10 results about "Heart rate variation" patented technology

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.

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 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 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.

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.