# 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

- Smith, R.W.; Freeston, I.L.; Brown, B.H. A real-time electrical impedance tomography system for clinical use-design and preliminary results. IEEE Trans. Biomed. Eng.
**1995**, 42, 133–140. [Google Scholar] [CrossRef] [PubMed] - Bodenstein, M.; David, M.; Markstaller, K. Principles of electrical impedance tomography and its clinical application. Crit. Care Med.
**2009**, 37, 713–724. [Google Scholar] [CrossRef] [PubMed] - Tallman, T.; Gungor, S.; Wang, K.; Bakis, C. Damage detection and conductivity evolution in carbon nanofiber epoxy via electrical impedance tomography. Smart Mater. Struct.
**2014**, 23, 045034. [Google Scholar] [CrossRef] - Knight, R.; Lipczynski, R. The use of EIT techniques to measure interface pressure. In Proceedings of the Twelfth Annual International Conference of the IEEE Engineering in Medicine and Biology Society, Philadelphia, PA, USA, 1–4 November 1990; pp. 2307–2308. [Google Scholar]
- Silvera-Tawil, D.; Rye, D.; Soleimani, M.; Velonaki, M. Electrical impedance tomography for artificial sensitive robotic skin: A review. IEEE Sens. J.
**2015**, 15, 2001–2016. [Google Scholar] [CrossRef] - Wang, H.; de Boer, G.; Kow, J.; Alazmani, A.; Ghajari, M.; Hewson, R.; Culmer, P. Design Methodology for Magnetic Field-Based Soft Tri-Axis Tactile Sensors. Sensors
**2016**, 16, 1356. [Google Scholar] [CrossRef] [PubMed] - Tomo, T.P.; Somlor, S.; Schmitz, A.; Jamone, L.; Huang, W.; Kristanto, H.; Sugano, S. Design and characterization of a three-axis hall effect-based soft skin sensor. Sensors
**2016**, 16, 491. [Google Scholar] [CrossRef] [PubMed] - Cirillo, A.; Ficuciello, F.; Natale, C.; Pirozzi, S.; Villani, L. A Conformable Force/Tactile Skin for Physical Human–Robot Interaction. IEEE Robot. Autom. Lett.
**2016**, 1, 41–48. [Google Scholar] [CrossRef] - Silvera Tawil, D.; Rye, D.; Velonaki, M. Interpretation of the modality of touch on an artificial arm covered with an EIT-based sensitive skin. Int. J. Robot. Res.
**2012**, 31, 1627–1641. [Google Scholar] [CrossRef] - Nagakubo, A.; Alirezaei, H.; Kuniyoshi, Y. A deformable and deformation sensitive tactile distribution sensor. In Proceedings of the IEEE International Conference on Robotics and Biomimetics, 2007 (ROBIO 2007), Sanya, China, 15–18 December 2007; pp. 1301–1308. [Google Scholar]
- Tallman, T.; Gungor, S.; Wang, K.; Bakis, C. Tactile imaging and distributed strain sensing in highly flexible carbon nanofiber/polyurethane nanocomposites. Carbon
**2015**, 95, 485–493. [Google Scholar] [CrossRef] - Holder, D.S. Electrical Impedance Tomography: Methods, History and Applications; CRC Press: Boca Raton, FL, USA, 2004. [Google Scholar]
- Xu, C.; Dong, X.; Shi, X.; Fu, F.; Shuai, W.; Liu, R.; You, F. Comparison of drive patterns for single current source EIT in computational phantom. In Proceedings of the 2nd International Conference on Bioinformatics and Biomedical Engineering, 2008, (ICBBE 2008), Shanghai, China, 16–18 May 2008; pp. 1500–1503. [Google Scholar]
- Gallo, G.J.; Thostenson, E.T. Spatial damage detection in electrically anisotropic fiber-reinforced composites using carbon nanotube networks. Compos. Struct.
**2016**, 141, 14–23. [Google Scholar] [CrossRef] - Demidenko, E.; Hartov, A.; Soni, N.; Paulsen, K.D. On optimal current patterns for electrical impedance tomography. IEEE Trans. Biomed. Eng.
**2005**, 52, 238–248. [Google Scholar] [CrossRef] [PubMed] - Kaipio, J.P.; Seppänen, A.; Voutilainen, A.; Haario, H. Optimal current patterns in dynamical electrical impedance tomography imaging. Inverse Probl.
**2007**, 23, 1201. [Google Scholar] [CrossRef] - Silva, O.L.; Lima, R.G.; Martins, T.C.; de Moura, F.S.; Tavares, R.S.; Tsuzuki, M.S.G. Influence of current injection pattern and electric potential measurement strategies in electrical impedance tomography. Control Eng. Pract.
**2017**, 58, 276–286. [Google Scholar] [CrossRef] - Russo, S.; Carbonaro, N.; Tognetti, A.; Nefti-Meziani, S. A Quantitative Evaluation of Drive Patterns in Electrical Impedance Tomography. In Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, Proceedings of the 6th EAI International Conference on Wireless Mobile Communication and Healthcare, Milan, Italy, 14–16 November 2016; Springer International Publishing: Cham, Switzerland, 2017; pp. 337–344. [Google Scholar]
- Polydorides, N.; Lionheart, W.R. A Matlab toolkit for three-dimensional electrical impedance tomography: A contribution to the Electrical Impedance and Diffuse Optical Reconstruction Software project. Meas. Sci. Technol.
**2002**, 13, 1871. [Google Scholar] [CrossRef] - Brandstatter, B. Jacobian calculation for electrical impedance tomography based on the reciprocity principle. IEEE Trans. Magn.
**2003**, 39, 1309–1312. [Google Scholar] [CrossRef] - Lionheart, W.R. EIT reconstruction algorithms: Pitfalls, challenges and recent developments. Physiol. Meas.
**2004**, 25, 125. [Google Scholar] [CrossRef] [PubMed] - Alirezaei, H.; Nagakubo, A.; Kuniyoshi, Y. A highly stretchable tactile distribution sensor for smooth surfaced humanoids. In Proceedings of the 7th IEEE-RAS International Conference on Humanoid Robots, 2007, Pittsburgh, PA, USA, 29 November–1 December 2007; pp. 167–173. [Google Scholar]
- Wilkinson, A.J.; Randall, E.; Cilliers, J.; Durrett, D.; Naidoo, T.; Long, T. A 1000-measurement frames/second ERT data capture system with real-time visualization. IEEE Sens. J.
**2005**, 5, 300–307. [Google Scholar] [CrossRef] - Adler, A.; Lionheart, W.R. Uses and abuses of EIDORS: An extensible software base for EIT. Physiol. Meas.
**2006**, 27, S25. [Google Scholar] [CrossRef] [PubMed] - Yasin, M.; Böhm, S.; Gaggero, P.O.; Adler, A. Evaluation of EIT system performance. Physiol. Meas.
**2011**, 32, 851. [Google Scholar] [CrossRef] [PubMed] - Gagnon, H.; Cousineau, M.; Adler, A.; Hartinger, A.E. A resistive mesh phantom for assessing the performance of EIT systems. IEEE Trans. Biomed. Eng.
**2010**, 57, 2257–2266. [Google Scholar] [CrossRef] [PubMed] - Bera, T.K.; Nagaraju, J. Studying the resistivity imaging of chicken tissue phantoms with different current patterns in Electrical Impedance Tomography (EIT). Measurement
**2012**, 45, 663–682. [Google Scholar] [CrossRef] - Isaacson, D. Distinguishability of conductivities by electric current computed tomography. IEEE Trans. Med. Imaging
**1986**, 5, 91–95. [Google Scholar] [CrossRef] [PubMed] - Geselowitz, D.B. An application of electrocardiographic lead theory to impedance plethysmography. IEEE Trans. Biomed. Eng.
**1971**, BME-18, 38–41. [Google Scholar] [CrossRef] - Brown, B.; Seagar, A. The Sheffield data collection system. Clin. Phys. Physiol. Meas.
**1987**, 8, 91. [Google Scholar] [CrossRef] [PubMed] - Cheney, M.; Isaacson, D.; Newell, J.C.; Simske, S.; Goble, J. NOSER: An algorithm for solving the inverse conductivity problem. Int. J. Imaging Syst. Technol.
**1990**, 2, 66–75. [Google Scholar] [CrossRef] - Shi, X.; Dong, X.; Shuai, W.; You, F.; Fu, F.; Liu, R. Pseudo-polar drive patterns for brain electrical impedance tomography. Physiol. Meas.
**2006**, 27, 1071. [Google Scholar] [CrossRef] [PubMed] - Kolehmainen, V.; Vauhkonen, M.; Karjalainen, P.; Kaipio, J. Assessment of errors in static electrical impedance tomography with adjacent and trigonometric current patterns. Physiol. Meas.
**1997**, 18, 289. [Google Scholar] [CrossRef] [PubMed] - Zhang, H.; Tao, X.; Yu, T.; Wang, S. Conductive knitted fabric as large-strain gauge under high temperature. Sens. Actuators A Phys.
**2006**, 126, 129–140. [Google Scholar] [CrossRef] - Li, L.; Au, W.M.; Wan, K.M.; Wan, S.H.; Chung, W.Y.; Wong, K.S. A resistive network model for conductive knitting stitches. Text. Res. J.
**2010**, 80, 935–947. [Google Scholar] [CrossRef] - Naushad, A.; Rashid, A.; Mazhar, S. Analysing the performance of EIT images using the point spread function. In Proceedings of the 2014 International Conference on Emerging Technologies (ICET), Islamabad, Pakistan, 8–9 December 2014; pp. 36–41. [Google Scholar]

**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