Radar-based vital sign monitoring, a promising technique for medical guidance and versatile applications, faces challenges in complex environments with low signal-to-noise ratio and substantial interference. This article focuses on accurate vital sign frequency estimation, particularly for weak heartbeat estimation in the presence of substantial harmonics interference. This innovative method involves constructing an enhanced multiplication spectrum and performing harmonic order recognition to effectively eliminate the effect of cardiopulmonary intermodulation, thereby ensuring accurate respiration frequency estimation. Moreover, we utilize the spectral redundancy in the second-order cyclic temporal moment function (CTMF) to derive the heartbeat frequency via a harmonic and cross-term (CT) suppressed enhanced spectrum. By establishing a harmonious coupling between respiration harmonics and heartbeat estimation, the proposed approach significantly improves the local signal-to-interference-plus-noise ratio (SINR) of weak heartbeat signals and thus acquires an enhanced heartbeat estimation, even in the substantial respiration harmonics interference. Simulations and experiments validate its superiority over conventional demodulation techniques.

For detecting the target echoes undergoing distortion in frequency and time dispersive channels, a novel robust active sonar detector is proposed. The proposed active detector firstly extracts the matching envelope (ME) of the multi-pulse linear frequency modulation (LFM) signals, and then the peaks of the matching envelope spectrum (MES) contained in the designed grid searching windows are detected. The grid searching windows are designed based on the theoretical analysis of the features of the MES. Lastly, the detector utilizes the detected peaks to make a judgement for determining whether there are target echoes existing in the received signal. In addition, the proposed detector can be applied to detect the multi-pulse single frequency target echoes. The proposed active detector shows a more stable performance in the frequency and time dispersive channels, compared to several classical methods, including the replica correlation (RC), the segmented RC (SRC), and the RC integration (RCI) detectors. Simulation results validate the improved performance of the active detector, showing a gain of 0.5-4 dB compared with more standard state-of-the-art detectors. Experimental results based on sea trial data have also verified the validity and feasibility of the proposed active detector.

Respiration is modulated by human emotional activity. Emotion recognition using physiological signals has recently gained considerable attention. However, most existing studies primarily focus on using electroencephalogram (EEG) signals for emotion recognition. This paper explores the potential of utilizing respiration signals collected by wearable devices for emotion recognition. We propose a hierarchical Transformer model to effectively extract emotional information from respiration signals. Furthermore, we introduce gender classification as an auxiliary task to further improve the accuracy of emotion recognition. Specifically, a Frame Transformer is employed to capture emotional information across frames of respiration signals. The extracted frame-level features are subsequently fused with segment-level embeddings through a specially designed fusion layer. Next, separate Segment Transformers are employed for emotion and gender to extract segment-level information, with a cosine similarity loss applied to promote shared feature learning. Finally, distinct classifiers are used for emotion and gender classification. To evaluate the effectiveness of the proposed method, extensive experiments are conducted on the DEAP and MAHNOB-HCI datasets under a subject-independent setting. For the DEAP dataset, the average classification accuracies are 72.42% for valence and 73.91% for arousal, while for the MAHNOB-HCI dataset, they are 80.45% and 79.69%, respectively. Compared to other physiological signals such as EEG, respiration signals exhibit comparable potential for emotion recognition.