# Augmenting Industrial Control Rooms with Multimodal Collaborative Interaction Techniques
## 4 The Study
### Experiments
#### 4.1.1 3D Immersive Scatterplot  
To address RQ 1, we have selected a 3D interactive scatterplot for our experiment as well as a simple data set representing cars and their performance indicators. The cube-shaped scatterplot consists of three axes and several points located in the 3D coordinate system. The points represent the cars. The dimensions of the coordinate system are bounded by the axes. Each horizontal dimension is assigned a performance characteristic in the first scenario, while the vertical dimension shows the price dimension. The origin of the axes is not zero. Instead, each axis has as its minimum value set to the lowest value of the respective characteristic from the set of cars. The maximum value is consequentially the largest value. Outside of the scatterplot itself are two smaller cubes located to manipulate the transformation of the scatterplot. In Figure 4(b), the blue plane shows the performance indicators of the selected car/point.  
The scatterplot is partly a self-developed solution. Some premade scripts are used, provided by the Mixed Reality Toolkit from Microsoft. For example, for constraining the transformation information of the two cubes and the scatterplot, premade scripts are used. The scatterplot itself is built with functions provided by the Unity Game Engine.  
Each car is defined by its manufacturer and model name and has a price, horsepower, curb weight, and tank size associated with it. The evaluation is split into two scenarios. In both of them, the user is supposed to find the best car in terms of performance per price using the scatterplot. In the first scenario (3D scenario) the user has to take into account the price dimension and two performance characteristics. Performance indicators are horsepower, curb weight or tank size. In the second scenario (4D scenario), the task is the same as in the 3D scenario, but performance indicators are also assigned to the vertical axis and the price dimension is visualized by coloring: the brightest car being the most expensive and the darkest the cheapest. By comparing the cars selected by the users with a list of cars sorted by their equally weighted price/performance ratio the evaluation is in parts quantifiable. Test persons were asked to use both--the 3D interactive scatterplot with the HoloLens 2 device (cf. Figure 4(b)) and an equivalent application for a desktop pc/laptop with adapted controls (cf. Figure 4(a)). Both applications are based on the Unity framework. Test persons alternately used the Desktop or the HoloLens application first to reduce skewing of the evaluation results through learning effects. This enables us to compare the performance of the test persons with respect to the different devices.  
Figure 4(b) shows the scatterplot in front of the smart warehouse demonstrator of the SmartFactoryOWL [32]. In this industrial context, the scatterplot could be used to position the different components, such as flat conveyers, in the space of key performance indicators, such as power consumption, speed, or voltage.  
In order to answer RQ 1, we asked the following questions to the test persons:  
* _Which of the two applications made the decision process easier for you?_
* _Which scenario made it easier for you to orientate yourself?_  
Figure 5: View of the scatterplot applications used in the experiment. (a) Desktop application; (b) HoloLens application.  
* _In which of the two scenarios was it easier to include the price information indexed by the colour in your decision making?_