A Quantitative Evaluation of Drive Patterns in Electrical Impedance Tomography

Russo, Stefania; Carbonaro, Nicola; Tognetti, Alessandro; Nefti‐Meziani, Samia

doi:10.1007/978-3-319-58877-3_43

Cited by 1 publication

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“…Therefore, even if the general topic of discussing which drive pattern is the optimal one to choose in EIT applications has been studied in the past, none have examined the relationship between the response of an EIT system and the contact position of the input stimuli. For this reason, in [ 18 ], we have preliminarily investigated how the boundary voltage data in an EIT sensor were affected by the target position using an 8-electrodes prototype system.…”

Section: Introductionmentioning

confidence: 99%

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

Russo

Nefti‐Meziani

Carbonaro

et al. 2017

Sensors

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Electrical Impedance Tomography (EIT) is a medical imaging technique that has been recently used to realize stretchable pressure sensors. In this method, voltage measurements are taken at electrodes placed at the boundary of the sensor and are used to reconstruct an image of the applied touch pressure points. The drawback with EIT-based sensors, however, is their low spatial resolution due to the ill-posed nature of the EIT reconstruction. In this paper, we show our performance evaluation of different EIT drive patterns, specifically strategies for electrode selection when performing current injection and voltage measurements. We compare voltage data with Signal-to-Noise Ratio (SNR) and Boundary Voltage Changes (BVC), and study image quality with Size Error (SE), Position Error (PE) and Ringing (RNG) parameters, in the case of one-point and two-point simultaneous contact locations. The study shows that, in order to improve the performance of EIT based sensors, the electrode selection strategies should dynamically change correspondingly to the location of the input stimuli. In fact, the selection of one drive pattern over another can improve the target size detection and position accuracy up to 4.7% and 18%, respectively.

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Section: Introductionmentioning

confidence: 99%