\section{User Study}
\label{experiment}

\subsection{The textures}
\label{textures}

The 100 visuo-haptic texture pairs of the \HaTT database \cite{culbertson2014one} were preliminary tested and compared using the apparatus described in \secref{apparatus} to select the most representative textures for the user study.
These texture models were chosen as they are visuo-haptic representations of a wide range of real textures that are publicly available online.
Nine texture pairs were selected (\figref{experiment/textures}) to cover various perceived roughness, from rough to smooth, as named on the database: \level{Metal Mesh}, \level{Sandpaper~100}, \level{Brick~2}, \level{Cork}, \level{Sandpaper~320}, \level{Velcro Hooks}, \level{Plastic Mesh~1}, \level{Terra Cotta}, \level{Coffee Filter}.
All these visual and haptic textures are isotropic: their rendering (appearance or roughness) is the same whatever the direction of the movement on the surface, \ie there are no local deformations (holes, bumps, or breaks).

\subsection{Apparatus}
\label{apparatus}

\figref{experiment/setup} shows the experimental setup, and \figref{experiment/view} the first person view of participants during the user study.
The user study was held in a quiet room with no windows, with one light source of \qty{800}{\lumen} placed \qty{70}{\cm} above the table.

Nine \qty{5}{\cm} square cardboards with smooth, white melamine surface, arranged in a \numproduct{3 x 3} grid, were used as real surfaces to augment.
Their poses were estimated with three \qty{2}{\cm} AprilTag fiducial markers glued on the surfaces grid.
Similarly, a \qty{2}{\cm} fiducial marker was glued on top of the vibrotactile actuator to detect the finger pose.
Positioned \qty{20}{\cm} above the surfaces, a webcam (StreamCam, Logitech) filmed the markers to track finger movements relative to the surfaces, as described in \secref[vhar_system]{virtual_real_registration}.
The visual textures were displayed on the real surfaces using the \OST-\AR headset Microsoft HoloLens~2 running a custom application at \qty{60}{FPS} made with Unity (v2021.1) and Mixed Reality Toolkit (v2.7).
A set of empirical tests enabled us to choose the best rendering characteristics in terms of transparency and brightness for the visual textures, that were used throughout the user study.

When a virtual haptic texture was touched, a \qty{48}{kHz} audio signal was generated using the rendering procedure described in \cite{culbertson2014modeling} from the corresponding \HaTT haptic texture model and the measured tangential speed of the finger (\secref[vhar_system]{texture_generation}).
The normal force on the texture was assumed to be constant at \qty{1.2}{\N} to generate the audio signal from the model, as \textcite{culbertson2015should}, who found that the \HaTT textures can be rendered using only the speed as input without decreasing their perceived realism.
The rendering of the virtual texture is described in \secref[vhar_system]{texture_generation}.
The vibrotactile voice-coil actuator (HapCoil-One, Actronika) was firmly attached to the middle index phalanx of the participant's dominant hand using a Velcro strap, similarly to previous studies \cite{asano2015vibrotactile,friesen2024perceived}.

\begin{subfigs}{setup}{Textures used and experimental setup of the user study. }[][
  \item The nine visuo-haptic textures used in the user study, selected from the \HaTT database \cite{culbertson2014one}.
    The texture names were never shown, to prevent the use of the user's visual or haptic memory of the textures.
  \item Experimental setup.
    Participant sat in front of the real surfaces, which were augmented with visual textures displayed by the Microsoft HoloLens~2 \AR headset and haptic roughness textures rendered by the vibrotactile haptic device placed on the middle index phalanx.
    A webcam above the surfaces tracked the finger movements.
  ]
  \subfig[0.49]{experiment/textures}
  \subfig[0.49]{experiment/setup}
\end{subfigs}

\subsection{Procedure and Collected Data}
\label{procedure}

Participants were first given written instructions about the experimental setup, the tasks, and the procedure of the user study.
Then, after having signed an informed consent form, they were asked to seat in front of the table with the experimental setup and to wear the \AR headset.
As the haptic textures generated no audible noise, participants did not wear any noise reduction headphones.
A calibration of both the HoloLens~2 and the finger pose estimation was performed to ensure the correct registration of the visuo-haptic textures and the real finger with the real surfaces, as described in \secref[vhar_system]{virtual_real_registration}.
Finally, participants familiarized with the augmented surface in a \qty{2}{min} training session with textures different from the ones used in the user study.

Participants started with the \level{Matching} task.
They were informed that the user study involved nine pairs of corresponding visual and haptic textures that were separated and shuffled.
On each trial, the same visual texture was displayed on the nine real surfaces, while the nine haptic textures were rendered on only one of the surfaces at a time, \ie all surfaces were augmented by the same visual texture, but each surface was augmented by a different haptic texture.
The placement of the haptic textures was randomized before each trial.
Participants were instructed to look closely at the details of the visual textures and explore the haptic textures with a constant pressure and various speeds to find the haptic texture that best matched the visual texture, \ie choose the surface with the most coherent visual-haptic texture pair.
The texture names were never given or shown to prevent the use of visual or haptic memory of the textures, nor a definition of what roughness is was given, to let participants complete the task as naturally as possible, similarly to \textcite{bergmanntiest2007haptic}.

Then, participants performed the \level{Ranking} task, employing the same setup as the matching task and the same 9 textures.
In this case, participants were asked to rank the textures according to their perceived roughness.
First, they ranked all the haptic textures (without any visual augmentation given), then all the visual textures (without any haptic augmentation given), and finally all the visuo-haptic texture pairs together, being informed that they were the correct matches as per the original \HaTT database.
The placement of the textures was also randomized before each trial.

The user study took on average 1 hour to complete.

\subsection{Experimental Design}
\label{design}

The user study was a within-subjects design with two tasks: \level{Matching} and \level{Ranking}.

In the \level{Matching} task, participants had to find the haptic texture that best matched a given visual texture.
It had one within-subjects factor, \factor{Visual Texture} with the following levels:
\level{Metal Mesh}, \level{Sandpaper~100}, \level{Brick~2}, \level{Cork}, \level{Sandpaper~320}, \level{Velcro Hooks}, \level{Plastic Mesh~1}, \level{Terra Cotta}, \level{Coffee Filter}.
To account for learning and fatigue effects, the order of \factor{Visual Texture} was counterbalanced using a balanced \numproduct{18 x 18} Latin square design.
A total of 9 textures \x 3 repetitions = 18 matching trials were performed per participant.

In the \level{Ranking} task, participants had to rank the haptic textures, the visual textures, and the visuo-haptic texture pairs according to their perceived roughness.
It had one within-subjects factor, \factor{Modality} with the following levels: \level{Visual}, \level{Haptic}, \level{Visuo-Haptic}.
Each modality level was ranked once per participant following the fixed order listed above (\secref{procedure}).

\subsection{Participants}
\label{participants}

Twenty participants took part in the user study (12 males, 7 females, 1 preferred not to say), aged between 20 and 60 years (\median{26.5}, \iqr{9}{}).
One participant was left-handed, all others were right-handed; they all performed the user study with their dominant hand.
All participants had normal or corrected-to-normal vision and none of them had a known hand or finger impairment.
They rated their experience with haptics, \AR, and \VR (\enquote{I use it every month or more}); 10 were experienced with haptics, 2 with \AR, and 10 with \VR.
Experiences were correlated between haptics and \AR (\spearman{0.53}), haptics and \VR (\spearman{0.61}), and \AR and \VR (\spearman{0.74}); but not with age (\spearman{-0.06} to \spearman{-0.05}) or gender (\spearman{0.10} to \spearman{0.27}).
Participants were recruited at the university on a voluntary basis.
They all signed an informed consent form before the user study.

\subsection{Collected Data}
\label{collected_data}

For each trial of the \level{Matching} task, the chosen \response{Haptic Texture} for the given displayed \factor{Visual Texture} was recorded.
The \response{Completion Time} was also measured as the time between the visual texture display and the haptic texture selection.
For each modality of the \level{Ranking} task, the \response{Rank} of each of the visual, haptic, or visuo-haptic pairs of the textures presented was recorded.

After each of the two tasks, participants answered to the following 7-item Likert scale questions (1=Not at all, 7=Extremely):
\begin{itemize}
  \item \response{Haptic Difficulty}: How difficult was it to differentiate the tactile textures?
  \item \response{Visual Difficulty}: How difficult was it to differentiate the visual textures?
  \item \response{Textures Match}: For the visual-tactile pairs you have chosen, how coherent were the tactile textures with the corresponding visual textures?
  \item \response{Haptic Realism}: How realistic were the tactile textures?
  \item \response{Visual Realism}: How realistic were the visual textures?
  \item \response{Uncomfort}: How uncomfortable was to use the haptic device?
\end{itemize}

In an open question, participants also commented on their strategy for completing the \level{Matching} task (\enquote{How did you associate the tactile textures with the visual textures?}) and the \level{Ranking} task (\enquote{How did you rank the textures?}).

The results were analyzed using R (v4.4) and the packages \textit{afex} (v1.4), \textit{ARTool} (v0.11), \textit{corrr} (v0.4), \textit{FactoMineR} (v2.11), \textit{lme4} (v1.1), and \textit{performance} (v0.13).
