# Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials

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## Abstract

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## 1. Introduction

## 2. Materials and Methods

#### 2.1. Participants

#### 2.2. The Sensor

#### 2.3. Data Selection

- Hitting the mannequin’s chest gently/strongly
- Hugging the mannequin gently/strongly
- Hitting the mannequin’s shoulder gently/strongly
- Rubbing the mannequin’s shoulder gently/strongly
- Rubbing the mannequin’s chest gently/strongly

- Hitting the mannequin’s chest gently (Touch1)
- Hugging the mannequin gently (Touch2)
- Hitting the mannequin’s shoulder gently (Touch3)
- Hitting the mannequin’s chest strongly (Touch4)
- Hugging the mannequin’s strongly (Touch5)
- Hitting the mannequin’s shoulder strongly (Touch6)

#### 2.4. Analysis

#### 2.4.1. Touch Feature Computation

- Maximum Touch Activated Area (MTAA): We first found the frame with maximum number of activated touch sensor’s cells (i.e., out of 180 frames, per touch scenario, per participant). MTAA was then computed as m × 1.12${}^{2}$ where m refers to the number of activated cells and 1.12${}^{2}$ is the area of a single cell.
- Cumulative Sum of Touch Intensity (CSTI): This was calculated as the sum of activation (i.e., measured pressure, per cell) of all cells in the maximally touch activated frame that was used for computing the Maximally Activated Touch Area above.
- Relative Time of Maximally Activated Frame (RTMAF): This was computed as the index of the frame (i.e., out of 180 frames, per touch scenario) that corresponded to the frame with Maximally Activated Touch Area above.

#### 2.4.2. Touch Classification

#### 2.4.3. Improving the RF Accuracy

- Reduction of Undesired Activity around a Mannequin’s Neck: In our data, we observed that the activity around the mannequin’s neck was present in all participants’ data and regardless of the touch gesture that they performed on this mannequin. Further investigation of these data revealed that such an undesired activity was present even prior to the start of the participants’ session. The latter observation verified that the observed noisy activity was due to the inadequate placement of the sensor vest on the mannequin’s upper body. To attenuate this effect, we extracted the sensor’s data of all the participants that pertained to the one frame prior to the start of their session, per touch scenario. Next, we located all the sensor’s cells that were commonly active in all of these frames (i.e., one per participant, per touch gesture). We then computed the MTAA and CSTI features (Section 2.4.2 and Figure 2) for this undesired activity around the mannequin’s neck. Subsequently, we subtracted them from all participants’ corresponding MTAA and CSTI features that were calculated during their sessions, per touch gesture.
- Introduction of an Additional Feature: We computed a new feature; largest connected component (LCC). In essence, LCC corresponded to the number of activated sensor’s cells in the largest connected area of the sensor vest. In this respect, whereas MTAA quantified the maximum number of activated sensor’s cells during a touch session, LCC represented the number of such cells that formed a connected neighboring cells that formed the largest subset of such pattern of activation. To compute LCC, we treated the sensor’s data in terms of a graph. Precisely, we first converted this data to an adjacency matrix by assigning a “1” or a “0” to every cell “c” if it was active or inactive:$$\begin{array}{c}\left\{\begin{array}{cc}{c}_{ij}=1\hfill & \mathrm{if}\phantom{\rule{4.pt}{0ex}}\mathrm{the}\phantom{\rule{4.pt}{0ex}}\mathrm{cell}\phantom{\rule{4.pt}{0ex}}\mathrm{at}\phantom{\rule{4.pt}{0ex}}\mathrm{row}\phantom{\rule{4.pt}{0ex}}\mathrm{i}\phantom{\rule{4.pt}{0ex}}\mathrm{and}\phantom{\rule{4.pt}{0ex}}\mathrm{column}\phantom{\rule{4.pt}{0ex}}\mathrm{j}\phantom{\rule{4.pt}{0ex}}\mathrm{was}\phantom{\rule{4.pt}{0ex}}\mathrm{active},\phantom{\rule{3.33333pt}{0ex}}\forall i,j=1,\cdots ,32\hfill \\ {c}_{ij}=0\hfill & \mathrm{otherwise}\hfill \end{array}\right.\end{array}$$
^{th}row and j^{th}column location on this frame. Next, we computed all the connected components [21] of this adjacency matrix. LCC was then the connected component that comprised the largest number of active cells among all of these connected components. Figure 3 shows the modified feature vectors, per touch gesture, per participant that included LCC.Figure 4 visualizes the pairwise cosine similarity distances between different touch gestures. This figure verifies that the use of [MTAA, CSTI, and RTMAF, LCC] feature vectors for quantification of these touch gestures quite effectively captured the similarity between participants’ data for each of these gestures. This can be seen by inspecting the nearly zero-valued larger-area squares, per touch pairs that lay along the diagonal. Looking at the larger-area squares along the row entries, these features were also able to capture considerable dissimilarities between touch gestures of different type. These observations indicated that [MTAA, CSTI, and RTMAF, LCC] feature vectors extracted substantial motion-related spatial information that were inherent characteristic/property of these touch gestures. This is due to the fact that the cosine similarity quantifies the similarities among a given set of vectors in terms of their directions in space.

#### 2.4.4. Statistics

#### 2.5. Ethics Statement

## 3. Results

#### 3.1. Overall Accuracies

#### 3.2. Between-Gesture Accuracies

#### Improving the RF Accuracy

## 4. Discussion

## 5. Limitations and Future Directions

## Author Contributions

## Funding

## Conflicts of Interest

## Appendix A. Verification of the Non-Overfitting Performance of Random Forest (RF)

**Figure A1.**Verification of the non-overfitting performance of Random Forest (RF). (

**A**) Learning curve of the RF during training (red) and cross-validation (green). The incremental pattern of cross-validated results as a function of training size is apparent in this figure. This subplot also verifies that the RF accuracy does not show a high variation between subsequent cross-validations. (

**B**) Boot strap (1000 simulation runs) test of significance at CI${}_{95.0\%}$ (i.e., p < 0.05) on overall (i.e., all touch gestures combined) accuracy of RF. (

**C**) Boot strap (1000 simulation runs) test of significance at 95.0% confidence interval CI${}_{95.0\%}$ (i.e., p < 0.05) on RF accuracy for each of touch gestures. In (

**B**,

**C**), the x-axis corresponds to the difference between the bootstrapped RF accuracy and its average accuracy. The lines in red mark the confidence interval of these differences at 95.0% confidence interval (i.e., p < 0.05). The lines in magenta mark the average values associated with these differences. In these subplots, the average differences were within the confidence interval of their respective subplot. This verifies that RF performance was consistent across all trials and with respect to its overall as well as each of the touch gestures separately.

**Table A1.**Bootstrap test of significance for RF overall accuracy (i.e., all touch gestures combined) and its accuracy for each of touch gestures separately. In this table, M, SD, and CI${}_{95.0\%}$ refer to mean, standard deviation, and 95.0% confidence interval (i.e., p < 0.05) of the bootstrapped estimate of RF accuracy.

Accuracy | M | SD | CI${}_{95.0\%}$ |
---|---|---|---|

Overall | 89.50 | 20.47 | [83.08 86.42] |

Touch 1 | 89.50 | 20.47 | [84.50 93.00] |

Touch 2 | 80.50 | 30.06 | [73.50 85.50] |

Touch 3 | 75.00 | 27.98 | [69.28 80.50] |

Touch 4 | 81.50 | 25.28 | [76.00 86.00] |

Touch 5 | 100.00 | 0.00 | [100.00 100.00] |

Touch 6 | 81.50 | 29.86 | [75.00 86.50] |

## Appendix B. RF Performance Using a Larger Number of Touch Gestures

- Rubbing the mannequin’s shoulder gently (Touch7)
- Rubbing the mannequin’s chest strongly (Touch8)
- Rubbing the mannequin’s chest gently (Touch9)

**Figure A2.**Feature vectors for three new touch gestures. From left: Rubbing the mannequin’s shoulder gently (Touch 7), Rubbing the mannequin’s chest gently (Touch 9), and Rubbing the mannequin’s chest strongly (Touch 8). MTAA, CSTI, RTMAF, LCC features are shown along the x-axis. In these subplots, MTAA, CSTI, and RTMAF are same as in Figure 2. The values in these subplots are z-normalized (i.e., each column is mean-subtracted and divided by its standard deviation). The y-axis corresponds to the participants included in this study (i.e., p1 through p9).

**Figure A3.**Random forest (RF) classifier’s performance using the extended feature space on nine touch gestures. The subplot on the right shows the accuracy of this classifier for each touch scenario (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of random forest (red-line in the right subplot) was 88.33% (chance level (black-line in the right subplot) ≈ 11.11%, given nine-class classification of balanced classes).

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**Figure 1.**(

**A**) Hitting the mannequin’s shoulder gently (

**B**) Hugging the mannequin strongly. In these figures, the heatmaps visualize the sensor values with red and blue indicating strength and gentle touches. These figures verify that whereas Touch3 (i.e., hitting the mannequin’s shoulder gently) corresponded to weakly activated sensor’s cells, the values associated with the sensors’ cells activation in the case of Touch5 (i.e., hugging the mannequin strongly) exhibited more variability.

**Figure 2.**Touch Features for (

**A**) Hitting the mannequin’s chest gently (left) and strongly (right) (

**B**) Hugging the mannequin gently (left) and strongly (right) (

**C**) Hitting the mannequin’s shoulder gently (left) and strongly (right). The calculated features are shown along the x-axis. They are Maximally Touch Activated Area (MTAA), Cumulative Sum of Touch Intensity (CSTI), and Relative Time of Maximally Activated Frame (RTMAF). The values depicted in these subplots are z-normalized (i.e., each column is mean-subtracted and divided by its standard deviation). The y-axis corresponds to the participants in this study (i.e., p1 through p9).

**Figure 3.**Modified Touch Features. (

**A**) Hitting the mannequin’s chest gently (left) and strongly (right); (

**B**) Hugging the mannequin gently (left) and strongly (right); and (

**C**) Hitting the mannequin’s shoulder gently (left) and strongly (right). MTAA, CSTI, RTMAF, and LCC features are shown along the x-axis. In these subplots, MTAA, CSTI, and RTMAF are same as in Figure 2. The values in these subplots are z-normalized (i.e., each column is mean-subtracted and divided by its standard deviation). The y-axis corresponds to the participants in this study (i.e., p1 through p9).

**Figure 4.**Pairwise cosine similarity distance between all touch gestures’ feature vectors. This figure verifies that MTAA, CSTI, RTMAF, and LCC quite effectively captured the similarity between participants’ data that pertained to same touch gestures. This is evident in the nearly zero-valued larger-area squares, per touch pairs that lay along the diagonal. Looking at the larger-area squares along the row entries, per touch pairs, these features were also able to extract considerable dissimilarities between gestures of different type. Each of these larger-area squares is a 9 × 9 matrix: i.e., one small square (a cosine similarity) per participant and every other participant (i.e., self-similarities included). As a result, each touch gesture is associated with a 9 × 54 (i.e., 9 participants × 6 touch gestures) cosine similarity sub-matrix that is extended along the row of the overall cosine similarity matrix of all touch gestures in this figure.

**Figure 5.**Models’ overall accuracies. Asterisks mark the significant differences between these classifiers’ performance as indicated by their pairwise Wilcoxon rank sum tests (*: p < 0.01, ***: p< 0.00001). In this figure, significantly higher overall accuracy (chance level 16.67%) of RF compared to other classifiers is evident.

**Figure 6.**Random forest (RF) classifier. The subplot on the right shows the accuracy of this classifier for each touch scenario (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of random forest (red-line in the right subplot) was 85.00% (chance level (black-line in the right subplot) ≈ 16.67, given six-class classification of balanced classes).

**Figure 7.**Naive Bayes (NB) classifier. The subplot on the right shows the accuracy of this classifier for each touch scenarios (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of Naive Bayes (red-line in the right subplot) was 77.62% (chance level (black-line in the right subplot) ≈ 16.67, given six-class classification of balanced classes).

**Figure 8.**Decision tree (DT) classifier. The subplot on the right shows the accuracy of this classifier for each touch scenarios (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of decision tree (red-line in the right subplot) was 66.67% (chance level (black-line in the right subplot) ≈ 16.67, given six-class classification of balanced classes).

**Figure 9.**K-nearest-neighbor (KNN) classifier. The subplot on the right shows the accuracy of this classifier for each touch scenarios (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of K-nearest-neighbor (red-line in the right subplot) was 47.45% (chance level (black-line in the right subplot) ≈ 16.67, given six-class classification of balanced classes).

**Figure 10.**Support vector classifier (SVC) with radial basis kernel. The subplot on the right shows the accuracy of this classifier for each touch scenarios (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of support vector classifier (red-line in the right subplot) was 60.74% (chance level (black-line in the right subplot) ≈ 16.67, given six-class classification of balanced classes).

**Figure 11.**Logistic regression (LR) classifier. The subplot on the right shows the accuracy of this classifier for each touch scenarios (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of logistic regression (red-line in the right subplot) was 58.28% (chance level (black-line in the right subplot) ≈ 16.67, given six-class classification of balanced classes).

**Figure 12.**Random forest (RF) classifier. The subplot on the right shows the accuracy of this classifier for each touch scenarios (i.e., Touch1 through Touch6). The subplot on the left presents the confusion matrix for this classifier. The overall accuracy of random forest (red-line in the right subplot) was 85.00% (chance level (black-line in the right subplot) ≈ 16.67, given six-class classification of balanced classes).

**Figure 13.**RF overall accuracy “before” and “after” original feature vectors (i.e., (MTAA, CSTI, RTMAF, LCC)) were extended by six additional features that were the Euclidean distances between the averaged cosine similarities (Section 2.4.3). Asterisks mark the significant differences between these classifiers’ performance as indicated by their pairwise Wilcoxon rank sum tests (***: p< 0.00001).

Technology | Piezoresistive |
---|---|

Pressure Range | 0.1 to 200 PSI (0.007 to 14.1 kg/cm^{2}) |

Matrix Size | Up to 64 × 256 lines |

Thickness | From 12 mils (0.3 mm) |

Mat Sensor Size | Customizable up to 150" (381 cm) |

1Scan Speed | Up to 1000 hertz |

Min Sensing Point Size | 0.188 in2 (1.21 cm^{2}) |

Stretchability | Up to 158% |

Accuracy | ±10% |

Repeatability | ±2% |

Hysteresis | ±5% |

Nonlinearity | ±1.5% |

Calibration | NIST Traceable |

**Table 2.**Overall (i.e., all touch gestures combined) average (i.e., 100 simulation runs) accuracy, precision, recall, and F1-score associated with random forest (RF), naive Bayes (NB), decision tree (DT), k-nearest-neighbor (KNN), support vector classifier (SVC), and logistic regression (LR). M and SD stand for the mean and the standard deviation of each model’s accuracies in 100 simulation runs.

Classifier | Accuracy | Precision | Recall | F1-Score |
---|---|---|---|---|

RandomForest | M = 85.00%, SD = 8.60 | 0.89 | 0.87 | 0.86 |

Naive Bayes | M = 77.62 %, SD = 10.65 | 0.86 | 0.83 | 0.82 |

DecisionTree | M = 66.67%, SD = 9.66 | 0.68 | 0.64 | 0.66 |

KNN | M = 47.45%, SD = 12.90% | 0.57 | 0.54 | 0.53 |

Support Vector | M = 60.74%, SD = 12.68% | 0.60 | 0.62 | 0.61 |

Logistic Regression | M = 58.28%, SD = 14.47 | 0.57 | 0.56 | 0.58 |

**Table 3.**Paired posthoc Wilcoxon rank sum tests between average (i.e., 100 simulation runs) accuracy of every pairs of models. The models are: random forest (RF), K-nearest-neighbor (KNN), support vector classifier (SVC), logistic regression (LR), Naive Bayes (NB), and decision tree (DT). W and r refer to the Wilcoxon test-statistics and effect size, respectively. Significantly superior performance of RF compared to all other models is evident in this table.

Models | p < | W(198) | r |
---|---|---|---|

RF vs. KNN | 0.00001 | 12.12 | 0.86 |

RF vs. SVC | 0.00001 | 10.86 | 0.77 |

RF vs. LR | 0.00001 | 11.06 | 0.78 |

RF vs. NB | 0.00001 | 4.54 | 0.32 |

RF vs. DT | 0.00001 | 10.08 | 0.71 |

NB vs. KNN | 0.00001 | 11.33 | 0.80 |

NB vs. LR | 0.00001 | 9.04 | 0.64 |

NB vs. SVC | 0.00001 | 8.42 | 0.60 |

NB vs. DT | 0.00001 | 7.07 | 0.50 |

KNN vs. SVC | 0.00001 | −7.07 | −0.50 |

KNN vs. LR | 0.00001 | −5.06 | −0.36 |

KNN vs. DT | 0.00001 | −9.67 | −0.68 |

SVC vs. LR | 0.00001 | 7.07 | 0.50 |

SVC vs. DT | 0.01 | −3.02 | −0.21 |

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

## Share and Cite

**MDPI and ACS Style**

Keshmiri, S.; Shiomi, M.; Sumioka, H.; Minato, T.; Ishiguro, H. Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials. *Sensors* **2020**, *20*, 3033.
https://doi.org/10.3390/s20113033

**AMA Style**

Keshmiri S, Shiomi M, Sumioka H, Minato T, Ishiguro H. Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials. *Sensors*. 2020; 20(11):3033.
https://doi.org/10.3390/s20113033

**Chicago/Turabian Style**

Keshmiri, Soheil, Masahiro Shiomi, Hidenobu Sumioka, Takashi Minato, and Hiroshi Ishiguro. 2020. "Gentle Versus Strong Touch Classification: Preliminary Results, Challenges, and Potentials" *Sensors* 20, no. 11: 3033.
https://doi.org/10.3390/s20113033