141 results about "Vital sign detection" patented technology

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention discloses a millimeter wave radar vital sign detection method based on an HHO-CEEMDAM algorithm, and the method comprises the steps: building a millimeter wave radar experiment system, and collecting an intermediate frequency signal of a human body echo; performing data reading and recombination, extracting phase features, and enhancing a target signal through non-coherent accumulation to determine the distance between the chest of the human body and the radar; recovering the phase of the vital sign signal from the incoherent accumulation FFT result by using the linear characteristic of arc tangent demodulation, and performing phase unwrapping and phase difference to obtain optimized phase information; setting a CEEMDAN parameter initialization range and designing a fitness function; a CEEMDAN parameter is optimized by using an HHO algorithm; performing CEEMDAN decomposition by using the optimized parameters, and screening breathing and heartbeat IMF components; and according to the IMF component, obtaining estimated values of the respiratory rate and the heart rate. The method improves the estimation precision of the respiratory rate and the heart rate of the millimeter wave radar in fatigue driving detection.

The invention discloses an OFDM radar vital sign signal separation method based on FGO-VMD and multi-feature clustering, and belongs to the technical field of radar communication integration. According to the method, an OFDM signal radar is used for carrying out human body detection, multi-subcarrier phase difference linear regression is used for estimating displacement, thoracic cavity micro-motion signals are extracted, an FGO-VMD joint optimization model is provided, the modal number and penalty factor parameters of a VMD decomposition algorithm are adaptively determined through minimum envelope entropy, and a multi-feature clustering and weighted reconstruction strategy is designed. And in combination with frequency band screening and correlation coefficient weighting, the separation robustness of the respiratory signal and the heartbeat signal is enhanced, and high-precision separation of the vital sign signal is realized. According to the method, non-contact vital sign detection based on OFDM radar signals is achieved, the problems that in a traditional algorithm, parameters of modal numbers and penalty factors need to be manually set, and signal amplitude attenuation and modal aliasing are caused under complex noise are solved, the vital sign monitoring precision is remarkably improved, and high-precision separation of vital sign signals is achieved.

The invention discloses a breath and heartbeat detection method and system based on an FMCW (Frequency Modulated Continuous Wave) radar. The method comprises the following steps: acquiring a continuous wave signal sent by an FMCW millimeter wave radar to detect a target object so as to obtain a reflected echo signal; the echo signals are preprocessed; separating a respiration signal from a heartbeat signal of the preprocessed echo signal based on an EPH multi-stage cooperative signal processing algorithm; and carrying out anomaly correction on the separated instantaneous frequency anomaly data based on a local weighted regression algorithm. Therefore, the vital sign detection accuracy of the radar can be improved under the interference conditions of background noise, signal harmonic waves and the like.

The invention provides a vital sign detection method and device based on a millimeter wave radar, electronic equipment and a storage medium. The method comprises the following steps: obtaining a distance frequency domain signal based on a radar echo signal; performing spectral analysis on the distance frequency domain signal, positioning the position of the to-be-detected object, and extracting phase information at the position; filtering through a band-pass filter to obtain a breathing frequency band interval signal and an initial heartbeat frequency band interval signal; performing real-time respiration rate estimation based on the respiration frequency band interval signal to obtain a respiration frequency, and eliminating respiration harmonics in the initial heartbeat frequency band interval signal by using an adaptive wave trap based on the respiration frequency to obtain a heartbeat frequency band interval signal; and with the minimum fuzzy entropy as an optimization target, decomposing the heartbeat frequency band interval signal by using a variational mode decomposition algorithm to obtain a heartbeat signal, and carrying out real-time heart rate estimation based on the heartbeat signal to obtain a real-time heart rate. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the accuracy of heart rate measurement can be improved.

The invention relates to the technical field of emergency scheduling management, and discloses a mobile scheduling management device, which comprises an information acquisition unit, an integration unit, a scheduling optimization unit, a decision-making auxiliary unit, a command unit and a communication unit, the information acquisition unit comprises a field information acquisition module, an environment monitoring module and a vital sign detection sensor, and the information acquisition unit, the integration unit, the scheduling optimization unit, the decision-making auxiliary unit and the command unit are arranged, so that the field condition can be quickly, comprehensively and accurately mastered in an emergency scene; precise allocation and dynamic optimization of resources are realized based on scientific data analysis and an intelligent algorithm, scientificity and efficiency of command decision making are improved by means of an immersive virtual reality technology, and close cooperation and efficient cooperation among departments are ensured through a cross-department integrated cooperative communication system. Therefore, the efficiency and effect of emergency rescue are remarkably improved, and casualties and property loss are reduced to the maximum extent.

An intermediate frequency signal phase demodulation method under a low signal-to-noise ratio condition belongs to the technical field of millimeter wave radar non-contact vital sign detection, retains key information of a phase signal and provides rich feature representation through Grubrum transform, and inhibits noise components without fixed modals; the phase demodulation network adopts an asymmetric U-Net structure to reduce the complexity of the network, the signal demodulation precision is not reduced while the low calculation amount is realized, and the noise suppression capability and the signal detail capture capability of the network are enhanced by introducing a SwinTransform module and a dynamic convolution attention module. Under the condition of low signal-to-noise ratio, the high anti-noise performance and the accurate phase demodulation effect are shown, the estimation deviation of the heart rate in millimeter wave radar vital sign detection is remarkably reduced, and the method has the wide application prospect in the research of non-contact vital sign detection.

The invention provides an improved RPSEMD-based millimeter wave radar vital sign detection method, which comprises the following steps of: capturing an echo signal of a target by using a frequency modulation continuous wave radar module to carry out a signal preprocessing step, carrying out distance dimension fast Fourier transform on the captured signal, and positioning a distance unit where a target object is located by using a distance gate locking technology; phase extraction is carried out on the signal, and phase unwrapping processing is carried out; signal stability is enhanced through differential operation, and signal quality is improved through a median filtering technology; vital sign signals are separated and extracted by improving an RPSEMD algorithm model, and millimeter wave radar vital sign detection is achieved. The quadratic sum of the cross correlation coefficients is used as a target function, Bayesian optimization is utilized to optimize parameters of a traditional RPSEMD algorithm, and a group of optimal parameters are found out in a given parameter range. The algorithm verification result of the actually measured signal shows that the modal aliasing problem of the traditional algorithm in the signal decomposition process is reduced.

The invention belongs to the field of non-contact vital sign detection, and particularly relates to a millimeter wave radar arrhythmia detection method based on particle swarm optimization variational mode decomposition and deep learning, and the method specifically comprises the steps: S1, obtaining a human chest micro-motion signal through a millimeter wave radar, performing static clutter filtering, phase extraction, unwrapping and detrending processing on the radar echo signal to obtain a chest displacement signal containing heartbeat and breathing information; s2, aiming at the thoracic cavity displacement signal, constructing a variational mode decomposition model, and carrying out adaptive optimization on a mode number and a penalty factor through a particle swarm optimization algorithm to obtain an optimal decomposition parameter and complete signal decomposition; s3, according to the center frequency and the energy distribution characteristics of each modal component, screening the modal components in the heartbeat frequency range and reconstructing the modal components to obtain heartbeat characteristic signals representing heart mechanical activities; s4, carrying out time sequence segmentation on the heartbeat characteristic signals, extracting local heart beat morphological characteristics by utilizing a convolutional neural network, and carrying out modeling on a long-time rhythm dependency relationship of the heartbeat signals in combination with a time sequence modeling network based on an attention mechanism to obtain heart rhythm depth characteristic representation; and S5, inputting the heart rhythm depth features into a heart rhythm discrimination model, analyzing the heart rhythm state of the detected person, and outputting an arrhythmia detection result. According to the method, adaptive selection of variational mode decomposition parameters is realized by introducing a particle swarm optimization mechanism, heartbeat signals and respiration and motion interference components are effectively separated, modeling is carried out on rhythm characteristics in combination with a deep learning model, and non-contact detection of arrhythmia is realized. The method does not need to wear an electrode or contact a human body, has the advantages of strong anti-interference capability, good adaptability and high detection precision, and has a good application prospect in the fields of heart rhythm health monitoring, disease screening and the like.

An inside cabin sensor system, having an integrated, single hardware part comprising a camera sub-system and an integrated radar sub-system is proposed. A proposed system provides related information for covering the following applications inside a vehicle: Child Presence Detection (CPD), Driver Drowsiness & Fatigue (F), Driver Distraction (DD) as a safety-relevant function, complemented by Intrusion & Proximity Alert (IPA), Seat Occupancy Detection (SOD), Face Recognition (FR), and optional applications such as Driver Emotion (ES), Passenger Classification (PC), Airbag Suppression (AS), Airbag Activation (AA), Mobile Phone Detection (MP), Gesture Detection (GD) and Vital Signs Detection (VS). A proposed system utilizes Artificial Intelligence (AI)-related data processing for data processing, using radar point cloud data, calculated by said radar sensors. The proposed system utilizes Artificial Intelligence (AI)-related data processing for data processing, using video-captured data. The proposed system utilizes Artificial Intelligence (AI) -related data processing for sensor data fusion, using radar sensor and video sensor data.

A variable sensor system for non-contact vital sign monitoring of animals is described. Non-contact vital sign monitoring may involve correcting measured signals for reflectance and variations in distance between measurements. Non-contact vital sign detection may include temperature detection, and / or measuring cardiovascular and respiratory information. Embodiments are described using a non-contact variable sensor integrated with a weight scale, integrated with a drone, having a pan-tilt monitoring feature, integrated with a kennel, integrated with an automatic pass-fail interface, and including a medication dispensing control module.

The invention relates to the technical field of millimeter wave radar vital sign detection, and discloses a millimeter wave radar vital sign signal processing system and method. According to the method, distance dimension processing and phase extraction are carried out on millimeter wave radar echo signals, heartbeat signals are enhanced through phase difference, and the time domain sparse characteristic of random motion interference is highlighted; then, a sparse optimization model combining a time domain and a frequency domain is constructed, differential sparse constraints are applied to a breathing frequency band, a heartbeat frequency band and a heartbeat harmonic frequency band in the frequency domain, and two-norm morphological constraints are applied to sparse peaks of random motion interference in the time domain; and finally, the model is solved to realize separation of interference and vital sign signals. Complex hardware configuration is not needed, weak heartbeat signal error suppression can be effectively avoided, the stability and accuracy of respiratory rate and heart rate estimation in a random human body motion scene are remarkably improved, and the method is suitable for a non-contact vital sign monitoring scene.

The invention discloses a life body feature separation method based on millimeter-wave radar signal enhancement, and the method comprises the following steps: obtaining an original radar signal, collected by a millimeter-wave radar, of a life body monitoring target, and carrying out the preprocessing of the original radar signal; six types of features are extracted from the preprocessed signals, and the preprocessed signals are divided into four scene types based on the six types of features; according to the divided scene categories, calling a corresponding signal processing model to enhance or reconstruct the preprocessed signal to obtain an enhanced radar signal; and performing signal quality evaluation on the enhanced radar signal, inputting the enhanced signal meeting the quality evaluation requirement into a variational mode decomposition module, and separating a respiration signal and a heartbeat signal of the life body monitoring target. According to the method, the millimeter wave radar vital sign signals in a complex scene are subjected to scene classification and adaptive calling of the model to enhance the signals, breathing and heartbeat are separated through variational mode decomposition, and the vital sign detection precision in a complex environment is improved.

Methods and systems for detecting and / or estimating a rate of vital signs from an occupant within a cabin of a vehicle. In some implementations, the method may include using a plurality of different distance bins to identify a repeating pattern of Doppler spectrum peaks in the RADAR signal. The signal data may then be used to identify an estimated frequency distance between adjacent peaks of the repeating pattern. The estimated frequency distance may then be used to calculate an estimated rate of repetitive vital signs of an occupant within a cabin of the vehicle.

The application provides a radar-based vital sign detection method and device, a radar, and a storage medium. The method comprises: when a user is in bed, extracting the vital sign of the user based on the echo signal obtained by the radar in the detection of the surface area of the bed; if the vital sign in the current vital sign monitoring period is less than a preset vital sign threshold, obtaining the distance unit where the user is and the corresponding signal amplitude in the echo signal of the previous N calculation periods before the current vital sign monitoring period; if the distance unit where the user is in the echo signal of the previous N calculation periods before the current vital sign monitoring period continuously increases and the corresponding signal amplitude continuously decreases, it is determined that the user has a get-out-of-bed action, otherwise it is determined that the user does not have a get-out-of-bed action, and an alarm signal of the weak vital sign in the current frame is generated. Through the above method, the application can accurately distinguish between the two cases of getting out of bed and weak heartbeat, reduce the false alarm rate, and ensure the accuracy of vital sign detection.

The present application provides a kind of vital signs detection signal denoising method and device, it is related to physiological perception and signal processing technical field, including: based on millimeter wave radar's Doppler technique, obtains the original phase signal containing physiological signal;Original phase signal is carried out variational mode decomposition based on the search algorithm of variational mode decomposition super parameter optimization algorithm optimized by particle swarm optimization algorithm, obtain the vibration modal function information corresponding to the original phase signal;Wherein, the particle swarm optimization algorithm uses permutation entropy and fuzzy entropy as fitness function;After denoising processing is carried out to the vibration modal function, the vibration modal function after denoising processing is recombined, and high-precision denoising processing is obtained after physiological signal;From the high-precision denoising processing physiological signal, extract respiratory and heartbeat physiological signal.

The invention relates to the field of agricultural equipment, and discloses an agricultural operation wearable device fused with a sensor, the agricultural operation wearable device comprises a waistband, the waistband is an annular belt with an opening at the front end, a buckle is mounted at the opening at the front end of the waistband, and the outer wall of the waistband is fixedly connected with a shoulder strap; the environment collection assembly is arranged on the outer wall of the shoulder strap and used for collecting environment information; the alarm device is fixedly connected to the outer wall of the shoulder strap and is used for prompting an operator; and the control assembly is in signal connection with the environment acquisition assembly and the alarm device and is used for acquiring physical sign information of the operating personnel and providing an interactive operation platform for the operating personnel. Operation environment parameters are captured in real time through a plurality of groups of temperature sensors and wind speed sensors, in combination with physiological data such as the body temperature of a person collected by an infrared vital sign detection module, a personalized exposure flux correction coefficient and residual safe operation duration can be dynamically calculated, and a traditional static management mode of a fixed standard is broken through.

The application discloses a sleep apnea detection method based on a millimeter wave radar, and relates to the technical fields of radar signal processing and vital sign detection.The method comprises the following steps: transmitting a detection signal to a target through a millimeter wave radar to obtain an intermediate frequency signal and a signal transformation matrix; calculating a dynamic energy change value based on the preprocessed signal transformation matrix, and determining a target distance bin of each frame according to a statistical result of the dynamic energy change value; determining whether the target is in a position stable state according to the discrete degree of the target distance bin of each frame in a preset time window; extracting a phase signal at the target distance bin of each frame in the window in the position stable state, obtaining a breathing signal waveform curve based on the phase signal, and detecting a sleep apnea event.The application can realize high-precision detection of the sleep apnea event under various complex conditions, and effectively solves the problems of unstable target locking, large noise interference and lack of distance adaptability in state determination.

This invention discloses a method for separating vital signs based on millimeter-wave radar signal enhancement, comprising the following steps: acquiring the original radar signal of the target being monitored by millimeter-wave radar and preprocessing it; extracting six types of features from the preprocessed signal and classifying the preprocessed signal into four scene categories based on the six types of features; according to the classified scene categories, calling the corresponding signal processing model to enhance or reconstruct the preprocessed signal to obtain the enhanced radar signal; performing signal quality assessment on the enhanced radar signal, and inputting the enhanced signal that meets the quality assessment requirements into a variational mode decomposition module to separate the respiratory and heartbeat signals of the target being monitored. This invention improves the accuracy of vital sign detection in complex environments by classifying millimeter-wave radar vital sign signals in complex scenes, adaptively calling the model to enhance the signal, and using variational mode decomposition to separate respiration and heartbeat.

The utility model relates to the technical field of medical equipment, in particular to an equipment containing device which comprises a main body, a vital sign detector is fixedly connected to the top of the main body, two sets of detection tool containing areas are formed in one side of the vital sign detector, a tray is arranged at the end, away from the vital sign detector, of the top of the main body, and the two sets of detection tool containing areas are arranged in the tray. The end, away from the detection tool placement area, of the body is slidably connected with a first drawer, the end, away from the detection tool placement area, of the body and located at the bottom of the first drawer is slidably connected with a second drawer, and the end, away from the detection tool placement area, of the body and located at the bottom is provided with a placement area. A containing groove is formed in the top of the tray, handheld grooves are formed in the two sides of the tray, two sets of first cylindrical grooves are formed in the end, close to the vital sign detector, of the top of the tray, and compared with an existing equipment containing device, the overall practicability of the equipment containing device can be improved through the design.

The present invention relates to a radar phase-based stationary human target detection method in complex scenes, comprising the following steps: shifting radar echo signals in the complex scene from the time domain to the frequency domain to obtain distance information for all target points in the complex scene; extracting a number of stationary target signals from the distance information for all target points; extracting a number of stationary target points in an area of ​​interest from the stationary target signals, and calculating the stationary target point phase for each stationary target point; unwrapping the stationary target point phase of each stationary target point to obtain the true phase of the stationary target point; calculating the absolute average of the true phase of each stationary target point within a target time period; and determining whether each stationary target point is a stationary human target based on the absolute average. This detection method overcomes the problems of existing vital sign detection, such as inaccurate and low sensitivity, resulting in misjudgment of stationary human targets, and slow detection speed, and is still applicable when dynamic and static targets coexist in complex scenes.

The invention relates to a precise positioning fire-fighting intelligent safety helmet which comprises a helmet body, and the helmet body comprises a controller arranged on the helmet body, and an environment detection module, a vital sign detection module, an imaging module, a positioning module, a communication module, an alarm module, a voice interaction module and a power supply module which are electrically connected with the controller; the environment detection module is connected with an environment detection device arranged on the helmet body and used for detecting the concentration, temperature and humidity of gas in the environment and transmitting signals to the controller. The vital sign detection module is connected with a vital sign detection device arranged on the helmet body and used for monitoring vital signs of firemen. A mask is arranged on the front side of the helmet body, an air inlet used for being connected with a respirator is formed in the middle of the mask, and goggles are arranged on the upper portion of the mask. A neck cover is arranged at the bottom of the helmet body and used for shielding the neck of a fireman and preventing high-temperature substances or fragments from entering a collar.

The invention provides a vertical rescue probe radar life detection system and method, and belongs to the technical field of radars, the vertical rescue probe radar life detection system comprises a radar probe, the radar probe is used for detecting trapped persons in an underground roadway, and the radar probe comprises a vertical rescue radar and a cylindrical metal probe; the cable reel is used for laying down the radar probe from the vertical shaft on the ground, and the cable reel is connected with the radar probe through a cable; the ground processing display terminal is used for processing and displaying data and results detected by the radar exploring tube; the radar probe tube is connected with the ground processing display terminal through wireless communication, the radar probe tube is simple and compact, and therefore the radar probe tube can be applied to small-diameter drill hole vital sign detection, and low-power-consumption detection can be adopted during detection.

The utility model relates to an accurate positioning fire-fighting intelligent safety helmet, which comprises a helmet body, and the helmet body comprises a controller arranged on the helmet body, and an environment detection module, a vital sign detection module, an imaging module, a positioning module, a communication module, an alarm module, a voice interaction module and a power supply module which are electrically connected with the controller, the environment detection module is connected with an environment detection device arranged on the helmet body and used for detecting the concentration, temperature and humidity of gas in the environment and transmitting signals to the controller. The vital sign detection module is connected with a vital sign detection device arranged on the helmet body and used for monitoring vital signs of firemen. A mask is arranged on the front side of the helmet body, an air inlet used for being connected with a respirator is formed in the middle of the mask, and goggles are arranged on the upper portion of the mask. A neck cover is arranged at the bottom of the helmet body and used for shielding the neck of a fireman and preventing high-temperature substances or fragments from entering a collar.

The present application discloses a flexible triboelectric sensor for detecting human vital signs, as well as its preparation method and intelligent detection method, belonging to the technical field of flexible triboelectric sensors. The sensor comprises a translucent silicone film layer, a multi-walled carbon nanotube composite sandpaper-modified silicone layer, a foam barrier layer, a conductive fabric electrode layer, a PET base layer, and a metal shielding layer, which are sequentially laminated. The electrode layer has a fabric structure, the multi-walled carbon nanotube composite sandpaper-modified silicone layer serves as the upper friction layer of the flexible triboelectric sensor, and the electrode layer serves as the lower friction layer of the flexible triboelectric sensor. The sensor has a rectangular through-hole, the size of which is the same as that of the multi-walled carbon nanotube composite sandpaper-modified silicone layer, and the multi-walled carbon nanotube composite sandpaper-modified silicone layer is located inside the rectangular through-hole. The sensor has a flexible electrode layer and a high-performance friction layer, which can improve accuracy, sensitivity, and response speed, and can recognize the user's posture, breathing, gestures, and pulse.

The invention provides a non-contact vital sign detection system, which relates to the technical field of non-contact detection and comprises an ultra-wideband radar module, an infrared temperature measurement module, a core processing unit and an output unit. The ultra wide band radar module is used for transmitting nanosecond narrow pulse detection waves to a monitoring space and receiving echo signals; the infrared temperature measurement module is used for collecting an infrared radiation signal sent by a monitoring target; the core processing unit is electrically connected with the ultra-wideband radar module and the infrared temperature measurement module, and is configured to process the echo signal and the infrared radiation signal so as to calculate and output quantitative vital sign data and spatial trajectory information of a monitored target; and the output unit is connected with the core processing unit and is used for outputting quantitative vital sign data and spatial trajectory information. According to the invention, high-precision, quantitative and non-contact synchronous monitoring of multi-target vital signs in a complex daily scene can be realized, so that the limitation that the prior art is affected by clothes shielding, human body movement and distance change is overcome.

A Life Sign Detection system (LDS) monitors one or more environments using radar. The radar signals are processed to generate one or more point clouds, each associated with an organism in the environment(s). Life signs for the organisms(s) are inferred based on the radar point clouds. For example, the coordinates of the point cloud may be tracked to determine if the organism is moving about the environment, frequency domain components of the points of the point cloud may be used to determine involuntary movements, or combinations thereof. The LDS may determine a status of the organism(s) based on the inferred life signs. For example, if the organism is moving or has involuntary movement, the organism may be determined to be alive. In some embodiments, the life signs may be compared to normal ranges to determine a status of the organism(s).

The invention discloses a vital sign detection method, device and equipment. The method comprises the steps that a first optical signal is collected, wherein the first optical signal is a combined signal of a first sub-optical signal used for detecting the health condition of a first user and a second sub-optical signal used for detecting the health condition of a second user; determining a signal abrupt change point of a first electric signal corresponding to the first optical signal as a segmentation point for segmenting the first electric signal; segmenting the first electric signal into a first sub-electric signal corresponding to the first sub-optical signal and a second sub-electric signal corresponding to the second sub-optical signal according to the segmentation point; determining a vital sign of the first user according to the first sub-electric signal; and determining the vital signs of the second user according to the second sub-electric signal. In the application, the signal mutation point of the first electric signal is taken as the segmentation point of the first electric signal, the electric signal corresponding to each optical signal can be accurately separated, so that the vital signs of multiple users are detected, the structure is simple, the cost is relatively low, and the implementation is easy.

The invention relates to the technical field of vital sign detection, in particular to a method for processing an arterial blood pressure waveform signal through a residual neural network. Comprising the following steps: preprocessing an arterial blood pressure waveform signal and extracting a characteristic value; performing short-time Fourier transform on the arterial blood pressure waveform signals to generate amplitude and phase images of the signals in a time-frequency domain, and inputting the images and characteristic values into a residual neural network model to obtain predicted values of hemodynamic parameters including cardiac output and stroke output; and predicting the blood pressure trend in a certain time in the future by using the time sequence prediction capability of the residual neural network. According to the method, more accurate heart and displacement parameter prediction and more accurate hypotension event prediction are provided; in addition, uncertainty quantization of hypotension event prediction is also increased.

The application discloses an omnidirectional biological radar device and a working method thereof, wherein a FMCW radar is installed on a 360-degree rotating platform, and the high-speed rotating radar is used to simultaneously realize omnidirectional vital sign detection on multiple human targets. The rotating radar platform can not only realize the identification of the existence of people in the surrounding environment, but also estimate the relative angle between the initial position of the radar and the object by using the virtual SIMO (Simple Input Multiple Output) method, so as to finally realize the simultaneous detection of the full-dimensional vital signs of multiple human targets. The realization of the task has important application value for future search and rescue and vital sign detection.