Toward a HumanMachine Interface Based on Electrical Impedance Tomography for Robotic Manipulator Control In this study, we proposed a novel humanmachine interface (HMI) for robotic manipulator control. The specific target was to adjust the impedance coefficients of the robot controller in real time by measuring the human forearm muscle contractions. We firstly designed a HMI system. Different from the frequently used sEMG technologies, the interface in our study could detect muscle morphological changes within the skin by the electrical impedance tomography (EIT). The sensing frontend was a soft elastic fabric band which was compatible to different arm shapes. With the specific designed sensing hardware and the reconstruction algorithms, EIT images indicating forearm muscle shapes were obtained. We then designed a hybrid positonimpedance controller on a UR5 with the impedance coefficients being tuned in real time by the grasp force estimation. A sigmoid regression algorithm was used to map the EIT images to the grasp forces. After implementation of the whole system, two experiments were carried out. The first experiment was the offline grasp force estimation. With the 1:1 cross validation, an average R2 of 0.83 pm 0.04 and an average of the relative root mean square error (RRMSE) of 0.31 pm 0.10 across 5 subjects were yielded. The second experiment was the realtime robot control. Trajectory tracking task with dynamic uncertainties were investigated and grasp forces were estimated in realtime. With higher muscle contraction levels, smaller position errors were observed and shorter time was needed to return to the expected trajectory when there were external disturbances. The results proved the feasibility of the new approach on humanrobot interaction tasks. Future endeavours will be made to get more promising results.

Deep Neural Network Approach in Electrical Impedance Tomographybased Realtime Soft Tactile Sensor Recently, a wholebody tactile sensing have emerged in robotics for safe humanrobot interaction. A key issue in the wholebody tactile sensing is ensuring largearea manufacturability and high durability. To fulfill these requirements, a reconstruction method called electrical impedance tomography (EIT) was adopted in largearea tactile sensing. This method maps voltage measurements to conductivity distribution using only a few number of measurement electrodes. A common approach for the mapping is using a linearized model derived from the Maxwellxe2x80x99s equation. This linearized model shows fast computation time and moderate robustness against measurement noise but reconstruction accuracy is limited. In this paper, we propose a novel nonlinear EIT algorithm through Deep Neural Network (DNN) approach to improve the reconstruction accuracy of EITbased tactile sensors. The neural network architecture with rectified linear unit (ReLU) function ensured extremely low computational time (0.002 seconds) and nonlinear network structure which provides superior measurement accuracy. The DNN model was trained with dataset synthesized in simulation environment. To achieve the robustness against measurement noise, the training proceeded with additive Gaussian noise that estimated through actual measurement noise. For real sensor application, the trained DNN model was transferred to a conductive fabricbased soft tactile sensor. For validation, the reconstruction error and noise robustness were mainly compared using conventional linearized model and proposed approach in simulation environment. As a demonstration, the tactile sensor equipped with the trained DNN model is presented for a contact force estimation.