What we offer to our clients

XR interaction data analytics

We have built up expertise on capturing and analysing behavioural data on experiences in virtual or augmented reality.
We provide valuable objective data and comprehensive analysis on human experiences.

  • All possible interactions between the user and objects in the virtual environment can be logged and visualized on a timeline. This can be useful to reflect back on critical events during the simulation or training or perform in-depth analysis

  • Cognitive load or the amount of information the human working memory can hold at any given time is an interesting measure for performance assessment and training. It can also be a good metric for optimizing designs and human-technology interactions.

  • A stress response includes physical and thought responses to concrete events in the simulation and can be measured through psychophysiological sensors. This stress or arousal response can also be used in training or user testing as a trigger to elicit certain behaviors (what is the performance under stress?)

  • Moments of hesitation and doubt and under certain circumstances even “freezing” can indicate that the user doesn’t understand the task or doesn’t know what to do anymore. By detecting these hesitation moments using gaze and posture tracking, we can optimize a training or design.

  • It is also possible to measure other cogntive-affective states like fatigue, distraction and attention with a combination of behavioral and psychophysiological sensors. These can help in designing the most optimal human-technology interactions.

Cognitive assessment in VR

With this tool, you will be able to capture data about how people behave within a virtual environment. This can be used to make a comparison with the benchmark of similar needed capabilities, provided by our extensive database.

  • We implemented the well-known N-back task in a 3D environment and designed a task that can discriminate between various levels of cognitive load. In a 10-minute experiment, we are able to assess someone mental workload capacitiy.

  • We implemented the well-known mental rotation task in a 3D environment and designed a task that can discriminate between various levels of spatial insight. In a 10-minute experiment, we are able to assess someone mental rotation capacities.

  • The individual performance of a user on the cognitive assessment tasks can be commpared to a reference group, making it possible to better match a person’s skills with the cognitive demands of a job.

  • Individual differences in human physiology can make it hard to correctly interpret sensor & wearable data. Using our tools, it is possible to obtain a personalized model and characterization of someone’s psychophysiological signals, making it possible to create better trainings and perform better user experience research.