How can you test the scalability of your VR model?

To test my VR model's scalability, I first define goals like user capacity and interaction complexity. I then select tools such as LoadRunner or JMeter, which simulate heavy loads and user behavior. Planning and executing tests is methodical, checking system response against benchmarks. Insights from these tests guide my revisions to enhance the VR model. I actively seek user feedback to ensure the experience stays true to user needs while scaling. This approach ensures our VR experience remains robust and engaging as it grows.

Testing your model, having user feedback. Scale also comes with new problems so keep that in mind, What might work for 100 people isn't the same that will work for 100,000 people.

Bonjour, l'évolutivité de mon modèle VR est lié à l'avancement de la recherche en matière de connaissance historique. La modélisation 3D se doit d'être la transcription fine, dans la mesure du possible de la connaissance à un instant précis. Cette 3D intègre des vides, des niveaux d'hypothèse (plusieurs layers donc). L'évolutivité signifie donc que la transcription de ce qui est connu, de ce qui ne l'est pas et de ce qui est possible, évolue telle une publication scientifique.

The end objective of the testing or the goal becomes very important as optimisation of assets is highly dependent on the way VR is experienced. e.g. Does the VR model reside inside the headset or outside? Will it have multiple users? Quality of the VR headset? Are the assets realistic or stylised? Some of these questions can help define the quality of end-user expreience which will in turn define the scalability of VR experience

Some effective scalability testing tools include: LoadRunner: This is a widely-used tool for performance testing, helping to simulate thousands of users and analyze system behavior under load. Apache JMeter: An open-source tool designed to load test functional behavior and measure performance, JMeter can simulate heavy loads on a server to test its strength or analyze overall performance under different load types. BlazeMeter: BlazeMeter provides cloud-based load and performance testing, compatible with JMeter, which is scalable and supports multiple users for large-scale tests. Gatling: Gatling is another open-source load testing tool that is designed for ease of use and maintaining high performance, with a focus on web applications.

I think Unreal Engine Performance and Profiling Tools is great, easily accessible way of testing performance of Unreal Engine based VR applications. While the target platforms might differ, possibility to capture low-level data directly from within engine can help to formulate more educated decisions about needed performance optimizations or identify not-so-evident bottlenecks. It is important to use such tools on regular basis and test often. Ideally in automated fashion.

To ensure my VR model scales effectively, I plan tests that scrutinize every facet graphics, audio, physics, networking, and user interaction. Adjusting test parameters like user numbers, object counts, and system settings helps me understand performance under various scenarios. I execute these tests multiple times, altering conditions such as resolution and latency, to gauge the model's robustness. Analyzing this data is key, it tells me not just where we are, but where we need to go to create an experience that's both technically sound and human-centered.

Post scalability tests, I faithfully review my VR model, pinpointing and rectifying any errors, bugs, or glitches. My goal is to optimize the model, enhancing performance while economizing resource use. It's a priority to elevate the user experience, ensuring seamless interaction. After refining the model, I retest and iterate this process, steadfast in meeting our scalability objectives and delivering an experience that feels both intuitive and engaging for the user.

There’s no scalability if the final users are not integrated into the whole development process. Coming from clinical work to developing VR content and managing innovation projects, I've seen firsthand the impact of early clinician involvement. The inclusion of clinicians as part of the team from the start is vital in healthtech development. It's not merely about technological advancement, but about creating clinically pertinent and seamlessly integratable solutions in healthcare settings. The absence of this input can lead to resistance and underutilization, regardless of the technological prowess. This integration is crucial, not just for initial development, but also for ensuring successful adoption and scalability.

In the final phase of VR scalability testing, I engage with real users for feedback. Conducting user tests across diverse groups, I observe interactions, gauging the model's quality and usability. Their reactions to challenges, along with their opinions and suggestions, are invaluable. I use this collective insight to refine the VR model, ensuring it aligns with the users' needs and enhances their virtual experiences.

During the development of a VR project aimed at medical training, seeking feedback and validation played a pivotal role. We organized user testing sessions involving medical students, clinicians, and even individuals with no prior VR experience. This diverse approach helped us gain valuable insights into how different user groups interacted with our VR model. We used questionnaires, one-on-one interviews, and observation notes to collect user opinions and suggestions systematically. Additionally, we encouraged users to rate their experience, which provided quantifiable data to guide our improvements.

Collaboration is always key! On a personal note, our Projecte Benestar, which focuses on preventing burnout among healthcare workers through an educational VR program, perfectly illustrates the importance of a collaborative effort. Following two years of pilot trials, the project has now been embraced as an institutional program. The partnership between our innovation team and the occupational health department was instrumental in advancing our project to this new phase, where we can expand from small initial groups to a broader participant base. Our ultimate goal is to reach the entire workforce, demonstrating how innovation, backed by collaborative efforts and strategic pilot testing, can lead to impactful institutional changes.