67 results about "Heart rate change" patented technology

The invention relates to a human motion data processing method, and belongs to the technical field of data processing. Comprising the following steps: controlling a microwave radar to emit a detection signal, and detecting a heart rate signal and a respiration signal of a human body; performing clustering analysis on the data according to the collected heart rate and respiratory rate, and extracting heart rate, respiratory rate change, duration and exercise intensity characteristics; according to the extracted heart rate change, duration, exercise intensity and other characteristics, the exercise stage is recognized, and according to the neural network model library, an energy consumption result is output in combination with data analysis. According to the human motion data processing method provided by the invention, the microwave radar is combined with the intelligent wearable device, so that the detection precision of heart rate and respiration signals is remarkably improved, multi-modal data fusion is realized, personalized heart rate prediction is performed, motion stage division is optimized in combination with acceleration data, and the accuracy of motion data processing is improved. The accuracy of motion mode recognition is improved, and personalized motion suggestions can be provided for the user.

The invention discloses an intelligent temperature control mattress system based on multi-mode sensing and adaptive learning, and the system comprises a data collection module which is used for collecting a mattress surface vibration signal, a mattress surface temperature and a mattress surrounding environment parameter; the data analysis module is used for performing deep analysis on the time-frequency domain features according to a biological recognition model to obtain human body or non-human body classification, performing self-learning to obtain body movement changes, accurately recognizing various typical sleeping postures and filtering out error data; and the model prediction module is used for monitoring the physiological parameters of the user according to the spectral analysis, inputting the physiological parameters, the collected temperature and the parameters of the surrounding environment of the mattress into a temperature control model composed of three neural networks of LSTM, GNN and Transform for comprehensive analysis and decision making, and outputting an optimal temperature control strategy. According to heart rate changes or body heat fluctuations, the sleep stage and the sleep and waking time are intelligently recognized, and therefore sleep environment adjustment better conforming to the human body rhythm is achieved.

The invention discloses an ultra-high heart rate electrocardio acquisition system, which comprises a signal acquisition unit adopting an ADS131E08 chip and supporting 8 channels, a 24-bit delta-sigma ADC (Analog to Digital Converter) and programmable gain amplification; the dynamic sampling rate adjusting unit is used for dynamically adjusting the sampling rate between 1kSPS and 64kSPS according to the heart rate change, and the sampling rate is increased to be greater than or equal to 2kSPS when the heart rate exceeds 200 times per minute; a low-noise design circuit has a common-mode rejection ratio greater than or equal to 110dB, a dynamic range greater than or equal to 118dB and total harmonic distortion less than or equal to-90dB, and power frequency interference is suppressed; the morphological enhancement module is used for enhancing QRS wave group characteristics by using digital morphological filtering; the multi-lead fusion module is used for fusing multi-lead signals through time sequence alignment and R-wave phase difference analysis; and the anti-interference module is integrated with a hardware comparator, and is used for lead falling detection and dynamic digital notch filtering. The system is suitable for researches on myocardial ischemia, arrhythmia and drug cardiotoxicity. Compared with the prior art, the device has the advantages of being simple in structure, low in cost, high in precision, high in reliability and the like, and can effectively solve the problem of animal ultrahigh heart rate electrocardio collection.

The invention discloses an emergency nursing system capable of dynamically adjusting priority, which belongs to the technical field of emergency nursing and comprises the following steps: acquiring heart rate detection data, blood pressure detection data and oxyhemoglobin saturation detection data of a patient at preset time intervals, and filtering; setting a sliding time window, and respectively calculating a heart rate change rate, a blood pressure change rate and an oxyhemoglobin saturation change rate for the filtered heart rate detection data, the filtered blood pressure detection data and the filtered oxyhemoglobin saturation detection data in the sliding time window; correlation analysis is conducted on the heart rate change rate, the blood pressure change rate and the blood oxygen saturation degree change rate, parameter correlation characteristics are obtained, and a comprehensive parameter deterioration index is calculated; the comprehensive parameter deterioration index is compared with a preset threshold value, and whether a priority improvement mechanism is triggered or not is judged; and executing the priority lifting mechanism. By establishing a dynamic monitoring mechanism based on the change rate of the physiological parameters, real-time capture and quantitative analysis of the change trend of the illness state of the patient are realized.

The invention relates to the technical field of resistance adjustment, in particular to a multifunctional spinning and a multifunctional spinning system, and the system comprises a pedaling frequency recognition module, a state judgment module, a stage control module, a load adjustment module and an output response module. According to the invention, through continuous analysis of the pedaling frequency and time information, accurate recognition of the rhythm fluctuation state, and combination of the linkage change of the flywheel rotation speed and the pedal pressure, the dynamic judgment of the motion stability and the matching mechanism of the training time and stage setting are realized; a resistance adjusting strategy is deduced according to the relation between flywheel acceleration and pedal pressure, the sensitivity and precision of regulation and control response are enhanced, the load weight is set by fusing the heart rate change trend, the resistance output is more matched with the individual physiological state, the load rhythm matching degree is optimized, and the training efficiency is improved. And the rhythm following capability, the resistance adjustment rationality and the training individuation level are integrally improved.

The invention discloses a children propofol anesthesia depth monitoring method based on permutation entropy and heart rate change, and belongs to the technical field of anesthesia depth monitoring, and the method comprises the following steps: extracting a children electroencephalogram, and carrying out preprocessing and spectral analysis on the children electroencephalogram to obtain permutation entropy; child heart rate data are extracted and processed to obtain the heart rate change degree; inputting the permutation entropy, the heart rate change degree and the age into the mixed prediction model to obtain a composite index, and indicating the narcotic depth of the propofol for the children. According to the children propofol anesthesia depth monitoring method based on the permutation entropy and the heart rate change, the mixed prediction model is constructed by combining the electroencephalogram permutation entropy, the heart rate change degree and the child patient age, and higher accuracy, real-time performance and reliability are shown in children propofol anesthesia depth monitoring; the anesthesia state change can be quickly responded, and the problem of misjudgment caused by age dependence and insufficient linear processing in the prior art is effectively solved.

The invention relates to the technical field of data processing, and provides a heart rate data analysis system and method.The heart rate data analysis method comprises the steps that heart rate monitoring signals are collected through wearable heart rate monitoring equipment, and motion acceleration data at all moments are recorded; analyzing the discrete and differential performance of the central rate monitoring data in the monitoring time period, and quantifying the heart rate change intense index in the monitoring time period; obtaining a motion transformation index of the monitoring time period based on the discrete representation of the motion acceleration data in the monitoring time period; obtaining the interference source possibility of each independent component signal; screening a plurality of interference source component signals according to the interference source possibility; obtaining the frequency decoupling degree of each interference source component signal; obtaining a preprocessing demand degree of each interference source component signal; and performing smoothing based on the preprocessing demand degree, performing reconstruction to obtain a processed heart rate monitoring signal, and performing data analysis. The invention aims to solve the problem that heart rate data monitoring is influenced by a motion mode to generate motion artifacts.

The invention relates to the field of rehabilitation monitoring, in particular to an intelligent monitoring device for exercise rehabilitation of chronic diseases, which comprises a box body with a top opening and a controller, a water cavity used for containing liquid is formed in the box body, a running function assembly is arranged at the bottom of the water cavity, and the running function assembly is used for enabling a patient to run or walk underwater after being started; a water body fluctuation sensor is arranged in the water cavity and used for detecting water body fluctuation caused by underwater running of the patient; the controller is used for judging and recording the exercise intensity of the patient according to the water body fluctuation amplitude detected by the water body fluctuation sensor, and the water body fluctuation amplitude is in positive correlation with the exercise intensity of the patient. According to the technical scheme, whether the patient enters the traveling state or not is judged by detecting the heart rate change of the patient, and the exercise intensity of the patient is analyzed through the water fluctuation amplitude.

The invention discloses a temperature adjusting method and system for moxibustion equipment controlled by an intelligent chip, and the method comprises the steps: firstly, collecting a heart rate sequence of a user during moxibustion through the intelligent chip, decomposing a long-term trend item and a short-term fluctuation item, and calculating the instantaneous heart rate change; thirdly, a causal model of the instant heart rate change and a temperature sensitivity variable is built according to the instant heart rate change; secondly, extracting historical moxibustion temperature setting data from the equipment database, and analyzing temperature setting preferences of a user in different temperature sensitivity states; splitting a basic preset temperature and a fluctuation temperature regulation part based on preference characteristics, and determining fluctuation temperature regulation coefficients in different heart rate reaction modes according to heart rate changes; meanwhile, the preference and change of the user to the moxibustion part are analyzed in combination with historical use records; and finally, storing the personalized temperature adjustment data of the user in the intelligent chip, and updating the personalized temperature adjustment data in real time after the personalized temperature adjustment data is used each time. The method can effectively grasp personalized demands of users and improve the moxibustion experience and effect.

A cardiopulmonary management system includes a multi-sensor device and a controller. The multi-sensor device includes an elongated body having a first end and a second end, a first electrode disposed at the first end of the body, a second electrode disposed at the second end of the body, an electrical circuitry disposed near a middle of the body, and a sound sensor. The multi-sensor device is configured to be disposed on a skin of a subject and to measure cardiopulmonary parameters from the subject. The controller is configured to derive a score of sound change, heart rate change, thoracic impedance change, respiratory rate change, QRS wavelength change, QT interval change, electromechanical activation time change, and left ventricular systolic time change based on the measured cardiopulmonary parameters. The controller is further configured to control an operation of the system when the derived score is greater than a threshold.

The present invention relates to the field of image processing technology, and in particular to a pain grading method and system based on image processing. When grading pain for an object to be graded, the present invention selects a pain grading sub-model based on changes in the object's heart rate, thereby improving the efficiency of pain prediction. Simultaneously, in a second pain grading sub-model, to reduce the amount of multi-feature fusion computation, a method for constraining the generation of facial feature vectors using heart rate data is employed, based on the characteristic that heart rate can better indicate the state to be assessed, thereby improving the efficiency of pain grading. Furthermore, by adjusting the weight adjustment parameters of each organ, the changing trend of each feature weight can be flexibly controlled. Furthermore, the present invention can more accurately reflect the contribution of each feature to facial expression at different heart rate levels, thereby improving the generalization capability of pain grading.

The invention discloses a non-contact real-time heart rate detection method and system based on rPPG and wavelet enhancement, and belongs to the technical field of medical health. Face video signals are collected through the camera, a sensor or an electrode does not need to be worn, real non-contact physiological signal detection is achieved, and the comfort level and use convenience of a user are improved; discrete wavelet transform denoising and enhancement processing is introduced on the basis of pulse signals extracted by a POS algorithm, and high-frequency noise caused by illumination variation, camera quantization errors and facial movement is removed through multi-scale decomposition and threshold filtering, so that the signal-to-noise ratio of the pulse signals is remarkably increased; the fast Fourier transform is combined with a sliding window mechanism, so that real-time updating and continuous calculation of the heart rate can be realized; the signal after wavelet enhancement is clearer in periodicity, so that the heart rate calculation error is remarkably reduced, a dynamic visual interface of a heart rate change curve and a pulse waveform is provided, a user can observe physiological state changes in real time, and good interactive experience is achieved.

The invention provides an intelligent data analysis method for a pension information service system, relates to the technical field of physiological data processing, and aims to judge behavior stages according to the daily moving speed of the elderly and the continuity of the speed in time, select stable effective behavior stages through deterministic scores, and improve the accuracy of the behavior stages. Judging a corresponding behavior state according to the change characteristic of the heart rate in the effective behavior stage, identifying the body function expression of the old person in the corresponding state according to the change rate characteristic of the central rate of each behavior state, and judging the reaction intensity in the first behavior state and the flat intensity in the second behavior state; and a heart function change index is constructed by combining the long-term comprehensive expression of the reaction intensity, the flat intensity and the heart rate in the third behavior state, so that the overall body function change characteristics are reflected and quantified, and a guardian can conveniently know the overall body condition change of the old.

The present disclosure provides an information processing device, alcohol intake estimation device, method for calculating an indicator for estimating alcohol intake, alcohol intake estimation method, biological examination method, biological examination device, estimation device, learning model, estimation method, learning model generation method, and computer program. The information processing device comprises a processing unit that acquires a total heart rate change representing the total sum of heart rate changes, which are amounts by which a subject's heart rate has changed. The processing unit calculates a first indicator for estimating alcohol intake, said indicator representing the total heart rate change in a state in which the subject is inactive and a state in which the heart rate of the subject exceeds a reference heart rate.

The invention relates to a method and system for monitoring the state of a person in the exercise process, and relates to the field of exercise monitoring, and the method comprises the steps: obtaining body fat data of a user before exercise, image information of the user during exercise, and a heart rate change diagram and a blood pressure value of the user during exercise, and determining a body type score of the user based on the body fat data and the image information, and performing feature recognition on the image information to obtain a motion posture of the user, determining a motion score of the user based on the body type score, the motion posture, the heart rate change diagram and the blood pressure value, and when the motion score is lower than a preset score threshold, outputting alarm information and stopping continuous motion. The method and the device have the effect of timely and accurately predicting fatigue or accidents of the hypertension user in the running process.

The invention relates to the technical field of data processing, in particular to a preoperative anxiety relieving method and system based on the virtual reality technology, and the method comprises the steps: obtaining preoperative physiological rhythm data of a patient, the physiological rhythm data at least comprising respiratory frequency and heart rate change information; constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, wherein the virtual natural scene comprises light and shadow fluctuation elements corresponding to the respiratory frequency; a patient wears a VR head-mounted display and a bone conduction earphone, the equipment automatically loads a customized virtual scene, breathing key points are prompted in real time through virtual guidance, and the patient is helped to establish perception association of rhythm elements; the physiological data of the patient is analyzed in real time to obtain the respiratory rate, the heart rate variability and the skin electrical activity value, and when the difference value between the light and shadow fluctuation frequency and the respiratory rate exceeds a threshold value, the light and shadow frequency is adjusted step by step to approach the respiratory rate. According to the multi-dimensional evaluation system, the anxiety state of the patient is reflected more comprehensively, and insufficient intervention caused by evaluation deviation is avoided.

The invention discloses a swimming pool automatic alarm system based on electrocardio monitoring, and relates to the field of electrocardio monitoring, and the system comprises a collection module which is used for collecting electrocardio data and equipment position correlation signals output by equipment worn by a swimmer in real time; the fusion module is used for carrying out de-noising processing on the collected two types of data, extracting heart rate change features and position movement features and generating a fusion feature data set; according to the method, relevant data are synchronously collected in real time and accurately denoised, key features of heart rate change and position movement are fully extracted, the abnormal linkage rule of the heart rate and the position is quantitatively analyzed, and the drowning state of the swimmer is accurately judged.

The present application relates to the technical field of non-contact vital sign monitoring, and specifically discloses a heart signal detection method based on feature decoupling millimeter wave radar based on contrast learning. The present application realizes the automatic separation of the rhythm feature and the morphological feature of the heart signal from the millimeter wave radar signal by constructing an adaptive filtering module, a sample generator, a multi-scale feature extraction module, a double-path feature decoupling module, a physical constraint module and a contrast learning loss function. The present application uses contrast learning to construct a positive sample pair, guides the model to learn a feature representation that is invariant to heart rate changes and waveform deformation, and combines physiological prior constraints to improve the robustness and interpretability of the features. The present application effectively solves the shortcomings of traditional methods in signal separation, noise suppression and generalization ability, and can be widely applied to heart rate variability analysis, arrhythmia detection and other heart health monitoring tasks.

The invention discloses an intelligent motion state recognition method and system based on big data analysis, and relates to the technical field of motion feature recognition, and the method comprises the steps: obtaining motion sensor information, obtaining user historical motion data according to the motion sensor information, classifying motion state types on the basis of the user historical motion data, and obtaining a motion state classification result; and obtaining motion state classification information. According to the method, different motion states are divided into multiple categories through the motion state classification information, a basis is provided for follow-up motion state recognition, the heart rate change condition of each motion state type is accurately analyzed through the heart rate difference reference coefficient, and the motion state recognition accuracy is improved. A data standard is provided for the time interval of heart rate change condition analysis through the heart rate increase reference time, the user motion state is recognized through the user motion state recognition index, the motion state recognition efficiency is improved, and the stability and accuracy of motion state recognition are ensured.

To improve the accuracy of fatigue assessment in subjects. [Solution] The calculation unit 36 ​​calculates, from the changes in the heart rate of a subject who has taken actions to recover after a temporary increase in heart rate, at least the RRI overshoot ratio, which is the ratio of the increase in RRI to the decrease in RRI, as a characteristic quantity of heart rate change. The determination unit 44 then determines the fatigue of the subject based on the characteristic quantity of heart rate change calculated by the calculation unit 36.

The embodiment of the invention discloses a non-contact special post competency assessment method and device and a medium, and the method comprises the steps: determining the movement data and flicker frequency of a target point location based on the work task information of a current special post, so as to control the target point location to carry out the first-stage movement; superimposing interference information on the first-stage movement based on a preset interference condition so as to carry out second-stage movement; canceling the interference information to control the target point location to move in the third stage; collecting pupil time sequence data of each stage of a tester through a preset eye tracker, collecting heart rate change data of each stage according to a preset camera, and determining input data corresponding to each stage according to the pupil time sequence data and the heart rate change data; and taking the input data as a data set, obtaining a psychological competency assessment model based on multi-layer perceptron training of a preset artificial neural network model, and inputting the input data of the current tester into the psychological competency assessment model to obtain a psychological competency assessment result.

The present invention relates to the technical field of home rehabilitation care, and discloses an intelligent management method for in-home care based on intelligent healthcare, including: obtaining relevant medical conditions and physiological data of patients, automatically generating a rehabilitation model, judging the rehabilitation progress of patients. When the rehabilitation progress is far from the estimated data, the system gives a timely warning and makes adjustments. This intelligent management method for in-home care based on intelligent healthcare judges the pain tolerance of patients, makes intelligent rehabilitation recommendations, generates a single-day change curve for the heart rate changes of patients under different rehabilitation programs and drug treatments, judges the rehabilitation progress of patients. When the rehabilitation progress of patients is quite different from the estimated model, it promptly prompts the attending physicians of patients to adjust the rehabilitation programs, facilitating the rehabilitation of patients and the work of nursing staff, improving the safety and effectiveness of patient rehabilitation, promoting the development of personalized medicine, improving the quality of medical services, reducing unnecessary medical costs, and enhancing the quality of life of patients.

The invention discloses a health anomaly detection system supporting long-term tracking, and relates to the technical field of health management, and the system comprises a historical data processing end, a dynamic health baseline modeling end, a real-time deviation quantification end, and an analysis evaluation and output end. The system obtains historical heart rate data of a user, constructs a sequence according to a period and analyzes the sequence, and establishes a personalized standard heart rate change curve as a health baseline; in the real-time monitoring process, the system collects current heart rate data and compares the current heart rate data with a baseline, and health abnormal events are recognized by calculating the deviation area between curves; in addition, the system can also distinguish continuous abnormal events and sudden abnormal events in combination with historical abnormal records, and finally pushes analysis results to the user and related medical personnel, so that long-term and automatic health risk monitoring and early warning are realized.

The application provides a heart rate monitoring and incentive device, which comprises a ring-shaped packaging shell, the inside of the ring-shaped packaging shell comprises, from top to bottom, a polarized piezoelectric film, a magnetoelastic film, an electromagnetic coil, a charge collection module, a Bluetooth module and a power module in sequence, the polarized piezoelectric film is connected with an electrode, the electrode is electrically connected with the charge collection module, the electromagnetic coil is electrically connected with the power module, the diameter of the polarized piezoelectric film is 17-19 mm, and the thickness is 2-4 mu m. The device can accurately detect the heart rate change of the human body, timely remind when the heart rate is abnormal, avoid accidents, and effectively solve the problems that the existing detection device cannot be portable real-time monitoring, the detection is inaccurate, and there is no wake-up function.

The invention relates to the technical field of physiological monitoring, in particular to a mental state real-time feedback system based on intelligent wearable equipment, which comprises a sequence construction module, a weight analysis module, a weight correction module, an index generation module and a hierarchical judgment module. According to the method, the multi-dimensional breathing fluctuation sequence containing the amplitude difference of the inspiration section, the duration of the expiration section and the change of the breathing rate is constructed, the multi-dimensional breathing fluctuation sequence and the heart beat interval sequence are mapped into the same multi-dimensional space, the Euclidean distance between the multi-dimensional breathing fluctuation sequence and the heart beat interval sequence is calculated to serve as the dynamic weighting coefficient, and the coupling strength between the cardiopulmonary systems can be quantified; therefore, the adjustment state of the autonomic nervous system is disclosed more deeply, state misjudgment possibly generated due to the fact that breathing activities cannot be associated when heart rate changes are analyzed in an isolated mode is avoided, meanwhile, the system can actively detect electrocardiosignal baseline drift and breathing waveform abnormal fluctuation, and the weighting coefficients are screened and corrected based on the interference degree.

The present application relates to the technical field of nursing decision, specifically to a nursing scheme auxiliary selection system for orthopedic patients, in the present application, in the rehabilitation training process, the moving track of the patient is collected in real time and deviation analysis is conducted with the ideal path, the deviation risk fragment set is established by means of the spatial coordinate sequence and the Hurst index calculation mode, the identification and positioning of the deviation trend of the rehabilitation action are strengthened, and the accuracy of the walking control stability evaluation is enhanced. The heart rate signal is mapped to a multi-dimensional density space, the local abnormal interval in the heart rate change is accurately calibrated through the local abnormal factor operation mode under the sliding window, and the identification of the slight physiological change is facilitated. The heart rate abnormal interval is classified and processed according to the duration, a heart rate fluctuation typing system is constructed, the transient physiological fluctuation and the continuous stress state can be effectively distinguished, and the identification degree of the pain inducement is improved.

This invention relates to a protective device, and more particularly to a protective device for impulsive behavior in patients with mental disorders. It includes a clothing-type airbag, an air pump, a deflation valve, and a fastening mechanism. The clothing-type airbag, when inflated, restricts the movement of the patient. The air pump is installed on the rear side of the clothing-type airbag, and its outlet communicates with the airbag. The deflation valve is also installed on the rear side of the clothing-type airbag, and a fastening mechanism for securing the airbag is located on the front side. This invention involves wearing the clothing-type airbag on the patient with a mental disorder. By monitoring the patient's heart rate changes with a heart rate monitor, it can detect impulsive behavior in real time. The inflating of the clothing-type airbag restricts the patient's movement, thus protecting people and objects around the patient without requiring medical personnel to approach, providing a high level of safety.

The invention relates to the technical field of limb training, in particular to a multi-joint constant-speed upper and lower limb assessment training method and system, and the method comprises the steps: obtaining the injury degree of the limbs of a user before the training, and the motion data of the joints during the training; any joint is selected as a target joint, and the joint function score of the target joint is calculated based on the operation data of the target joint after the training is completed. According to the method, the movement data of the joints of the user in the training process are obtained, the injury degree of the limbs before training is evaluated, and the initial resistance of the training device is dynamically adjusted in combination with the joint function score after training. Quantitative evaluation of the rehabilitation state of the user is achieved through multi-dimensional physiological indexes such as electromyographic signals and heart rate changes, the initial resistance is intelligently adjusted through a clustering algorithm, the resistance is automatically increased or decreased according to the training effect, the influence of improper resistance setting on the training process is effectively avoided, and the training effect on the limbs of the user is improved.

1. The name of the design product: smart ring (saddle). 2. The use of the design product: for wearing decoration and monitoring user's heart rate changes and other physical data. 3. The design points of the design product: in shape. 4. The picture or photo that best indicates the design points: perspective view 1.

The present application relates to the technical field of data processing, in particular to a patient rehabilitation scheme design method and system based on multi-modal fusion, comprising: acquiring physiological rhythm data of a patient before surgery, the physiological rhythm data at least including respiratory frequency and heart rate variability information; constructing a virtual natural scene with dynamic light and shadow changes based on the physiological rhythm data, the virtual natural scene containing light and shadow fluctuation elements corresponding to the respiratory frequency; wearing a VR headset and a bone conduction earphone for the patient, the device automatically loading a customized virtual scene, and real-time prompting of breathing points through a virtual guide to help the patient establish a perception association of the rhythm elements; real-time analysis of the patient's physiological data to obtain the respiratory frequency, heart rate variability and skin electrical activity value, and step-by-step adjustment of the light and shadow frequency to approach the respiratory frequency when the difference between the light and shadow fluctuation frequency and the respiratory frequency exceeds a threshold value. The multidimensional evaluation system of the present application more comprehensively reflects the anxiety state of the patient and avoids insufficient intervention caused by evaluation bias.