21 results about "Heart rate variation" patented technology

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 relates to a three-dimensional electrocardio scatter diagram analysis method and device based on heart rate variability and a medium, and relates to the technical field of biomedical signal processing. According to the method, the heart rate data of the user can be mapped into the three-dimensional points in the three-dimensional coordinate system after being preprocessed, the coordinate axes of the three-dimensional coordinate system are the time domain index, the frequency domain index and the nonlinear index, the heart rate change is displayed from three dimensions, compared with a traditional two-dimensional scatter diagram, the heart rate change characteristics can be more comprehensively and stereoscopically displayed, and the user experience is improved. According to the method, more potential heart disease related information is captured, heart rate data features are deeply excavated by constructing a three-dimensional electrocardiogram scatter diagram containing more heart rate change information, heart rate scatter indexes are quantified, and risk levels of heart diseases such as arrhythmia and atrial fibrillation are scientifically and accurately evaluated according to the scatter indexes. And the accuracy and reliability of heart disease risk assessment are improved.

The emotion assessment apparatus has a detecting unit detecting heartbeat information including the heart rate of a subject, an emotion assessment unit assessing whether the emotion of the subject is a negative emotion or a positive emotion based on the heartbeat information, a counting unit counting the number of heart rate variations within a predetermined time period, an emotional expression assessment unit assessing the mental state of the subject based on the number of heart rate variations, and using the assessment results from the emotion assessment unit to assess the mental state, and an output unit outputting the assessment results from the emotional expression assessment unit.

The application relates to the technical field of intelligent sensors and big data processing, in particular to a smart watch multi-source data fusion method and system based on environment sensing. The method comprises the following steps: obtaining an original data set through a smart watch; generating a data frame sequence according to a preprocessing result of the original data set, and generating a fusion feature vector based on the data frame sequence; inputting each fusion feature vector into a preset health mode classification model, and outputting an initial recognition result; generating a health risk level and an environmental influence factor according to the initial recognition result, and outputting user feedback information; extracting features such as a heart rate variation interval, a body temperature change rate and an air quality fluctuation cycle, constructing a fusion feature vector, combining a pre-trained health mode classification system, analyzing a linkage relationship between environmental changes and physiological reactions, and accurately identifying a stress mode caused by environmental pollution; and finally outputting personalized health prompt information based on a risk score mechanism and a historical mode library.

The application relates to a human-vehicle intelligent security method and device, computer equipment, a storage medium and a computer program product. The method comprises the following steps: acquiring heart rate variation information of a driver, each facial image of the driver and multi-angle photographed vehicle images, and identifying facial expression information corresponding to each facial image; identifying psychological state information of the driver based on the heart rate variation information of the driver, and identifying emotion change distribution information of the driver based on each facial expression information; identifying vehicle abnormal information of the vehicle based on each vehicle image, and identifying intention information of the driver through an abnormal emotion analysis strategy based on the psychological state information of the driver and the emotion change distribution information of the driver; and determining a target detection result of the vehicle based on the intention information of the driver and the vehicle abnormal information of the vehicle. The method can improve the comprehensiveness of human-vehicle security.

An apparatus is disclosed. The invention relates to monitoring lung condition. The solution comprises non-invasive optical measurements (300) on a patient utilising at least two wavelengths; determining (302), based on the optical measurements, oxygen saturation, heart rate and heart rate variations of the patient; determining (304), based on heart rate and / or heart rate variations, breathing effort of the measurement subject; detecting (306) a change in breathing effort; detecting (308) a change in oxygen saturation level; determining (310), based the detected changes, a probability index for lung problem; comparing (312) the probability index to a given threshold and generating (314) an indication if the probability index is over a given threshold.

The invention relates to the technical field of power consumption management, in particular to a wearable device power consumption adaptive management method and system based on deep learning, and the method comprises the following steps: obtaining skin resistance, gait frequency, heart rate variation coefficient and body temperature data, calculating fluctuation degree and marking stability, adjusting stable parameter sampling frequency to generate dynamic sampling frequency information; evaluating the remaining electric quantity and the charging time to obtain operation grades, classifying and identifying user behaviors, and combining behavior and grade matching to generate a periodic power consumption control instruction set. According to the method, parameters in a stable state are screened based on physiological data volatility indexes, the sampling frequency of the parameters is compressed, the scarcity degree of the equipment operation electric quantity is calculated by combining the residual electric quantity and the predicted charging time difference value, and the user behavior identification tag is matched with the operation level identifier; the energy consumption control strategy is accurately matched according to the actual electric quantity state and the user behavior characteristics, the self-adaptive cruising ability of the equipment in the daily use period is enhanced, and the power consumption waste is reduced.

The utility model discloses an intelligent wearable rehabilitation monitoring system which comprises elastic bridle rings, the elastic bridle rings are symmetrically erected front and back, first protection pads are embedded and fixed to the opposite sides of the bottoms of the elastic bridle rings, and pulse monitors are embedded and fixed to the centers of the tops of the first protection pads. A springback ring pad is embedded in the top sensing end of the pulse monitor, a sponge pad is fixedly connected to the outer side of the springback ring pad in a sleeving mode, the sponge pad is downwards embedded and fixedly connected with a first protection pad, a second protection pad is fixedly embedded in the opposite side of the top of an elastic strap ring, and a monitoring meter is fixedly embedded in the center of the top of the second protection pad. The springback ring pad is nested and matched at the sensing end of the pulse monitor and is in extrusion fit with the pulse beating area of the wrist, and the pulse beating state is clearly reflected to the pulse monitor in real time for accurate health data recording, so that accurate heart rate change is obtained, and the overall health monitoring effect is improved.

This invention provides a method, system, storage medium, and device for grading chronic stress emotional states. The method includes continuously and non-interfering acquisition of ultra-low frequency physiological data such as heart rate and blood oxygen saturation via a wrist-worn device; extraction of characteristic indicators such as diurnal heart rate variation, recovery time, heart rate entropy, and low blood oxygen duration; simultaneous acquisition of psychological scale and personality trait data, construction of an associated database, and analysis of patterns; and training a machine learning model based on selected physiological features and psychological grading labels to achieve automatic grading of chronic stress states (mild, moderate, and severe). This invention effectively characterizes changes in autonomic nervous function under long-term stress by utilizing ultra-low frequency physiological rhythm characteristics and combines them with personality traits for personalized modeling, solving the problem of objectively, continuously, and accurately assessing the chronic stress state of individuals in real high-risk work environments.

The invention discloses a smart ward platform based on the Internet of Things, and relates to the technical field of smart wards. A bedside interaction unit, an infusion unit, a nursing electronic whiteboard unit and a ward display screen are arranged in the ward; a patient wears an electrocardiogram monitoring bracelet, and the electrocardiogram monitoring bracelet comprises a millimeter wave blood flow monitoring module which is used for collecting physical sign data and electrocardiogram monitoring data; the physical sign fusion monitoring module is used for collecting real-time heart rate variation coefficient data through the electrocardiogram monitoring bracelet; the infusion optimization system comprises a piezoelectric film sensing array arranged in an infusion device, the identity authentication unit is integrated with a near-infrared structured light projection module and is used for comparing face 3D depth information with HIS system data; and the multi-data coordination unit is used for recommending an execution scheme based on the medical record similarity map and generating a conflict report and a multi-dimensional data comparison thermodynamic diagram. Individualized precise regulation and control of the infusion rate are achieved, the defects of traditional infusion management are overcome, and the safety and effectiveness of the infusion link are improved.

The present invention provides a method, device, system, and electronic device for verifying the legitimacy of an identity, relating to the field of security monitoring technology. The method comprises: performing video heart rate detection on a pedestrian in a video image captured by a monitoring device to obtain a first heart rate value; if the first heart rate value does not fall within a preset heart rate range, determining a baseline heart rate variation range based on a preset heart rate road network map, and determining the pedestrian's heart rate variation value based on the first heart rate value and adjacent monitoring devices; if the heart rate variation value does not fall within the baseline heart rate variation range, verifying whether the pedestrian's identity is legitimate based on the pedestrian's human biometrics; if the first heart rate value falls within the preset heart rate range or the heart rate variation value falls within the baseline heart rate variation range, determining that the pedestrian's identity is legitimate. The technical solution provided by the present invention combines video heart rate detection with map technology to verify the legitimacy of an identity, thereby reducing computing power costs and improving the efficiency of identity legitimacy verification.

The invention discloses a control method of an intelligent mattress and a control system based on the intelligent mattress, and relates to the technical field of smart home. According to the embodiment of the invention, processing, separation and feature extraction are sequentially carried out on collected original composite signals to obtain heart rate variation features and micro body movement features; calculating a pressure index and a sleep stage according to heart rate variation characteristics and micro body movement characteristics; and finally, the intelligent mattress is controlled based on the processed target composite signal, the heart rate variation characteristics, the pressure index and the sleep stage, so that a complete closed-loop path from'non-sensing 'to'accurate evaluation' and then to'dynamic intervention 'is constructed, and active and intelligent adjustment on the emotion of the monitored object is realized.

The invention discloses an agricultural machinery driving fatigue recognition method based on the change of the heart rate of a driver, and belongs to the technical field of agricultural machinery intelligent monitoring and man-machine work efficiency engineering crossing. The problems that an existing physiological signal detection method for driving fatigue is difficult in feature extraction and poor in universality due to individual differences are solved. Comprising the following steps: performing feature extraction based on a heart rate sequence to obtain a time-domain heart rate feature and a nonlinear heart rate feature; meanwhile, frequency domain heart rate characteristics are obtained through calculation based on the power spectrum density; processing is carried out to obtain a corrected time domain fatigue feature, a corrected frequency domain fatigue feature, a corrected nonlinear fatigue complexity feature and a corrected multi-time-mark driving fatigue cooperation feature; calculating multi-feature coupling fatigue energy, and calculating a fatigue accumulated response amount and a fatigue discrimination probability value; and calculating a fatigue strength index and a self-adaptive fatigue grading threshold value, and finally obtaining a fatigue grade judgment result. The method is used for identifying the fatigue state of the agricultural machinist.

The present application relates to a kind of physical training examination method, system and intelligence capacity training system.Solve the problem that traditional physical training examination statistical method cannot objectively and quantitatively examine trainee.Method includes the completion accuracy of each action based on training process data and evaluation standard, then according to personal basic information, the number of times of completing standard examination action, heart rate variation value and facial expression as examination result information, through accurate, quantitative statistics and analysis, obtain objective examination result, remove the subjective influence factor when examination, can be efficiently, comprehensively, objectively to trainee examination and ranking in large-scale examination.Intelligence capacity training system includes mechanical training device, relevant sensor module, man-machine interaction module, control module and statistical analysis system;Control module obtains examination result information, and statistical analysis system receives the examination result information of all trainees, the score of each person's examination situation is calculated, and is sorted according to score.

The utility model relates to the technical field of psychological intervention, and discloses a wireless Bluetooth pectoral girdle using electrocardiogram heart rate change to monitor vegetative nerve function, which comprises a fixed shell, the outer wall of the front end of the fixed shell is fixedly connected with a mounting seat, and the middle part of the outer wall of the front end of the mounting seat is provided with a storage groove. A detection block is hinged to one side of the outer wall of the front end of the mounting base, a data storage module, a storage battery, a central processing module and a Bluetooth signal module are arranged in the detection block, a plurality of detection interfaces are formed in one side of the upper surface of the detection block, and connectors are arranged on the inner walls of the detection interfaces; the end, away from the detection block, of the connector is fixedly connected with a connecting line. According to the wireless Bluetooth chest strap, the wireless Bluetooth chest strap can be in Bluetooth connection with mobile equipment such as an intelligent mobile phone, a user can directly check real-time data and historical records on the mobile phone, additional reading equipment is not needed, and therefore the use convenience of the device is improved.

Methods, systems, and apparatus for disease detection are described. The methods may include receiving heart rate variability (HRV) data associated with a user from a wearable device, the HRV data being collected via the wearable device over a first time interval and a second time interval following the first time interval. The method may include feeding the HRV data into a classifier and identifying a deviation criterion between a first subset of the HRV data collected during the first time interval and a second subset of the HRV data collected during the second time interval. The method may include causing a graphical user interface (GUI) of the user device to display a disease risk measure for the user based on the satisfaction of the deviation criterion, the disease risk measure being associated with the relative probability that the user will transition from a healthy state to an unhealthy state.

The present invention provides a motion assessment method and system based on a closed-loop cardiopulmonary function digital twin model. The motion assessment method includes the following steps: obtaining multi-dimensional physiological signals collected synchronously based on a single contact point; constructing a time dynamic feature library using long-term continuous monitoring data; analyzing individual physiological pattern characteristics in combination with a deep learning algorithm; establishing a progressively optimized closed-loop cardiopulmonary function digital twin model; achieving maximum exercise volume, heart rate variation range and cardiac output changes in a virtual space based on the closed-loop cardiopulmonary function digital twin model, and issuing a warning signal before reaching the patient's cardiopulmonary function limit. The present invention has the advantages of continuous monitoring and early prediction of danger, especially the ability to accurately predict activity restrictions caused by cardiac function, lung function, and metabolism, which is of great benefit to disease management and the implementation of daily exercise prescriptions, ensuring that patients can obtain maximum benefits while avoiding injuries caused by excessive exercise.

The application relates to the field of intelligent medical treatment, in particular to a children propofol anesthesia state monitoring method, device and program product based on a sensitive awakening index. The method comprises the following steps: acquiring electroencephalogram data and heart rate data of children injected with propofol; performing spectrum calculation on the electroencephalogram data to obtain a spectrum beta ratio, and calculating a heart rate variation degree through the heart rate data; performing anesthesia awakening index prediction through the spectrum beta ratio and the heart rate variation degree; and evaluating the anesthesia state of children through the predicted anesthesia awakening index. The application can sensitively detect the change of the anesthesia state of children and has good clinical value.

The invention provides a driving monitoring system and a driving monitoring method. The driving monitoring method comprises the following steps: detecting a first heart beat signal of driving through a radar; extracting a heartbeat interval signal from the first heart beat signal; generating heart rate variability data according to the heartbeat interval signal, wherein the heart rate variability data comprises low-frequency power and high-frequency power; judging whether driving is fatigue or not according to the heart rate variation data; and outputting an alarm message in response to the judgment of the driving fatigue.

An apparatus is disclosed. The invention relates to monitoring lung condition. The solution comprises non-invasive optical measurements (300) on a patient utilising at least two wavelengths; determining (302), based on the optical measurements, oxygen saturation, heart rate and heart rate variations of the patient; determining (304), based on heart rate and / or heart rate variations, breathing effort of the measurement subject; detecting (306) a change in breathing effort; detecting (308) a change in oxygen saturation level; determining (310), based the detected changes, a probability index for lung problem; comparing (312) the probability index to a given threshold and generating (314) an indication if the probability index is over a given threshold.

The invention relates to the technical field of treatment safety early warning, and relates to an intelligent moxibustion treatment method and system based on multi-modal interaction, and the method comprises the steps: obtaining a PPG signal of a patient and an infrared thermogram comprising all moxibustion regions when moxibustion is carried out in a plurality of moxibustion regions of the patient; calculating a thermal load coefficient of each moxibustion area; calculating a heart rate variation coefficient for the PPG signal; calculating a tolerance coefficient according to the heart rate variation coefficient and the thermal load coefficients of all the moxibustion areas; and determining whether to give an alarm or not according to the tolerance coefficient, the heart rate variation coefficient and the thermal load coefficients of all the moxibustion areas. According to the method, the physical thermal load is comprehensively and objectively evaluated, and the scalding risk caused by subjectively judging the skin temperature and the tolerance condition of the patient is avoided.