosunclassified

UX-Tracking: Web and Multimodal Tool for User Experience Evaluation

Lima¹,

Moura²,

Alves³

et al. 2022

Anais Estendidos Do XXVIII Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2022)

View full text Add to dashboard Cite

Different markets have increasingly sought to offer personalized experiences in the use of their products and/or services. From that, this work presents UX-Tracking, a web tool for multimodal capture that adds a set of tracking techniques to allow User Experience (UX) evaluation. UX-Tracking allows you to simultaneously track mouse, eye, keyboard, history and, in the future, voice data in a single session with the user. This factor contributes to greater accuracy in UX assessments, since the interaction can be analyzed from different perspectives. As such, UX-Tranking is expected to be an alternative for researchers and professionals from academia and industry to improve quality of interaction with products and services in digital interfaces accessed via a web browser.

show abstract

Section: Trabalhos Relacionadosunclassified

UX-Tracking: Web and Multimodal Tool for User Experience Evaluation

Lima¹,

Moura²,

Alves³

et al. 2022

Anais Estendidos Do XXVIII Simpósio Brasileiro De Sistemas Multimídia E Web (WebMedia 2022)

View full text Add to dashboard Cite

show abstract

“…Significance of these tests lies in their ability to determine the optimal placement of interface components by aligning with high-priority zones. This strategic alignment not only enhances efficiency but also significantly contributes to reducing the user's mental load during interaction [21], [22]. Thus, mouse tracking tests provide a detailed perspective that facilitates the creation of more intuitive interfaces focused on user needs [23].…”

Section: Introductionmentioning

confidence: 99%

Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

Chanchí G.,

Chanchí P.,

Ospina A.

2024

TEM Journal

View full text Add to dashboard Cite

Mouse tracking tests play a crucial role in evaluating software usability, but their efficient image processing remains challenging, especially with high-resolution images and numerous participants. This article introduces a novel method, leveraging parallel computing, for the efficient analysis of interaction zones in mouse tracking test images. Following Pratt's iterative research pattern, the proposed method is implemented using Python libraries Dask and OpenCV, validated through a proof of concept on Eclipse software. Results demonstrate the parallel approach's remarkable efficiency, being 252 times faster than the sequential method across various executions. The method's potential impact is discussed, providing a valuable reference for developing tools in usability and other application contexts. The open-source tools employed, Dask and OpenCV, prove suitable for parallel image analysis, offering versatility for broader application in diverse fields. This work contributes to advancing the field of mouse tracking analysis by significantly improving processing efficiency and lays the groundwork for future tools and methodologies.

show abstract

“…Although there are many studies on UX evaluation, few works have addressed the optimization and automation of this process (Aviz et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

UX and Machine Learning – Preprocessing of Audiovisual Data Using Computer Vision to Recognize UI Elements

Čejka,

Masner,

Jarolímek

et al. 2023

AOL

View full text Add to dashboard Cite

This study explores the convergence of user experience (UX) and machine learning, particularly employing computer vision techniques to preprocess audiovisual data to detect user interface (UI) elements. With an emphasis on usability testing, the study introduces a novel approach for recognizing changes in UI screens within video recordings. The methodology involves a sequence of steps, including form prototype creation, laboratory experiments, data analysis, and computer vision tasks. The future aim is to automate the evaluation of user behavior during UX testing. This innovative approach is relevant to the agricultural domain, where specialized applications for precision agriculture, subsidy requests, and production reporting demand streamlined usability. The research introduces a frame extraction algorithm that identifies screen changes by analyzing pixel differences between consecutive frames. Additionally, the study employs YOLOv7, an efficient object detection model, to identify UI elements within the video frames. Results showcase successful screen change detection with minimal false negatives and acceptable false positives, showcasing the potential for enhanced automation in UX testing. The study’s implications lie in simplifying analysis processes, enhancing insights for design decisions, and fostering user-centric advancements in diverse sectors, including precision agriculture.

show abstract

Comparative study of user experience evaluation techniques based on mouse and gaze tracking

Cited by 8 publications

References 9 publications

UX-Tracking: Web and Multimodal Tool for User Experience Evaluation

UX-Tracking: Web and Multimodal Tool for User Experience Evaluation

Application of Parallel Computing in the Analysis of Usability Tests, Under the Mouse Tracking Approach

UX and Machine Learning – Preprocessing of Audiovisual Data Using Computer Vision to Recognize UI Elements

Contact Info

Product

Resources

About