Evaluating Usability Aspects of a Mixed Reality Solution for Immersive Analytics in Industry 4.0 Scenarios

Summary

This protocol delineates the technical setting of a developed mixed reality application that is used for immersive analytics. Based on this, measures are presented, which were used in a study to gain insights into usability aspects of the developed technical solution.

Abstract

In medicine or industry, the analysis of high-dimensional data sets is increasingly required. However, available technical solutions are often complex to use. Therefore, new approaches like immersive analytics are welcome. Immersive analytics promise to experience high-dimensional data sets in a convenient manner for various user groups and data sets. Technically, virtual-reality devices are used to enable immersive analytics. In Industry 4.0, for example, scenarios like the identification of outliers or anomalies in high-dimensional data sets are pursued goals of immersive analytics. In this context, two important questions should be addressed for any developed technical solution on immersive analytics: First, is the technical solutions being helpful or not? Second, is the bodily experience of the technical solution positive or negative? The first question aims at the general feasibility of a technical solution, while the second one aims at the wearing comfort. Extant studies and protocols, which systematically address these questions are still rare. In this work, a study protocol is presented, which mainly investigates the usability for immersive analytics in Industry 4.0 scenarios. Specifically, the protocol is based on four pillars. First, it categorizes users based on previous experiences. Second, tasks are presented, which can be used to evaluate the feasibility of the technical solution. Third, measures are presented, which quantify the learning effect of a user. Fourth, a questionnaire evaluates the stress level when performing tasks. Based on these pillars, a technical setting was implemented that uses mixed reality smartglasses to apply the study protocol. The results of the conducted study show the applicability of the protocol on the one hand and the feasibility of immersive analytics in Industry 4.0 scenarios on the other. The presented protocol includes a discussion of discovered limitations.

Introduction

Virtual-reality solutions (VR solutions) are increasingly important in different fields. Often, with VR solutions (including Virtual Reality, Mixed Reality, and Augmented Reality), the accomplishment of many daily tasks and procedures shall be eased. For example, in the automotive domain, the configuration procedure of a car can be supported by the use of Virtual Reality¹ (VR). Researchers and practitioners have investigated and developed a multitude of approaches and solutions in this context. However, studies that investigate usability aspects are still rare. In general, the aspects should be considered in the light of two major questions. Fi....

Protocol

All materials and methods were approved by the Ethics Committee of Ulm University, and were carried out in accordance with the approved guidelines. All participants gave their written informed consent.

1. Establish Appropriate Study Environment

NOTE: The study was conducted in a controlled environment to cope with the complex hardware setting. The used mixed reality smartglasses (see Table of Materials) and the laptop for the 2D application were&.......

Discussion

Regarding the developed mixed reality smartglasses (see Table of Materials) application, two aspects were particularly beneficial. The use of spatial sounds for the outlier’s detection task was positively perceived on one hand (see the results of RQ3). On the other, the use of voice commands was also perceived positively (see Figure 10).

Regarding the study participants, although the number of recruited participants was rather small for an e.......

Materials

Name	Company	Catalog Number	Comments
edaMove	movisens
HoloLens	Microsoft
Matlab R2017a	MathWorks
RPY2	GNU General Public License v2 or later (GPLv2+) (GPLv2+)		https://pypi.org/project/rpy2/
SPSS 25.0	IBM