The non-contact monitoring of vital signs by radar has great prospects in clinical monitoring. However, the accuracy of separated respiratory and heartbeat signals has not satisfied the clinical limits of agreement. This paper presents a study for automated separation of respiratory and heartbeat signals based on empirical wavelet transform (EWT) for multiple people. The initial boundary of the EWT was set according to the limited prior information of vital signs. Using the initial boundary, empirical wavelets with a tight frame were constructed to adaptively separate the respiratory signal, the heartbeat signal and interference due to unconscious body movement. To verify the validity of the proposed method, the vital signs of three volunteers were simultaneously measured by a stepped-frequency continuous wave ultra-wideband (UWB) radar and contact physiological sensors. Compared with the vital signs from contact sensors, the proposed method can separate the respiratory and heartbeat signals among multiple people and obtain the precise rate that satisfies clinical monitoring requirements using a UWB radar. The detection errors of respiratory and heartbeat rates by the proposed method were within ±0.3 bpm and ±2 bpm, respectively, which are much smaller than those obtained by the bandpass filtering, empirical mode decomposition (EMD) and wavelet transform (WT) methods. The proposed method is unsupervised and does not require reference signals. Moreover, the proposed method can obtain accurate respiratory and heartbeat signal rates even when the persons unconsciously move their bodies.

In recent years, auxiliary diagnosis technology for cardiovascular disease based on abnormal heart sound detection has become a research hotspot. Heart sound signals are promising in the preliminary diagnosis of cardiovascular diseases. Previous studies have focused on capturing the local characteristics of heart sounds. In this paper, we investigate a method for mapping heart sound signals with complex patterns to fixed-length feature embedding called HS-Vectors for abnormal heart sound detection. To get the full embedding of the complex heart sound, HS-Vectors are obtained through the Time-Compressed and Frequency-Expanded Time-Delay Neural Network(TCFE-TDNN) and the Dynamic Masked-Attention (DMA) module. HS-Vectors extract and utilize the global and critical heart sound characteristics by masking out irreverent information. Based on the TCFE-TDNN module, the heart sound signal within a certain time is projected into fixed-length embedding. Then, with a learnable mask attention matrix, DMA stats pooling aggregates multi-scale hidden features from different TCFE-TDNN layers and masks out irrelevant frame-level features. Experimental evaluations are performed on a 10-fold cross-validation task using the 2016 PhysioNet/CinC Challenge dataset and the new publicly available pediatric heart sound dataset we collected. Experimental results demonstrate that the proposed method excels the state-of-the-art models in abnormality detection.

The evaluation of clinical status and vital signs is a crucial component of remote medical care. Radar provides a non-contact monitoring measurement without consideration of lighting or privacy. However, the multipath effect generated by multiple people gathered in a small indoor space seriously affects the detection of vital sign information. This study presents a non-contact measurement of the vital signs based on stepped-frequency continuous wave ultra-wideband(SFCW-UWB) and impulse radio ultra-wideband(IR-UWB) radars. Additionally, this study suggests the Spectral Unmixing Successive Variational Mode Decomposition (SUSVMD) algorithm since the respiratory components have a probability of mixing with the heartbeat signal’s frequency. The suggested approach extracts band-limited modes through back projection and spectral unmixing optimization. The alternate direction method of multiplication is used to address the optimization problem. The experiments are conducted under two radar measurements and three subjects. The results show that the suggested method is superior and effective.

Under low signal-to-noise-and-clutter ratio circumstances, non-contact multi-subjects vital sign assessment based on ultrawideband (UWB) radar is extremely difficult. It is challenging to obtain an accurate measurement since the respiratory frequency harmonics are mixed with noise during detection and are extremely near to the frequency of the heartbeat signal. We use an impulse radio ultrawideband (IR-UWB) radar and a stepped-frequency continuous-wave ultrawideband (SFCW-UWB) radar in measurement to get reliable vital signs from several participants. For adaptively extracting respiratory and heartbeat patterns, it is proposed to use the learning refined integral null space pursuit algorithm (LR-INSP), which is based on the learning refined integral null space pursuit method and optimum route learning. The refined-INSP technique aims to provide fine-grained multi-layer and multi-branch harmonic components. Then, to obtain a reliable and stable vital sign path from the multi-branch decomposition, an optimum path learning technique is presented. According to the experimental findings, the average signal accuracy assessment (SAA) of the heartbeat and respiratory frequencies for IR-UWB radar is 95.41% and 89.97%, respectively. The average SAA of the heartbeat and respiratory frequencies for SFCW-UWB radar is 94.39% and 99.48%, respectively.

Fetal heart rate (fHR) is critical for assessing fetal health and diagnosing disorders such as fetal distress, congenital heart disease, and intrauterine growth retardation. With the rapid development of the Internet of Medical Things (IoT), fetal R-peak detection plays an important role in diagnosing heart defects during pregnancy. However, due to the nonlinear mixing of multiple sources in the non-invasive signals and the low signal-to-noise ratio (SNR), it is difficult to obtain accurate R-peak detection result. This article presents a dual Self-calibrating system based on a Spectral Attention Kernel Independent Component Analysis (SA-KICA) module and a Selfcalibrating fetal R-peak detection (SC-FRD) module. SA-KICA is an ICA-based calibration module constructed by SA mechanism, which was sought from Short-Time Fourier Transform (STFT) and was shipped back to original signal with convolution to achieve perfect maternal electrocardiogram (MECG) separation in high-dimensional linear separable space. Then a periodic and morphological based Channel Selector is designed to select the optimal MECG. After MECG removal, to further improve the performance of fetal R-peak detection, the SC-FRD module is introduced to utilize the interior peak information and selfcalibrating strategy, which includes variance-based fetal R-peak seed selection, time-varying coarse prediction and adaptive probability mask calibration. The proposed framework is a primary attempt to concurrently introduce the nonlinear feature, spectral information and self-calibrating strategy in the field of fetal ECG processing. The framework achieved excellent performance in fetal R-peak detection accuracy on a simulated dataset and two public datasets with varying divergence and richness of resources. The experimental results show that our framework is superior to existing methods and can be used as a potential fetal monitoring method in the application of Internet of Medical Things. The code is released in https://github.com/bfyjr/NI-FECG-Extraction.

Hand gesture recognition with radar sensors is essential because they can detect gestures despite environmental factors like lighting, dust, and complex backgrounds. Considering the complexity of a system, it is challenging to design CNNs on CPU devices and realize the carry-on mid-air gesture recognition. We propose a mid-air gesture recognition method based on a novel discriminant feature, and it be used as part of a measurement system of hand movements using an ultrawideband (UWB) radar. The Gesture-ProxylessNAS (GPNAS) is presented to enhance the adaptability of model search and overcome the challenge of the network's computational complexity. In order to fully extract local spatial discriminant features and prevent information loss, Local Binary Pattern (LBP) encoders are utilized to extract local spatial information. In the meantime, multi-layer ShuffleNet with depthwise separable convolution is used to gradually leverage high-level spatial features. The Gesture-ProxylessNAS module revisits the multi-layer ShuffleNet's design spaces using an optimization problem, greatly reducing the network's parameters and computational complexity. According to experimental verification on real UWB hand gestures, the proposed framework provides more satisfactory recognition performance and efficiency with a deeper network structure and fewer parameters. The proposed hand gesture recognition system can recognize gestures with a promising accuracy of 96.52% on the UWB-gestures public dataset [1].