Dynamic Hand Gesture Recognition Using Electrical Impedance Tomography.

Electrical impedance tomography (EIT) has been applied in the field of human-computer interaction due to its advantages including the fact that it is non-invasive and has both low power consumption and a low cost. Previous work has focused on static gesture recognition based on EIT. Compared with static gestures, dynamic gestures are more informative and can achieve more functions in human-machine collaboration. In order to verify the feasibility of dynamic gesture recognition based on EIT, a traditional excitation drive pattern is optimized in this paper. The drive pattern of the fixed excitation electrode is tested for the first time to simplify the measurement process of the dynamic gesture. To improve the recognition accuracy of the dynamic gestures, a dual-channel feature extraction network combining a convolutional neural network (CNN) and gated recurrent unit (GRU), namely CG-SVM, is proposed. The new center distance loss is designed in order to simultaneously supervise the intra-class distance and inter-class distance. As a result, the discriminability of the confusing data is improved. With the new excitation drive pattern and classification network, the recognition accuracy of different interference data has increased by 2.7~14.2%. The new method has stronger robustness, and realizes the dynamic gesture recognition based on EIT for the first time.

One-dimensional convolutional neural network (1D-CNN) image reconstruction for electrical impedance tomography.

In recent years, due to the strong autonomous learning ability of neural network algorithms, they have been applied for electrical impedance tomography (EIT). Although their imaging accuracy is greatly improved compared with traditional algorithms, generalization for both simulation and experimental data is required to be improved. According to the characteristics of voltage data collected in EIT, a one-dimensional convolutional neural network (1D-CNN) is proposed to solve the inverse problem of image reconstruction. Abundant samples are generated with numerical simulation to improve the edge-preservation of reconstructed images. The TensorFlow-graphics processing unit environment and Adam optimizer are used to train and optimize the network, respectively. The reconstruction results of the new network are compared with the Deep Neural Network (DNN) and 2D-CNN to prove the effectiveness and edge-preservation. The anti-noise and generalization capabilities of the new network are also validated. Furthermore, experiments with the EIT system are carried out to verify the practicability of the new network. The average image correlation coefficient of the new network increases 0.0320 and 0.0616 compared with the DNN and 2D-CNN, respectively, which demonstrates that the proposed method could give better reconstruction results, especially for the distribution of complex geometries.

Accelerated reconstruction of electrical impedance tomography images via patch based sparse representation.

Electrical impedance tomography (EIT) reconstruction is a nonlinear and ill-posed problem. Exact reconstruction of an EIT image inverts a high dimensional mathematical model to calculate the conductivity field, which causes significant problems regarding that the computational complexity will reduce the achievable frame rate, which is considered as a major advantage of EIT imaging. The single-step method, state estimation method, and projection method were always used to accelerate reconstruction process. The basic principle of these methods is to reduce computational complexity. However, maintaining high resolution in space together with not much cost is still challenging, especially for complex conductivity distribution. This study proposes an idea to accelerate image reconstruction of EIT based on compressive sensing (CS) theory, namely, CSEIT method. The novel CSEIT method reduces the sampling rate through minimizing redundancy in measurements, so that detailed information of reconstruction is not lost. In order to obtain sparse solution, which is the prior condition of signal recovery required by CS theory, a novel image reconstruction algorithm based on patch-based sparse representation is proposed. By applying the new framework of CSEIT, the data acquisition time, or the sampling rate, is reduced by more than two times, while the accuracy of reconstruction is significantly improved.