646 results about "RR interval" 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 relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

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 relates to the technical field of training assistance, in particular to a training quality prediction method and system based on training damage analysis, and the method comprises the steps: multi-modal data collection and accumulative superposition, causal graph construction, attitude risk modeling, data enhancement, dynamic prediction and training plan optimization. According to the method, daily exercise data of a user is continuously collected, a chronic load accumulative calculation model is established, and a time sequence analysis method is adopted to perform long-term tracking and accumulative calculation on core indexes such as exercise amount, heart rate variability and joint activity, so that a complete personal exercise state database is formed; the chronic load accumulation mechanism breaks through the limitation of traditional single training evaluation, comprehensive monitoring and long-term trend analysis of the motion state of the user are achieved, a potential risk mode can be found earlier, a reliable data basis is provided for personalized risk evaluation and scientific training optimization, and the method is suitable for popularization and application. The problems that a traditional motion monitoring system is single in data and one-sided in evaluation are effectively solved.

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 discloses an anesthesia depth monitoring system and method based on multiple physiological parameters, and the system comprises a data collection unit which is used for obtaining a perfusion index value, an electrocardiogram signal, a brain wave signal, a blood pressure signal of a patient and the individual feature information of the patient; the signal processing unit is electrically connected to the data acquisition unit and used for processing the signals acquired by the data acquisition unit and calculating the perfusion index change rate, the heart rate variability, the anesthesia consciousness index and the blood pressure fluctuation rate; the parameter fusion unit is electrically connected to the signal processing unit and used for combining the parameters calculated by the signal processing unit with the individual feature information of the patient; the individualized correction unit is electrically connected to the parameter fusion unit and is used for adjusting the comprehensive anesthesia depth score generated by the parameter fusion unit; the method has the advantages that multi-dimensional physiological data and individual characteristics can be integrated, precise evaluation and individualized regulation and control of the anesthesia depth are realized, and the anesthesia safety is improved.

The invention relates to the technical field of health assessment, in particular to a newborn health assessment method and system based on visual analysis, and the method comprises the following steps: obtaining a newborn image frame sequence, extracting a hue curve, recognizing a variation region to generate a layer, and extracting a mutation region bitmap group in combination with a heart rate RR interval difference value; and analyzing an included angle mapping grid between the center of gravity of the pigment and the heart rate slope, superposing a respiratory rate curvature, performing co-occurrence clustering to form a trend graph block, and evaluating a risk distribution interface formed by joint mutation. According to the method, a dynamic coupling relation between an image and a physiological signal is established through slope direction angle change and included angle deviation, cross-dimension mapping of heart rate trend change and visual feature change is achieved, the heart rate, skin color tracks and curvature change of respiratory frequency are fused, a trend gathering map is constructed, and the dynamic coupling relation between the image and the physiological signal is obtained. The risk judgment and display are completed according to the joint grading of the block density, the time span and the parameter quantity in the atlas, and the health level fluctuation trend is visually presented in the risk evolution sorting.

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 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 sleep state real-time monitoring method and system, and belongs to the technical field of sleep monitoring, and the method specifically comprises the steps: collecting a heart rate variability signal, an electroencephalogram signal and body movement data of a user in real time through a non-invasive sleep pad integrating a piezoelectric sensor, a flexible dry electrode and a pressure sensor; based on the physiological data, whether the user reaches an autonomous sleep state or not is judged through a first algorithm model; if not, starting an active intervention program for playing the adjustable music, and dynamically adjusting the music volume, the playing speed or the track in combination with the physiological data feedback until the user enters an autonomous sleep state; after the user falls asleep, physiological data are continuously collected, and a sober period, a light sleep period, a deep sleep period and a rapid eye movement period are divided through a second algorithm model; if the staging result is a waking period, the autonomous sleep state judgment is executed again; according to the invention, non-intrusive monitoring and personalized intervention are combined, and the monitoring comfort and accuracy are improved.

The invention particularly relates to a quartered sleep state monitoring method and a quartered sleep state monitoring system based on a non-contact vibration sensor. The quartered sleep state monitoring method and the quartered sleep state monitoring system are particularly suitable for realizing non-sensitive sleep quality evaluation through an intelligent bed in a family environment. Comprising the following steps that 1, vibration signals of a human body in the sleep process are continuously collected through a non-contact vibration sensor; step 2, preprocessing the vibration signal, including denoising and segmentation processing, to obtain time domain vibration data; step 3, performing frequency domain transformation on the time domain vibration data, and extracting frequency domain features including respiratory rate, heart rate and heart variability parameters; 4, time alignment is carried out on the frequency domain features and sleep staging labels synchronously obtained through a polysomnography, and a training data set is constructed; an XGBoost classification model based on time-frequency domain feature fusion is provided, fundamental frequency and harmonic energy proportions of respiratory signals are extracted through short-time Fourier transform, and quartering classification is achieved in combination with the LF / HF ratio of heart rate variability.

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.

A biometric wearable device (e.g. finger ring or smart watch) has optical sensors to measure body oxygenation level, hydration level, glucose level, heart rate, heart rate variability, and / or blood pressure. Light from light emitters is transmitted through body tissue and changes in the light are analyzed. The angles and / or vectors along which the light is transmitted through body tissue can be automatically changed by the device in order to scan different tissue regions and / or different tissue depths.

The invention discloses a sleep mild wake-up method based on a user sleep curve, and belongs to the technical field of sleep monitoring, and the method comprises the steps: collecting a physiological signal of a user, generating a sleep stage curve by using a pre-trained sleep stage model, and carrying out the parallel analysis of heart rate variability and respiratory coordination to generate a mood index curve; overlapping and fusing the two curves to form a sleep-mood alignment feature set; in a preset wake-up time range, analyzing the sleep stage and psychological state of the user according to the feature set, and dynamically determining an optimal wake-up starting opportunity; when the clock arrives, an instruction is sent to the linkage alarm clock, and sound, light and touch multi-mode stimulation is triggered in sequence in a cooperative mode; in the wake-up process, the sleep depth of the user is continuously monitored, if it is detected that the depth recovery exceeds the critical threshold value, the stimulation intensity is adaptively adjusted until the depth falls back, it is ensured that the user naturally wakes up under the condition that the discomfort is the lowest, and intelligent mild wake-up is achieved.

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 relates to the technical field of medical health monitoring, and discloses a multi-modal data fusion health monitoring analysis early warning method and system, and the method comprises the steps: synchronously collecting motion and heart rate data streams through a wearable device, generating a coupling vector of the motion intensity and the heart rate change rate in a short time window, and carrying out the long-time accumulation to form a coupling vector cluster, and judging the physiological regulation stability by evaluating the change of the morphological dispersion. Micro-motion is converted into a natural probe, through dynamic evolution of a motion-heart rate coupling relation, transition from observation of vital sign values to evaluation of autonomic nerve regulation ability is achieved, early decline of body functions can be captured before conventional indexes are abnormal, and meanwhile evaluation reliability in a complex environment is ensured through frequency domain feature filtering.

The invention provides a cognitive ability decline detection method and system based on physiological indexes of wearable equipment, and relates to the technical field of feature selection and machine learning. Comprising the following steps of multi-dimensional physiological data acquisition, data preprocessing and time alignment, cognitive ability state label definition, feature engineering and data balance, cognitive ability decline detection model training and optimization, and output of cognitive ability state prediction. Multi-dimensional physiological indexes and time information of a user are collected in real time through a wearable device, and the physiological indexes comprise heart rate fluctuation features, heart rate statistical features, body temperature features, blood oxygen saturation features, motion data, electroencephalogram state features, skin electrical features, near infrared spectrum features and the like. The wearable device is used for integrating multiple types of physiological signal sensors, and continuous and non-inductive collection of multi-dimensional physiological data such as heart rate variability, electrodermal response and oxyhemoglobin saturation is achieved.

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 personalized medical health service recommendation system based on big data analysis, and belongs to the technical field of medical health services. The synchronization module is used for generating a standardized data stream according to dynamic blood glucose monitoring data and an original heart rate variability signal; the analysis module is used for generating a diet time sequence data stream according to the intake event timestamp; the blood glucose feature extraction module is used for extracting data from the standardized data stream; the insulin sensitivity analysis module is used for generating an insulin sensitivity factor matrix; the nutrition analysis module is used for generating a carbohydrate equivalent time distribution vector and a dietary fiber intake intensity value; the feature fusion module is used for generating a fusion feature vector according to the blood glucose drift trend coefficient, the insulin sensitive factor matrix and the carbohydrate equivalent time distribution vector; the risk assessment module is used for generating a hypoglycemia risk probability value according to the fusion feature vector; and the recommendation generation module is used for generating a personalized service recommendation set according to the hypoglycemia risk probability value.

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 discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention discloses a wearable fatigue monitoring and feedback method based on deep learning. The method comprises the steps that a heart rate variability signal, a gamma wave band electroencephalogram signal and body movement posture information of a user are collected in real time; denoising, normalizing and synchronously fusing the acquired multi-mode signals; extracting a fatigue state representation vector in real time by adopting a Mamba linear state space sequence model; estimating a user fatigue index in real time based on a lightweight full-connection neural network decoder and constructing an individual fatigue threshold dynamic model; calculating a phase synchronization index of the electroencephalogram signal in real time; and generating and outputting an individualized 40Hz gamma wave band sensory nerve stimulation feedback signal in real time based on the fatigue index and the phase synchronization index. According to the invention, high-robustness fatigue identification and low-delay feedback adjustment in a complex motion noise environment are realized.

The invention provides a nerve electrical stimulation method, device and system based on electroencephalogram signals, and the method comprises the steps: obtaining pain related features in real time, the pain related features comprising electroencephalogram index features and heart rate variability features; generating a comprehensive pain score value in real time based on the pain related characteristics, and generating an electrical stimulation parameter in real time based on a target difference value between the comprehensive pain score value and the baseline score value; the vagus nerve of the patient is electrically stimulated based on the electrical stimulation parameters. The problem that in the prior art, due to immobilization of electrical stimulation parameters, dynamic adjustment cannot be conducted according to real-time neural activity of a patient, and therefore the treatment effect is affected is solved.

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.

The invention provides a pulse wave signal (BVP signal) extraction method based on remote photoplethysmography. The method comprises the following specific steps: step 1, acquiring a face video and detecting a face area; in the process, the system captures, recognizes and extracts all facial feature points; 2, the rPPG signal intensities of different face areas are different, and in order to maximize the signal quality, the face areas are divided according to the intensities of remote photoplethysmography signals (rPPG signals); 3, training a convolutional neural network model, calculating a weighting coefficient of each region according to the characteristics of the signals of each region, and fusing the signals of each region into a single-channel signal; 4, removing low-frequency noise and interference signals from the fused signals by using a filtering technology, wherein the superposition operation helps to enhance the effective rPPG signals; through the processing steps, the quality of the BVP signals can be improved, and high-quality input data is provided for subsequent extraction of blood oxygen, respiration, heart rate variability and other signals.