Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing
<p>Structure of this study.</p> "> Figure 2
<p>Information of plastic plates. The plates were as follows: #1, polystyrene; #2, unknown; #3, polypropylene; #4, polyethylene; #5, polycarbonate; #6, polymethylmethacrylate; #7, unknown; #8, polyethylene. (<b>a</b>) The enlarged views of test samples (scale bar: 5 mm). (<b>b</b>) Dynamic friction coefficient, <span class="html-italic">µ’</span> (mean ± SD, n = 10).</p> "> Figure 3
<p>Tactile sensing system with developed tactile sensor. (<b>a</b>) Actual image of the tactile sensor. (<b>b</b>) Schematic diagram of the tactile sensor structure. (<b>c</b>) Overall view of the sensing system.</p> "> Figure 4
<p>Conceptual diagram for calculation of the features.</p> "> Figure 5
<p>Dendrogram obtained from cluster analysis.</p> "> Figure 6
<p>Results of feature calculation for the eight samples. (<b>a</b>) <span class="html-italic">D</span><sub>SAI</sub>, (<b>b</b>) <span class="html-italic">D</span><sub>SAISAIIFAI</sub>, (<b>c</b>) <span class="html-italic">D</span><sub>SAISAIIFAII</sub>, (<b>d</b>) <span class="html-italic">D</span><sub>ALL</sub>, (<b>e</b>) <span class="html-italic">D</span><sub>SAIIFAII</sub>, (<b>f</b>) <span class="html-italic">D</span><sub>FAII</sub>, (<b>g</b>) <span class="html-italic">D</span><sub>SAISAII</sub>, (<b>h</b>) <span class="html-italic">D</span><sub>SAIFAII</sub> (mean ± SD, <span class="html-italic">n</span> = 11, NS: no significant difference at 5% significance probability, *: <span class="html-italic">p</span> < 0.05, **: <span class="html-italic">p</span> < 0.01, †: significant differences are noted in the text).</p> "> Figure 7
<p>Relationship between the actual value and the predicted value. (<b>a</b>) Cluster 1, (<b>b</b>) Cluster 2.</p> ">
Abstract
:1. Introduction
2. Materials and Methods
2.1. Strategy of Tactile Estimation Modeling
2.2. Target Samples
2.3. Sensory Evaluation Test
2.4. Vibration Measurement System and Procedure
2.5. Data Processing Methods
2.6. Tactile Estimation Models
3. Results
3.1. Sensory Evaluation Results
3.2. Feature Values Extracted from Vibration
3.3. Regression Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
Abbreviations
Abbreviation | Definition |
PCA | principal component analysis |
PCs | principal components |
FFT | fast Fourier-transformation |
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Model Type | Feature Extraction Method | |
---|---|---|
Previously Reported Method [27] | Proposed Method | |
Linear | A-1 | B-1 |
Logarithmic | A-2 | B-2 |
Interaction | A-3 | B-3 |
Polynomial | A-4 | B-4 |
Evaluation Word | Principal Component Load | ||||
---|---|---|---|---|---|
Cluster 1 | Cluster 2 | ||||
PC1 | PC2 | PC1 | PC2 | PC3 | |
Smooth | −0.933 | −0.055 | −0.646 | 0.186 | −0.517 |
Sticky | 0.913 | 0.136 | 0.695 | 0.300 | −0.257 |
Pasty | 0.872 | 0.120 | 0.724 | 0.385 | 0.088 |
Feel friction-drag | 0.877 | 0.000 | 0.741 | 0.220 | 0.099 |
Moisten | 0.840 | 0.236 | 0.466 | 0.371 | 0.276 |
Sleek | −0.845 | 0.196 | −0.617 | 0.356 | −0.033 |
Slippery | −0.561 | 0.427 | −0.200 | 0.725 | 0.075 |
Velvety | −0.215 | 0.836 | −0.603 | 0.319 | 0.318 |
Fine | −0.048 | 0.810 | −0.452 | 0.356 | 0.507 |
Rough | −0.188 | −0.772 | −0.011 | −0.673 | 0.461 |
Eigenvalue | 5.50 | 2.26 | 3.18 | 1.79 | 1.00 |
Contribution rates (%) | 50.4 | 22.8 | 26.3 | 18.8 | 14.6 |
Cumulative contribution rates (%) | 50.4 | 73.2 | 26.3 | 45.1 | 59.7 |
Cluster | Principal Component | Model | |||||||
---|---|---|---|---|---|---|---|---|---|
A-1 | A-2 | A-3 | A-4 | B-1 | B-2 | B-3 | B-4 | ||
Cluster 1 | PC1 | 0.134 | 0.115 | 0.876 | 0.138 | 0.052 | 0.018 | 0.061 | 0.876 |
PC2 | 0.539 | 0.506 | 1.133 | 0.795 | 0.545 | 0.535 | 0.451 | 0.338 | |
Cluster 2 | PC1 | 0.328 | 0.231 | 0.211 | 0.426 | 0.227 | 0.209 | 0.307 | 0.542 |
PC2 | 0.268 | 0.325 | 0.303 | 0.733 | 0.046 | 0.048 | 0.138 | 0.321 | |
PC3 | 0.418 | 0.416 | 0.688 | 0.360 | 0.386 | 0.385 | 0.337 | 0.441 |
Cluster | Principal Component | Equation | R2 | R’2 | p |
---|---|---|---|---|---|
Cluster 1 | PC1 | (10) | 0.995 | 0.986 | 0.000854 |
PC2 | (11) | 0.458 | 0.241 | 0.217 | |
Cluster 2 | PC1 | (12) | 0.837 | 0.772 | 0.0107 |
PC2 | (13) | 0.935 | 0.887 | 0.0077 | |
PC3 | (14) | 0.308 | −0.211 | 0.651 |
Objective Variable | Explanatory Variable | p | |
---|---|---|---|
yC1PC1 | 0.797 | 0.004 | |
−0.426 | 0.011 | ||
0.469 | 0.018 | ||
0.438 | 0.013 | ||
yC2PC1 | 0.515 | 0.039 | |
0.645 | 0.018 | ||
yC2PC2 | 0.357 | 0.072 | |
−0.787 | 0.005 | ||
0.798 | 0.005 |
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Sagara, M.; Nobuyama, L.; Takemura, K. Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing. Sensors 2022, 22, 6697. https://doi.org/10.3390/s22176697
Sagara M, Nobuyama L, Takemura K. Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing. Sensors. 2022; 22(17):6697. https://doi.org/10.3390/s22176697
Chicago/Turabian StyleSagara, Momoko, Lisako Nobuyama, and Kenjiro Takemura. 2022. "Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing" Sensors 22, no. 17: 6697. https://doi.org/10.3390/s22176697
APA StyleSagara, M., Nobuyama, L., & Takemura, K. (2022). Nonlinear Tactile Estimation Model Based on Perceptibility of Mechanoreceptors Improves Quantitative Tactile Sensing. Sensors, 22(17), 6697. https://doi.org/10.3390/s22176697