Handgrip force prediction is widely used in the rehabilitation of the arm and prosthetic control. To investigate the effects of different measurement positions and feature parameters on the results of handgrip force prediction, a model based on electrical impedance myography (EIM) and long short-term memory (LSTM) networks was proposed to compare and determine a better scheme for handgrip force prediction. We conducted the signal acquisition experiments of impedance and handgrip force on the anterior forearm muscles and brachioradialis muscle. Afterwards, three evaluation metrics were introduced to compare the prediction results of various models, and the variability between models was analyzed using paired sample t-tests. The results showed that the model of handgrip force prediction based on anterior forearm muscles exhibited better performance in predicting. The evaluation metrics of $\mathbf {R^{2}}$ , explained variance score (EVS) and normalized mean square error (NMSE) for the model fusing the feature parameters resistance (R) and reactance (X) were 0.9023, 0.9173 and 0.0114, respectively. Therefore, the feature parameters fusing R and X are the optimal input for the handgrip force prediction model. The anterior forearm muscles are the preferred position for impedance measurement over the brachioradialis muscle. This paper validated the feasibility of EIM for handgrip force prediction and provided a new reference and implementation scheme for muscle rehabilitation training and prosthetic control.

The respiratory rate is one of the crucial indicators for monitoring human physiological health. The purpose of this paper was to introduce a head-mounted respiratory monitoring solution based on electrical impedance sensing. Firstly, we constructed a finite element model to analyze the feasibility of using head impedance for respiratory sensing based on the physiological changes in the pharynx. After that, we developed a circuit module that could be integrated into a head-mounted respiratory monitoring device using a bioelectrical impedance sensor. Furthermore, we combined adaptive filtering and respiratory tracking algorithms to develop an app for a mobile phone. Finally, we conducted controlled experiments to verify the effectiveness of this electrical impedance sensing system for extracting respiratory rate. We found that the respiration rates measured by the head-mounted electrical impedance respiratory monitoring system were not significantly different from those of commercial respiratory monitoring devices by a paired t-test (p > 0.05). The results showed that the respiratory rates of all subjects were within the 95% confidence interval. Therefore, the head-mounted respiratory monitoring scheme proposed in this paper was able to accurately measure respiratory rate, indicating the feasibility of this solution. In addition, this respiratory monitoring scheme helps to achieve real-time continuous respiratory monitoring, which can provide new insights for personalized health monitoring.

The concealed object detection in millimeter wave (MMW) image is of great significance in non-contact body inspection. At present, MMW radar has been able to obtain 3D images, which arc simply compressed into 2D images in current methods in general. However, such a rough processing does not take the information along the depth direction into account which results in a bottleneck of detection accuracy. To address this issue, a novel framework for MMW image concealed object detection is proposed, in which a 3D image is regarded as a sequence of 2D cross-sectional images and the most of the internal logic relations of features in the crosss sectional images can be explored along the sequential direction, i. e. the depth direction of the 3D image. The framework consists of a Convolutional Neural Network (CNN) and a Long Short-Term Memory (LSTM) network. The former is used to extract the multiscale features in each 2D cross sectional image while the latter is used to explore the global correlation of the above features along the depth direction to achieve feature-level information fusion and improve the accuracy of 2D location prediction. Experimental results show that the proposed method achieves remarkable results comparing to the known detection method based on 2D MMW images.