**Note:** This preprint has been accepted for publication in [ IEEE Sensors Journal ]. The published version is available on IEEE Xplore: [DOI link]( https://ieeexplore.ieee.org/document/10659376 ).   A high-resolution two-dimensional (2D) electrical impedance tomography (EIT) system requires a larger number of electrodes and a finer mesh than its traditional counterpart. This increases the required number of measurements and, in turn, the amount of computation for the image reconstruction. Given the inverse and ill-posed nature of the EIT systems, they require a high signal-to-noise ratio (SNR) acquisition system as well as a high-precision hardware accelerator platform. In this paper, we present a field programmable gate array (FPGA)-based acquisition system with a tunable single-frequency current source that can reach an acquisition speed of more than 500 and 2400 frames per second (fps) for an excitation signal frequency of 500 kHz using 32 and 16 electrodes, respectively. The data processing and reconstruction are carried out using the most recent embedded Graphical Processing Unit (GPU, Nvidia Jetson Orin) by utilizing multiple Cuda cores to perform parallel high-speed 2D image reconstruction. Five different algorithms, namely linear back projection (LBP), Tikhonov regularization (TK), one-step Gauss Newton (GN), Landweber (LW), and iterative Tikhonov (ITK), were used for investigation. A gain in speed-up of at least 4 times was observed over the traditional implementations on recent general-purpose computers (PCs). Extensive experiments indicate that the proposed system can yield a throughput of more than 2500 fps for a 16-electrode system with around 8192 mesh elements. This paves the way for EIT systems to be potentially used in high-speed imaging applications as well as in 3D EIT applications which involve even larger amount of mesh elements.

This paper suggests a high resolution, high frame rate strain mapping sensor that uses three layers to measure the position as well as the value of the strain applied at different locations of the sensor. The top layer which should be highly conductive can be connected to either a current source or a voltage source, depending on the value of conductivity separating it from the ground during the switching sequence. The middle layer which consists of a strain-compressible material, such as piezoresistive foam, exhibits a variable electric resistance the value of which decreases with the increase of the applied strain. In this paper, the sensor sensitivity ranges from 50 to 500 kPa, however it can accommodate any other type of piezoresistive material provided that an adequate calibration is done. The lower layer consists of segmented highly electrically conductive tracks, the pattern of which allows to detect both the location, and the strain intensity at the points of contacts. The sensor is designed to also compensate for eventual changes of the electric resistance function of the temperature. Additionally, it has the advantage to mitigate eventual crosstalks that may occur between adjacent electrodes, since it keeps grounding utmost two electrode, forcing the electric current to flow into only two points. To our best knowledge, these simultaneous attributes have not been reported by a single system. This yields the advantage of using the sensor for a wide range of applications, including rehabilitation and human-computer interaction. A series of experiments and FEM simulations reveal that the sensor is highly accurate and can provide both the location and intensity of multiple contacts with an accuracy of 97.5 % at a frame rate of 20 frames/s when using 29 electrodes.