Fangli Yu

In this paper, a patch array antenna with wideband circular polarization and high gain is proposed by utilizing a hybrid metasurface (MS). A corner-cut slotted patch antenna was chosen as the source due to the possible generation of CP mode. The hybrid MS (HMS), consisting of a receiver MS (RMS) arranged in a 2 × 2 array of squared patches and a linear-to-circular polarization conversion (LCPC) MS surrounding it was then utilized as the superstrate driven by the source. The LCPC MS cell is a squared-corner-cut patch with a 45° oblique slot etched, which has the capability for wideband LCPC. The LCPC unit cell possesses wideband PC capabilities, as demonstrated by the surface current analysis and S-parameter simulations conducted using a Floquet–port setup. The LP EM wave radiated by the source antenna was initially received by the RMS, then converted to a CP wave as it passed through the LCPC MS, and ultimately propagated into space. To further enhance the LCPC properties, an improved HMS (IHMS) was then proposed with four cells cut at the corners, based on the original HMS design. To verify this design, both CMA and E-field were utilized to analyze the three MSs, indicating that the IHMS possessed a wideband LCPC capability compared to the other two MSs. The proposed antenna was then arranged in a 2 × 2 array with sequential rotation to further enhance its properties. As demonstrated by the measurements, the array antenna achieved an S11 bandwidth of 60.5%, a 3 dB AR bandwidth of 2.85 GHz, and a peak gain of 15.1 dBic, all while maintaining a low profile of only 0.09λ₀.

Motion pattern classification is one of the important research fields in lower extremity exoskeleton robot, it refers to acquiring motion data from multiple sensors installed on the exoskeleton. We designed a wearable lower limb exoskeleton robot with multiple sensors mainly including force sensitive resistors (FSRs) inside smart shoes and encoders inside joints. The wearable robot was used to help people carry the heavy load in the scenes of ascending stairs and descending stairs. The experiments of stair walking were carried out by the subjects who wore the exoskeleton to ascend stairs and descend stairs for a designated time. Before or after the stair walking, the subject would turn to move on flat ground with the result that there existed four transition motions between the stair and flat ground walking. As known, there is less research focusing on the classification of transition motions. The aim of this paper is to classify these motion patterns through a learning algorithm. The convolutional neural network (CNN) and gated recurrent unit (GRU) framework were combined to improve the classification accuracy. Specifically, CNN was used to extract the features of the motion pattern, while GRU was used to extract the temporal correlation during walking. Experimental works showed that the proposed CNN-GRU possessed a significantly high prediction accuracy in motion pattern classification. Compared with CNN, GRU and LSTM-CNN models whose accuracy score does not exceed 93.22%, the proposed CNN-GRU gained a high accuracy of 95.51%.

A new approach is proposed to detect the real-time gait patterns adaptively through measuring the ground contact forces (GCFs) by force sensitive resistors (FSRs). Published threshold-based methods detect the gait patterns by means of setting a fixed threshold to divide the GCFs into on-ground and off-ground statuses. However, the threshold-based methods in the literature are neither an adaptive nor a real-time approach. To overcome these drawbacks, this study utilized the constant false alarm rate (CFAR) to analyze the characteristics of GCF signals. Specifically, a sliding window detector is built to record the lasting time of the curvature of the GCF signals and one complete gait cycle could be divided into three areas, such as continuous ascending area, continuous descending area and unstable area. Then, the GCF values in the unstable area are used to compute a threshold through the CFAR. Finally, the new gait pattern detection rules are proposed which include the results of the sliding window detector and the division results through the computed threshold. To verify this idea, a data acquisition board is designed to collect the GCF data from able-bodied subjects. Meanwhile, in order to test the reliability of the proposed method, five threshold-based methods in the literature are introduced as reference methods and the reliability is validated by comparing the detection results of the proposed method with those of the reference methods. Experimental results indicated that the proposed method could be used for real-time gait pattern detection, detect the gait patterns adaptively and obtain high reliabilities compared with the reference methods.