Xiao Yu

Building collapse leads to mechanical injury, which is the main cause of injury and death, with crush syndrome as its most common complication. During the post-disaster search and rescue phase, if rescue personnel hastily remove heavy objects covering the bodies of injured individuals and fail to provide targeted medical care, ischemia-reperfusion injury may be triggered, leading to rhabdomyolysis. This may result in disseminated intravascular coagulation or acute respiratory distress syndrome, further leading to multiple organ failure, which ultimately leads to shock and death. Using bio-radar to detect vital signs and identify compression states can effectively reduce casualties during the search for missing persons behind obstacles. A time-domain ultra-wideband (UWB) bio-radar was applied for the non-contact detection of human vital sign signals behind obstacles. An echo denoising algorithm based on PSO-VMD and permutation entropy was proposed to suppress environmental noise, along with a wounded compression state recognition network based on radar-life signals. Based on training and testing using over 3000 data sets from 10 subjects in different compression states, the proposed multiscale convolutional network achieved a 92.63% identification accuracy. This outperformed SVM and 1D-CNN models by 5.30% and 6.12%, respectively, improving the casualty rescue success and post-disaster precision.

As pets are considered members of the family, their health has received widespread attention. Since pets cannot talk and complain when they feel uncomfortable, monitoring vital signs becomes very helpful in disease detection, as well as observing their progression and response to treatment. In this study, we proposed an ultra-wideband radar-based, non-contact animal vital sign monitoring scheme that could monitor the breathing and heart rate of a pet in real-time. The primary advantage of the ultra-wideband radar was its ability to operate remotely without electrodes or wires and through any clothing or fur. Because of the existing noise and clutter in non-contact detection, background noise removal was applied. Furthermore, the respiration rate was directly obtained through spectrum analysis, while the heartbeat signal was extracted by the variational mode decomposition algorithm. By using electrocardiogram measurements, we verified the accuracy of the radar technology in detecting the anesthetized animals’ respiratory rate and heart rate. Besides, three beagles and five cats in a non-sedated state were measured by radar and contact pressure sensors simultaneously; the experimental results showed that radar could effectively measure the respiration of cats and dogs, and the accuracy rate was over 95%. Due to its excellent performance, the proposed method has the potential to become a new choice in application scenarios, such as pet sleep monitoring and health assessment.

Research work on distinguishing humans from animals can help provide priority orders and optimize the distribution of resources in earthquake- or mining-related rescue missions. However, the existing solutions are few and their stability and accuracy of classification are less. This study proposes an accurate method for distinguishing stationary human targets from dog targets under through-wall condition based on ultra-wideband (UWB) radar. Eight humans and five beagles were used to collect 130 samples of through-wall signals using the UWB radar. Twelve corresponding features belonging to four categories were combined using the support vector machine (SVM) method. A recursive feature elimination (RFE) method determined an optimal feature subset from the twelve features to overcome overfitting and poor generalization. The results after ten-fold cross-validation showed that the area under the receiver operator characteristic (ROC) curve can reach 0.9993, which indicates that the two subjects can be distinguished under through-wall condition. The study also compared the ability of the proposed features of four categories when used independently in a classifier. Comparison results indicated that wavelet entropy-corresponding features among them have the best performance. The method and results are envisioned to be applied in various practical situations, such as post-disaster searching, hostage rescues, and intelligent homecare.

Ultra-wideband radar-based penetrating detection and recognition of human activities has become a focus on remote sensing in various military applications in recent years, such as urban warfare, hostage rescue, and earthquake post-disaster rescue. However, an excellent micro-Doppler signature (MDS) extracting method of human motion with high time-frequency resolution, outstanding anti-interference ability, and extensive adaptability, which aims to provide favorable and more detailed features for human activity recognition and classification, especially in the non-free space detection environment, is in great urgency. To cope with the issue, a multiple Hilbert-Huang transform (MHHT) method is proposed for high-resolution time-frequency analysis of finer-grained human activity MDS hidden in ultra-wideband (UWB) radar echoes during the through-wall detection environment. Based on the improved HHT with effective intrinsic mode function (IMF) selection according to the cosine similarity (CS) principle, the improved HHT is applied to each channel signal in the effective channel scope of the UWB radar signal and then integrated along the range direction. The activities of swinging one or two arms while standing at a spot 3 m from a wall were used to validate the abilities of the proposed method for extracting and separating the MDS of different moving body structures with a high time-frequency resolution. Simultaneously, the corresponding relationship between the frequency components in MHHT-based spectra and structures of the moving human body was demonstrated according to the radar Doppler principle combined with the principle of human body kinematics. Moreover, six common finer-grained human activities and a piaffe at different ranges under the through-wall detection environment were exploited to confirm the adaptability of the novel method for different activities and pre-eminent anti-interference ability under a low signal-noise-clutter ratio (SNCR) environment, which is critical for remote sensing in various military application, such as urban warfare, hostage rescue, earthquake post-disaster rescue.

High frequency millimeter-wave (MMW) radar-like sensors enable the detection of speech signals. This novel non-acoustic speech detection method has some special advantages not offered by traditional microphones, such as preventing strong-acoustic interference, high directional sensitivity with penetration, and long detection distance. A 94-GHz MMW radar sensor was employed in this study to test its speech acquisition ability. A 34-GHz zero intermediate frequency radar, a 34-GHz superheterodyne radar, and a microphone were also used for comparison purposes. A short-time phase-spectrum-compensation algorithm was used to enhance the detected speech. The results reveal that the 94-GHz radar sensor showed the highest sensitivity and obtained the highest speech quality subjective measurement score. This result suggests that the MMW radar sensor has better performance than a traditional microphone in terms of speech detection for detection distances longer than 1 m. As a substitute for the traditional speech acquisition method, this novel speech acquisition method demonstrates a large potential for many speech related applications.