# A Quantitative Evaluation of Drive Pattern Selection for Optimizing EIT-Based Stretchable Sensors

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## Abstract

**:**

## 1. Introduction

## 2. Electrical Impedance Tomography

#### 2.1. EIT Mathematical Formulation

#### 2.2. Regularization

#### 2.3. Image Reconstructions

## 3. Methods

#### 3.1. Performance Parameters

#### 3.1.1. Voltage Data Parameters

#### 3.1.2. Image Parameters

#### 3.2. Drive Patterns

#### 3.3. Sensor Model

#### 3.4. Experiments

#### 3.5. Simulation Studies

## 4. Experimental Results and Discussion

#### 4.1. Voltage Data Parameters

#### 4.2. Image Parameters

#### 4.3. Discussion

## 5. Conclusions

## Acknowledgments

## Author Contributions

## Conflicts of Interest

## References

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**Figure 1.**Flow chart of Electrical Impedance Tomography (EIT) image reconstruction. The grey shaded boxes show the calculations done in the offline setup of the system. Then, the second set of potentials ${V}^{1}$ is updated online and an image showing the conductivity changes inside the sensor is reconstructed.

**Figure 2.**Performance parameters for the reconstructed image. On the top left, the EIT-based sensor, and in green the target placed over it. After the voltage data is acquired, the image of the conductivity change is reconstructed (top right, $\left(\widehat{{x}_{O}}\right)$). Then, the Region Of Interest (ROI) is selected and the parameters of Size Error (SE), Position Error (PE) and Ringing (RNG) are calculated from the post-processed image $\left(\widehat{{x}_{P}}\right)$.

**Figure 3.**From top to bottom, the first and last of sixteen steps for the (

**a**) Adjacent (AD); (

**b**) Pseudo-Polar (PP) and (

**c**) PP-PP drive patterns for a 16-electrode system. For each injection step, current is applied between a pair of electrodes and the resulting voltage is measured between the remaining pairs. The current excitation and voltage measurement is then rotated until the last step.

**Figure 4.**Our EIT-based stretchable sensor. In (

**a**), the conductive fabric material in shown; and, in (

**b**), the material when stretched; in (

**c**), a touch pressure is applied over the sensor and, in (

**d**), an image showing the conductivity change is reconstructed from the boundary voltage data. Red colour indicates a positive changes in the conductivity.

**Figure 6.**In (

**a**), the experiments are conducted with the load applied each time step at $\mathrm{x}=0.75$, $\mathrm{x}=0.5$, $\mathrm{x}=0.25$ and finally at $\mathrm{x}=0$. The last experiment in (

**b**) is the simultaneous two-point pressure at locations $\mathrm{x}1=-0.5$ and $\mathrm{x}2=0.5$.

**Figure 7.**Performance parameters from simulated data are shown in the case of single point input positions.

**Figure 8.**On the left, the real contact locations are shown, and for each one the reconstructed and processed images in the case of AD, PP and PP-PP pattern are presented.

**Figure 9.**Performance parameters from experimental data are shown in the case of single point input positions.

Position | AD | PP | PP-PP | ||||||
---|---|---|---|---|---|---|---|---|---|

SE | PE | RNG | SE | PE | RNG | SE | PE | RNG | |

$\mathrm{x}=0.75$ | 1.4% | 0.7% | 0.204 | 3.5% | 2.3% | 0.301 | 4.6% | 7.8% | 0.411 |

$\mathrm{x}=0.5$ | 1.6% | 0.7% | 0.197 | 2.8% | 1.7% | 0.423 | 3.2% | 4.2% | 0.401 |

$\mathrm{x}=0.25$ | 3.0% | 0.8% | 0.340 | 1.6% | 1.7% | 0.471 | 3.3% | 6.3% | 0.432 |

$\mathrm{x}=0$ | 4.2% | 1.1% | 0.370 | 1.1% | 1.1% | 0.465 | 3.7% | 2.5% | 0.402 |

$\mathrm{x}1=0.5$ and $\mathrm{x}2=0.5$ | 4.4% | 23.4% | 0.451 | 2.5% | 22.6% | 0.472 | 1.5% | 14.4% | 0.428 |

AD | PP | PP-PP |
---|---|---|

54.98 | 58.93 | 76.06 |

**Table 3.**Boundary voltage changes (mV) for the three different drive patterns at different target locations.

Position | AD | PP | PP-PP |
---|---|---|---|

$\mathrm{x}=0.75$ | 55 | 182 | 587 |

$\mathrm{x}=0.5$ | 52 | 156 | 900 |

$\mathrm{x}=0.25$ | 49 | 273 | 547 |

$\mathrm{x}=0$ | 16 | 368 | 697 |

$\mathrm{x}1=0.5$ and $\mathrm{x}2=0.5$ | 129 | 1324 | 1487 |

Position | AD | PP | PP-PP | ||||||
---|---|---|---|---|---|---|---|---|---|

SE | PE | RNG | SE | PE | RNG | SE | PE | RNG | |

$\mathrm{x}=0.75$ | 1.8% | 2.2% | 0.263 | 3.2% | 3.6% | 0.332 | 3.1% | 6.7% | 0.324 |

$\mathrm{x}=5$ | 2.0% | 3.3% | 0.299 | 2.5% | 3.1% | 0.313 | 2.3% | 6.5% | 0.318 |

$\mathrm{x}=0.25$ | 2.6% | 3.7% | 0.328 | 1.6% | 2.5% | 0.396 | 3.4% | 1.4% | 0.355 |

$\mathrm{x}=0$ | 3.3% | 3.9% | 0.300 | 0.6% | 2.4% | 0.380 | 5.3% | 4.4% | 0.369 |

$\mathrm{x}1=0.5$ and $\mathrm{x}2=0.5$ | 1.7% | 38.0% | 0.353 | 1.3% | 22.5% | 0.372 | 1.0% | 20.0% | 0.361 |

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**MDPI and ACS Style**

Russo, S.; Nefti-Meziani, S.; Carbonaro, N.; Tognetti, A.
A Quantitative Evaluation of Drive Pattern Selection for Optimizing EIT-Based Stretchable Sensors. *Sensors* **2017**, *17*, 1999.
https://doi.org/10.3390/s17091999

**AMA Style**

Russo S, Nefti-Meziani S, Carbonaro N, Tognetti A.
A Quantitative Evaluation of Drive Pattern Selection for Optimizing EIT-Based Stretchable Sensors. *Sensors*. 2017; 17(9):1999.
https://doi.org/10.3390/s17091999

**Chicago/Turabian Style**

Russo, Stefania, Samia Nefti-Meziani, Nicola Carbonaro, and Alessandro Tognetti.
2017. "A Quantitative Evaluation of Drive Pattern Selection for Optimizing EIT-Based Stretchable Sensors" *Sensors* 17, no. 9: 1999.
https://doi.org/10.3390/s17091999