Smart Lighting (Hue) Experiment

Nowadays there is a shift happening in light control. Smart lighting systems (such as Philips hue) are now available for use at home. At this moment the only way to control this light is through a graphical interface on a smart device. It is interesting to see how tangible interfaces can play a role in the control of connected lighting. Therefore we decided to set up an experiment to determine the difference between graphical and tangible interfaces and gain insight into the usability of the interaction possibilities.

Design explorations

In order to make the design interesting we decided to design 3 prototypes that vary in mapping of abstraction. This means that within the interaction; the parameters such as hue, saturation and brightness are indirectly mapped to the control of the light in varying amounts. How much abstract control can a user handle and what is the general opinion on the different kinds of interaction? These questions can give valuable insights into the way tangible light control interfaces should be designed.

Through this approach we can not only validate a hypothesis by comparison but also gain valuable insights into the opinion of users on the interaction methods.

Prototype 1. Hue App: This app is the original way to control the hue lamps and allows you to drag individual virtual lamps over a color palette to choose a color. Underneath you can set the intensity of the lights. (little abstraction)

Prototype 2. Sliders: This prototype is an almost literal translation of hue, saturation and brightness through the use of sliders in a tangible way. Each slider is mapped to a technical parameter (little abstraction).

Prototype 3. Mouse: The way a user moves the mouse is translated in light around him. Speed determines the brightness of all lamps and the direction translates into color (x-axis) and color intensity (y-axis). (high abstraction level)

Prototype 4. Button: The way the button is pressed is reflected in the lights underneath the button. The harder and longer a user touches the button results in cold (white) light, softer and slower touch makes the light warmer (yellow). The expression of the user is shown in the form of light around them. The user can increase the amount of light over time on the first touch and decrease on the second touch. (high abstraction level)

Hypothesis

In the interaction with connected lighting we see a clear difference in how people perceive a graphical app and a tangible interface.

Method

SONY DSC

To set up a realistic test we could test 4 different prototypes to control 4 Philips Hue lamps in a room. The prototypes were designed to be different in abstraction level. By using tangible interfaces we count on people’s experience to help in dealing with the amount of parameters. By manipulating a tangible device the user can feel what he/she is doing and perceive the changes around them. By changing the level of abstraction we can gain insight if users would prefer these more abstract ways of controlling connected lighting.

Experiment

We had 4 prototypes connected to the 4 hue lamps.

1. Entrances and Explanation

On entrance the users was asked to sit down and explained where the lamps are and that the lamps could be controlled through the interfaces. Each prototype had a brief explanation of the interaction in front of them.

2. Tasks

For each prototype the user was asked to create two scenes: a scene that would support reading a book and a party scene. First they read the description and then they started to make the reading scene. While making this scene the users exploring the functionality do each interface and setting the scenes. (The scene setting was timed)

3. Comparison Questionnaire

The users were asked to put each prototype on a scale of 6 categories Freedom (of interaction), Joy, Effort, Desirability, Abstraction and Functionality. In this way the user could compare the prototypes and we could say something about this comparison in the analysis.

Analysis

11 participants performed the test. During the test data was collected on the categories: Freedom (of interaction), Joy, Effort, Desirability, Abstraction and Functionality. Together with this data the time it took for the participants to perform the exercises were logged, the time would not be included in the analysis because the emphasis of the test is on the experience rather than performance.

For each category the participants would scale all the prototypes on one scale, 90 mm wide, the idea behind this was to force the participants to create differences between prototypes. The scales where then measured and put into a table.

The data for each category was imported separately into Illmo, in this way the results between the 4 prototypes for each category would become apparent. Much of the data wouldn’t have a fit with a regular Gaussian model, within the data there sometimes appeared to be two populations, which could be a result of the method used in the scaling.

In order to make the Gaussian model fit the data splines where used in order to work with the double populations, furthermore a few outliers in the data were eliminated. After the fitting the app was determined as our reference and the analysis could be made.

Abstraction:

abstraction2

From the beginning we considered the two last prototypes to be abstract and the 2nd prototype as a tangible interpretation from the app. The data shows the same interpretation. The last two prototypes are significantly more abstract than the first two.

Freedom:

freedom2

Within this category we see that every prototype that we made had a lesser degree of freedom than the original app. It appears to be that freedom is less present in the more abstract models (3&4). But we have to consider that all participants did not equally interpret freedom.

Effort:

effort2

In the scaling we scaled from much to little effort, which means that much effort resulted in a lower score than little effort. The data from this test shows that all of the prototypes took more effort than the app by Philips.

Functionality:

functionality2

All prototypes seem to be less functional than the original app. This is especially the case for the last two prototypes.

Joy:

joy2

The 2nd and 3rd prototype seem to be give a more joyful experience than the first and the last, which are almost equally joyful.

Desirabilty:

desirability2

Although the differences are not very big, the app seems to be the most desired tool for changing the ambient lighting in the given tasks

Discussion and conclusion

The goal of making more abstract and tangible prototypes to control the Hue lighting than the app seems to be reached, all the prototypes, especially the mouse and button, are perceived as more abstract than the app. Since the participants did notice a increased level of abstraction in the three tangible prototypes we can safely say when looking at the other data that they also experienced the three tangible prototypes different from the app.

When comparing data there seems to be a link between the level of abstraction and the level of effort and functionality, where the more abstract prototypes take more effort and offer less functionality than the app. Overall the Philips app scores better than the other prototypes, which is not a surprise since the prototypes are designed and built in a day where the app has had a far longer developing time. The app is only less joyful as the other prototypes, but joy does not appear to alter the desirability of the app over the other prototypes

Issues with this test where that the method of scaling which forces persons to make decisions and differences are not applicable on this test and do not provide discrete data. The inconsistencies in where the positive and the negative aspects on the scales are placed could also make the scaling hard to make for the participants. Also some categories where not good enough defined leaving room for different interpretations.

This test gave us insight in how abstraction can affect other aspects of design; furthermore the real goal of the test was to gain insight into generating experimental data and to analyze it, experiencing what the added value of software such as Illmo is for industrial design. In that goal the test succeeded, we have seen what data can mean, how we can interpret it and how to gain confidence in design decisions and conclusions.

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