# Tactile Perception of Woven Fabrics by a Sliding Index Finger with Emphasis on Individual Differences

^{*}

## Abstract

**:**

## 1. Introduction

## 2. Materials and Methods

#### 2.1. Samples

#### 2.2. Participants

#### 2.3. Measurements

#### 2.3.1. Physical Properties: Surface, Compression, and Heat Flow Properties

^{2}), which is related to the warm/cool feeling, was also measured using a KES Thermo Labo II (Kato Tech Co., Ltd., Kyoto, Japan) in accordance with the JIS L1927 standard.

#### 2.3.2. Descriptive Sensory Evaluation

#### 2.3.3. Interface Parameters: Index Finger Skin Vibration, Contact Force, and Translation Speed

#### 2.4. Analysis

- Differences in the use of attributes and measurement scale;
- Discrimination ability;
- Differences in sensitivity (perception and recognition);
- Misunderstanding of the meaning of attributes;
- Confusion of similar attributes;
- Repeatability.

#### 2.4.1. Datasets

#### 2.4.2. Structure of the Sensory Dataset

#### 2.4.3. Descriptive Statistical Analysis

#### 2.4.4. ANOVA

#### 2.4.5. PCA and Tucker-1

#### 2.4.6. Consonance Analysis—Visualization of Agreement among Participants

#### 2.4.7. Principal Component Regression (PCR)

**X**represents the sensory dataset,

**T**represents the PCA scores,

**P**represents the loadings, and

**E**represents the residuals. PCA was conducted on each attribute separately, with rows as samples and columns as participants.

**y**represents a physical or interface attribute,

**T**is the PCA score,

**q**is the regression coefficients for

**y**on

**T**, and

**f**is the residuals. The regression coefficients are also known as regression loadings because they assume the same role as ${\mathbf{P}}^{\mathbf{T}}$ in Equation (8). The regression loading plots provided a direct relationship between the predictors (sensory data) and the response (physical attributes and interface attributes).

## 3. Results and Discussion

#### 3.1. Descriptive Analysis

#### 3.2. ANOVA

#### 3.3. Consonance Analysis

#### 3.3.1. Vertical Unfolding—Sensory Dataset

#### 3.3.2. Horizontal Unfolding—Sensory Dataset and Delta Power

## 4. PCR

## 5. Conclusions

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Abbreviations

KES-F | Kawabata Evaluation System for Fabrics |

PCA | Principal Component Analysis |

DA | Sensory Descriptive Analysis |

MIU | Coefficient of Friction |

MMD | Mean Deviation of MIU |

SMD | Surface Roughness |

LC | Linearity of Compression |

WC | Compression Energy |

RC | Compression Resilience |

PSD | Power Spectral Density |

PCR | Principal Component Regression |

TWFA | Three-way Factor Analysis |

## Appendix A

Property | Symbol | Characteristic Value | Unit | Measurement Condition |
---|---|---|---|---|

Compression | LC | Linearity of compression displacement curve | — | Maximum pressure, Pm, = 5 kPa |

WC | Compression energy | J/m^{2} | Rate of compression = 20 µm/s | |

RC | Compression resilience | % | ||

Surface | MIU | Coefficient of friction | — | 20 steel piano wires with 0.5 mm diameter and 10 mm length. |

MMD | Mean deviation of MIU | — | Contact force = 0.49 N | |

SMD | Geometrical roughness | µm | Steel piano wire with 0.5 mm diameter and 5 mm length. Contact force = 0.1 N | |

Thickness | T_{0} | Thickness at pressure of 49.0 Pa | mm | |

Weight | W | Fabric weight per unit area | g/m^{2} | |

q_{max} | q_{max} | Maximum value of heat flux | W/cm^{2} | ΔT = 10 °C |

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**Figure 1.**Photographs of the surface structures of the samples with weave directions. (

**A**–

**F**) Six woven fabric samples with different weave structures and surface roughness values were used in this study.

**Figure 2.**

**Measurement of surface properties**. (

**a**) Measurement of surface friction using 20 piano wires ($\varphi =0.5$ mm, probe size of 1 cm × 1 cm) (MIU and MMD). (

**b**) Surface geometry measurement using a U-type piano wire ($\varphi =0.5$ mm) (SMD).

**Figure 3.**

**Experimental setup**. Starting from the stage, the participants slid the index finger over the sample from left to right (slide direction) in a smooth and continuous motion. On the index finger, a motion capture marker and a skin vibration sensor were attached to measure the speed and the vibration signal, respectively. Beneath the sample, a pressure plate was used to measure contact force. Two markers on the pressure were defined as the effective experimental areas where all the parameters were calculated.

**Figure 4.**

**Three-way matrix unfolding**. The figure depicts the two unfolding strategies used in Tucker-1 analysis. The three-way dataset is arranged either (a) vertically as an ($mn\times p$) matrix or (b) horizontally as an ($n\times mp$) matrix, resulting in unique sample–participant or participant–attribute pairs. This forms the foundation for the agreement (consonance) analysis, where participants with similar perceptions of samples (a) cluster together in the principal component space and participants with similar perceptions of attributes or (b) cluster together similarly.

**Figure 5.**

**Two-way ANOVA (sensory and interface data)**. Model with effects for the sample and the participants. (

**a**) Sensory attributes and (

**b**) fabric–finger interface attributes. The bar length represents the F-value. The attributes in red have a p-value > 0.05 and were regarded as non-significant.

**Figure 6.**

**Consonance analysis—sensory data (vertical unfolding)**. The agreement in the samples can be inspected. Attributes flagged by the ANOVA have been dropped. Cross-validation was performed, leaving one participant out. “Scores” represents the distribution of the samples, as seen by the participants compressed in the first two PCs. Bold colors represent the sample median. “Loadings” represents the attributes of the PCA space. Subjective preference attributes are highlighted in red. “Correlation loadings” is an alternative scaling of the loadings plot, where each original attribute is correlated with the score components. The outer ellipse corresponds to a 100% correlation and the inner to a 50% correlation.

**Figure 7.**

**Consonance analysis (scores)—sensory and delta power (horizontal unfolding)**. The left scores plot corresponds to the sensory data and the right corresponds to delta power.

**Figure 8.**

**Consonance analysis (loadings)—sensory and delta power (horizontal unfolding)**. Correlation loadings plot for each sensory attribute. Each graph represents the same data. The same graph is repeated highlighting each sensory attribute at a time. The level of agreement is given by the level of the clustering of the participants.

**Figure 9.**

**Principal component regression—correlation loadings plots**. Physical properties and delta power were regressed onto the principal components of the sensory dataset. The regression coefficients (regression loadings) are plotted in red. The percentages shown on the left indicate the explained variance in the component. On the right, the mean R

^{2}values for all the response variables in the component are shown. Delta power was regressed without averaging and the medium value (blue) is highlighted. Roughly, values inside the inner ellipse are not significant (p-value > 0.05).

Sample | Fiber Ratio (%) | Weave Structure | Density (cm) | Thickness mm | Weight g/m ^{2} | L* (D65), a*, b*, c* (*1) | |
---|---|---|---|---|---|---|---|

Ends | Picks | ||||||

A | Polyester 99/polyurethane 1 | Wedge slab | 28 | 20 | 0.90 | 214 | 15.28, 0.21, 0.08, 0.23 |

B | Wool 100 | 3 × 1 twill | 22 | 12 | 1.80 | 406 | 13.81, 0.03, −0.84, 0.80 |

C | Polyester 100 | Satin | 95 | 39 | 0.24 | 9 | 18.78, 0.47, −0.56, 0.73 |

D | Wool 100 | 2 × 1 twill | 38 | 30 | 1.13 | 243 | 13.22, 0.17, −1.37, 1.38 |

E | Mohair 56/wool 35/water soluble vinylon 9 | Plain | 35 | 30 | 0.38 | 155 | 15.98, −0.10, −1.38, 1.34 |

F | Wool 56/paper 40/cotton 4 | 2 × 2 twill | 60 | 64 | 3.40 | 415 | 16.91, −0.18, −1.39, 1.40 |

Code No | Direction | Surface | Heat Flow | Compression | ||||
---|---|---|---|---|---|---|---|---|

SMD mm | MIU — | MMD — | q_{max}W/cm ^{2} | WC J/m ^{2} | RC % | LC — | ||

A-1 | Weft | 5.91 | 0.160 | 0.016 | 0.124 | 0.29 | 40.0 | 0.32 |

A-2 | Warp | 7.92 | 0.177 | 0.012 | ||||

B-3 | Weft | 8.16 | 0.286 | 0.018 | 0.057 | 0.59 | 50.6 | 0.34 |

B-4 | Warp | 2.90 | 0.170 | 0.009 | ||||

B-5 | Cross | 32.0 | 0.213 | 0.019 | ||||

B-6 | Parallel | 4.85 | 0.197 | 0.011 | ||||

C-7 | Weft | 1.54 | 0.177 | 0.003 | 0.208 | 0.07 | 37.6 | 0.38 |

C-8 | Warp | 0.68 | 0.140 | 0.002 | ||||

D-9 | Weft | 2.53 | 0.134 | 0.006 | 0.086 | 0.37 | 56.5 | 0.30 |

D-10 | Warp | 2.56 | 0.124 | 0.006 | ||||

E-11 | Weft | 3.52 | 0.131 | 0.015 | 0.188 | 0.11 | 52.7 | 0.30 |

E-12 | Warp | 7.52 | 0.158 | 0.020 | ||||

F-13 | Weft | 3.81 | 0.212 | 0.008 | 0.050 | 1.82 | 49.0 | 0.45 |

F-14 | Warp | 3.27 | 0.215 | 0.007 |

Dataset | Samples | Attributes | Participants |
---|---|---|---|

Sensory | 11 | 28 | |

Interface | 14 | 3 | 28 |

Physical | 7 | — |

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## Share and Cite

**MDPI and ACS Style**

Romao Santos, R.; Nakanishi, M.; Sukigara, S.
Tactile Perception of Woven Fabrics by a Sliding Index Finger with Emphasis on Individual Differences. *Textiles* **2023**, *3*, 115-128.
https://doi.org/10.3390/textiles3010009

**AMA Style**

Romao Santos R, Nakanishi M, Sukigara S.
Tactile Perception of Woven Fabrics by a Sliding Index Finger with Emphasis on Individual Differences. *Textiles*. 2023; 3(1):115-128.
https://doi.org/10.3390/textiles3010009

**Chicago/Turabian Style**

Romao Santos, Raphael, Masumi Nakanishi, and Sachiko Sukigara.
2023. "Tactile Perception of Woven Fabrics by a Sliding Index Finger with Emphasis on Individual Differences" *Textiles* 3, no. 1: 115-128.
https://doi.org/10.3390/textiles3010009