Usability Testing of Virtual Reality Applications-The Pilot Study

Development of a virtual reality system usability questionnaire (VRSUQ)

Virtual reality (VR) has gained significant attention as a technology that provides immersive experiences similar to the real world. In order for a VR system to be accepted, usability needs to be guaranteed. Accordingly, VR-related researchers are continuing their efforts to improve VR systems by conducting usability evaluations. However, existing studies have limitations in that they cannot comprehensively evaluate the detailed properties of VR systems by using questionnaires developed for general product usability evaluation or focusing only on some usability aspects of VR systems. This suggests it may be difficult to fully capture usability issues in a VR system, and that it is necessary to develop a usability evaluation tool that reflects the specific characteristics of the VR system. Therefore, this study develops and proposes the Virtual Reality System Usability Questionnaire (VRSUQ). In the development of the questionnaire, items were structured based on a literature review and discussions with experts. To account for the diverse characteristics of VR systems, the validity of the questionnaire was verified through both exploratory and confirmatory factor analysis, utilizing data obtained from three distinct experimental studies that employed different VR systems. In addition, by comparing the results of VRSUQ with the results from the System Usability Scale, which is widely used for perceived usability evaluation, alternative possibilities for using VRSUQ are presented. Further testing on various VR platforms is needed to ensure the reliability and validity of VRSUQ, and as results from using VRSUQ are accumulated, it is expected to be widely used as a more powerful and robust VR-specific perceived usability evaluation tool.

Heuristic smartphone usability evaluations of the mobile application NANDA, nursing interventions classification, and nursing outcomes classification customized for nursing home registered nurses

PURPOSE

We aimed to evaluate the usability of a smartphone application consisting of standardized nursing language (SNL) using NANDA, Nursing Intervention Classification, and Nursing Outcome Classification for nursing home nurses.

DATA SOURCES

Applying convenience sampling, a total of 14 experts and 15 real users were invited to test and evaluate the smartphone application independently. For the usability evaluation of the developed application, the Korean version of the Mobile Application Rating Scale for experts and Mobile Application Rating Scale: User Version developed by Stoyanov et al. were used.

DATA SYNTHESIS

Both groups determined that the SNL application was quite informative about SNL and efficient function; however, the engagement was quite lower than other categories.

CONCLUSIONS

Although SNLs were scientifically developed for several decades, the widely available technological application for registered nurses in different languages is urgently needed to improve quality of nursing care.

IMPLICATIONS FOR NURSING PRACTICE

The identified problems and recommendations by users and experts using heuristic evaluation will be reflected in the application's final version to be used for research.

The Performance of Inertial Measurement Unit Sensors on Various Hardware Platforms for Binaural Head-Tracking Applications

Binaural synthesis with head tracking is often used in spatial audio systems. The devices used for head tracking must provide data on the orientation of the listener's head. These data need to be highly accurate, and they need to be provided as fast and as frequently as possible. Therefore, head-tracking devices need to be equipped with high-quality inertial measurement unit (IMU) sensors. Since IMUs readily include triaxial accelerometers, gyroscopes, and magnetometers, it is crucial that all of these sensors perform well, as the head orientation is calculated from all sensor outputs. This paper discusses the challenges encountered in the process of the performance assessment of IMUs through appropriate measurements. Three distinct hardware platforms were investigated: five IMU sensors either connected to Arduino-based embedded systems or being an integral part of one, five smartphones across a broad range of overall quality with integrated IMUs, and a commercial virtual reality unit that utilizes a headset with integrated IMUs. An innovative measurement method is presented and proposed for comparing the performance of sensors on all three platforms. The results of the measurements performed using the proposed method show that all three investigated platforms are adequate for the acquisition of the data required for calculating the orientation of a device as the input to the binaural synthesis process. Some limitations that have been observed during the measurements, regarding data acquisition and transfer, are discussed.

Data Collection Framework for Context-Aware Virtual Reality Application Development in Unity: Case of Avatar Embodiment

Virtual Reality (VR) has been adopted as a leading technology for the metaverse, yet most previous VR systems provide one-size-fits-all experiences to users. Context-awareness in VR enables personalized experiences in the metaverse, such as improved embodiment and deeper integration of the real world and virtual worlds. Personalization requires context data from diverse sources. We proposed a reusable and extensible context data collection framework, ManySense VR, which unifies data collection from diverse sources for VR applications. ManySense VR was implemented in Unity based on extensible context data managers collecting data from data sources such as an eye tracker, electroencephalogram, pulse, respiration, galvanic skin response, facial tracker, and Open Weather Map. We used ManySense VR to build a context-aware embodiment VR scene where the user's avatar is synchronized with their bodily actions. The performance evaluation of ManySense VR showed good performance in processor usage, frame rate, and memory footprint. Additionally, we conducted a qualitative formative evaluation by interviewing five developers (two males and three females; mean age: 22) after they used and extended ManySense VR. The participants expressed advantages (e.g., ease-of-use, learnability, familiarity, quickness, and extensibility), disadvantages (e.g., inconvenient/error-prone data query method and lack of diversity in callback methods), future application ideas, and improvement suggestions that indicate potential and can guide future development. In conclusion, ManySense VR is an efficient tool for researchers and developers to easily integrate context data into their Unity-based VR applications for the metaverse.