The identification of weak vital signs has always been one of the difficulties in the field of life detection. In this paper, a novel vital sign detection and extraction method with high efficiency, high precision, high sensitivity and high signal-to-noise ratio is proposed. Based on the NVA6100 pulse radar system, the radar matrix which contains several radar pulse detection signals is received. According to the characteristics of vital signs and radar matrices, the Singular Value Decomposition (SVD) is adopted to perform signal denoising and decomposition after preprocessing, and the temporal and spatial eigenvectors of each principal component are obtained. Through the energy proportion screening, the Wavelet Transform decomposition and linear trend suppression, relatively pure vital signs in each principal component, are obtained. The human location is detected by the Energy Entropy of spatial eigenvectors, and the respiratory signal and heartbeat signal are restored through a Butterworth Filter and an MTI harmonic canceller. Finally, through an analysis of the performance of the algorithm, it is proved to have the properties of efficiency and accuracy.

Life detection technology using ultra-wideband (UWB) radar is a non-contact, active detection technology, which can be used to search for survivors in disaster rescues. The existing multi-target detection method based on UWB radar echo signals has low accuracy and has difficulty extracting breathing and heartbeat information at the same time. Therefore, this paper proposes a new multi-target localization and vital sign detection method using ultra-wide band radar. A target recognition and localization method based on permutation entropy (PE) and K means++ clustering is proposed to determine the number and position of targets in the environment. An adaptive denoising method for vital sign extraction based on ensemble empirical mode decomposition (EEMD) and wavelet analysis (WA) is proposed to reconstruct the breathing and heartbeat signals of human targets. A heartbeat frequency extraction method based on particle swarm optimization (PSO) and stochastic resonance (SR) is proposed to detect the heartbeat frequency of human targets. Experimental results show that the PE—K means++ method can successfully recognize and locate multiple human targets in the environment, and its average relative error is 1.83%. Using the EEMD–WA method can effectively filter the clutter signal, and the average relative error of the reconstructed respiratory signal frequency is 4.27%. The average relative error of heartbeat frequency detected by the PSO–SR method was 6.23%. The multi-target localization and vital sign detection method proposed in this paper can effectively recognize all human targets in the multi-target scene and provide their accurate location and vital signs information. This provides a theoretical basis for the technical system of emergency rescue and technical support for post-disaster rescue.

Detection of vital signs for motional human targets in complex environment has always been a major challenge in the field of remote detection, remote healthcare and emergency rescue, because polytropic and multimodal interferences make intelligent signal processing more difficult. In this paper, a systematic intelligent signal processing scheme which contains signal preprocessing, vital signs identification, motion trajectory estimation and respiratory signal and heartbeat signal extraction is established. Based on CNN (Convolutional Neural Networks) model, accurate identification of motional vital signs getting rid of the interference of harmonics and distortion can be realized. Then, the misidentified outliers are eliminated with K-means clustering algorithm. Next, the motion trajectory of human targets can be estimated with Kalman filtering algorithm. Finally, the SVD-EEMD algorithm is proposed for respiratory signal and heartbeat signal extraction of dynamic human targets. The introduction of deep learning algorithms makes the proposed method have good performance of high accuracy, good robustness, strong adaptability and high efficiency, which can be observed in actual detection tasks contrast experiments.

Giving robot hands more powerful functions has always been one of the goals pursued by scholars in this field. In this paper, an electromagnetic switching multiple grasping modes robot hand (ESMGM hand) is proposed, which integrates three typical grasping modes and therefore has versatile usage and improved performance. The switchable CPS mechanism developed in this paper integrated the parallel grasping and coupled grasping modes, which are incompatible with each other, through ingenious design. The partial effective transmission mechanism guarantees the fusion and connection to self-adaptive grasping mode from both parallel grasping mode and coupled grasping mode. Based on the above two essential mechanisms, the specific structure of the ESMGM robot hand is designed. Theoretical analyses for the kinematic and grasping forces are performed, and the results show that the ESMGM hand not only has multiple grasping functions but also has the characteristics of equitable grasping motions, adequate grasping forces, and stable grasping effects. To further verify the performance of the ESMGM hand, the prototype of the ESMGM hand is manufactured, and grasping experiments are performed. The grasping forces distribution results are consistent with the theoretical analysis results. The general grasping experiments also illustrate that the ESMGM hand has the features of fast electromagnetic switching speed, good adaptability, high stability, fast response, and broad application prospects.