52 results about "High heart rate" patented technology

The invention relates to the technical field of heart rate detection, in particular to a wearable underwear heart rate detection method and system.The method comprises the following steps that motion information of a user is obtained, and specific body posture changes are recognized based on the motion information of the user; generating a physiological artifact signal related to the specific body posture change based on the specific body posture change; removing a physiological artifact signal from the original physiological signal to obtain a purified physiological signal; heart rate abnormity judgment is conducted on the purified physiological signals. According to the scheme, the physiological artifact signals can be effectively removed, and the accuracy of heart rate detection is improved.

The present invention relates to computer vision technology and RPG heart rate detection technology, and specifically to a heart rate detection method based on CardiA2Net. The present invention utilizes CardiA2Net to combine a self-attention convolutional hybrid network (ACmix) with an attention-based long short-term memory (ALSTM) network to perform enhanced feature extraction from raw data and improve time series learning with an integrated attention mechanism. The present invention also proposes a comprehensive set of preprocessing methods for facial video datasets, effectively reducing noise interference in facial videos and improving the accuracy of heart rate detection. The present invention calculates heart rate through facial video analysis and simultaneously processes noise in the video, effectively reducing the difficulty of contactless heart rate detection and improving the accuracy of contactless heart rate detection.

The invention provides a heart rate and breath prediction method, a program product, electronic equipment and a storage medium. The method comprises the following steps: acquiring an original signal sequence acquired from a human body through an ultra-wideband radar; determining a thoracic cavity displacement signal according to a mapping relation between the phase change and thoracic cavity displacement; performing multi-domain feature extraction on the thoracic cavity displacement signal to obtain multi-domain features; the multi-domain features comprise time domain features, frequency domain features and time-frequency domain features; on the basis of the multi-domain features, prediction values are obtained through a pre-trained prediction model, and the prediction values comprise the heart rate and the respiratory rate in the future time period. The robustness of the algorithm is enhanced through multi-dimensional feature fusion, high precision is still kept in an electromagnetic interference or multi-target scene, and the reliability of heart rate and respiratory rate prediction in a complex environment is improved. The physical signal advantage of the ultra-wideband radar is complementary with the environmental adaptability of the neural network, and the prediction accuracy of the heart rate and respiratory rate is improved.

The invention provides a vital sign detection method and device based on a millimeter wave radar, electronic equipment and a storage medium. The method comprises the following steps: obtaining a distance frequency domain signal based on a radar echo signal; performing spectral analysis on the distance frequency domain signal, positioning the position of the to-be-detected object, and extracting phase information at the position; filtering through a band-pass filter to obtain a breathing frequency band interval signal and an initial heartbeat frequency band interval signal; performing real-time respiration rate estimation based on the respiration frequency band interval signal to obtain a respiration frequency, and eliminating respiration harmonics in the initial heartbeat frequency band interval signal by using an adaptive wave trap based on the respiration frequency to obtain a heartbeat frequency band interval signal; and with the minimum fuzzy entropy as an optimization target, decomposing the heartbeat frequency band interval signal by using a variational mode decomposition algorithm to obtain a heartbeat signal, and carrying out real-time heart rate estimation based on the heartbeat signal to obtain a real-time heart rate. According to the method, the device, the electronic equipment and the storage medium provided by the invention, the accuracy of heart rate measurement can be improved.

The invention discloses an ultra-high heart rate electrocardio acquisition system, which comprises a signal acquisition unit adopting an ADS131E08 chip and supporting 8 channels, a 24-bit delta-sigma ADC (Analog to Digital Converter) and programmable gain amplification; the dynamic sampling rate adjusting unit is used for dynamically adjusting the sampling rate between 1kSPS and 64kSPS according to the heart rate change, and the sampling rate is increased to be greater than or equal to 2kSPS when the heart rate exceeds 200 times per minute; a low-noise design circuit has a common-mode rejection ratio greater than or equal to 110dB, a dynamic range greater than or equal to 118dB and total harmonic distortion less than or equal to-90dB, and power frequency interference is suppressed; the morphological enhancement module is used for enhancing QRS wave group characteristics by using digital morphological filtering; the multi-lead fusion module is used for fusing multi-lead signals through time sequence alignment and R-wave phase difference analysis; and the anti-interference module is integrated with a hardware comparator, and is used for lead falling detection and dynamic digital notch filtering. The system is suitable for researches on myocardial ischemia, arrhythmia and drug cardiotoxicity. Compared with the prior art, the device has the advantages of being simple in structure, low in cost, high in precision, high in reliability and the like, and can effectively solve the problem of animal ultrahigh heart rate electrocardio collection.

The invention discloses a safety condition monitoring method and device for old people, and relates to the technical field of heart rate monitoring. Acquiring a face video and an infrared video of a target user to obtain a first final space-time diagram and a second final space-time diagram; respectively inputting the first final space-time diagram and the second final space-time diagram into a space-time feature extraction module for feature extraction to obtain RGB features and NIR features, and fusing the RGB features and the NIR features to obtain fused features; the heart rate of the target user is determined according to the fusion features, and state monitoring is conducted. Acquiring a face and an infrared video of a target user, and extracting spatio-temporal features of the face video according to a preset rule to obtain a first spatio-temporal diagram; the infrared video features are extracted and amplified frame by frame to obtain a second space-time diagram, RGB and NIR features are extracted through a space-time feature extraction module and fused into fused features, non-contact heart rate monitoring is achieved, the accuracy and efficiency of heart rate monitoring are improved, the heart rate is determined according to the fused features, and the health condition of a user is monitored and evaluated in real time.

The present application relates to a PPG sensor module, electronic device, wearable device and physiological information detection method. The PPG sensor module includes: at least three light-emitting components for emitting light signals; a light detection module for generating multiple detection signals based on the light signals emitted by each light-emitting component; each light sensor and at least two light-emitting components respectively form a first measurement channel, and the light sensor also forms a second measurement channel with at least one light-emitting component other than the first measurement channel; the length of the first measurement channel is less than the length of the second measurement channel; a controller for obtaining each detection signal and determining the heart rate information of the detection object based on the detection signal obtained from each first measurement channel, and the controller is also used to determine blood oxygen information based on the detection signal obtained from each first measurement channel and the detection signal generated based on each second measurement channel. The PPG module can simultaneously improve the accuracy of heart rate and blood oxygen detection.

The invention relates to a driver heart rate monitoring method, a driving risk early warning method, a medium, a controller and a vehicle. The method comprises the steps that a face video of a vehicle driver is collected; based on the face video, determining a face region of interest for heart rate calculation; and processing the facial region of interest through a deep learning network to obtain a heart rate value of the driver. By collecting the face video of the driver and applying the face video to follow-up determination of the heart rate value, compared with the heart rate value measured through hardware equipment connected with the driver in the related technology, direct contact with the driver can be avoided, and then the driving process of the driver can be prevented from being limited. And the face region of interest is processed through the deep learning network to obtain the heart rate value of the driver, so that the accuracy of the heart rate value can be improved.

The invention discloses a heart rate measurement method and system based on time-frequency fusion and dynamic gating attention, and the method comprises the steps: firstly carrying out the preprocessing of an acquired face video sequence, and obtaining a stable input image sequence; thirdly, extracting a three-dimensional feature map and time sequence features which change along with time through a time-frequency convolutional network; a time-frequency fusion module is constructed, complementary time-frequency features are obtained through time-domain convolution and frequency-domain wavelet decomposition, and feature adaptive fusion is realized by using a dynamic gating mechanism; and the local time relevance of the features is enhanced through a local sliding window attention module. And inputting the three-dimensional convolution features into a prediction module, performing regression to generate a remote photoelectric volume pulse wave signal, and performing frequency domain analysis on the signal to estimate the heart rate. According to the method, pulse related signals can be stably extracted under the conditions of illumination variation and slight head movement, and the method has high heart rate estimation precision and environment robustness and can be used for non-contact vital sign monitoring application.

The present invention relates to the technical field of information processing, and in particular to a heart rate and respiration monitoring method based on a millimeter-wave radar. The method includes determining whether the driver is speaking; acquiring a first reflected wave signal; analyzing the first reflected wave signal; determining whether monitoring is abnormal; determining a second heart rate and a second respiration rate; determining whether the driver is abnormal; and correcting a standard distance range. By combining head image recognition and mouth feature analysis, the present invention ensures that heart rate and respiration monitoring is only performed in a target stationary state, significantly improving the efficiency of heart rate and respiration monitoring. By dynamically adjusting the duration of the transmitted wave and the monitoring strategy according to the actual distance between the driver and the transmitter, it effectively adapts to changes in the target position, avoids monitoring errors, and improves the accuracy and effectiveness of monitoring. When a wrong judgment occurs, the standard distance range is automatically corrected according to the feedback, continuously optimizing the monitoring parameters, improving the monitoring accuracy, and providing a more reliable guarantee for driving safety.

A system for testing medications for cardiac activity at elevated heart rates utilizes an array of electrodes connected to a computer and cardiomyocyte samples on the electrodes. Software implements an electrical stimulation protocol in a method of delivering an electrical signal at a baseline frequency to stimulate respective cardiomyocyte samples and calculating a functional refractory period (mISI) for the respective cardiomyocytes at the baseline frequency along with increased respectively applied frequencies of consecutive electrical signals.

The application provides a heart rate detection module and an electronic device; wherein the heart rate detection module comprises a light emitting element, a light receiving element and a Fresnel film; the light emitting element and the light receiving element are arranged in optical isolation, the Fresnel film is located on the same side of the light emitting element and the light receiving element, and the light emitting surface of the light emitting element faces the Fresnel film; the Fresnel film has a light absorbing medium therein, and the light absorbing medium is located between the light emitting element and the light receiving element in the direction perpendicular to the Fresnel film. According to the heart rate detection module and the electronic device provided by the application, the crosstalk phenomenon of the detection light emitted by the light emitting element in the Fresnel film can be reduced / attenuated, that is, the light leakage in the Fresnel film can be effectively inhibited to weaken the light noise of the heart rate measurement, and the signal-to-noise ratio of the heart rate detection module can be improved.

A heart rate detection method based on joint attention and multi-scale fusion constructs a multi-scale image sequence to ensure the integrity of the scale, then extracts signal features from multiple scales, performs multi-scale feature fusion through an inter-layer attention mechanism, and finally sends the fused features into the rPPG signal extraction network. The designed rPPG signal extraction network consists of channel-time joint attention (CTJA) and spatio-temporal joint attention (STJA) to extract signals in terms of time, space, and channels. The present invention can effectively improve the accuracy of heart rate detection.

The invention relates to the technical field of physiological signal processing, in particular to a device and method for monitoring vital signs during sleep, and the device comprises a signal collection module which is used for collecting physiological signals of a user in a non-contact manner and sending the collected physiological signals Sraw (t) to a noise filtering module; the noise filtering module is used for de-noising the physiological signal Sraw (t) and outputting a de-noised signal Scanan (t); the signal enhancement module is used for carrying out time sequence feature extraction and signal enhancement on the de-noised signal Scanan (t) so as to improve the stability and detectability of the heart rate signal, and outputting an enhanced signal Senhanced (t); the signal classification module is used for classifying the enhanced signal Senhanced (t) and outputting heart rate HR (t), respiratory rate RR (t) and body movement information M (t); the data analysis module is used for extracting characteristic parameters from the output data of the signal classification module, identifying abnormal events and outputting an analysis result; and the display and alarm module is used for displaying the analysis result in real time and giving an alarm when an abnormal condition is detected.

The present application relates to the technical field of heart rate detection, and particularly relates to a wearable underwear heart rate detection method and system, the method comprising the following steps: acquiring motion information of a user, identifying a specific body posture change based on the motion information of the user; generating a physiological artifact signal related to the specific body posture change based on the specific body posture change; removing the physiological artifact signal from an original physiological signal to obtain a purified physiological signal; and performing heart rate anomaly judgment on the purified physiological signal. Through the above scheme, the physiological artifact signal can be effectively removed, and the accuracy of heart rate detection is improved.

The present application relates to the technical field of wearable devices, in particular to a method for calibrating earphone heart rate error based on a sports knowledge graph and related devices. The method comprises the following steps: obtaining multi-modal physiological data of a user wearing an earphone during sports, the multi-modal physiological data including photoplethysmographic wave signals collected by an optical heart rate sensor and the like. The present application structures the complex mapping relationship between different sports state types, sports intensity levels and heart rate error characteristics by constructing a sports knowledge graph, thereby achieving explicit modeling of the correlation between sports artifacts and heart rate signals. Compared with traditional heart rate calibration methods based on statistical filtering or a single deep learning model, the present application uses the topological structure information of the knowledge graph to better capture the deep semantic correlation between sports states and heart rate errors, thereby achieving higher heart rate monitoring accuracy in various sports scenarios.

A computer game as presented from spectator side may use clusters of marginally high heart rate to infer something interesting occurring on platform, which may be used in turn for video bookmarking highlights, other uses on the platform.

The invention discloses an ultrahigh heart rate anomaly detection method based on real-time signal processing, and relates to the technical field of pet health monitoring. According to the ultrahigh heart rate anomaly detection method, local low-delay processing is achieved by fusing multi-modal physiological signals and utilizing edge calculation, signal preprocessing is conducted through band-pass filtering, self-adaptive Kalman filtering and the like, and the abnormal heart rate is obtained. Multi-time scale feature extraction is combined with pet state vectors to dynamically adjust feature weights, an improved attention mechanism neural network and support vector machine secondary classification and a posterior probability fusion algorithm are used to improve classification precision, and a dynamic threshold mechanism and a grading early warning mechanism are used to match response intensity according to abnormal duration, so that the classification accuracy is improved. According to the invention, accurate and real-time monitoring of the abnormal ultrahigh heart rate of the pet is realized, an efficient and reliable detection process is provided for health management of the pet, and the method has important significance for early detection of potential diseases of the pet.

An ergonomic heart rate tracking and bias detecting system and method is provided. The system is configured to determine if users exhibit biases by measuring their heartrates and / or recording audio via the smartwatch during simulated tests or real-world situations. The system is intended to help those supervisors of employees by requiring employees to wear the smartwatch and using the system track and record their behavior. The system is configured to transmit data from the smartwatch to the system application for analysis to measure the heartrate of users. A low or resting heartrate may denote a pass situation, where a high heartrate may denote a failure situation, meaning a potential bias is detected in the user. Audio recorded by the system is also analyzed for tone and language to further determine the presence of bias in the user. Supervisors may then decide whether to hire / fire / or retrain the user.

The application is suitable for the field of remote physiological parameter monitoring, and provides a heart rate determination method and device based on a region of interest and an electronic device, which comprises the following steps: acquiring a video stream comprising a face of a user to be measured; performing region of interest detection on the video stream by using at least two region of interest templates to obtain corresponding regions of interest; for each region of interest, determining a time sequence signal corresponding to the region of interest, performing blind source separation on the time sequence signal by using an independent component algorithm to obtain an independent component signal related to a heart rate, and calculating the heart rate according to the independent component signal to obtain a heart rate corresponding to the region of interest; determining a weight value of each region of interest; and determining a heart rate of the user to be measured according to the weight value and the heart rate corresponding to each region of interest. By using the above method, local abnormal disturbance can be inhibited, and the accuracy of the heart rate can be improved.

This application provides a method, program product, electronic device, and storage medium for predicting heart rate and respiratory rate. The method includes: acquiring the original signal sequence collected from the human body via ultra-wideband radar; determining the chest cavity displacement signal based on the mapping relationship between phase change and chest cavity displacement; extracting multi-domain features from the chest cavity displacement signal to obtain multi-domain features, including time-domain features, frequency-domain features, and time-frequency-domain features; and obtaining predicted values ​​based on the multi-domain features using a pre-trained prediction model, the predicted values ​​including heart rate and respiratory rate in the future time period. Multi-dimensional feature fusion enhances the robustness of the algorithm, maintaining high accuracy even under electromagnetic interference or multi-target scenarios, improving the reliability of heart rate and respiratory rate prediction in complex environments. The physical signal advantages of ultra-wideband radar complement the environmental adaptability of neural networks, improving the accuracy of heart rate and respiratory rate prediction.

The invention discloses a watch heart rate monitoring method and device based on TEC temperature control, and solves the problem of inaccurate heart rate monitoring caused by the fact that the influence of temperature on a photoelectric sensor is not considered when a smart watch photoelectric measurement method is used for measuring the heart rate in the prior art. Comprising the following steps: acquiring the surface temperature of LED lamp beads at the bottom of the watch or the ambient temperature in real time; according to the target temperature range and the current temperature value, the TEC working mode is adjusted based on a preset temperature control algorithm; continuously detecting the temperature of the LED lamp bead, and dynamically adjusting the working power of the TEC according to the temperature change trend; after the temperature of the watch is stabilized within the target temperature range, photoelectric signals are collected for heart rate monitoring. The light source temperature can be stabilized in the optimal working temperature interval, and the accuracy of heart rate measurement is improved.

The invention discloses a heart rate monitoring method, system and device based on millimeter wave radar and a storage medium. The method comprises the following steps: extracting a phase signal of a target monitoring object in a radar echo signal; performing second-order differential processing to obtain an acceleration signal segment set corresponding to the phase signal; positioning an acceleration signal segment most similar to the periodic heartbeat signal from the acceleration signal segment set; segmenting beat-by-beat heartbeat signals from the positioned acceleration signal segments, and calculating the heart rate according to the segmented beat-by-beat heartbeat signals. According to the method, the acceleration signal segment of the distance angle bin with the optimal heartbeat is positioned based on the periodicity of the heartbeat signals, and the beat-by-beat heartbeat signals are segmented based on the acceleration signal segment, so that the accurate heart rate value is calculated, the misjudgment problem of a spectrum peak value detection method under the coexistence of multiple persons and the interference of respiration harmonics and body movement is effectively solved, and the accuracy of the detection result is improved. And the heart rate calculation precision is improved.

The present application provides a data acquisition method and an electronic device. The method includes: obtaining first heart rate information of a target user corresponding to a first scenario within a recent first time period, where the first heart rate information is collected when the signal quality of a photoplethysmogram (PPG) signal meets a first condition, and the first heart rate information includes a step frequency value and a heart rate value; constructing a first heart rate map corresponding to the target user in the first scenario according to the first heart rate information; receiving a target step frequency value of the target user in the first scenario, where the target step frequency value is collected when the signal quality of the PPG signal does not meet the first condition; and determining a target heart rate value corresponding to the target step frequency value according to the first heart rate map. In this way, by generating a heart rate map using the user's historical heart rate information and determining the heart rate value when the PPG signal quality is poor according to the heart rate map, the abnormal value output of heart rate monitoring can be reduced, thereby improving the reliability of heart rate monitoring and enhancing the user experience.

The application discloses a heart rate estimation method and device based on a face video, which estimates a heart rate value through analysis of the face video, comprises the following steps: pre-processing a face video to obtain a video data set, and dividing the video data set into a training set, a verification set and a test set; constructing a fusion network comprising a ResNet network and a ConvLSTM network, the ResNet network comprising a bottleneck residual module to improve the performance of the ResNet network; inputting the training set into the fusion network and training to obtain a network training model; inputting the verification set into the network training model for verification, and calculating a loss through a loss function to obtain a final network training model; inputting the test set into the final network training model to obtain a heart rate estimation value; and cross-dataset testing to improve the precision of the model. The application increases the bottleneck residual module in the ResNet network, improves the calculation efficiency of the ResNet network, enables the ResNet network to better train a neural network with a large depth, and through the fusion of the ConvLSTM network, global features can be fully utilized to improve the accuracy of the heart rate estimation value.

The application provides a heart rate warning method and an electronic device. The method comprises the following steps: obtaining a maximum heart rate value of a target user based on the age of the target user and a preset first algorithm model, wherein the first algorithm model is a nonlinear relationship between the maximum heart rate value and the age parameter; determining a warning heart rate value corresponding to the target user according to the maximum heart rate value of the target user; collecting a first heart rate value of the target user in real time, and starting timing if the first heart rate value is greater than or equal to the warning heart rate value and the current timing is not started; monitoring the first heart rate value of the target user collected after the timing is started, and issuing a heart rate warning information if the timing time reaches a first time length and all the first heart rate values monitored in the first time length are greater than or equal to the warning heart rate value. In this way, a more accurate maximum heart rate of the user can be obtained, the estimation accuracy of the maximum heart rate is improved, the accuracy of the heart rate warning is improved, and the health risk is reduced.

The present invention discloses a heart rate determination method and system based on millimeter-wave radar and neural networks, which relates to the field of heart rate detection technology. The method comprises: synchronously collecting raw radar echo data and raw electrocardiogram (ECG) signals; using an initial sliding window to determine a target ECG signal based on the raw ECG signals to generate multiple initial instantaneous heart rates; performing data removal according to high-frequency body movement time to output the target radar echo data and the target instantaneous heart rate; constructing an initial radar complex data matrix corresponding to each target instantaneous heart rate based on the target radar echo data using the initial sliding window; performing data equalization based on the initial radar complex data matrices belonging to multiple preset heart rate intervals; determining a training radar complex data matrix and a true instantaneous heart rate; determining a trained heart rate prediction network based on the training radar complex data matrix and the true instantaneous heart rate; and predicting the measured instantaneous heart rate of the measured radar echo data. The above scheme is beneficial for improving the accuracy of heart rate determination.

The application discloses a kind of heart sound abnormality detection method and system based on earphone, belong to signal processing technical field.The method includes: obtaining the earphone microphone collected ear sound signal;Based on the physical information neural network constructed in advance, the ear sound signal is calculated inversely;Based on the constructed neural network, the mapping of inverse signal and real heart sound label is completed, and the reconstructed heart sound chart is obtained;Based on the reconstructed heart sound chart, abnormality detection identification is carried out;Physical information neural network includes source pulse wave inversion network HemoNet and source heart sound inversion network CardiNet;HemoNet is obtained in ear drum at arterial pulse pressure wave by ear sound signal inversion, and source pulse wave is obtained at heart;CardiNet is inverted into source heart sound signal at heart by heart sound collected in thoracic cavity;MappingNet generates heart sound signal by source pulse wave signal mapping, and obtains reconstructed heart sound chart.Improve the physical consistency and accuracy of reconstructed signal, realize accurate inversion source pulse wave signal and heart sound reconstruction, improve the convenience and accuracy of heart rate monitoring.

The invention relates to a PPG sensing module, electronic equipment, wearable equipment and a physiological information detection method. The PPG sensing module comprises at least three light-emitting assemblies which are used for emitting light signals; the light detection module is used for generating a plurality of detection signals according to the light signals emitted by the light-emitting assemblies; each light sensor and at least two light-emitting assemblies form a first measurement channel, and the light sensor and at least one light-emitting assembly except the first measurement channel form a second measurement channel; the length of the first measuring channel is smaller than that of the second measuring channel. The PPG module can improve the heart rate and blood oxygen detection accuracy at the same time.

The invention discloses a millimeter wave radar heart rate estimation method and device based on Hilbert transform. The method comprises the steps that echo data containing human chest wall infinitesimal displacement information is collected through a millimeter wave radar, the chest wall position is positioned, and phase information is extracted to obtain vital sign signals; the method comprises the following steps of: processing a signal by adopting a sliding time window, solving an instantaneous phase in each window through improved Hilbert transform, periodically calculating a candidate heart rate estimated value through the slope of a regression fitting straight line based on the corresponding relationship between time and the phase in a window sliding process, and carrying out time sequence constraint on the candidate heart rate value, so as to obtain an estimated value of the heart rate. And isolated abnormal values and non-physiological jumps are inhibited. According to the method, improved Hilbert transform is applied, the problem that hopping is prone to occurring at the two ends of a window in a conventional method is solved, and the accuracy and stability of heart rate estimation are improved in combination with a heart rate estimation method based on regression fitting and heart rate time sequence constraint.