Continuous human motion recognition with a dynamic range-Doppler trajectory method based on FMCW radar

Radar-based human motion recognition is crucial for many applications, such as surveillance, search and rescue operations, smart homes, and assisted living. Continuous human motion recognition in real-living environment is necessary for practical deployment, i.e., classification of a sequence of activities transitioning one into another, rather than individual activities. In this paper, a novel dynamic range-Doppler trajectory (DRDT) method based on the frequency-modulated continuous-wave (FMCW) radar system is proposed to recognize continuous human motions with various conditions emulating real-living environment. This method can separate continuous motions and process them as single events. First, range-Doppler frames consisting of a series of range-Doppler maps are obtained from the backscattered signals. Next, the DRDT is extracted from these frames to monitor human motions in time, range, and Doppler domains in real time. Then, a peak search method is applied to locate and separate each human motion from the DRDT map. Finally, range, Doppler, radar cross section (RCS), and dispersion features are extracted and combined in a multidomain fusion approach as inputs to a machine learning classifier. This achieves accurate and robust recognition even in various conditions of distance, view angle, direction, and individual diversity. Extensive experiments have been conducted to show its feasibility and superiority by obtaining an average accuracy of 91.9% on continuous classification

Radar signal processing for sensing in assisted living: the challenges associated with real-time implementation of emerging algorithms

This article covers radar signal processing for sensing in the context of assisted living (AL). This is presented through three example applications: human activity recognition (HAR) for activities of daily living (ADL), respiratory disorders, and sleep stages (SSs) classification. The common challenge of classification is discussed within a framework of measurements/preprocessing, feature extraction, and classification algorithms for supervised learning. Then, the specific challenges of the three applications from a signal processing standpoint are detailed in their specific data processing and ad hoc classification strategies. Here, the focus is on recent trends in the field of activity recognition (multidomain, multimodal, and fusion), health-care applications based on vital signs (superresolution techniques), and comments related to outstanding challenges. Finally, this article explores challenges associated with the real-time implementation of signal processing/classification algorithms

Tangential Human Posture Recognition with Sequential Images Based on MIMO Radar

Recent research on radar-based human activity recognition has typically focused on activities that move toward or away from radar in radial directions. Conventional Doppler-based methods can barely describe the true characteristics of nonradial activities, especially static postures or tangential activities, resulting in a considerable decline in recognition performance. To address this issue, a method for recognizing tangential human postures based on sequential images of a Multiple-Input Multiple-Output (MIMO) radar system is proposed. A time sequence of high-quality images is achieved to describe the structure of the human body and corresponding dynamic changes, where spatial and temporal features are extracted to enhance the recognition performance. First, a Constant False Alarm Rate (CFAR) algorithm is applied to locate the human target. A sliding window along the slow time axis is then utilized to divide the received signal into sequential frames. Next, a fast Fourier transform and the 2D Capon algorithm are performed on each frame to estimate range, pitch angle, and azimuth angle information, which are fused to create a tangential posture image. They are connected to form a time sequence of tangential posture images. To improve image quality, a modified joint multidomain adaptive threshold-based denoising algorithm is applied to improve the image quality by suppressing noises and enhancing human body outline and structure. Finally, a Spatio-Temporal-Convolution Long Short Term Memory (ST-ConvLSTM) network is designed to process the sequential images. In particular, the ConvLSTM cell is used to extract continuous image features by combining convolution operation with the LSTM cell. Moreover, spatial and temporal attention modules are utilized to emphasize intraframe and interframe focus for improving recognition performance. Extensive experiments show that our proposed method can achieve an accuracy rate of 96.9% in classifying eight typical tangential human postures, demonstrating its feasibility and superiority in tangential human posture recognition

SGNet: Salient Geometric Network for Point Cloud Registration

Point Cloud Registration (PCR) is a critical and challenging task in computer vision. One of the primary difficulties in PCR is identifying salient and meaningful points that exhibit consistent semantic and geometric properties across different scans. Previous methods have encountered challenges with ambiguous matching due to the similarity among patch blocks throughout the entire point cloud and the lack of consideration for efficient global geometric consistency. To address these issues, we propose a new framework that includes several novel techniques. Firstly, we introduce a semantic-aware geometric encoder that combines object-level and patch-level semantic information. This encoder significantly improves registration recall by reducing ambiguity in patch-level superpoint matching. Additionally, we incorporate a prior knowledge approach that utilizes an intrinsic shape signature to identify salient points. This enables us to extract the most salient super points and meaningful dense points in the scene. Secondly, we introduce an innovative transformer that encodes High-Order (HO) geometric features. These features are crucial for identifying salient points within initial overlap regions while considering global high-order geometric consistency. To optimize this high-order transformer further, we introduce an anchor node selection strategy. By encoding inter-frame triangle or polyhedron consistency features based on these anchor nodes, we can effectively learn high-order geometric features of salient super points. These high-order features are then propagated to dense points and utilized by a Sinkhorn matching module to identify key correspondences for successful registration. In our experiments conducted on well-known datasets such as 3DMatch/3DLoMatch and KITTI, our approach has shown promising results, highlighting the effectiveness of our novel method

SPEEK Membrane of Ultrahigh Stability Enhanced by Functionalized Carbon Nanotubes for Vanadium Redox Flow Battery

Proton exchange membrane is the key factor of vanadium redox flow battery (VRB) as their stability largely determine the lifetime of the VRB. In this study, a SPEEK/MWCNTs-OH composite membrane with ultrahigh stability is constructed by blending sulfonated poly(ether ether ketone) (SPEEK) with multi-walled carbon nanotubes toward VRB application. The carbon nanotubes disperse homogeneously in the SPEEK matrix with the assistance of hydroxyl group. The blended membrane exhibits 94.2 and 73.0% capacity retention after 100 and 500 cycles, respectively in a VRB single cell with coulombic efficiency of over 99.4% at 60 mA cm−2 indicating outstanding capability of reducing the permeability of vanadium ions and enhancing the transport of protons. The ultrahigh stability and low cost of the composite membrane make it a competent candidate for the next generation larger-scale vanadium redox flow battery

Liver X Receptor Activation Alleviates Hepatic Ischemia‐Reperfusion Injury in Diabetes by Inhibiting NF‐κB–NLRP3 Activation

ABSTRACT Introduction Hyperglycemia has been reported to be a crucial factor that aggravates liver ischemia‐reperfusion injury (IRI). Macrophage–mediated inflammatory injury is vital to liver IRI. A positive effect of Liver X receptor (LXR) on diabetes has been proven; however, the function and mechanism of LXR in diabetic liver IRI remain unclear. Accordingly, our study concentrates on the mechanism underly. Materials and Methods Streptozotocin (STZ, 40 mg/kg)‐treated diabetic mice were used to establish a liver IRI model. Bone marrow–derived macrophages (BMDMs) were used in studying the role of macrophage inflammation in diabetic liver. GW3965 hydrochloride was used to activate LXR in vivo and in vitro. QD394, a lipid peroxidation agonist, was used to verify the underlying mechanism. Results Hyperglycemia exacerbates liver ischemia‐reperfusion injury (IRI) by promoting hepatic cell death and inflammation in vivo. In diabetic livers, the expression of liver X receptors (LXRs) is significantly reduced. Furthermore, the ischemia‐reperfusion process itself further decreases LXR levels. Application of the LXR agonist GW3965 mitigates macrophage lipid peroxidation and inflammasome NLRP3 (NOD‐like receptor thermal protein domain associated protein 3) inflammasome activation in vitro, thereby protecting the liver from severe IRI. The results were further confirmed by the rescue experiments. Conclusions LXRs play an important role in diabetic liver IRI and macrophage‐associated inflammation. Pharmacologic activation of LXRs alleviates macrophage inflammatory activation in diabetic liver IRI, and may serve as a potential therapeutic target for diabetes‐related liver injury