Typo

3-perception/vhar-system/2-method.tex

@@ -24,9 +24,9 @@ The system consists of three main components: the pose estimation of the tracked
   These poses are used to move and display the virtual model replicas aligned with the \RE.
   A collision detection algorithm detects a contact of the virtual hand with the virtual textures.
   If so, the velocity of the finger marker ${}^c\dot{\mathbf{X}}_f$ is estimated using discrete derivative of position and adaptive low-pass filtering, then transformed onto the texture frame $\poseFrame{t}$.
-  The vibrotactile signal $s_k$ is generated by modulating the (scalar) finger velocity ${}^t\hat{\dot{X}}_f$ in the texture direction with the texture period $\lambda$ (\eqref{signal}).
-  The signal is sampled at 48~kHz and sent to the voice-coil actuator via an audio amplifier.
-  All computation steps except signal sampling are performed at 60~Hz and in separate threads to parallelize them.
+  The vibrotactile signal $r$ is generated by modulating the (scalar) finger velocity ${}^t\hat{\dot{X}}_f$ in the texture direction with the texture period $\lambda$ (\eqref{signal}).
+  The signal is sampled at \qty{48}{\kilo\hertz} and sent to the voice-coil actuator via an audio amplifier.
+  All computation steps except signal sampling are performed at \qty{60}{\hertz} and in separate threads to parallelize them.
 ]
 
 \section{Description of the System Components}

3-perception/vhar-textures/4-discussion.tex

@@ -16,7 +16,7 @@ Indeed, the majority of users explained that, based on the roughness, granularit
 Several strategies were used, as some participants reported using vibration frequency and/or amplitude to match a haptic texture.
 It should be noted that the task was rather difficult (\figref{results_questions}), as participants had no prior knowledge of the textures, there were no additional visual cues such as the shape of an object, and the term \enquote{roughness} had not been used by the experimenter prior to the \level{Ranking} task.
 
-The correspondence analysis (\figref{results/matching_correspondence_analysis}) highlighted that participants did indeed match visual and haptic textures primarily on the basis of their perceived roughness (\percent{60} of variance), which is in line with previous perception studies on real \cite{baumgartner2013visual} and virtual \cite{culbertson2014modeling} textures.
+The correspondence analysis (\figref{results/matching_correspondence_analysis}) highlighted that participants did indeed match visual and haptic textures primarily on the basis of their perceived roughness (\percent{60} of variance), which is in line with previous perception studies on real textures \cite{baumgartner2013visual} and virtual textures \cite{culbertson2014modeling}.
 The rankings (\figref{results/ranking_mean_ci}) confirmed that the participants all perceived the roughness of haptic textures very similarly, but that there was less consensus for visual textures, which is also in line with roughness rankings for real haptic and visual textures \cite{bergmanntiest2007haptic}.
 These results made it possible to identify and name groups of textures in the form of clusters (\figref{results_clusters}), and to construct confusion matrices between these clusters and between visual texture ranks with haptic clusters (\figref{results/haptic_visual_clusters_confusion_matrices}), showing that participants consistently identified and matched haptic and visual textures.
 \percent{30} of the matching variance of the correspondence analysis was also captured with a second dimension, opposing the roughest textures (\level{Metal Mesh}, \level{Sandpaper~100}), and to a lesser extent the smoothest (\level{Coffee Filter}, \level{Sandpaper~320}), with all other textures (\figref{results/matching_correspondence_analysis}).