40 results about "Heart rate analysis" patented technology

The invention relates to the field of electronic health detection, and discloses a non-contact type automatic heart rate measurement system and a measurement method. The non-contact type automatic heart rate measurement system adopts a non-contact mode to remotely collect video data of a measured person and performs data conversion to realize automatic heart rate measurement, and is characterized by comprising a video collection module, a framing extraction ROI (region of interest) module, a primary color component separation module, a time-domain signal generation module, a blind source separation module, a signal screening module and a heart rate analysis module. Compared with the prior art, the non-contact type automatic heart rate measurement system and the measurement method are based on an automatic face track and blind source separation technology, adopt a noninvasive and non-contact type remote physiological signal detection method, overcome influence of motion artifact in video record, has motion tolerance, is high in measurement accuracy, and can perform heart rate measurement automatically and simultaneously.

A method, system, and computer program product related to the detection of physical activity using changes in heart rate. The method, system, and computer program product evaluates short term glucose demand and long term insulin action due to physical activity. The method, system, and computer program product is further related to the improvement of open and closed loop control of diabetes by accounting for the metabolic changes due to physical activity. The method, system, and computer program product is directed to detecting in real time the short and long term effects of physical activity on insulin action via heart rate analysis, and recommending changes in insulin dosing to compensate for the effects of physical activity. With these recommendations, the open and closed loop control of diabetes can be improved and steps can be taken to prevent hypoglycemia that may result from increased insulin sensitivity due to physical activity.

The invention discloses a system and method for intelligent video monitoring of vital signs. The system comprises a heart rate analysis and extraction module, a cloud side database and an intelligent application module, wherein the heart rate analysis and extraction module and the intelligent application module are equipped with wireless communication modules respectively. The heart rate analysis and extraction module is used to shoot a human face video image, conduct human face detection, determine a human face area, process the human face area, determine a skin area, extract an interested area, compute gray averages of three RGB channels, apply independent component analysis to conduct blind source separation of signals and obtain a heart rate by a heart beat interval mean value method; the wireless communication modules upload the real-time heart rate obtained by the heart rate analysis and extraction module to the cloud side database and are connected to wireless intelligent communication equipment in the intelligent application module; and the intelligent application module is used to conduct the real-time monitoring and store heart rate information, and is also used to conduct early warning for physical health conditions. The system and the method disclosed by the invention are effective and reliable and have the advantages that vital sign signals can be monitored in real time; monitoring accuracy is high; timeliness is high; and applications are wide.

The invention provides a monitoring device controlled based on an intelligent mobile phone for preventing obstructive sleep sudden death. The monitoring device includes the intelligent mobile phone, an image detection and signal treatment device and an oximeter. The image detection and signal treatment device includes: an image recognition and signal treatment module based on Digital Signal Processor (DSP), an RGB camera shooting module, a storage module and a wireless module. The recognition and signal treatment module includes: a heart rate signal analysis module, a snore signal analysis module and a breathe frequency analysis module. The heart rate analysis module is respectively connected to the RGB camera shooting module and the storage module and measures the sleep heart rate, the breathe, and the snore in non-contact manner, measures the blood oxygen saturation through the oximeter, fits a function according to negative correlation of the blood oxygen saturation and sleep apnea time, thereby improving the veracity and effectiveness of the oximeter, systematically analyzing the signal variation to recognize the sleep condition and to prevent the sleep sudden death.

The invention relates to an electrocardiogram-based heart rate analysis method and equipment, wherein the heart rate analysis method comprises the following steps: obtaining particular cardiac cycle data based on electrocardiogram; drawing a heart rate scatter diagram based on the cardiac cycle data; respectively connecting two scatter points adjacent in time domain in the heart rate scatter diagram to obtain a plurality of vectors; drawing a fence diagram of vectors in chronological order of the appearance of vectors, wherein the chronological order of the appearance of vectors is consistent with the chronological order of the appearance of the scatter points for forming the vectors; judging whether the cardiac cycle data is in a predetermined range of normal values; drawing the cardiac cycle data beyond the range of normal values in the fence diagram based on the time correspondence. According to the invention, the cardiac cycle data in the electrocardiogram are superimposed into the fence diagram of the vectors, so that doctors can not only see the starting point of sustained arrhythmia visually, but also see the duration of sustained arrhythmia, so as to be beneficial to the diagnosis of doctors.

The invention discloses a real-time dynamic heart rate monitoring device and monitoring method. The device comprises a baseline drift removal module, a band-pass filter module, a frequency domain analysis module and a heart rate frequency point selection module, wherein the baseline drift removal module is used for removing an interference signal of causing drift of the baseline of a pulse wave signal; the band-pass filter module is used for removing a noise signal out of a heart rate frequency band when obtaining a frequency signal component belonging to the heart rate frequency band; the frequency domain analysis module is used for obtaining a signal frequency spectrum of the pulse wave signal in a specific frequency band interval set in advance; the heart rate frequency point selection module is used for obtaining the frequency point corresponding to the heart rate and outputting the frequency point. The influences of human motion and muscle motion on heart rate analysis are eliminated; the hardware cost is reduced; the concerned frequency band interval can be flexibly selected; the frequency domain accuracy of the signal is improved only within the concerned frequency band; excessive computing and storage overheads are avoided.

The invention discloses a heart rate detecting device which comprises a finger clamp, a signal collecting amplification circuit, a single-chip microcomputer, a WIFI module and APP installed on an intelligent device. The finger clamp is connected with a display screen. A battery is installed in the finger clamp and electrically connected with the signal collecting amplification circuit, the single-chip microcomputer and the WIFI module. The output end of the signal collecting amplification circuit is connected with an external interrupt port of the single-chip microcomputer. The output end of the single-chip microcomputer is electrically connected with the WIFI module and the display screen. The single-chip microcomputer is in wireless communicating connection with the intelligent device through the WIFI module. The intelligent APP obtains heart rate information through the WIFI module. A patient can use the device for detecting the heart rate every day, obtained heart rate data is stored in the intelligent device through the intelligent APP, when it is necessary, the patient directly transmits the heart rate data to a hospital from the intelligent APP, a doctor analyzes the heart rate of the patient within a certain period of time according to the heart rate data, some diseases can be observed easily for a long term, the patient does not need to be hospitalized to be monitored, and labor is saved.

The invention is a smart campus system and relates to the field of network technology. The smart campus system of the invention includes an attendance positioning subsystem, a heart rate monitoring subsystem, a sleep analysis subsystem, a motion analysis subsystem, a mobile payment subsystem, an environment monitoring subsystem, and a campus application subsystem, which are connected with a campuscenter management system through the network. The attendance positioning subsystem includes a personnel positioning module, a campus attendance module, an RFID sign-in module, and a Bluetooth sign-inmodule. The heart rate monitoring subsystem includes a heart rate monitoring module, a heart rate analysis module, and a health early warning module. The sleep analysis subsystem includes a sleep molecular module and a sleep aid module. The motion analysis subsystem includes a step count statistics module, a riding statistics module, a running statistics module, and a motion trajectory module. The mobile payment subsystem includes an intelligent traffic module, a cafeteria consumption module, a merchant consumption module, and a UnionPay sub card module.

The invention discloses a fear nervous emotion recognition method based on video eye movement and heart rate analysis, and proposes that a Relief feature selection method is used for optimal selection analysis of two physiological signals by utilizing a mode of combining eye movement in a video and remote non-contact heart rate estimation. Finally, a k-nearest neighbor (kNN) algorithm and a least squares support vector machine (LS-SVM) algorithm are adopted to recognize and judge the fear and tension mental state of the person. The method relates to the technical field of video analysis. The fear nervous emotion recognition method based on video eye movement and heart rate analysis can solve the problems that in the prior art, an abnormal emotion recognition method is restricted by the cooperation degree of a detected person, a test method is not secret and the test efficiency is low, an abnormal data processing mechanism is introduced through eye movement data analysis, the algorithm efficiency is improved, and a feature selection method is adopted, so that the feature dimension is reduced, and the abnormal emotion recognition of similar criminal knowers is effectively improved while the training time is shortened.

The invention discloses a heart rate analysis method. Focusing on the problem that in the prior art, heart rate detection data is not fully utilized, the following technical scheme is provided. Heartbeat data and respiration data of a person to be tested in a period are collected, and the maximum heart rate Hmax, the minimum heart rate Hmin, the maximum respiration frequency Rmax and the minimum respiration frequency Rmin of the person to be tested are determined; the heart rate H and respiration frequency R of the person to be tested in any time t of a period T are calculated according to the formula as shown in the description, wherein P is the heart rate deviation ratio, Q is the respiration deviation ratio and U is fatigue; if the fatigue U is larger than the maximum fatigue value M, it is determined that the heart of the person to be tested is in an over-fatigue state, and a rest is needed. The fatigue reflects the fatigue state of the heart of a person, whether or not the person to be tested is tired is judged through the calculated fatigue, so that the person to be tested is informed in time to take a rest when the heart is tired, and the heart rate detection data is utilized more fully.

The present invention discloses a method and signal processing device (620) for analyzing a ballistocardiogram (BCG). The actual energy content of the cardiac part of the BCG in a predefined time window is determined and compared with a reference value. A difference between the actual energy content and the reference energy content exceeding a predefined threshold is indicative of the presence of an arrhythmia in the evaluated part of the BCG because the force exerted by an arrhythmic ventricular contraction, if at all present, is typically substantially smaller than the force exerted by a ventricular contraction of a regular heartbeat. This insight facilitates a more accurate detection of the presence of arrhythmias in a BCG compared to BCG detection schemes based on a heart rate analysis.

The present invention relates to a heart rate analysis method and equipment based on an electrocardiogram, wherein the heart rate analysis method includes: obtaining specific cardiac cycle data according to the electrocardiogram; drawing a heart rate scatter diagram based on the cardiac cycle data; Two adjacent scatter points to obtain multiple vectors; draw the raster diagram of the vectors in the time order of the vectors, where the time order of the vectors is consistent with the time order of the scatter points used to form the vector; determine the cardiac cycle whether the data is within the predetermined normal value range; and plot the cardiac cycle data not within the normal value range in the grid chart according to the time correspondence. The present invention superimposes the cardiac cycle data in the electrocardiogram into the vector raster diagram, so that the doctor can not only visually see the starting point of the continuous arrhythmia, but also can visually see the duration of the continuous arrhythmia, which is beneficial Doctor's diagnosis.