21 results about "Heart rate average" patented technology

A human body breathing and heartbeat signal estimation method based on a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) is characterized by comprising the following steps of (1) obtaining an intermediate frequency signal of a multi-channel frequency modulation continuous wave millimeter wave radar (FMCW) system, (2) carrying out distance dimension FFT and phase extraction and unwrapping on a Chirp intermediate frequency signal channel by channel, and (3) carrying out phase extraction and unwrapping on the Chirp intermediate frequency signal channel by channel. (3) calculating signal energy of interested breathing and heartbeat frequency bands in the phase time sequence under all distances, and weighting amplitudes of all distance dimension FFT under all distances to realize accurate positioning of a human body target, and (4) regarding the phase time sequence under the accurate distance and the adjacent distance of each channel as potential multipath signals, and performing fusion enhancement on the signals to realize accurate positioning of the human body target. And (5) processing the enhanced phase time sequences of the channels on the basis of multivariate variational mode decomposition, separating and estimating respiratory signals and heartbeat signals of the human body, and calculating an average respiratory rate and an average heart rate in an observation time period.

Example embodiments relate to a computer-implemented method for determining endurance performance of a subject based on sensor measurements; the computer-implemented method comprising: obtaining (201) workload data measured by at least one workload sensor, comprising a time series (212) of the workload exerted by the subject during one or more physical activities; obtaining (202) heart rate data measured by at least one heart rate sensor, comprising a time series (211) of the heart rate of the subject during the one or more physical activities; selecting (203) time intervals (213 - 219) within the workload data and the heart rate data based on a set of selection criteria (204); for the respective time intervals (213 - 219), determining (205) an average workload (207) and, for a final portion of the respective time intervals (213 - 219), determining (205) an average heart rate (206); and determining (208) at least a portion of a workload-heart rate relationship (221), indicative for the endurance performance of the subject, by fitting a linear equation (224) to the average workloads and the average heart rates (230) of the respective time intervals (213 - 219).

The invention relates to the technical field of heart rate data processing, in particular to a health state change recognition method and device based on a time sequence heart rate mode. Acquiring a night heart rate sequence of the user for continuous M days; acquiring night heart rate sequences of the user for continuous N days from the night heart rate sequences of the user for continuous M days; calculating a daily heart rate average value of the continuous N days of the user based on the night heart rate sequence of the continuous N days of the user; k-means clustering is carried out on the daily heart rate average values of the user in continuous N days to obtain a plurality of first clustering clusters; k-shape clustering is carried out on the night heart rate sequence corresponding to the daily heart rate average value in each first clustering cluster to obtain a plurality of second clustering clusters; calculating a sample ratio of each second cluster, and determining the second cluster with the maximum sample ratio as a normal mode cluster; and determining whether other second clusters are abnormal mode clusters based on the normal mode clusters. In this way, early warning of potential health risks and long-term tracking of individual health can be achieved.

The invention provides a mobile skill training quality evaluation method and system, and relates to the technical field of mobile skill training.The method comprises the steps that a human body joint image sequence and a face video sequence of a trainee in the mobile skill training process are collected respectively; performing skeleton recognition on the human body joint image sequence to obtain a current skeleton posture and determine a current skeleton posture category; obtaining an average heart rate based on the face video sequence; comparing the current skeleton posture with a standard skeleton posture, calculating to obtain a posture similarity and a posture related error between the current skeleton posture and the standard skeleton posture, and evaluating the standard of the current skeleton posture action; performing cross-modal interaction fusion on the current skeleton posture and the average heart rate to obtain a training intensity value; and the standard and the training intensity value of the current skeleton posture are judged, so that evaluation of the mobile skill training quality is realized. According to the invention, the problems of large error and low accuracy of skill training quality evaluation at present are solved.

The application discloses a kind of fatigue driving detection method, device, equipment, storage medium and program product, belong to automobile technical field, the method includes: obtaining the current angular acceleration of steering wheel rotation at current time, and obtaining the average heart rate of vehicle driver and the average grip force of vehicle driver to steering wheel in a period of time including current time;According to current angular acceleration, average heart rate and average grip force, determine whether vehicle driver is fatigue driving.The application is based on the data of steering wheel angular acceleration, steering wheel grip force and vehicle driver heart rate three aspects, the fatigue driving state of vehicle driver is determined, improve the accuracy of fatigue driving detection result, to reduce the traffic accident caused by fatigue driving, improve the safety of driving.

The invention discloses a multi-modal dynamic weight action evaluation method and system and a storage medium. The method comprises the following steps: acquiring action posture data from an action video stream; processing the collected electrocardiogram data, and extracting heart rate features including average heart rate, heart rate variability and breathing periodicity; for the joint point sequence of each part, an improved DTW algorithm is used to calculate a similarity score with a standard action, and the improved DTW algorithm introduces a curved path optimization mechanism to reduce an invalid path calculation amount; a dynamic weight model is constructed based on a random forest algorithm, the weight of each part is dynamically distributed according to the motion complexity, the importance of the parts and the physiological indexes, the motion posture data and the heart rate data are fused for comprehensive evaluation, and evaluation grades are divided. Dynamic weights are distributed according to the motion complexity and the like, comprehensive evaluation is obtained by fusing multi-modal data, and accurate and comprehensive evaluation of the motion is achieved.

The application discloses a kind of trustable devices for unit physical training detection, specifically relates to physical detection technical field, including physical examination integrated equipment, backend system and blockchain bottom layer, physical examination integrated equipment and backend system are connected with each other to exchange data, physical examination integrated equipment and backend system are also connected with blockchain bottom layer, and data is chained and exchanged;The physical examination integrated equipment includes running physical information acquisition module, data cache, blockchain gateway and TEE trusted environment, the running physical information acquisition module is used to collect average heart rate, maximum heart rate, step number, kilometer number, time parameter, and the data cache is used to cache the data obtained by running physical information acquisition module.The application can ensure the accuracy of the collected parameters by chaining the collected running-related parameters, avoid human factors affecting the assessment results, and the chained data cannot be modified again.

The application belongs to the technical field of automobiles and provides a safety belt control method and device based on the state of a child, a medium and equipment, which comprises the following steps: in response to a lock buckle locking instruction, controlling the safety belt lock buckle of the child to be locked; judging whether the vehicle has collided, and if so, unlocking the safety belt lock buckle; otherwise, acquiring the heart rate of the child, the safety belt stretch length and the voice of the child at a plurality of continuous time points; based on the heart rate of the child at the plurality of continuous time points, calculating the average heart rate and judging the heart rate range in which the child is located; based on the safety belt stretch length at the plurality of continuous time points, identifying the safety belt state by calculating the variance of the safety belt stretch length; based on the voice of the child at the plurality of continuous time points, extracting the tone features and identifying the emotion of the child; based on the heart rate range in which the child is located, the safety belt state and the emotion of the child, judging the state of the child; and based on the state of the child, controlling the safety belt stretch length. The risk that the child moves around and struggles to break free from the safety belt can be avoided, and the safety of the child is protected.

This invention discloses an intelligent heart rate prediction method. Relating to the field of heart rate monitoring technology, the method involves continuously collecting heart rate data from patients using a heart rate monitoring instrument, constructing short-term heart rate sequences and corresponding waveforms, segmenting the waveforms into intervals, and extracting typical heart rate features for each interval, including the interval's average heart rate and standard deviation. These features are combined into a feature sequence, and a heart rate feature library is established. In the prediction phase, using the current time as a benchmark, the method acquires the patient's heart rate data from the past traceability period, generating the latest traceability short-term heart rate waveform and its feature sequence. By searching for matching historical feature sequences in the heart rate feature library, the difference rate is calculated to determine the optimal match. Finally, based on the optimal heart rate waveform and feature sequence, the method outputs a prediction result for the future heart rate. By leveraging historical data patterns and associating them with the patient's own characteristics, the method achieves intelligent prediction, improving the accuracy and timeliness of heart rate monitoring and providing support for clinical decision-making.

Systems and methods are described for predicting the likelihood of myocardial infarction and / or accident, such as road traffic accident. Patient or user heart rate is detected and each of a plurality of heart rate n-variability (HRnV) and average heart rate n-variability (avHRnV) parameters are determined from the heart rate signal, from which a predictive model can predict the likelihood of myocardial infarction occurrence for the patient or user and, if necessary, alert the patient or user.

A system for processing electrocardiogram (ECG) signals is disclosed. A receiving module receives ECG signals corresponding to at least one channel. The ECG signal for each channel includes a plurality of beats. A beat processing module, for each channel: calculates an instantaneous heart rate (IHR) for each beat; forms a plurality of beat groups from the plurality of beats, each beat group including a set of beats of the plurality of beats; calculates an average heart rate (AHR) for each beat group based upon the IHR of the set of beats; and generates a representative beat for each beat group based upon the set of beats. A feature extraction module extracts, for each channel, a set of features based upon the plurality of representative beats. A classifier generates an indicator indicating a risk associated with a cardiac disease based upon the set of features for the at least one channel.

This application discloses a method and system for extracting heart rate and heart rate variability features based on electrocardiogram (ECG) signals. The process is as follows: acquiring ECG signals and preprocessing them to improve the accuracy of subsequent R-peak detection; performing R-peak detection; setting a time window and calculating heart rate and heart rate variability feature indicators based on the NN interval sequence. The time window is selected and set to a standard duration of 5 minutes or a short duration of 1-2 minutes. The heart rate feature indicators include instantaneous heart rate and average heart rate within the time window. The heart rate variability feature indicators include time-domain indicators and frequency-domain indicators. The time-domain indicators include the standard deviation SDNN of all NN intervals and the root mean square difference RMSSD of adjacent NN intervals. The frequency-domain indicators include low-frequency power LF, high-frequency power HF, and the LF / HF ratio. The heart rate and heart rate variability feature indicators are output at predetermined time intervals to form time-series data, and the changing trend is displayed in real time or analyzed offline. This application adapts to dynamic monitoring needs and can jointly calculate multiple types of feature indicators.

A method of detecting sleep apnea includes generating a cardiac signal indicating activity of a heart of a patient. The method further includes determining a short-term average heart rate and a long-term average heart rate. The method further includes determining a start and end of a heart rate cycle based on the short-term average heart rate and the long-term average heart rate. The method further includes determining physiological parameter values occurring during the heart rate cycle. The method further includes determining whether patient has or has not experienced a sleep apnea event based on whether one or more conditions are satisfied by one or more parameter values for one or more heart rate cycles and responsively generating an indication that patient has or has not experienced a sleep apnea event.

The invention discloses a QRS wave group detection method and device, a storage medium and computer equipment. The method comprises the following steps: acquiring original electrocardio data, and performing low-pass smoothing processing, point-by-point differential square processing and re-smoothing processing on the original electrocardio data to obtain a QRS region enhanced characteristic signal; performing down-sampling processing on the QRS region enhanced characteristic signal to obtain a down-sampled QRS wave group enhanced characteristic signal; solving a local maximum value of the down-sampling QRS wave group enhanced characteristic signal window by window, and determining a detection threshold value based on the solved local maximum value; carrying out point-by-point judgment on sampling points higher than a detection threshold value in the down-sampling QRS wave group enhanced characteristic signal to obtain a QRS wave group position; and obtaining a cardiac cycle sequence based on the QRS wave group position, screening the cardiac cycle sequence based on the cardiac cycle range, and obtaining an average heart rate based on the screened target cardiac cycle. According to the method, real-time heart rate extraction can be achieved through an embedded system.

A wearable physiological monitoring system comprises commercially available off-the shelf components. With the growth of interrelated systems of computing devices, mechanical and digital machines, objects, animals or people connected by the Internet, there is a significant interest in the use of wearable sensors such as cell watches, and smart phones. These wearable sensors may be used to monitor physiological signals and provide health information. An edge-intelligent Internet based wearable assists in substance-abuse detection by monitoring and interpreting an individual's physiological signals on continuous basis. The wearable device helps in monitoring cravings and substance abuse of the individual and help the healthcare provider to start an early intervention as required. The proposed system is developed as a dedicated substance abuse wearable system. An example of a wearable device is a medical quality wearable which yielded a correlation of 0.89 for accelerometer measurements and 0.92 for average heart rate measurements in tests.

A signal is received 302 from a non-invasive heart sensor, positioned to detect the heartbeat of a driver. It is used to predict the likelihood of myocardial infarction, inferring likelihood of a road traffic accident. A plurality of values for heart rate n-variability (HRnV) 304 and average heart rate n-variability (avHRnV) 306 are determined, from RR intervals derived from the heart rate signal. The intervals have summation parameter n and stride parameter m, and the plurality of values includes different values of at least one of n and m to allow for consideration of HRV behaviour in varying scales. The values are passed to a predictive model 308, resulting in an alert if the likelihood exceeds a threshold. The heart rate sensor may be contactless, e.g. an infrared detector, and configured for use in a motor vehicle. The predictive model may also take medical history and demographic information into account.

The invention discloses an intelligent heart rate prediction method based on deep learning, and relates to the technical field of heart rate monitoring, and the method comprises the steps: continuously collecting heart rate data of a patient through a heart rate monitoring instrument, constructing a short-term heart rate sequence and a corresponding oscillogram, carrying out the interval segmentation of the oscillogram, and extracting the typical heart rate features of each interval; comprising interval average heart rates and interval heart rate standard deviations, combining the interval average heart rates and the interval heart rate standard deviations into a feature sequence and establishing a heart rate feature library; in the prediction stage, the current time is taken as a reference, heart rate data of a patient in a past traceability period is acquired, a latest traceability short-term heart rate oscillogram and a feature sequence thereof are generated, and optimal matching is determined by searching a matched historical feature sequence in a heart rate feature library and calculating a difference rate. And finally, outputting a future heart rate prediction result based on the optimal heart rate oscillogram and the feature sequence. The characteristics of the patient are associated through a historical data mode to achieve intelligent prediction, the accuracy and timeliness of heart rate monitoring are improved, and support is provided for clinical decision making.

The application provides a high-precision heart rate monitoring system fusing non-contact electrocardio and heart vibration signals, which comprises a capacitive coupling type non-contact electrocardio and piezoelectric film heart vibration signal acquisition module, a heart rate channel selection module based on signal quality evaluation, and a heart rate calculation module.The specific implementation comprises the following steps: in the first step, the capacitive coupling type non-contact electrocardio and piezoelectric film heart vibration signal acquisition module is used for acquiring non-contact electrocardio signals and heart vibration signals; in the second step, the heart rate channel selection module based on signal quality evaluation is used for calculating the quality of the acquired signals; and in the third step, the heart rate calculation module is used for calculating the heart rate according to the signal type, and the calculation result comprises high-precision heart rate information based on RR interval and average heart rate information based on heart vibration; the system is oriented to long-time high-precision physiological signal acquisition in a sleep scenario, fuses the advantages of two modes of non-contact electrocardio and heart vibration, and can effectively acquire continuous, long-time and high-precision heart rate information.

The invention relates to the technical field of electrocardiogram analysis, in particular to a method and system for predicting ventricular fibrillation, and the method comprises the steps: obtaining long-time electrocardiogram data of a first preset time length, calculating characteristic parameters and average heart rate of the long-time electrocardiogram data, fitting each characteristic parameter and the average heart rate, and obtaining a characteristic curve corresponding to each characteristic parameter; the method comprises the following steps: acquiring short-time electrocardiogram data of a preset monitoring time length, calculating characteristic parameters and an average heart rate of the short-time electrocardiogram data, calling a characteristic curve of each characteristic parameter, correcting the characteristic parameters of the short-time electrocardiogram data in combination with the average heart rate of the short-time electrocardiogram data, and acquiring standardized characteristic parameters; and inputting the standardized characteristic parameters into the constructed prediction model, and predicting the risk probability of the occurrence of the ventricular fibrillation. According to the scheme, the accuracy of ventricular fibrillation prediction can be improved, and high-risk groups can be helped to carry out early warning on the sudden cardiac arrest risk of the high-risk groups.

The invention provides a heart failure recognition method and device.The method comprises the steps that a first target heart rate and a first target pulsation index are obtained, and the first target heart rate and the first target pulsation index are the average heart rate and the average pulsation value of the pumping flow in a first period when a ventricular assist device operates at a target rotating speed respectively; calculating a first target parameter according to the first target heart rate and the first target pulsation index, wherein the first target parameter is used for measuring the heart failure increase risk of the heart; and if the first target parameter is greater than the preset value, determining that the risk of right heart failure or left heart failure deterioration exists. Whether the heart failure of the patient deteriorates or not is identified according to the change conditions of the heart rate and the pulsation index during operation of the ventricular auxiliary device, and the heart failure deterioration risk is monitored in real time.

This invention discloses a multimodal dynamic weighted motion evaluation method, system, and storage medium. It acquires motion posture data from motion video streams; processes the acquired electrocardiogram (ECG) data to extract heart rate features, including average heart rate, heart rate variability, and respiratory periodicity; for each joint sequence, an improved DTW algorithm is used to calculate the similarity score with standard motion. This improved DTW algorithm incorporates a curved path optimization mechanism to reduce the computation of invalid paths; a dynamic weight model is constructed based on the random forest algorithm, dynamically allocating weights to each part according to motion complexity, part importance, and physiological indicators; and a comprehensive evaluation is performed by fusing motion posture data and heart rate data to classify evaluation levels. By allocating dynamic weights based on motion complexity and other factors, and fusing multimodal data to obtain a comprehensive evaluation, it achieves accurate and comprehensive motion evaluation.