10 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 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 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.

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.

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 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.

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.