# Physically encoding data with material flexibility

Bahare Bakhtiari, Eric Huber, Sowmya Somanath, Charles Perin

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Bahare Bakhtiari, Eric Huber, Sowmya Somanath, Charles Perin. Physically encoding data with material flexibility. IEEE VIS 2025 Electronic Conference Proceedings, Nov 2025, Vienna, Austria. hal-05514439

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# Physically encoding data with material flexibility

Bahare Bakhtiari* University of Victoria

Eric Huber† University of Victoria

Sowmya Somanath‡ University of Victoria

Charles Perin§ University of Victoria

<figure><img src="image_2.png" /><figcaption>Figure 1: Left: 3D printed Thermoplastic polyurethane (TPU) with different print properties resulting in different flexibility and elasticity levels. Right: printing with different wall thickness (left), infill pattern (middle), and print orientation (right)</figcaption></figure>

## ABSTRACT

Data physicalizations utilize non-visual sensory modalities to encode data. There is a need to study how different material properties affect user interaction and perception with physicalizations. In this work, we investigate how material flexibility can be used to encode data, by studying i) how people interact with flexible material for understanding data and ii) how they perceive flexibility to decode encoded data values.

## 1 INTRODUCTION

Data physicalization as defined by Jansen et al. is about represent data through material properties and geometries [5]. Data physicalizations engage multiple human senses, both visual and non-visual. This presents an opportunity to communicate data to a broader range of people, including those with visual impairments. In educational contexts, it is essential to actively engage various senses, rather than relying solely on visual interactions with screens, when teaching concepts to children that aim at enhancing their cognitive skills [5]. There is also evidence that physical 3D bar charts can outperform on-screen 3D bar charts for some basic visualization tasks and that touch plays a major role when exploring the physical chart [4].

Despite a growing interest in data physicalization research and practice, and in stark contrast to the exhaustive exploration of perception of visual variables that has been going on in the visualization community for the past 40 years, we still know relatively little when it comes to how people perceive information when is is encoded through (non-visual) physical variables.

An early study focusing on the isolated exploration of tactile and visual sensory experiences was conducted by Klatzky et al. in 1987, investigating the cognitive representation of objects with and without visual input. The study utilized free sorting, prompting participants to categorize 'similar' objects into common bins. Results indicated that when participants relied solely on haptic cues, their judgments of similarity were inclined to be based on material and its associated properties, such as hardness and texture, while shape was the most dominant similarity measure when the study was involving vision. This study underscores the significance of studying the effects of material properties in tactile interactions and their influence on the perception of various physical and visual variables [6].

In this work, we focus on flexibility as a physical variable. Flexible material is widely used in interaction design. For example, deformable controllers are used as input or output modality in deformable screens [3](e.g., [11, 1, 7]). However, only recently the research community has started to explore data physicalizations with flexible materials [2, 10]. Flexible materials enable different types of interactions than traditional 2D screens do, such as bending, folding, twisting, and stretching [9]. Such interactions open new doors to understanding data represented in physical environments. We aim to answer two main questions about flexibility as a physical variable:

* Q1 – How do people interact with flexible materials to decode data?

* Q2 – How do people perceive data when it is encoded through flexibility?

We structure the remainder of this document according to these two questions.

## 2 Q1 – How do people interact with flexible materials to decode data?

To answer this question, we conducted a pilot psychophysical study to understand how people interact with various materials with different levels of rigidity, elasticity, and flexibility to decode data using tactile sensory.

Drawing upon the framework of eight exploratory procedures as defined by Lederman and Klartzky [8], our examination delved into participant interactions with different material samples. We selected a range of flexible material (both elastic and non-elastic: fabric, elastic band, silicone, and paper) and rigid material (wood and glass). We devised a size ratio estimation task in which participants were asked to interact with the materials provided inside a box where they did not have visual access. For each material, we asked participants to estimate the ratio of the size compared to a control sample of the same material. They could have and interact with both samples at the same time. We video-recorded their interactions and then went through those videos, open-coding extracts of how people interact with material with different properties, arriving at a set of 11 distinct interactions. We then analyzed the videos a second time, counting the number of interactions that participants performed with each material. In that second-round stage, we observed major similarities in interactions, particularly in the forms of enclosure and contour following. We also identified new interactions and diverse variations of enclosure and contour following. Figure 3 shows a summary of those results.

We found that for **rigid materials**, participants focused on interactions such as single contour following, two-handed full enclosure, one-handed full enclosure, partial enclosure, comparative contour

<footnote>*e-mail: baharebakhtiari@uvic.ca</footnote>

<footnote>†e-mail: erichuber@uvic.ca</footnote>

<footnote>‡e-mail: sowmyasomanath@uvic.ca</footnote>

<footnote>§e-mail: cperin@uvic.ca</footnote>
<figure><img src="image_3.png" /><figcaption>Figure 2: Flexible material affordances: a) on-ground contour following, b) applying pressure/force, c) two-handed full enclosure, d) folding, e) comparative contour following, f) moving samples alongside each other. Rigid materials affordances: g) two-handed full enclosure, h) partial enclosure, i) single contour following, j) moving samples alongside each other, k) turning Samples alongside each other, l) comparative contour following</figcaption></figure>

<table><caption>Figure 3: Total number of performed interactions with each material across all participants, in the ratio estimation task.</caption><thead><tr><th></th><th>Fabric</th><th>EW fabric</th><th>Silicone</th><th>Paper</th><th>Glass</th><th>Wood</th></tr></thead><tbody><tr><td>One-Handed full enclosure</td><td>0</td><td>0</td><td>0</td><td>0</td><td>0</td><td>1</td></tr><tr><td>Two-Handed full enclosure</td><td>5</td><td>6</td><td>2</td><td>4</td><td>3</td><td>8</td></tr><tr><td>Partial enclosure</td><td>0</td><td>0</td><td>0</td><td>0</td><td>2</td><td>0</td></tr><tr><td>Single contour following</td><td>6</td><td>4</td><td>2</td><td>5</td><td>4</td><td>2</td></tr><tr><td>Comparative contour following</td><td>11</td><td>20</td><td>12</td><td>17</td><td>18</td><td>18</td></tr><tr><td>On-ground contour following</td><td>7</td><td>0</td><td>8</td><td>6</td><td>0</td><td>6</td></tr><tr><td>Moving samples alongside each other</td><td>2</td><td>0</td><td>0</td><td>3</td><td>11</td><td>13</td></tr><tr><td>Turning samples alongside each other</td><td>0</td><td>0</td><td>2</td><td>2</td><td>5</td><td>4</td></tr><tr><td>Hand sizing</td><td>5</td><td>1</td><td>3</td><td>5</td><td>3</td><td>7</td></tr><tr><td>Applying pressure/force</td><td>0</td><td>6</td><td>1</td><td>0</td><td>0</td><td>0</td></tr><tr><td>Folding</td><td>11</td><td>19</td><td>19</td><td>8</td><td>0</td><td>0</td></tr></tbody></table>

following, moving and turning samples alongside each other, and hand sizing.

For flexible materials, participants focused on interactions such as single contour following, applying pressure and force, two-handed full enclosure, on-ground comparative contour following, folding, and moving samples alongside each other (see Figure 2).

Upon counting the number of interaction types observed throughout the entire ratio estimation task across different materials, it became evident that the dominant technique for interacting with two samples was comparative contour following (see Figure 3 l). Additionally, folding (see Figure 3 d) emerged as a dominant technique with flexible materials, while moving samples alongside each other (see Figure 3 j) was a dominant technique with rigid materials.

## 3 Q2 – HOW DO PEOPLE PERCEIVE DATA WHEN IT IS EN-CODED THROUGH FLEXIBILITY?

In this in-progress work, we are currently investigating several aspects that will allow us to understand how people perceive data when it is encoded through flexibility.

1. **Resolution** – what is the resolution of flexibility as a physical variable? In other words, how many levels of flexibility can people differentiate? To answer this question, we are considering Just Noticeable Difference protocols. We create various 3D printed bars by modifying the print properties (e.g., wall thickness, infill pattern, print orientation) and the filament properties (TPU filament with shore hardness of 60A-95A). Then we will use these printed sample for conducting ratio estimation tasks to gain insights toward the just noticeable differences.

2. **Accuracy** – how accurate are people's readings of values when encoded through flexibility? To answer this question, we are considering ratio estimation tasks in which participants will be asked to compare pairs of materials with all material properties being identical, except their flexibility. This would allow us to build a model of flexibility perception.

We are currently developing a study setup that will allow us to answer those research questions.

## 4 IMPLICATIONS FOR THE DESIGN OF PHYSICALIZATIONS

From the insights gained in the studies of this work and the work in progress, we will propose design implications for future data physicalization endeavors. The choice of material and its properties is critical for effective tactile interactions. Flexible materials, for instance, can enrich tactile experiences by offering affordances such as folding, contributing to a more versatile interaction. Conversely, rigid materials facilitate ease of interaction and prove more effective for analytical tasks, as demonstrated in tasks like ranking.

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