1. Pervasive Augmented Reality to support real-time data monitoring in industrial scenarios: Shop floor visualization evaluation and user study.
- Author
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Maio, Rafael, Araújo, Tiago, Marques, Bernardo, Santos, André, Ramalho, Pedro, Almeida, Duarte, Dias, Paulo, and Santos, Beatriz Sousa
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AUGMENTED reality , *HEAD-mounted displays , *WEB-based user interfaces , *DATA visualization , *BIG data , *COMPUTER science , *INDUSTRY 4.0 , *ASSEMBLY line methods - Abstract
Augmented Reality (AR) is a key technology in the transition to Industry 4.0 and smart manufacturing, gaining reputation in a wide range of industrial fields One promising application is in another field of Industry 4.0, the data monitoring, where AR can be used to visualize and interact with complex and big data in real time, potentially improving the efficiency and accuracy of decision-making In this work, we propose a Pervasive AR tool for data monitoring, developed in collaboration with industry partners. An web application of the same data monitoring function is also created for comparison purposes A Human-Centered Design (HCD) methodology was used to identify the needs and requirements of industrial analysts, which led to the development of these systems Preliminary user studies, with 17 participants having distinct levels of expertise in industry, data analysis and computer science, were conducted to collect opinions and suggestions for improvements, as well as, quantitative data regarding the technologies considered A succeeding user study was then prepared, in which 12 participants used two conditions (C1 — Hands-free Pervasive AR tool for HMDs and C2 — Web tool for tablet devices) to fulfill a set of data monitoring tasks in an industry assembly line The result of these studies confirm the potential of Pervasive AR for data monitoring, as it engages users, promote environmental awareness, contextualizes data and allows fast self-localization. [Display omitted] • Two user studies with more than 10 participants each, industrial settings tasks. • Understand which method (Web or AR) stands out according to the task characteristic. • The findings reveal that certain methods were more effective for types of task. • Display methods significantly influenced participants across analysis. • These studies confirm the potential of Pervasive AR for data monitoring. [ABSTRACT FROM AUTHOR]
- Published
- 2024
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