It is necessary to detect the fall of the elderly in time. As a noncontact monitoring device, radar can monitor users without their knowledge and protect their privacy. The unsupervised fall detection method does not need to collect and label fall samples, which avoids the difficulty of collecting fall data and saves researchers time and cost. The current unsupervised fall detection studies consider fewer types of actions and do not test the generalization of their models in new environments and subjects. This article proposes a new unsupervised fall detection system, including a feature extractor and predictor. We first use 3-D convolution and 3-D transposed convolution to construct a feature extractor to extract the range–velocity–time features of radar signals. Then, we construct a predictor to learn the pattern of nonfall action. Finally, we design an unsupervised training method based on hard sample mining to improve the ability of the model to identify hard negative samples. We train the model using only unlabeled nonfall samples and test it in new scenarios. The system’s accuracy in the data set containing 52 kinds of nonfall actions and 12 kinds of fall actions is 95.54%, the false alarm rate is 1.07%, and the area under the receiver operating characteristic is 0.9974.

Sleep apnea, characterized by its high prevalence and serious health consequences, faces a critical bottleneck in diagnosis. Polysomnography (PSG), the gold standard, is costly and cumbersome, while wearable devices struggle with quality control and patient compliance, rendering them as unsuitable for both large-scale screening and continuous monitoring. To address these challenges, this research introduces a contactless, low-cost, and accurate event-level sleep apnea detection method leveraging frequency-modulated continuous-wave (FMCW) radar technology. The core of our approach is a novel deep-learning model, built upon the U-Net architecture and augmented with self-attention mechanisms and squeeze-and-excitation (SE) modules, meticulously designed for the precise event-level segmentation of sleep apnea from FMCW radar signals. Crucially, we integrate blood oxygen saturation (SpO₂) prediction as an auxiliary task within a multitask-learning framework to enhance the model’s feature extraction capabilities and clinical utility by capturing physiological correlations between apnea events and oxygen levels. Rigorous evaluation in a clinical dataset, comprising data from 35 participants, with synchronized PSG and radar data demonstrated a performance exceeding that of the baseline methods (Base U-Net and CNN–MHA), achieving a high level of accuracy in event-level segmentation (with an F1-score of 0.8019) and OSA severity grading (91.43%). These findings underscore the significant potential of our radar-based event-level detection system as a non-contact, low-cost, and accurate solution for OSA assessment. This technology offers a promising avenue for transforming sleep apnea diagnosis, making large-scale screening and continuous home monitoring a practical reality and ultimately leading to improved patient outcomes and public health impacts.

Most researches on indoor target trajectory tracking using millimeter-wave radar have faced challenges such as interference from multipath effects, which led to corrupted point clouds, and large tracking errors in multi-target scenarios. This study aims to improve the accuracy of human multiple-object tracking, addressing the practical challenges of target trajectory tracking in indoor environments. We proposed an extended Distributed Radar Multi-Target Tracking (DRMTrack) framework that enabled the fusion of point clouds from multiple radar nodes. Additionally, by exploiting the spatial distribution characteristics of the target point clouds for clustering, tracking filtering and integrating historical data for tracking association, the framework enhances multiple-object trajectory tracking performance. Experimental results demonstrate that for various trajectory paths, the minimum position tracking error for multi-target is 5.2 cm. In the five-target tracking scenario, the 90th percentile tracking error is 18.8 cm, representing a 27.7% improvement in accuracy compared to single-radar tracking. The DRMTrack system effectively reduces interference from clutter point clouds, enhances multiple-object tracking precision, and supports real-time computation. This system is suitable for motion monitoring in indoor environments such as homes and hospital rooms, effectively fulfilling the needs of real-time, non-contact health management.

Multi-label electrocardiogram (ECG) classification is of great significance for the diagnosis of cardiovascular diseases, and it is also a challenging task because of the various of pathological patterns across different cardiac abnormalities. This paper analyzes the complex spatial-temporal variations information contained in the 12-lead ECG from a new perspective, and proposes an advanced deep neural network framework to accurately identify various multi-label ECG abnormalities. Firstly, the 12-lead ECG was modeled as 3-dimensional (3-D) spatial-temporal variations involving the intra-period, inter-period temporal dimensions, and cross-lead spatial dimension. Then, a 3-D spatial-temporal learning network (3D-STLNet) was carefully designed to extract the complex pathological features from the 3-D spatial-temporal variations. Secondly, the multi-head attention (MHA) mechanism was introduced into the proposed network to extract global temporal dependencies in ECG segments, which further improved the performance of the proposed network. By integrating a residual backbone network, a 3D-STLNet, and the MHA module, our proposed network allows end-to-end learning and prediction in a unified framework. The influence of several classic threshold selection strategies on the performance of multi-label ECG classification algorithm was also analyzed, which provides a reference for subsequent research on optimal threshold selection for multi-label ECG classification. Finally, our proposed network was evaluated on multiple large multi-label ECG public datasets, and the performance of our network outperforms that of current state-of-the-art networks, which demonstrates that our network has great competitiveness in identifying a wider range of multi-label ECG abnormalities.

It is essential to find and treat the elderly in time after they fall. Millimeter wave radar sensor not only solves the problem of invasion of privacy caused by the camera but also avoids letting patients wear wearable devices, which has received more and more attention in the field of fall detection and motion recognition. Due to the different modulation of radar signals by different users and environments, the performance of the fall detection model is inevitably reduced. In this paper, we propose a highly generalized radar-based fall detection framework to improve the generalization of the fall detection system in the new domain. We use a feature extractor with a ResNet structure constructed from three-dimensional convolution to extract features of the signal, and a multilayer perceptron as a classifier for fall detection. We measure the cross-domain cross entropy of the features by setting subclassifiers for each domain to measure the cross-domain invariance of the features, enabling the feature extractor to extract domain-invariant features. We propose a specific data augmentation technology for radar time series signals, which is combined with the proposed model to achieve more generalized fall detection. We have conducted sufficient experiments to explore the relationship between the number of users and domains and the model’s generalization. The proposed method achieves an F1 score of 0.9747 on the proposed dataset containing 10 scenarios, 21 subjects, 52 non-falling actions and 12 falling actions, and an F1 score of 0.9949 on the public dataset, which shows superior effectiveness.