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.

Microwave/millimeter-wave imaging has a wide prospect in applications of concealed weapons detection. The main problem of the existing techniques lies in the contradiction between the imaging speed and system cost. In this respect, a cylindrical sparse MIMO array topology is proposed, which has no mechanical scanning so as to achieve fast sampling process. Further, it can provide more angular observations than the mainly used systems with a planar aperture. To speed up the image reconstruction process, a phase center approximationbased transformation is made to convert the multistatic sampling scheme to its monostatic counterpart for which there are more fast and tractable imaging algorithms. Then, a phase calibration method is presented after we analyze the error of such an equivalence. In so doing, the cylindrical MIMO topology is transformed into an approximate monostatic one. Finally, a wavenumber domain algorithm is employed to attain the fast image reconstruction. Numerical simulations are provided to demonstrate the performance improvement of the proposed technique.

Radar has been widely applied in many scenarios as a critical remote sensing tool for non-contact vital sign monitoring, particularly for sleep monitoring and heart rate measurement within the home environment. For non-contact monitoring with radar, interference from house pets is an important issue that has been neglected in the past. Many animals have respiratory frequencies similar to those of humans, and they are easily mistaken for human targets in non-contact monitoring, which would trigger a false alarm because of incorrect physiological parameters from the animal. In this study, humans and common pets in families, such as dogs, cats, and rabbits, were detected using an impulse radio ultrawideband (IR-UWB) radar, and the echo signals were analyzed in the time and frequency domains. Subsequently, based on the distinct in-body structure between humans and animals, we propose a parameter, the respiratory and heartbeat energy ratio (RHER), which reflects the contribution rate of breathing and heartbeat in the detected vital signs. Combining this parameter with the energy index, we developed a novel scheme to distinguish between humans and animals. In the developed scheme, after background noise removal and direct-current component suppression, an energy indicator is used to initially identify the target. The signal is then decomposed using a variational mode decomposition algorithm, and the variational intrinsic mode functions that represent human respiration and heartbeat components are obtained and utilized to calculate the RHER parameter. Finally, the RHER index is applied to rapidly distinguish between humans and animals. Our experimental results demonstrate that the proposed approach more effectively distinguishes between humans and animals in terms of monitoring vital signs than the existing methods. Furthermore, its rapidity and need for only minimal calculation resources enable it to meet the needs of real-time monitoring.

The coherence factor (CF) is defined as the ratio of coherent power to incoherent power received by the radar aperture. The incoherent power is computed by the multi-antenna receiver based on only the spatial variable. In this respect, it is a one-dimensional (1-D) CF, and thereby the image sidelobes in down-range cannot be effectively suppressed. We propose a two-dimensional (2-D) CF by supplementing the 1-D CF by an incoherent sum dealing with the frequency dimension. In essence, we employ both spatial diversity and frequency diversity which, respectively, enhance imaging quality in cross-range and range. Simulations and experimental results are provided to demonstrate the performance advantages of the proposed approach.

In this article, an ultrawideband (UWB) radar is first employed to probe through the opaque wall media to detect behind-the-wall human motions. By employing such a radar, a high-resolution time-range map with different body parts’ reflections highly discriminable in range direction can be obtained. Second, a high-pass filter is applied to remove the wall effects in the raw time-range map. Then, with the aim of exploiting the rich range information so as to enhance their corresponding micro-Doppler features, a novel range-max enhancement strategy is proposed to extract the most significant micro-Doppler feature of each time–frequency cell along range direction for a specific motion. Finally, the effectiveness of the proposed motion feature enhancement strategy is investigated by means of onsite experiments. Comparative classifications using different convolutional neural network (CNN) structures show that the proposed approach outperforms other state-of-the-art micro-Doppler feature extraction methods. The comparison with the narrowband detection case also proves its superiority in feature enhancement in the narrowband detection scene.

The purpose of this study was to describe the pattern of meniscus and cartilage pathology in multiligament knee injuries (MLKIs) and determine the relationship between surgical timing and injury mechanism with degree of intra-articular injury. Patients with surgically treated MLKIs over a 15-year period were retrospectively reviewed and grouped based on surgical intervention, time to intervention, and injury mechanism. The presence or absence of meniscus and chondral injury were recorded at the time of surgery. Surgical intervention within 6 weeks of injury was deemed acute, while surgery occurring more than 6 weeks from injury was classified as delayed. Over the 15-year study period, 207 patients with MLKIs were identified. Compared with acutely managed patients, the delayed intervention group had significantly more meniscus (p = 0.03) and cartilage (p < 0.01) pathology. Meniscus injury rates in MLKIs sustained during sporting activity did not differ from nonsporting injuries (p = 0.63). However, the nonsporting group had significantly more chondral injuries (p < 0.01). High-energy injury mechanism was associated with increased cartilage (p = 0.02), but not meniscus (p = 0.61) injury rates. In conclusion, surgical reconstruction of MLKIs delayed for more than 6 weeks was associated with increased meniscus and cartilage pathology.