Design of a Wearable Parallel Electrical Impedance Imaging System for Healthcare

A wireless wearable electrical impedance tomography (EIT) system has been developed using the AD5933 for real-time lung respiration imaging. The system uses a current injection method tailored to the human body's impedance characteristics. It applies a voltage excitation and measures the resulting current as the voltage passes through the body. Additionally, measures are taken to suppress the effects of parasitic capacitance, which can lead to signal oscillations and leakage currents. Additionally, to improve data acquisition speed, five AD5933 units are used for parallel measurements, with multiple measures taken to ensure high synchronization during the parallel acquisition process. The results demonstrate conductivity images generated from the EIT system, with data collected from a water tank, human lungs during respiration, and a human calf at rest, confirming that this portable EIT system can measure biological tissues with high precision and low cost.

sEMG-based Fine-grained Gesture Recognition via Improved LightGBM Model

Surface electromyogram (sEMG), as a bioelectrical signal reflecting the activity of human muscles, has a wide range of applications in the control of prosthetics, human-computer interaction and so on. However, the existing recognition methods are all discrete actions, that is, every time an action is executed, it is necessary to restore the resting state before the next action, and it is unable to effectively recognize the gestures of continuous actions. To solve this problem, this paper proposes an improved fine gesture recognition model based on LightGBM algorithm. A sliding window sample segmentation scheme is adopted to replace active segment detection, and a series of innovative schemes such as improved loss function, Optuna hyperparameter search and Bagging integration are adopted to optimize LightGBM model and realize gesture recognition of continuous active segment signals. In order to verify the effectiveness of the proposed algorithm, we used the NinaproDB7 dataset to design the normal data recognition experiment and the disabled data transfer experiment. The results showed that the recognition rate of the proposed model was 89.72% higher than that of the optimal model Bi-ConvGRU for 18 gesture recognition tasks in the open data set, it reached 90.28%. Compared with the scheme directly trained on small sample data, the recognition rate of transfer learning was significantly improved from 60.35% to 78.54%, effectively solving the problem of insufficient data, and proving the applicability and advantages of transfer learning in fine gesture recognition tasks for disabled people.

Lower Limb Movements Recognition Based on Feature Recursive Elimination and Backpropagation Neural Network

Surface electromyographic (sEMG) signal serve as a signal source commonly used for lower limb movement recognition, reflecting the intent of human movement. However, it has been a challenge to improve the movements recognition rate while using fewer features in this area of research area. In this paper, a method for lower limb movements recognition based on recursive feature elimination and backpropagation neural network of support vector machine is proposed. First, the sEMG signal of five subjects performing eight different lower limb movements was recorded using a BIOPAC collector. The optimal feature subset consists of 25 feature vectors, determined using a Recursive Feature Elimination based on Support Vector Machine (SVM-RFE). Finally, this study used five supervised classification algorithms to recognize these eight different lower limb movements. The results of the experimental study show that the combination of the BPNN classifier and the SVM-RFE feature selection algorithm is able to achieve an excellent action recognition accuracy of 95\%, which provides sufficient support for the feasibility of this approach.