## 3 Proposed Pipeline
### Process  
In the process stage, we mostly focused on the data acquisition, preparation and analysis steps. The other aspect that we wished to incorporate by design into the process block was mechanisms that allowed analysts with different skills to work collaboratively. The process block software is open-source (GPL v3) and it is a collaborative analytics tool that can be operated on a single workstation or in a cloud infrastructure as a service with more analysts actively manipulating the shared data. As mentioned in the previous section, given a remote capture dataset that may be continuously updated, we have a \(groupID\) variable that refers to a specific scene, an experimental treatment or some general groping condition. Then, we have the records, which, for example, may be user sessions or records of any variable that can be tracked. Figure 1 shows a process block schema. In this schema, the recorded data obtained from the capture session are referred to as raw data. After a preliminary filtering step, in which the data are directly inspected and gated, the data become processed data. In the final step, we implement statistics and machine learning methods. The outputs of these methods and algorithms are named aggregated data. Eventually, the interpretation of the aggregated data allows the analyst to elaborate insights into the analytic question. For the first data processing step, we developed two tools: a data inspection tool and a gating tool. The data inspection tool is a web application developed with Voila and Jupyter. This web application allows the direct exploration of the raw data records through tables and graph representations, which show the evolution in time of the records. Figure 2A shows the evolution in time of the captured variables. Some variables are floating point or integer variables, since they refer to spatial coordinates or directions, and other variables are binary or nominal variables, such as the navigation mode (e.g., VR, AR). Furthermore, the tool allows the analyst to crop a specific time interval of the record or to drop the entire record if necessary. In the pilot case that we consider in this manuscript, the analysis can crop the CSV tables to the relevant parts of the records and discard records that were captured by error. This feature was used when different navigation modes were present in a single record and we were interested in studying only one mode. In general, data should be discarded only with well-defined criteria to avoid bias in the statistical outcomes; see Figure 2A. Another set of graphs shows 3D representations of the variable's trajectories in space. In the case of user sessions exploring a virtual room, the 3D representations of the variables are the trajectories of the positions of the user, and, attached to each position recorded in the trajectory, there are arrows pointing in the direction of the user VR camera. Figure 1: Process block schema. Remote capture data are composed of a group variable and a session variable. The group variable may identify a scene or an experimental treatment. The record variable may identify specific user sessions or sensor acquisitions that belong to the group. We refer to the data acquired with the capture block as raw data. These data are filtered through a sequence of direct inspections and gating procedures; thus, they become processed data. Finally, we implement machine learning and eventually AI methods to generate aggregate data and extract insights. In the gating step, we examine global metrics regarding the records, such as the total elapsed time or the variables' total variance to exclude gross outliers, by gating the values that are greater or lower than certain thresholds (Figure 2B). To give a practical example, gross outliers could be records that last only a few seconds compared to an average of minutes or that have very low total variance because the user was not interacting with the environment for the entire time.