Experimental Setup

5.3.1 Study Design, Participants, and Apparatus

The study used a single-factor within-participant design with interaction technique as four-level factor (Touch Scroll, Touch Flat, Screen Pointing, and Room Pointing). The order of appearance was balanced using a Latin square.

I recruited 16 participants (4 female, 12 male; ages 21 − 36, 𝑥̅ = 27 years; 3 left-, 13 right- handed) from a local university. All participants had experience with traditional computer

systems and owned a smart phone; six participants have previously used full-arm gesture control. They received a $10 honorarium for participating in this one-hour-long study.

The study was carried out in a laboratory, in which I recreated a living-room-like setting with a couch, a 42” TV screen (2.1 𝑚 to the couch), and a mobile 7” touch screen with 5: 3 aspect ratio

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and 133 𝑑𝑝𝑖 resolution (see Figure 59, left) that was connect to my experiment computer via USB. I chose this distance so that the field of view for the TV and the touch screen were comparable. For both pointing-based techniques, I tracked participant’s gestures using an OptiTrack infrared tracker. I set its sampling rate to 40 𝐻𝑧 and used a Butterworth-filter to remove frequencies above 12.5 𝐻𝑧 to remove the effects of hand jitter.

5.3.2 Adding Digital Artifacts to the Environment

In this study, I also wanted to simulate a behavior I deemed typical in domestic smart

environments: adding more digital artifacts. One can imagine, for example, adding more cooking recipes or e-books to one’s repository. I therefore added 10 digital artifacts in the final two blocks of each condition (trails+).

5.3.3 Study Conditions and Procedures

I implemented four different interaction techniques, two touch- and two pointing-based. Both

Touch Scroll and Touch Flat mimicked current smart-phone like interaction. In Touch Scroll,

participants could only see 15 buttons at a time, so they had to scroll left or right if the desired button was currently not displayed. In Touch Flat, all 30 (40 in trials+) buttons were shown concurrently, although at half the size (area) than in Touch Scroll. The two pointing-based techniques were Screen Pointing and Room Pointing.

After filling out a consent form and an initial questionnaire, participants were seated on the couch. In both touch-based conditions, participants were handed the touch-sensitive screen (see Figure 59, left); in both pointing conditions, the touch screen was placed on a stool in front of the couch, and the participants were handed a tracked Wii Remote (see Figure 59, right). I used the trigger button (“B”) on the Wii Remote for selection confirmation and calculated participants’ pointing direction using the rigid bodies taped to the Wii Remote (Screen Pointing) or to the participant’s index and middle finger (Room Pointing). Every 1 − 2 𝑠 (randomized, uniformly distributed), a pop-up on the touch screen asked participants to select a given digital artifact. There were a total of 30 (40 in trials+) artifacts, divided in five categories: books, movies, TV series, environment commands, and bookmarks. Each selection technique had a different set of artifacts to avoid learning effects across conditions; I picked artifacts from known sources (Academy and Emmy Award winners, Alexa Ranking, B&N bestseller list) to make the sets comparable. At the beginning of the experiment, the system randomly selected 7 of the 30

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possible artifacts. These were the 7 artifacts participants were asked to select during the

experiment. In my analysis, I considered seven selections (each artifact once in random order) as one block.

Figure 59: Touch interface (left) and pointing controllers (right)

Each participant went through three phases (practice, trials, trials+) and performed a total of 28 + 56 + 14 = 98 selections per technique (or 4 + 8 + 2 = 14 blocks). Generally, the practice and the trials phases were identical and just separated by a pop-up window that gave the

experimenter a chance to check in with the participants; for Room Pointing, however, I turned off the continuous feedback about participant’s current pointing target (see Figure 60, top right) with the beginning of the trials phase. This made Room Pointing a system-feedback-free

technique. In the trials+ phase, 10 new artifacts were added to the user interface, resulting in more scrolling (Touch Scroll) , more and smaller on-screen buttons (Touch Flat: new button size 1.1 × 2.0 𝑐𝑚2 _{; and Screen Pointing), and smaller target zones (Room Pointing, see Figure 64).} In both Touch Scroll and Touch Flat, participants had to click a home button on the touch-screen, which brought them back to the main menu. After this, they had to start the “Room Control” app by tapping on a prominently located icon on the main screen. Finally, they had to find the correct item to select on the screen (see Figure 60, top left and top center).

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In the Screen Pointing condition, participants could use their arm to move an on-screen cursor and a click with the B-button on the Wii Remote to confirm their selection. For the selection, participants had to click once with the Wii Remote to bring up the “Room Control”-menu and then select the artifact from the menu (see Figure 60, bottom).

Figure 60: Touch Scroll (top left), Touch Flat (top center),

Room Pointing (top, right), and Screen Pointing (bottom)

In the Room Pointing condition, participants had to point at the correct real-world objects and confirm the selection using the Wii Remote’s B-button. While pointing during the practice-

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phase, participants received feedback about the current selection and the associated real-world proxy (see Figure 60, top right).

After each interaction technique, participants filled out a NASA TLX form; after the experiment, which lasted for 1 hour, they were paid a $10 honorarium.

5.3.4 Data Analysis

As an initial step, I removed all 3𝜎-outliers from the time-data in order to account for unusual participant behavior, such as playing around with the system; I calculated 𝜎 per participant and per phase (practice, trials, trials+). This amounted 3.2 % of all data to be removed, which is higher than expected (99.7 %) and could indicate that my data was not perfectly normal- distributed.

For RM-ANOVAs, I used Greenhouse-Geisser correction for non-spherical data and Bonferroni correction for post-hoc tests.