  Enabling dexterous manipulation and safe human-robot interaction, soft robots
are widely used in numerous surgical applications. One of the complications
associated with using soft robots in surgical applications is reconstructing
their shape and the external force exerted on them. Several sensor-based and
model-based approaches have been proposed to address the issue. In this paper,
a shape sensing technique based on Electrical Impedance Tomography (EIT) is
proposed. The performance of this sensing technique in predicting the tip
position and contact force of a soft bending actuator is highlighted by
conducting a series of empirical tests. The predictions were performed based on
a data-driven approach using a Long Short-Term Memory (LSTM) recurrent neural
network. The tip position predictions indicate the importance of using EIT data
along with pressure inputs. Changing the number of EIT channels, we evaluated
the effect of the number of EIT inputs on the accuracy of the predictions. The
least RMSE values for the tip position are 3.6 and 4.6 mm in Y and Z
coordinates, respectively, which are 7.36% and 6.07% of the actuator's total
range of motion. Contact force predictions were conducted in three different
bending angles and by varying the number of EIT channels. The results of the
predictions illustrated that increasing the number of channels contributes to
higher accuracy of the force estimation. The mean errors of using 8 channels
are 7.69%, 2.13%, and 2.96% of the total force range in three different bending
angles.
