474 results about "Fetal Heart Rate Variability" patented technology

The invention provides a multi-source biological signal fusion moxibustion electric control method and system. In the moxibustion applying process, a multi-source biological signal flow is formed by obtaining an epidermal impedance dynamic waveform and a heart rate variability signal, a skin impedance baseline map of a physiological feature parameter library and a heart rate variability association rule are dynamically matched, and an acupoint sensitivity index and a heat tolerance threshold value are generated; triggering an infrared thermal imaging device to scan a moxibustion application area based on the sensitivity index, reconstructing a heat conduction model in combination with the epidermal layer thickness and the acupoint distribution density, comparing an epidermal impedance dynamic waveform with a skin impedance baseline map through a phase offset, and calculating a targeted thermal field compensation gradient of the acupoint area; and finally, coupling the thermal tolerance threshold and the space-time parameters of the target thermal field compensation gradient to generate a control instruction set. According to the technical scheme, accurate regulation and control of thermal field distribution and optimization of the motion path of the mechanical arm in the moxibustion applying process are achieved.

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention discloses a nerve disease detection and analysis method and system, and the method comprises the steps: obtaining a bracelet collection signal, a sphygmomanometer collection signal, a movement behavior image and behavior test data, and extracting tremor intensity features, gait symmetry features and autonomic nerve rhythm features through multi-band decomposition of the bracelet collection signal; analyzing the motion behavior image and the standardized motion test to obtain a motion function score; carrying out heart rate variability analysis to identify a neural function abnormality mode; constructing a neural function state map and calculating a feature weight; predicting a disease progress trend in combination with historical monitoring data; and dynamically adjusting a prediction result through subsequent feedback correction information. Through a mode of combining short-time intensive monitoring and long-term intermittent acquisition, long-term trend prediction and dynamic correction based on initial data are realized, and the reliability and practicability of nerve disease risk assessment in a home scene are remarkably improved.

The invention relates to the technical field of fire fighting systems, in particular to an intelligent cockpit system for fire fighting, which comprises a perception analysis layer, a decision processing layer, a command execution layer and a reinforcement learning closed-loop architecture for mixed reward shaping, and integrates video streams, audio communication and firefighter physiological data containing heart rate variability through a deep multi-modal fusion module. Generating a global fire scene situation of physical constraint verification; through a risk sensitive type three-dimensional fire scene deduction module, a fire extinguishing strategy considering efficiency and safety is generated; through an immersive augmented reality visual interface, in combination with a synchronous positioning and mapping technology, precise navigation in a complex environment and superposed display of situation information containing risk levels are realized; a Q learning algorithm continuous optimization strategy including domain knowledge intermediate process rewards is adopted, closed-loop optimization of perception-analysis-decision-execution-feedback is achieved, and therefore the overall efficiency of fire rescue operation in modern complex disaster scenes is remarkably improved.

The invention relates to the technical field of intelligent medical systems, and discloses a cerebral apoplexy onset risk assessment and reminding method and system.The method comprises the steps that continuous medical structured detection data are collected, and the data comprise carotid artery blood flow parameters, brain oxygen saturation, heart rate variability and metabolic indexes; inputting a bidirectional LSTM, a differential convolutional network, a wavelet residual network and a multi-layer perceptron to extract nonlinear features; constructing a neural function coupling structure diagram of four nodes of cerebral blood supply, oxygen supply, autonomous regulation and metabolic steady state; calculating inter-node time sequence offset correlation and a stable factor to obtain a coupling anomaly coefficient; and driving the embedded network by using a graph structure and a node feature input mechanism, and outputting a risk state assessment result. According to the method, the neural function coupling structure diagram is constructed and the mechanism is introduced to drive the embedded network, so that high-precision identification of the multi-system collaborative abnormal state and dynamic evaluation of the stroke risk level are realized.

The invention belongs to the technical field of intelligent education, and particularly relates to a classroom attention detection method and system based on multi-modal data fusion. Aiming at the problems of high equipment cost, low multi-source data fusion efficiency, insufficient privacy protection and the like in the prior art, the invention provides the following solutions: collecting face, eye movement, posture, voice signals and heart rate variability data of a student through a sensor; multi-modal data synchronization is realized by adopting a time sequence alignment algorithm; respectively extracting a visual attention feature, a voiceprint matching feature and a physiological wake-up feature by using a lightweight deep learning model; constructing a multi-modal data fusion network, and dynamically adjusting a feature weight in combination with a classroom scene; attention anomaly detection is realized by adopting a hybrid model, and real-time early warning is output through edge computing equipment. The method has the beneficial effects that the hardware cost is greatly reduced while the detection precision is ensured, and the privacy of students is effectively protected; a dynamic weight distribution mechanism improves the adaptability of different teaching scenes.

The invention provides a depression state assessment method and device based on heart rate variability characteristics and a parallel neural network. Real-time electrocardiosignals are collected in real time through wearable equipment and transmitted to a mobile terminal, the mobile terminal transmits the electrocardiosignals to a cloud server, and work of signal preprocessing, heart rate variability (HRV) feature extraction, feature selection and data enhancement and depth model construction and optimization is carried out on the cloud server. And evaluating the depression state of the subject. The cloud outputs an evaluation result to a display screen of the mobile terminal to be displayed, the depression state of the subject is finally displayed, and the four evaluated depression states are healthy, mild depression, moderate depression and severe depression; in addition, the terminal also supports depression state historical record query and key HRV feature tracing.

The invention discloses a multi-modal emotion analysis method and system, and the method comprises the steps: carrying out the heart rate variability analysis of a collected vital sign signal, so as to extract a time domain feature, a frequency domain feature and a nonlinear feature corresponding to a heart rate variability signal obtained after the analysis, fusing the time domain feature, the frequency domain feature and the nonlinear feature to generate a heart rate variability feature corresponding to the vital sign signal; calculating a power spectrum and a power spectrum entropy corresponding to the collected electroencephalogram signals, and extracting electroencephalogram characteristics corresponding to the electroencephalogram signals according to the power spectrum and the power spectrum entropy; extracting skin electric characteristics corresponding to the collected skin electric signals; respectively inputting the heart rate variability feature, the electroencephalogram feature and the galvanic skin feature into a pre-trained emotion recognition model, and outputting a plurality of emotion analysis results; and acquiring an emotion regulation scheme matched with each emotion analysis result to output a corresponding emotion regulation strategy, thereby improving the accuracy of the emotion analysis and regulation strategy.

The invention relates to a motion evaluation method, device and equipment based on multi-modal physiological data and a medium, and the method comprises the steps: solving the problem of space-time mismatch of the multi-modal data through sampling timestamps of a hardware clock protocol for multi-source physiological signals such as a makeup rate, myoelectricity, blood lactic acid and the like; equipment interference and motion artifacts are eliminated, and the signal quality is improved; dynamic characteristics such as heart rate variability, myoelectricity root mean square and blood lactic acid gradient in the sliding window are calculated; dividing exercise intensity intervals based on the individually calibrated heart rate percentage and the myoelectricity activation degree threshold, and detecting conversion candidate points; and recognizing a motion intensity critical state in real time through a self-adaptive threshold model driven by historical data, and generating a comprehensive evaluation result containing a thermodynamic diagram and an early warning report. According to the method, the limitation of a traditional fixed threshold model is broken through, multi-modal data deep fusion and individual dynamic adaptation are achieved, the exercise intensity critical point detection precision is improved, and real-time decision support is provided for training load optimization and rehabilitation progress evaluation.

The invention discloses a fatigue state detection method, system and device and a storage medium, and relates to the technical field of fatigue detection.The method comprises the steps that electroencephalogram, electrocardio, myoelectricity and blood oxygen physiological signals of a user are collected; a Morlet wavelet transform method is adopted to extract time-frequency characteristics in the electroencephalogram signals; the method comprises the following steps: calculating a heart rate signal and heart rate variability by detecting the position of an R wave in an electrocardiosignal, and extracting a time sequence characteristic of the heart rate signal and a heart rate variability characteristic in the heart rate variability; utilizing an LSTM network to respectively extract electromyographic activity features and blood oxygen level features in the electromyographic signals and the blood oxygen signals; inputting the plurality of extracted features into a feature fusion network for feature fusion; the fused features are converted into fatigue indexes through a regression network; detecting the fatigue state of the user according to the fatigue index; by integrating a plurality of physiological signals, real-time monitoring and accurate evaluation of the fatigue state of the pilot are improved.

The invention discloses a pressure sensitivity classification evaluation method based on heart rate variability and wearable equipment, and relates to the technical field of biomedical signal processing. According to the method, the collected signals can be processed to form the RR interval sequence and the resultant acceleration, the motion state is judged, the motion state mark is set, the RR interval sequence and the motion state mark are aligned according to the timestamp, the structured data are generated, the pressure sensitivity level of an individual is judged based on the structured data, and the pressure sensitivity level of the individual is calculated. The subjective influence of pressure sensitivity evaluation can be avoided, and the classification precision and adaptability are improved.

The invention discloses a heart disease prediction system based on an electrocardiosignal and a large model, and relates to the field of electrocardiosignal processing, and the system is characterized in that the electrocardiosignal of a user is collected by using a heart coat, and baseline offset suppression and noise processing operation are carried out on the electrocardiosignal by using a data processing center; a variability sequence is extracted from the processed electrocardiosignal, an electrocardiograph scatter diagram is drawn according to variability, and scatter points in the scatter diagram are numbered and coded in a partitioned mode to obtain the sequence information entropy of the heart rate variability sequence; segmenting the electrocardiosignal based on the sequence information entropy and the wave crest, obtaining a waveform segment of a single cardiac pulse according to a segmentation result, inputting the waveform segment into the large medical prediction model, and outputting the characteristic change of the electrocardiosignal; and comparing the feature change with a pre-stored disease feature standard and outputting a disease prediction result. According to the method, the complex characteristics of the electrocardiosignals and the prediction capacity of the large model are combined, and the diagnosis precision and the early-stage discovery rate of heart diseases can be remarkably improved.

The invention provides a fatigue driving monitoring method and system and a storage medium, and the method comprises the steps: obtaining a DMS video stream, carrying out the feature extraction of the DMS video stream, and obtaining a face ROI region; inputting the face ROI region into a regression model to extract skin brightness change characteristics of the driver, and obtaining multiple vital signs of the driver based on the skin brightness change characteristics; the vital signs at least comprise heart rate, respiratory rate, oxyhemoglobin saturation, blood pressure and heart rate variability; the vital signs are judged according to a preset time period and multiple vital sign threshold ranges, and when at least two of the multiple vital signs exceed the corresponding vital sign threshold ranges, it is judged that the driver is in the fatigue driving state. According to the method provided by the invention, multiple vital signs are detected by depending on one independent regression model, and the accuracy and timeliness of fatigue driving detection are improved.

The invention discloses a depression assessment system based on resting heart and brain coupling analysis and an implementation method. The system performs depression scale score prediction through a depression symptom assessment model; the implementation method comprises the steps that synchronous multi-lead electroencephalogram signals and electrocardiosignals with the same time duration are collected for a plurality of users; the collected electroencephalogram signals and electrocardiosignals are preprocessed; feature sequence extraction is carried out on the preprocessed electroencephalogram signals and electrocardiosignals, and traditional heart rate variability features are extracted; calculating causal nonlinear coupling strength between each frequency band feature sequence and the RR interval feature sequence of the electroencephalogram to obtain a heart and brain coupling feature set; based on the heart rate variability feature set and the heart and brain coupling feature set, establishing a depressive symptom evaluation model; according to the depression feature set obtained through calculation, the depression symptom evaluation model outputs the quantitative score of the depression degree. The method can avoid the limitation that a traditional scale depends on subjective evaluation, and can be used for early screening of depression.

The invention discloses an oxygenerator remote control system and method based on the Internet of Things, and belongs to the technical field of intelligent medical equipment and remote health management, and the method comprises the steps: S1, collecting multi-dimensional physiological data of a user in real time through a multi-source physiological signal collection module, a heart rate variability index and a body movement signal; s2, based on the multi-dimensional physiological data, processing is performed through a preset physiological state prediction model, and a unified physiological stress index is solved; s3, determining a predictive oxygen supply flow rate value in combination with the physiological stress index and a preset user basic oxygen supply flow rate; s4, according to the predictive oxygen supply flow velocity value, a control signal for a physical execution component of the oxygen generator is generated so as to dynamically adjust oxygen supply parameters, and conversion from passive compensation to active prediction is achieved.

The invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention provides wearable cardiovascular health monitoring equipment based on artificial intelligence, an environment adaptability analysis system of the wearable cardiovascular health monitoring equipment and a use method of the wearable cardiovascular health monitoring equipment, and aims to provide real-time and comprehensive cardiovascular health monitoring and risk assessment. And the health condition of the user and the influence of the external environment are comprehensively monitored. Through a built-in wireless communication module, the equipment can transmit collected data to mobile equipment or a cloud server of a user in real time. In addition, the system also comprises an AI analysis module which performs complex data analysis by using indexes such as heart rate variability (HRV) and the like so as to evaluate the cardiovascular health state of the user and predict the potential risk. The device also provides a personalized suggestion generator which provides lifestyle adjustment suggestions to the user according to the analysis result. A user can intuitively obtain own health data, risk assessment and life improvement suggestions through interactive interfaces such as a mobile application program (APP) and a smart watch / bracelet.

The invention provides an atrial fibrillation postoperative recurrence prediction method fusing electrocardiosignals and clinical characteristics. The method comprises the following steps: acquiring data of a patient before an ablation operation, and carrying out resampling, denoising and normalization preprocessing and data segment segmentation on an electrocardiosignal; extracting spatio-temporal features by using a deep network containing a residual convolutional block and a long and short term memory module; screening high-discrimination clinical baseline features through statistical analysis and a machine learning model; extracting time-frequency domain and nonlinear features of short-time heart rate variability; designing a cross-modal attention fusion module to carry out feature adaptive weighted fusion; and outputting a recurrence probability through a multi-layer perceptron based on the fusion features. The method improves the prediction precision through feature complementarity, facilitates the recognition of high-recurrence-risk patients, is suitable for sinus heart rhythm signals or atrial flutter and atrial fibrillation signals, and has a certain application value in the field of cardiovascular precision medical treatment. The method can be popularized to all prediction researches based on the electrophysiological signals.

A system for guiding a user with heart rate variability feedback may include a computing device comprising a heart rate sensor, one or more processors, and memory having stored thereon computer program code that, when executed by the one or more processors, is configured to cause the one or more processors to receive user input, responsive to receiving the user input, cause an audio output device to output an audio content associated with a default breathing pattern, dynamically receive interbeat interval data from the heart rate sensor, dynamically extract one or more characteristics from the interbeat interval data, cause the display to indicate a dynamic visual pattern based on the one or more characteristics, dynamically determine a second breathing pattern based on the one or more characteristics, and dynamically cause the audio content to change based on the second breathing pattern.

The invention discloses an HRV estimation method of a millimeter wave radar. The method comprises the following steps: firstly, positioning a human body by using the range resolution characteristic of an FMCW radar, and preprocessing noise and interference existing in an echo signal; secondly, a grey wolf optimization algorithm is introduced, the maximum mutual information coefficient and the minimum reconstruction error serve as fitness functions, a traditional variational mode decomposition method is optimized, and self-adaptive separation of vital sign signals is achieved. Meanwhile, an optimal heartbeat component is selected from decomposed modals based on a modal selection criterion of a correlation coefficient. And finally, extracting a time parameter corresponding to the heartbeat wave crest, further obtaining IBI, and calculating the time-frequency domain characteristics of the HRV through the IBI. According to the method, the heartbeat signals are effectively extracted through the improved mode decomposition method and the mode selection criterion, and then high-precision estimation of the heart rate variability is achieved.

The invention provides a meditation effect intelligent scoring method and system based on big data analysis, and relates to the technical field of data processing.The method comprises the steps that 1, brain wave signals, heart rate variability, skin conductance original waveforms and respiratory rhythm signals of a user during meditation are collected in real time through a wearable device, and the brain wave signals, the heart rate variability, the skin conductance original waveforms and the respiratory rhythm signals are sent to the wearable device; environment noise decibel values, illumination intensity and environment temperature parameters are obtained through an environment sensor, and a multi-dimensional original data set fusing the physiological signals and the environment parameters is generated; 2, according to the resting state physiological parameters in the multi-dimensional original data set, Gaussian mixture probability distribution is adopted, personalized reference features are generated, and the personalized reference features comprise mean vectors and covariance matrixes of brain wave signals, heart rate variability, skin conductance and respiratory rhythm parameters; according to the invention, through multi-source data acquisition and fusion, personalized reference construction, multi-dimensional physiological and environmental index analysis and dynamic score adjustment, quantitative evaluation of a meditation effect, which is comprehensive, accurate and real-time and fits individuals, is realized.

There is described an artificial intelligence wearable ECG skin patch (400) to detect sudden cardiac arrest. The wearable ECG monitoring patch (400) with AI based predictive analytics and remote based cardiac monitoring (615) system that can detect cardiac arrhythmias automatically in real-time and make a diagnosis with AI models trained with acquired data. The wearable skin has a biocompatible polymer patch (400) which captures the electrical signal through a flexible printed electronic technology based conducting ink and a substrate. The microcontroller controls (201), store and transmit the data packets. The IoT connected signal transmission is capable of recording and transferring the data packets through wireless communication. The AI engine is capable of analysing, evaluating, testing and providing the data packets of sudden cardiac arrest through a peak detector algorithm. The ECG skin patch (400) to detect and measure the sudden cardiac arrest with the R-R interval time series to obtain heart rate variability.

The invention discloses a Parkinson's disease assessment method and system based on face video multi-modal physiological feature fusion, and relates to multiple technical fields of computer vision, physiological signal processing and the like, and the method comprises the following steps: S1, based on a continuous face video stream, extracting rPPG signals; then heart rate variability key parameters are calculated, and heart rate variability characteristics are obtained; s2, analyzing the dynamic change of an eye fixation point in the face video based on IPAST, extracting key eye movement behavior parameters, and obtaining eye movement behavior characteristics through a convolution gating loop unit; and S3, inputting the heart rate variability characteristics and the eye movement behavior characteristics into a multi-modal fusion network structure, and outputting a continuous risk score or an illness state label for assisting a doctor in early Parkinson risk assessment. According to the method, multiple physiological signals acquired through videos are deeply integrated, a multi-modal feature collaborative analysis framework is constructed, the subjective limitation of traditional scale evaluation is broken through, and the one-sidedness defect of single biomarker detection is overcome.

The invention relates to the field of traditional Chinese medicine pulse condition collection, and provides a traditional Chinese medicine six-channel identification cognition method and system based on heart rate variability, and the method comprises the steps: obtaining heart rate variability data of a detected object, and extracting parameters such as time frequency from the heart rate variability data; constructing a hierarchical feature extraction network, inputting parameters such as time frequency into the hierarchical feature extraction network, and distributing weights for the parameters such as time frequency based on a qi-blood-body fluid theory to obtain a fusion feature vector; inputting the fused feature vector into a particle swarm optimization algorithm, and performing feature selection by adopting a six-channel transmission constraint function and a syndrome affinity particle update strategy to obtain an optimized feature combination; the optimized feature combination is converted through the semantic mapping relation between the heart rate variability parameters and the pulse condition descriptors, and pulse condition feature parameters are obtained; and performing syndrome classification calculation based on the pulse condition characteristic parameters, and outputting six-channel syndrome types. According to the invention, automatic identification conversion from physiological signals to traditional Chinese medicine syndromes is realized, and the precision and reliability of six-channel syndrome identification are improved.

The embodiment of the invention discloses an ovarian health assessment method and related equipment, the method is applied to first electronic equipment, the first electronic equipment can display a first interface, and the first interface comprises a first control; receiving a first operation acting on the first control; acquiring first data, wherein the first data comprises heart rate variability data of the user in a preset time period; according to the physical sign data of the user, determining an ovary health assessment result; the user sign data comprises first data; the ovarian health assessment result is used for indicating the ovarian health condition of the user in a preset time period; after the preset time period, a second interface is displayed, and the display content of the second interface comprises the ovarian health assessment result. By adopting the method, the ovarian function abnormity of the user can be found in time.

The invention provides a sleep apnea real-time early warning system based on heart rate variability analysis, belongs to the technical field of computer data processing, and provides a data processing method which is used for dynamically selecting a monitoring terminal, adaptively adjusting an analysis threshold value based on a sleep situation and carrying out time sequence correlation verification on a heart rate and a sound signal. And the sleep apnea event monitoring accuracy and the system operation efficiency are improved. The monitoring terminals are dynamically selected, the predictive switching strategy is adopted, continuity and high quality of heart rate data streams input into the data processing system are ensured, the data acquisition modes caused by body position changes of the user in the sleep period are switched, subsequent analysis errors are avoided, and the user experience is improved. By determining situation information such as sleeping postures of a user in real time and dynamically adjusting an analysis threshold value of heart rate variability, a data processing model can intelligently adjust the sensitivity of the data processing model according to the risk level, and deep time sequence correlation verification is carried out in combination with heart rate and sound signals, so that collaborative analysis of multi-modal information is realized.

The invention relates to the technical field of equipment management, in particular to an industrial equipment on-line state and configuration state management system which comprises an emotion perception regulation and control module for collecting heart rate data and skin electricity data of a working area, analyzing physiological index fluctuation through heart rate variability measurement in combination with skin electricity response, recognizing a working pressure state and obtaining emotion characteristics. According to the invention, by comprehensively collecting and analyzing the physiological and environmental data of the working area, the dynamic adjustment ability of the production environment emotion is improved. By monitoring the heart rate variability and the electrodermal response, the pressure state of the employee is effectively recognized, and emotion data is further supplemented through sound analysis. The data comprehensive analysis not only optimizes the operation parameters of the equipment, such as adjusting speed and temperature, but also realizes the humanization of the production environment, reduces the pressure of employees, and improves the production efficiency.

The invention discloses a paroxysmal atrial fibrillation screening method based on heart rate variability characteristics, and relates to the technical field of medical diagnosis, and the paroxysmal atrial fibrillation screening method comprises the following steps: collecting an electrocardiosignal of a subject, decomposing the electrocardiosignal into heart rate variability sub-signals, and mapping the heart rate variability sub-signals to a characteristic space; calculating a critical moderation index and a dynamic stability parameter, generating an atrial fibrillation precursor state map, and determining a state transition critical point; constructing a pulse coupled oscillator network, and measuring system synchronization change and chaotic transition characteristics; measuring the desynchronization degree of the pulse coupled oscillator network and the atrial fibrillation precursor state map matching degree, extracting atrial fibrillation precursor signal features by using a stochastic resonance technology, and generating an atrial fibrillation occurrence probability value; and establishing an individualized calibration mechanism, carrying out individualized adjustment on the occurrence probability of the atrial fibrillation, and carrying out early warning. According to the method, a multi-level individualized calibration mechanism is established, the atrial fibrillation occurrence probability is dynamically adjusted in combination with the historical attack mode, time distribution and induction factors of the patient, and precise early warning is achieved.

The invention provides a brain-heart coupling feature extraction method, a brain-heart coupling feature extraction device and electronic equipment, and belongs to the field of electric digital signal processing. The method comprises the steps that electroencephalogram signals and electrocardiosignals are obtained, analysis is conducted on the basis of objective physiological data, power spectrum density information corresponding to the electroencephalogram signals is obtained, heart rate variability data are obtained on the basis of the electrocardiosignals, the time-varying regulation and control coupling strength of the brain to low-frequency and high-frequency components of heart activity is further determined, and then the time-varying regulation and control coupling strength is obtained. And performing fusion analysis based on the time-varying regulation coupling strength and the power spectrum density information corresponding to the electroencephalogram signal, depicting a physiological mechanism related to cognition, and capturing features of interaction between the brain and the heart, thereby obtaining first coupling feature information of the brain corresponding to the target user for the heart. The cognitive level misjudgment rate can be effectively reduced, the extracted first coupling feature information can reflect the real cognitive state of the target user more accurately, and a more accurate basis is provided for cognitive level evaluation.

A precise cardiac data reconstruction method is provided, which may also be referred to herein as radar cardiography (RCG). RCG can reconstruct cardiac data, such as heart rate and / or electrocardiogram (ECG)-like heartbeat waveform signals wirelessly by using advanced radar signal processing techniques. For example, heartbeat and related characteristics can be monitored by isolating cardiovascular activity from strong respiratory interference in spatial spaces: azimuth and elevation. This results in significant improvements to pulse signal-to-noise-ratio (SNR) compared to conventional approaches, facilitating heart-rate variability (HRV) analysis.