2748results about "Diagnostic signal processing" patented technology

The invention discloses an intelligent system and method for early screening of depression based on electroencephalogram-eye movement multi-modal data fusion. According to the system, in a virtual reality situation, emotion and cognitive response of a subject are stimulated through an elaborately-designed cognitive task, and brain electrical activity and sight behavior data are synchronously collected by adopting a wearable EEG device and a high-precision eye tracker. After signal preprocessing and feature extraction, deep fusion of multi-modal data is realized by using a weighted fusion formula or a graph neural network, the weight of each modal is automatically adjusted, interaction features between electroencephalogram and eye movement are extracted, and then the depression risk of a subject is accurately judged through a classifier. Experiments show that the method can effectively capture weak physiological abnormalities of mild depression patients, has the advantages of high sensitivity, high accuracy and real-time online screening, and meets the requirements of non-invasive, portable and intelligent clinical application.

The invention discloses a pig breeding intelligent management and control system based on multi-modal data monitoring, and belongs to the technical field of pig breeding management, and the system comprises a data collection and individual recognition module which comprises an RFID ear tag, an inspection robot, an environment sensor and an intelligent feeder, and is used for determining the identity information of a pig individual based on the data collected by the RFID ear tag and the inspection robot, associating the collected multi-modal data of the live pigs in the pig house, and uploading the multi-modal data to edge computing equipment through an MQTT protocol for preprocessing; the feature extraction module extracts physiological and behavior features of live pigs by using a multi-modal deep learning fusion model; the key breeding index analysis module constructs a live pig health index and an environment quality index; the abnormity early warning module adopts a CUSUM control chart to accumulate index deviation and respond to abnormity; the management regulation and control model constructs a Markov decision process by minimizing energy consumption and maximizing health indexes, intelligently adjusts the rotating speed of a fan and a feeding strategy, and realizes precise environmental control and feeding optimization.

The invention relates to the technical field of data processing, and discloses a mental health monitoring method and system based on artificial intelligence. The method comprises the following steps: acquiring environmental parameters through a multi-source sensor network, and identifying scene features through an environmental context inference engine; according to the environmental features, extracting physiological features by adopting an adaptive algorithm; analyzing an environment and behavior relationship by using a Bayesian model; fusing the multi-dimensional features through a graph attention network; constructing an environment adaptive baseline based on transfer learning; and evaluating a psychological state and outputting an early warning in combination with multi-level anomaly detection. According to the method and the device, the psychological health state of the user can be accurately monitored in real time under the condition of considering the dynamic change of the environment, and the monitoring deviation caused by individual difference is overcome.

The invention provides a rectal cancer neoadjuvant chemoradiotherapy curative effect prediction method and system based on magnetic resonance image analysis, and aims to improve accurate prediction of curative effects of patients with local advanced rectal cancer (LARC). According to the method, multi-modal magnetic resonance images (T1WI, T2WI, DWI and DCE-MRI) are obtained, qualitative and quantitative analysis is combined, tumor morphology, diffusion characteristics and microcirculation parameters are extracted, clinical and pathological data of a patient are integrated, and a multi-dimensional feature set is constructed. Curative effect prediction is performed through machine learning and a deep learning model, and the model can predict treatment results of complete remission, partial remission, ineffectiveness and the like in a classified manner. The system accurately predicts the reaction of a patient to neoadjuvant radiotherapy and chemotherapy through a non-invasive method, supports formulation of a personalized treatment scheme, reduces excessive treatment, optimizes the treatment effect, and has good clinical application value.

The invention discloses a self-adaptive music intervention system based on multi-modal physiological feedback, and belongs to the technical field of monitoring and intervention of emotional states, the self-adaptive music intervention system comprises a multi-modal emotional state evaluation module and a self-adaptive music intervention module, the multi-modal emotional state evaluation module acquires multi-modal physiological information of a user, and the self-adaptive music intervention module performs music intervention on the multi-modal emotional state evaluation module; feature extraction and fusion are carried out on the multi-modal data, and the emotional state is evaluated according to the fused data; and the adaptive music intervention module generates music features matched with the current emotion according to the evaluated emotion state, the music features and preference data of the user, and selects music according to the music features to intervene the user. By the adoption of the method, the wearable forehead lobe three-lead electroencephalogram equipment and the intelligent bracelet are combined, physiological signals of the central nervous system and the autonomic nervous system are collected and analyzed in real time, music intervention is automatically adjusted, a personalized emotion adjusting scheme free of side effects is provided, and a closed-loop mental state regulation and control mechanism is formed.

A system for determining electrophysiological data comprising an electronic control unit configured to acquire electrophysiology signals from a plurality of electrodes (130) of one or more catheters, select at least one clique of electrodes from the plurality of electrodes (136) to determine a plurality of local E field data points, determine the location and orientation of the plurality of electrodes, process the electrophysiology signals from the at least one clique from a full set of bipole subcliques to derive the local E field data points associated with the at least one clique of electrodes, derive at least one orientation independent signal from the at least one clique of electrodes (138) from the information content corresponding to weighted parts of electrogram signals, and display or output catheter orientation independent electrophysiologic information to a user or process.

The invention provides an emotion recognition method based on electroencephalogram eye movement multi-mode cross-attention feature fusion, and the method comprises the steps: firstly carrying out the preprocessing of an emotion recognition public data set, and building a corresponding training set and a test set; secondly, constructing an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model; then carrying out model training and performance testing; finally, an electroencephalogram eye movement multi-mode online emotion recognition system is built, and the effectiveness of the method is verified. The method has the advantages that an electroencephalogram eye movement multi-mode cross-attention feature fusion emotion recognition model is designed, a dynamic graph convolutional network and an attention mechanism are flexibly applied, meanwhile, multi-dimensional data are used, the problem that single-mode emotion recognition information is insufficient is solved, and the recognition accuracy is improved; and an electroencephalogram eye movement multi-mode online emotion recognition system is built, so that the interactivity is enhanced. The average recognition accuracy of the method reaches 97.94% and is superior to that of an existing optimal method, and meanwhile the accuracy of online emotion recognition reaches 87.4%.

In one embodiment, a medical system includes a catheter configured to be inserted into a chamber of a heart of a living subject, and including multiple electrodes configured to capture electrical activity from electrical activation signals propagating in tissue of the chamber, a display, and processing circuitry configured to automatically select bipolar signals to be captured into an electro-anatomical map from respective electrode pairs of the multiple electrodes responsively to an alignment of the respective electrode pairs with a direction of propagation of the electrical activation signals, and render the electro-anatomical map to the display.

The present application discloses a multifunctional health monitoring method and system based on a millimeter wave radar, and a computer readable storage medium. The method comprises: receiving an echo signal in a space, and on the basis of the echo signal, calculating point cloud data of a user and phase change data caused by the respiration and heartbeat of the user; calculating behaviors of the user on the basis of the point cloud data; calculating vital signs of the user on the basis of the phase change data, wherein the vital signs comprise respiration rate and heart rate; and on the basis of the behaviors and / or the vital signs, determining that the user triggers a set alarm event, and on the basis of the type of the triggered alarm event, performing a corresponding alarm operation.

The invention provides a sleep stage detection method based on a multi-modal and visual transformation network, and the method comprises the steps: extracting EEG channel signals and EOG channel signals in PSG, carrying out the feature extraction of different frequency bands through a multi-scale convolutional network, and fusing the multi-modal features to enhance the classification accuracy. Thirdly, the time-space relevance of network modeling signals is transformed through vision, and then different stages of sleep are effectively recognized; the method also provides confidence evaluation of the classification result, and the reliability of the detection result is enhanced. The automatic detection method can be used for clinical sleep staging to assist doctors in evaluating sleep quality and diagnosing sleep disorders.

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

The invention discloses a postoperative physical sign monitoring system based on medical information processing. The postoperative physical sign monitoring system comprises a data acquisition module, a physical sign feature extraction module, a postoperative physical sign evaluation model establishment module and a postoperative physical sign monitoring module. The invention belongs to the field of medical information processing, and particularly relates to a postoperative physical sign monitoring system based on medical information processing. Quantifying the total change trend of the heart rate fluctuation in the monitoring time; the quantitative basis is provided for the postoperative recovery process by integrating the blood pressure and heart rate changes; the sign monitoring effect is improved; focusing on dynamic adjustment through a time weight function, a recovery stage weight and an environment correction factor in a postoperative key time period; deviation weights are introduced for fluctuations of heart rates and blood pressures, loss coefficients are dynamically adjusted, segmented loss functions are designed according to physical sign values, conditions of different risk intervals are processed respectively, and the method adapts to uncertainty of individual differences and physical sign fluctuations of postoperative patients; and thus, the accuracy of post-operation sign monitoring is improved.

The invention discloses a vagus nerve stimulation parameter closed-loop adjustment method and system based on electroencephalogram characteristics. The method comprises the steps that real-time electroencephalogram signals of a patient are obtained, and time domain information and frequency domain information of the electroencephalogram signals of all frequency bands in the real-time electroencephalogram signals are extracted; on the basis of the time domain information and the frequency domain information of the electroencephalogram signals of all the frequency bands, electroencephalogram signal index features are calculated, and the electroencephalogram signal index features comprise an electroencephalogram signal power spectrum, a brain dynamic symmetry feature, a brain function connection feature and a rhythm feature; determining the state type of the patient according to the electroencephalogram signal index characteristics; determining parameter configuration information of vagus nerve stimulation according to the state type of the patient; and adjusting parameters of the vagus nerve electrical stimulation equipment according to the parameter configuration information of vagus nerve stimulation. According to the invention, timely and accurate electrical stimulation can be applied to a patient, the treatment accuracy is improved, the stimulation efficiency and the treatment effect are enhanced, side effects are reduced, and more accurate and individualized treatment management is realized.

The invention relates to the field of man-machine interaction training, virtual reality and intelligent evaluation, and particularly discloses a psychological exercise ability training method and system based on mixed reality and behavior analysis. The method comprises the steps that a multi-scene psychological training environment is constructed and presented in mixed reality equipment, action behavior data, eye movement path data and operation response time data of a pilot in the training process are collected, multi-modal original behavior characteristics are constructed, and then a psychological exercise ability label set is constructed; and realizing capability-task modeling through matching of a capability label and a task structure, generating a personalized training path and a capability difference atlas based on difference analysis with a preset capability standard, adjusting task parameter configuration data, dynamically updating a training scene, generating a capability evolution trajectory, and outputting a training analysis result. In this way, accurate ability modeling, task dynamic adaptation and visual tracking of the training effect can be achieved, and the individuation and intelligence level of psychological exercise ability training is remarkably improved.

The invention discloses a transcranial electrical stimulation intracranial vector electric field detection device and method, and belongs to the transcranial electrical stimulation intracranial electric field detection technology, the device inputs an individual head model in a stimulation system, sets a target spot and calculates transcranial electrical stimulation parameters; the implantation position and angle of the vector detection electrode are planned through the navigation system, electrical stimulation is applied to a brain target area through the stimulation electrode, and intracranial electric signals are collected in real time through the vector detection electrode; performing high-precision amplification on the acquired weak signal, and converting the weak signal into a digital signal; the collected electric field signals are calculated and processed in real time, and electric field distribution generated by transcranial electrical stimulation in the brain is analyzed and visualized; according to the method, time domain and frequency domain analysis is carried out on electric signals before and after stimulation, the influence of stimulation on neural signals in a detection area is evaluated, and high-precision measurement of the intensity and direction of a three-dimensional electric field is achieved through the design of a high-precision and multi-layer vector detection electrode.

The invention provides a closed-loop nerve regulation method and device, a medium and a program product, and relates to the technical field of nerve regulation. The method comprises the steps that an electroencephalogram collection module collects electroencephalogram signals in real time and provides real-time feedback data for nerve regulation and control; the magnetic resonance imaging module provides high-precision brain structure and function positioning information; the three different stimulation modules (transcranial alternating current stimulation, low-field magnetic field stimulation and transcranial magnetic stimulation) can stimulate brain regions with different depths and ranges; the multi-mode navigation module carries out accurate positioning based on the magnetic resonance image; the electroencephalogram detection module analyzes the collected signals; and the central control module coordinates the work of each module. According to the multi-module cooperative working mode, accurate stimulation of different areas and different depths of the brain is achieved, meanwhile, the stimulation effect is ensured through real-time monitoring, and therefore the accuracy and effectiveness of nerve regulation and control are improved.

The invention provides an intensive care method and system based on intelligent analysis, and the method comprises the steps: collecting multiple physiological data, including electrocardiogram, blood pressure and the like, of a critical patient in real time through a sensor network; cleaning and standardizing the data; calculating the weight of each physiological data by using a dynamic weight evaluation formula; predicting an abnormal trend; calculating a risk score of the patient by using a comprehensive risk assessment model according to the weight and the abnormal trend; when the score exceeds a threshold value, the system automatically gives an early warning and sends information to the medical staff, and provides preliminary treatment suggestions at the same time. The method combines a traditional intensive care technology with a modern sensor network and a data processing technology, and aims to improve the treatment efficiency and level of critical patients.

The invention discloses a remote monitoring and guiding system for rehabilitation training in the orthopedics department, particularly relates to the technical field of medical rehabilitation intelligent monitoring, and is used for solving the problems that in an existing remote rehabilitation system, timing sequence dislocation exists in action recognition and regulation and control instruction execution, and joint movement safety early warning is delayed. According to the method, localized time sequence alignment processing is performed on joint movement data and electromyographic signals based on edge computing nodes, a joint linkage action time sequence difference is generated, and an abnormal action risk level is judged in real time by combining an electromyographic signal pre-activation feature and a conflict detection mechanism of a standard action intention; historical abnormal records are matched through the cloud platform to generate a hierarchical regulation and control instruction set, and progressive safety intervention is triggered by the patient end equipment according to the dynamic deviation of the joint activity; through a collaborative mechanism of edge side action stage accurate analysis, electromyographic signal feed-forward verification and multi-stage instruction dynamic binding, millisecond-level response of joint linkage abnormity in staged rehabilitation training is realized, and the risk of joint over-limit activity is effectively avoided.

The invention discloses a thoracic surgery patient rehabilitation guidance system and method, particularly relates to the field of medical rehabilitation guidance, and solves the problem of insufficient evaluation precision caused by motion interference suppression in a family environment. Evaluation precision is improved through multi-modal data fusion and a causal reasoning technology. Inertial sensor data is synchronously calibrated based on a physical switch time sequence, and anti-interference frequency band energy characteristics are extracted to suppress environmental noise; constructing a twinning network to quantify joint track difference in the enabled / disabled state of the instrument, and revealing a motion mode reconstruction rule; driving a causal forest model by taking the use frequency of an instrument as a tool variable, analyzing causal association between a sensor channel and a rehabilitation effect, fusing a respiratory resistance dynamic spectrum and a joint space-time cooperation index, and screening out hybrid factors through confrontation pre-training; time domain feature clusters are dynamically divided based on the breathing phase, cross-scale fusion is implemented in combination with a frequency band energy attention mechanism, and an evaluation index with time sequence sensitivity and pathological interpretation is generated.

The invention discloses an intelligent auxiliary fracture monitoring system for orthopedic nursing, which comprises the following steps that: the system acquires mechanical parameters, movement track data and biological signals of a fracture patient through a multi-mode sensor, and intelligently analyzes the data by using a deep learning algorithm; generating a fracture state diagnosis result and recovery trend prediction data; the low-power-consumption wireless communication module realizes real-time transmission of diagnosis data and supports remote system parameter adjustment; the personalized nursing scheme generation module generates a rehabilitation plan, a load adjustment strategy and an auxiliary device configuration scheme through an optimization algorithm based on the patient biomechanical model and the diagnosis result; and the adaptive feedback adjustment module dynamically adjusts a data acquisition strategy and processing parameters to ensure the real-time response capability of the system to the state of the patient. The system is suitable for the technical field of intelligent auxiliary monitoring, and can realize real-time monitoring, intelligent diagnosis and personalized rehabilitation guidance of fracture patients.

The invention relates to the technical field of physical sign monitoring, in particular to a physical sign parameter monitoring method accompanying the medication process of a severe mental disease patient. According to the method, the dynamic change of the physical sign parameters is captured by acquiring the physical sign parameter time sequence curves of the to-be-monitored patient in the two stages before and after medication. For each time sequence curve, data value differences and fluctuation duration are analyzed, and abnormal fluctuation data segments are identified. Furthermore, a historical normal time sequence curve is utilized to determine a historical normal fluctuation section corresponding to the abnormal fluctuation, and the overall state of the physical sign parameters is visually reflected through comparative analysis and calculation of a stable index. Meanwhile, the physical sign condition of the patient is monitored by combining the stability index of the same physical sign parameter before and after medication and the time sequence characteristics of the abnormal fluctuation data segment. According to the method, the physical sign state of the patient is reflected more accurately by comparing the change of the physical sign parameters of the patient with the historical normal condition, and data support with higher credibility is provided for doctors to carry out medicine effect judgment or illness state analysis.

The invention belongs to the technical field of medical robot control, and discloses a physiotherapy robot self-adaptive control method and system based on multi-mode biological feedback, and the method comprises the steps: carrying out the preprocessing of a biomechanical sensing signal obtained in advance, carrying out feature extraction and dynamic weight fusion on the preprocessed biomechanical sensing signal by utilizing wavelet transform and frequency domain transform to obtain a joint feature; establishing a system dynamics model based on a Lagrange equation, generating a time-varying impedance trajectory model by utilizing a joint state acquired in real time and combining a rolling optimization algorithm, and dynamically adjusting impedance parameters of the time-varying impedance trajectory model according to joint features to generate a safe motion trajectory; and performing double closed-loop control on the physiotherapy robot according to the safe movement track and a biomechanical sensing signal obtained in real time to obtain a control signal of the physiotherapy robot, and realizing joint motor driving of the physiotherapy robot based on the control signal. The device can be flexibly changed according to individual differences of patients and rehabilitation processes.

The invention discloses a sleep state monitoring and analyzing system based on multi-sensor fusion, and relates to the technical field of health monitoring, the sleep state monitoring and analyzing system comprises a multi-modal sensor module used for collecting multi-dimensional data related to a sleep state, the multi-dimensional data comprises a bio-electricity signal, a physiological parameter, body movement data and an environment parameter, and the multi-modal sensor module is used for collecting the multi-dimensional data; the multi-modal sensor module comprises a non-contact sensor, a flexible electronic skin sensor and a bio-electricity signal sensor, and the data fusion and processing module is used for carrying out preprocessing, dynamic self-adaptive fusion and federal learning modeling on collected original data. According to an existing contact type sleep state monitoring scheme, more flexible and accurate sleep state data are realized through a non-contact type monitoring sensor in cooperation with dynamic adjustment of sleep monitoring content and adjustment of weights of various sensors for monitoring the sleep state; and a more accurate and intuitive reference report is provided for the sleep state and the health state of the subsequent user.

The invention relates to the technical field of brain-computer interfaces, in particular to a method, a device and equipment for detecting the cognitive state of attention glasses in real time. The method comprises the following steps: acquiring multi-channel electroencephalogram data of attention glasses, acquiring multi-dimensional features corresponding to the multi-channel electroencephalogram data, and generating a detection sequence; performing time sequence analysis on the detection sequence, extracting change characteristics to construct a cognitive sequence, and generating state description according to the cognitive sequence; performing trend analysis on the state description, performing feature extraction on an analysis result corresponding to the trend analysis, constructing a conversion rule according to the extracted features, and generating a prediction sequence according to the conversion rule; performing threshold division on the prediction sequence, marking state intervals, constructing a criterion sequence, and generating an evaluation result according to the criterion sequence for performing signal detection to obtain detection information; and performing data conversion on the detection information, constructing a display sequence to mark a state type, and generating cognitive state information. And data continuity and signal integrity in a feature extraction process are realized.

The invention discloses an intensive care data acquisition and early warning system, and relates to the technical field of medical early warning, abnormal points in data can be effectively identified through a data processing module, an abnormal data set is generated, and the method is beneficial to screening out possible instantaneous abnormal conditions. And on the basis of the anomaly sets, corresponding monitoring time periods are identified, the relevance between the behavior state and the physiological parameters of the patient is further analyzed, influence factors are calculated, and the relevance-based analysis method effectively solves the problem of false positive anomaly which is difficult to distinguish in a traditional intensive care system. On the basis, the hierarchical response module constructs a screening model by using a convolutional neural network technology, fits and outputs a screening index, distinguishes abnormal types according to the numerical value of the index and sends out a corresponding early warning instruction, and meanwhile, ensures that dynamic adjustment and switching of the early warning instruction are more intelligent and accurate through a preset time window mechanism.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention provides a pilot brain consciousness state detection method, an electronic device and a detection system, which are applied to the technical field of biomedicine, and the method comprises the following steps: collecting a dual-channel photoelectric volume pulse wave signal of a forehead part of a to-be-detected pilot; determining a forehead blood oxygen value and a blood perfusion degree of the pilot to be detected based on the dual-channel photoelectric volume pulse wave signal; inputting the forehead blood oxygen value and the blood flow perfusion degree of the pilot to be detected into a pre-trained brain consciousness state score prediction model, so that the brain consciousness state score prediction model performs weighted calculation on the forehead blood oxygen value and the blood flow perfusion degree based on a pre-trained weight coefficient, outputting the brain consciousness state score of the pilot to be detected; and determining the brain consciousness state of the pilot to be detected based on the brain consciousness state score and a preset state score threshold. In this way, the brain consciousness state of the pilot can be comprehensively detected, and the accuracy of detecting the brain consciousness state of the pilot can be improved.

The invention discloses a respiration monitoring analysis method and system based on multiple sensors, and relates to the technical field of respiration monitoring. The method comprises the following steps: collecting various signal data and collecting the signal data into a multi-dimensional signal data set; performing data preprocessing on the signal data; performing first-stage data fusion and second-stage feature fusion based on the preprocessed signal data; adopting a long-short-term memory network and a convolutional neural network to construct a respiratory anomaly detection model, and performing respiratory anomaly detection; and analyzing an anomaly detection result and carrying out visual display. Based on an advanced data fusion technology and an intelligent analysis algorithm, the defects of a traditional single-sensor monitoring method can be overcome, multi-dimensional and multi-level analysis of respiration signals is achieved, and the accuracy and real-time performance of respiration monitoring can be greatly improved; through multi-modal fusion, feature extraction and intelligent analysis of sensor data, more accurate and reliable technical support can be provided for early diagnosis and health monitoring of diseases.

Computer-implemented methods are disclosed for assessing the effect of musculoskeletal activities on disease and / or clinical events, the method comprising: receiving a patient-specific vascular and musculoskeletal model of a patient's anatomy, including at least one vessel of the patient; receiving at least one characteristic of the patient's musculoskeletal activity; generating or updating a computational anatomic vascular and musculoskeletal model of the patient's anatomy based on the received at least one characteristic of musculoskeletal activity; performing at least one of a computational fluid dynamics analysis or a structural mechanics simulation on the computational anatomic vascular and musculoskeletal model; and estimating at least one of the patient's risk of disease or clinical events based on the performed computational fluid dynamics analysis and / or structural mechanics simulation. Systems and computer readable media for executing these methods are also disclosed.

The invention provides a life sleep sign monitoring method, and relates to the technical field of sign data monitoring, and the method comprises the following steps: collecting an original echo signal of a monitoring area, and extracting a respiration signal, a heartbeat signal and a body movement signal corresponding to a body movement event from the original echo signal; obtaining relevance characteristics of the respiratory signal and the heartbeat signal and adjusting characteristics of the body movement event on the respiratory signal and / or the heartbeat signal; according to the relevance features and the adjustment features, biological features representing individual physiological differences are generated; based on the biological characteristics, target identity probability distribution is obtained; and performing interference suppression processing on the original echo signal according to the target identity probability distribution so as to separate a target respiration waveform and a target heartbeat waveform of the target individual from the original echo signal. The method is suitable for practical application scenes such as multi-person same-bed monitoring and family bedroom monitoring, and the technical bottleneck that the monitoring precision is reduced along with the increase of the target number in the prior art is broken through.