How do you track VR user behavior over time?

We use data driven and in-office usability testing for this. We generate heat maps of where the user is looking at. This helps us figure out if we are successfully driving attention to the subject or not. We also try to drive users to the subject using intuitive UI elements. However, I would say our on-site usability testing is our gem. We invite people to try our simulations in person and we closely observe if we are successfully able to get the user to do what we want them to do or they're just confused. This usually happens in a one to one session and everything is recorded and reported. It is a very lean way with a lot of trial and error but we seem to be improving over time at doing the right things.

As with many forms of observational research where rich, multifaceted "qualitative" data can be gleaned, it can take much repetition over time before reliable trends of repeated behaviours are observed. While a single actions, or small numbers of repeated behaviours over a short time could be considered anecdotal (and therefore of little value in reliably predicting the likelihood or frequency of their repeated in future), the greater numbers that might be observed over a longer timeframe can become "statistically significant" enough that predictions and circulations can be made based on them.

User behavioral analytics, driven by machine learning, are reshaping gesture detection technology. This innovation not only enhances the authenticity of user interactions, providing a more immersive experience, but it also contributes to long-term safety improvements. By analyzing user behavior, we can identify potential risks and refine safety measures over time, making this a transformative development with profound benefits.

because AI will be able to identify diseases based on physical activity as well as be able to copy you like a digital ghost for future generations to interact with you. More accurate data overtime leads to better softward and hardware

In addition, observing the frequency with which certain of the aspects mentioned above correlate with each other, additional meaningful insights can be gleaned. For example, in a given VR emvironment, heightened skin conductivity and heart rate in visitors (which could be merely indicators of physical exertion or overheating) might correlate most frequently with increased eye movement and pupil dilation (which together would indicate heightened stress or anxiety).

Based on my experience, facial expression data is a highly underrated data type in the behavioural data category. Headset manufacturers continue to increase facial tracking capabilities to enhance avatar expressions for a more realistic experience. Furthermore, these data types are easily accessible for capture and mining purposes. For instance, headsets such as Oculus Meta Quest Pro and HTC Vive Pro Eye offer over 50 facial expression data points. These data are invaluable in determining the user's psychological state and tracking their continuous changes.

It is here that the application of artificial intelligence based analysis systems can be very helpful, automating the logging of many and diverse data points and intelligently identifying correlation patterns without the bias or time/monetary investment inherent in having humans carry out the same observations and analyses. It should be borne in mind that AI can, in some cases, exhibit its own form of "bias", although this is often caused by the parameters set by its human developers, or by limitations in the size or diversity of the sample it is working upon.

VR glasses are equipped with an array of sensors, providing a treasure trove of data. These sensors capture everything from eye and head movements to environmental factors. The data collected varies based on the sensors within the goggles. Harnessing this diverse information offers endless possibilities for analytics, making VR a goldmine for understanding user behavior and optimizing immersive experiences.

To ensure accurate behavioural trend capture across multiple sessions, maintaining consistency in VR setup and calibration is of utmost importance. This necessitates ensuring that the VR equipment is configured and calibrated in an identical manner for each session. Furthermore, it is important to properly normalize data before feeding it into neural network models to adjust the data to a standard scale or range that facilitates more effective neural network model input.

The appropriate means of interpreting VR data is therefore inherently linked to the volume & variety of that data: (high volume, more binary types of data are generally referred to as "quantitative" and can frequently be graphed, while low volumes of richer data can be referred to as "qualitative" and generally require longform written, verbal or even video explanation to fully describe). As stated, the former is generally regarded as more objective and reliable, whilst the latter, whilst more likely to uncover unexpected behaviours, is susceptible to subjectivity and bias in its interpretation. Best practice is to begin with qualitative analysis, using this to form questions for proving or disproving in a subsequent quantitative analysis.

The combined volume, richness and dynamic (ever changing) range of inputs that VR can generate is incelreasigly highlighting the shortcomings of any of the above communication methods alone. Interestingly this has given rise to a number of immersive, multi-particupant virtual reality analysis environments. These have the three advantages of being able to display information dynamically (changing over time or with manual adjustments from its viewers), in three rather than two dimensions (even adding appropriate spatial and visual context such as in a "digital twin") and including the communication and differing perspectives of multiple, sometimes diverse, stakeholder groups (providing a social and, collectively, more objective perspective)

One of the most significant risks associated with immersive behaviour tracking is the possibility of creating identity profiles based on the data collected. Unlike other types of biometric data, behavioural data can reveal many aspects of a user, including their physical and mental health status including their sexual tendencies. The most dangerous aspect of behaviour profiling is that once released to a malicious user, it can never be recovered using traditional methods as these data are tightly bound to the user's natural behaviour. Ex: eye blinking pattern. This makes it crucial to be mindful of what kind of data is being collected and how it is being used, as the consequences of a data breach could be severe.