1262 results about "Heart beat rate" patented technology

The present invention provides a data processing method for a heart rate monitoring system, and belongs to the technical field of data processing. The present invention effectively solves the problems that heart rate monitoring systems have low accuracy in acquiring raw heart rate data of patients, have difficulty in removing heart rate interference factors, and require precise heart rate value data and analysis results. The present invention is used in a heart rate monitoring system, and the heart rate monitoring system comprises: a power supply apparatus, an information acquisition apparatus, an information conditioning module, a data fusion unit, a data analysis module, an anomaly detection alarm module, a display unit, and a storage unit. The present invention comprises the following steps: S1, information acquisition; S2, information conditioning; S3, data fusion; and S4, data analysis. The present invention can ensure that the heart rate monitoring system can accurately and reliably monitor and interpret heart rate data during data processing, thereby providing more reliable support for clinical practice.

The invention discloses a pressure sensitivity classification evaluation method based on heart rate variability and wearable equipment, and relates to the technical field of biomedical signal processing. According to the method, the collected signals can be processed to form the RR interval sequence and the resultant acceleration, the motion state is judged, the motion state mark is set, the RR interval sequence and the motion state mark are aligned according to the timestamp, the structured data are generated, the pressure sensitivity level of an individual is judged based on the structured data, and the pressure sensitivity level of the individual is calculated. The subjective influence of pressure sensitivity evaluation can be avoided, and the classification precision and adaptability are improved.

The invention relates to a hearing aid intelligent noise reduction and human voice enhancement system based on electroencephalogram signals, and belongs to the field of biomedical engineering and acoustic signal processing. The system comprises an electroencephalogram signal acquisition module, a multi-channel acoustic sensor array, an embedded neural signal processor, an adaptive beam forming module, a dynamic speech enhancement engine and a dual-mode output device, and constructs electroencephalogram-acoustics joint features by extracting an alpha / theta wave power ratio, a P300 component and auditory cortical Gamma phase synchronism. A deep network is driven to separate target voice, a wave beam direction and a frequency response curve are dynamically adjusted based on neural feedback, a closed-loop calibration unit is innovatively adopted, gain is reversely adjusted according to N1-P2 wave amplitude, heart rate variability and eye movement data are fused to optimize decisions, and when the signal-to-noise ratio is-5dB, the voice recognition rate reaches 89%, the auditory fatigue is reduced by 37%, and the decision conflict rate is smaller than 6%. The defects of attention blind area, noise separation failure and physiological adaptation of a traditional hearing aid are overcome. The system is suitable for the fields of hearing impairment rehabilitation, special communication and intelligent cabins.

The invention relates to a video heart rate detection method based on deep learning, and belongs to the technical field of image processing. The method comprises the following steps: preprocessing a to-be-detected video to generate a preprocessed face video sequence; inputting the preprocessed face video sequence into a double-flow collaborative spatial-temporal feature enhancement module group to generate spatial-temporal features; inputting the spatial-temporal feature representation into a multi-scale spatial-temporal convolution module, and extracting spatial-temporal features of different scales in parallel; training the detection model by adopting a time-frequency domain joint constraint composite loss function; and inputting the spatiotemporal features of different scales into a trained detection model to obtain a video heart rate detection result output by the detection model. The invention aims to solve the technical problem that the anti-interference capability and the detection precision are difficult to guarantee in the prior art.

The invention discloses a hand-eye coordination and attention evaluation method based on a mobile phone, and relates to the technical field of man-machine interaction and user behavior evaluation, and the method comprises the following steps: S1, in the process that a user executes a symbol matching test task of a mobile phone terminal, obtaining a heart rate variability sequence and an operation sequence interruption frequency, filtering and segmenting the pupil diameter change and the brain wave rhythm to obtain an original feature set; according to the hand-eye coordination and attention evaluation method based on the mobile phone, the continuity, reliability and objectivity of an evaluation conclusion are improved, the method is suitable for cognitive evaluation, man-machine interaction analysis and related intelligent application scenes, and the scientificity and practical value of hand-eye coordination and attention evaluation based on the mobile phone are improved.

The invention discloses a convolutional neural network information processing method for heart function dynamic monitoring, and belongs to the technical field of medical detection and monitoring. Comprising the following steps that multi-mode electrocardiosignal data and current physiological state parameters of a patient are obtained, and initial configuration of the intelligent heart function monitoring device is obtained; determining the signal quality grade of each signal channel, and generating a dynamic filtering adjustment strategy of the multi-modal signal acquisition module; configuring a feature extraction strategy of a double-branch convolutional neural network module based on the signal features of the target analysis signal segment and the physiological state parameters; performing multi-scale time sequence feature extraction and frequency domain autonomic nerve regulation feature extraction on the target analysis signal segment by using a double-branch convolutional neural network module according to a feature extraction strategy; executing arrhythmia classification, heart rate variability parameter quantification, heart function evaluation grade and graded early warning tasks, and outputting multi-stage intelligent early warning information from normal monitoring, potential risk and abnormal early warning to an emergency state.

The invention discloses a wearable fatigue monitoring and feedback method based on deep learning. The method comprises the steps that a heart rate variability signal, a gamma wave band electroencephalogram signal and body movement posture information of a user are collected in real time; denoising, normalizing and synchronously fusing the acquired multi-mode signals; extracting a fatigue state representation vector in real time by adopting a Mamba linear state space sequence model; estimating a user fatigue index in real time based on a lightweight full-connection neural network decoder and constructing an individual fatigue threshold dynamic model; calculating a phase synchronization index of the electroencephalogram signal in real time; and generating and outputting an individualized 40Hz gamma wave band sensory nerve stimulation feedback signal in real time based on the fatigue index and the phase synchronization index. According to the invention, high-robustness fatigue identification and low-delay feedback adjustment in a complex motion noise environment are realized.

The invention belongs to the technical field of wireless sensing and artificial intelligence, and discloses a high-robustness non-contact accurate electrocardiogram monitoring method based on a millimeter wave radar. Firstly, the distance and angle of a potential target are obtained through distance fast Fourier transform and digital beam forming technologies, and static background removal and thoracic cavity position detection are achieved in combination with mean filtering and a two-dimensional constant false alarm rate algorithm. And then a continuous phase is extracted by using a differential cross multiplication method, a two-step heartbeat-related phase extraction scheme is designed, body micro-motion and breathing interference are removed by adopting B-spline fitting and differential operation respectively, and a stable heartbeat-related phase signal is obtained. A heart rate-guided adaptive wavelet decomposition method is designed to obtain multiband features, and time-frequency joint features are extracted through a double-branch attention mechanism and a gating fusion part. Finally, the time-frequency joint features are input into an electrocardiosignal time domain reconstruction module based on a TransUNet architecture, high-quality reconstruction of electrocardiosignals is achieved, and the method has the advantages of being non-contact, continuous and convenient.

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 health monitoring module and electronic equipment. The health monitoring module comprises a shell, an optical heart rate module, a circuit board and a light transmitting part, the shell is fixed to one side of the circuit board, and the shell and the circuit board jointly define a containing space. The shell comprises a conductive part made of conductive materials, one part of the conductive part is exposed out of the top face of the shell, and the other part of the conductive part is electrically connected with the circuit board. The optical heart rate module is located in the containing space, the optical heart rate module is fixed to the circuit board and electrically connected with the circuit board, the optical heart rate module and the light hole are oppositely arranged, and the optical heart rate module emits and receives light through the light hole and the light transmitting piece. The optical heart rate module can be used for acquiring PPG signals of a user. The conductive part can be used for collecting electric signals of a user. And the ECG signal of the user can be obtained after the electric signal is processed. Devices for measuring ECG signals and PPG signals are integrated in one module, so that the module can realize multiple functions in a small size.

The invention discloses a pet wearable vital sign monitoring method based on a flexible sensor, which comprises the following steps: constructing a flexible multi-mode sensing interface, embedding a sensing layer into a fitting layer and fixing the sensing layer at the neck or chest position of a pet; a low-power thermal pulse is output to detect the contact characteristic, and a stable fitting interface is formed; multi-mode signals are collected and synchronously corrected, and filtering and baseline processing are executed to obtain a processed signal set; multi-modal features are extracted, a physical tensor is constructed, and vital sign parameters are calculated; body temperature, heart rate and respiratory rhythm components are extracted, a rhythm resonance curve is generated, and target configuration parameters are determined; sampling and communication are adjusted according to the target configuration, and an alarm is triggered and uploaded to a remote terminal when abnormity occurs. According to the pet vital sign monitoring system, high-precision, low-power-consumption and long-term stable monitoring of pet vital signs is realized by constructing a flexible multi-mode sensing interface and combining a physical consistency driven time sequence reasoning and rhythm energy scheduling mechanism.

There is described an artificial intelligence wearable ECG skin patch (400) to detect sudden cardiac arrest. The wearable ECG monitoring patch (400) with AI based predictive analytics and remote based cardiac monitoring (615) system that can detect cardiac arrhythmias automatically in real-time and make a diagnosis with AI models trained with acquired data. The wearable skin has a biocompatible polymer patch (400) which captures the electrical signal through a flexible printed electronic technology based conducting ink and a substrate. The microcontroller controls (201), store and transmit the data packets. The IoT connected signal transmission is capable of recording and transferring the data packets through wireless communication. The AI engine is capable of analysing, evaluating, testing and providing the data packets of sudden cardiac arrest through a peak detector algorithm. The ECG skin patch (400) to detect and measure the sudden cardiac arrest with the R-R interval time series to obtain heart rate variability.

The invention relates to the technical field of interactive systems, and particularly discloses a game motor home interactive demonstration control system which comprises a sensing interaction module, an intelligent scene engine, a multi-mode output module, a cooperative control module and a safety monitoring module. The perception interaction module integrates physiological, action and environment sensors, deeply analyzes explicit instructions and implicit demands (such as inferring emotions through a heart rate variability rate) of a user in combination with a Transform model, and generates a structured interaction intention label; according to the physiological, action and environment multi-dimensional data fusion mechanism, the system can accurately capture the user state, an accurate basis is provided for follow-up scene adjustment, and the fitting degree of interaction response is remarkably improved; the intelligent scene engine constructs a user immersion model through deep reinforcement learning, calculates an immersion index based on parameters such as heart rate, micro-expression and action angular velocity, and dynamically triggers a high immersion, balance or guide mode.

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 belongs to the technical field of smart old-age care product behavior debugging, and particularly discloses a smart old-age care product behavior dynamic adjustment method based on interactive data perception. According to the method, a sole mechanical fragment, a lower limb movement fragment and a heart rate fragment are extracted based on gait cycle alignment, gait stability, gait coordination and physiological conformity indexes are calculated, and coupling sensing of user movement behaviors and physiological states in the dynamic walking assisting process is achieved; the gait stability and the physiological conformity are fused to construct a gait physiological synchronism criterion, a hierarchical adaptive control strategy is implemented accordingly, and the limitation that a traditional supporting robot only depends on kinematics feedback control is broken through, so that on the premise that the user safety is guaranteed, response type physical and mental collaborative intelligent assistance is achieved, and the user experience is improved. And the risk prevention and control capability of rehabilitation training is effectively improved.

The invention discloses a self-adaptive music playing intelligent headphone and a system thereof, and relates to the technical field of headphones, the headphone comprises a head frame and earmuffs, the head frame comprises an outer shell, a fixing frame is fixedly mounted in the outer shell, and slideways are fixedly mounted at two ends of the fixing frame; a movable connecting rod is arranged in the slide way, and a limiting groove and a limiting block are arranged at one end of the connecting rod; the earmuff comprises an earmuff shell, a connecting piece used for being connected with the connecting rod is fixedly installed in the earmuff shell, and a touch screen control module is installed on one side of the outer portion of the earmuff shell. According to the system, the mood state of the user is automatically recognized and the emotional state of the user is judged by integrating various sensors (such as a heart rate sensor and a skin electric reaction sensor), so that the accuracy of emotion recognition is improved, subtle emotion changes can be captured, more appropriate music recommendation is provided, and the use effect of equipment is improved.

The invention provides a sleep apnea real-time early warning system based on heart rate variability analysis, belongs to the technical field of computer data processing, and provides a data processing method which is used for dynamically selecting a monitoring terminal, adaptively adjusting an analysis threshold value based on a sleep situation and carrying out time sequence correlation verification on a heart rate and a sound signal. And the sleep apnea event monitoring accuracy and the system operation efficiency are improved. The monitoring terminals are dynamically selected, the predictive switching strategy is adopted, continuity and high quality of heart rate data streams input into the data processing system are ensured, the data acquisition modes caused by body position changes of the user in the sleep period are switched, subsequent analysis errors are avoided, and the user experience is improved. By determining situation information such as sleeping postures of a user in real time and dynamically adjusting an analysis threshold value of heart rate variability, a data processing model can intelligently adjust the sensitivity of the data processing model according to the risk level, and deep time sequence correlation verification is carried out in combination with heart rate and sound signals, so that collaborative analysis of multi-modal information is realized.

The embodiment of the invention discloses a blood glucose index evaluation method, electronic equipment and a storage medium, and the method comprises the steps: obtaining the corresponding heart rate data of a target user in an evaluation time period, carrying out the feature extraction processing of the heart rate data, obtaining the corresponding target heart rate feature of the target user in the evaluation time period, and obtaining the target heart rate feature of the target user in the evaluation time period; inputting the target heart rate feature and a target calibration parameter corresponding to the target user into a blood glucose evaluation model, and predicting blood glucose control index data corresponding to the target user in the evaluation time period through the blood glucose evaluation model. The target heart rate features and the target calibration parameters are used as input of a blood glucose evaluation model, and the blood glucose management and control index data of the target user in the evaluation time period are predicted through the blood glucose evaluation model, so that it is ensured that the obtained heart rate data corresponding to the target user in the evaluation time period are utilized; and the accuracy of the blood glucose management and control index data corresponding to the target user in the evaluation time period is determined, and meanwhile, the cost of blood glucose monitoring is greatly reduced.

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 relates to the technical field of nervous system dysfunction, in particular to a nerve regulation and control system for degenerative disease treatment based on multi-modal physiological feedback, which comprises a central processing unit, an optical radiation applicator, a mechanical vibration applicator, an integrated biological signal sensing array and a man-machine interaction module, cooperative stimulation is applied by using a multispectral light source and a broadband vibrator, and multi-dimensional physiological signals such as heart rate variability, myoelectricity, galvanic skin and the like are monitored in real time through a sensor array. A multi-parameter adaptive control algorithm built in the central processing unit can dynamically and intelligently adjust stimulation parameters based on the feedback signals to form an accurate personalized treatment closed loop. Meanwhile, the safety monitoring module based on the biological thermal model ensures the safety boundary of the treatment process. According to the invention, intelligent, self-adaptive and non-invasive treatment of nerve dysfunction is realized.

A heart rate detection earphone and an earphone device are provided. The heart rate detection earphone includes an earphone housing and an earphone functional component. The earphone housing includes an ear hook part, a connection part, and an earphone part. One end of the connection part is connected to the ear hook part, and the other end of the connection part is connected to the earphone part. The earphone functional component includes an earphone circuit board, an earphone battery module, an earphone speaker module, and a heart rate detection module. The earphone battery module is electrically connected to the earphone circuit board. The earphone speaker module is fixedly provided on the earphone part. The heart rate detection module is provided on the earphone part and electrically connected to the earphone circuit board. The heart rate detection earphone has more diverse functions and meets special needs of users.

The invention discloses a heart sound, electrocardio and echocardiogram multi-modal data phase synchronization method, which belongs to the technical field of medical image processing, and comprises the following steps: A1, obtaining an original echocardiogram video and independently recorded heart sound audio data information; a2, preprocessing the acquired data and performing sample automatic construction; a3, a double-path neural network architecture is constructed, electrocardiogram waveform extraction is carried out, and a digital electrocardiogram signal sequence is obtained; a4, periodic feature points of the heart sound and the electrocardio are recognized based on the digital electrocardio signals; and A5, aligning the asynchronous data based on linear phase resampling. According to the method, the video frames, the electric signals and the heart sound signals under different sampling rates are forcibly mapped into the unified phase coordinate system through a linear interpolation algorithm, absolute synchronization of the video frames, the electric signals and the heart sound signals in cardiac pulsation physiological logic is finally achieved, and the influence of individual heart rate differences on data alignment is eliminated.

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.

When a gamer's sensed heart rate or other biometric indicator indicates that the gamer is excited or otherwise that an interesting part of a computer game is commanding the gamer's attention, a clip of the game is automatically recorded. A heat map of heart rate may be presented to the gamer showing the gamer his reactions as he moves around the game space. Also, a specific heart rate capture mechanism on a game controller is presented.

Various implementations disclosed herein include devices, systems, and methods that embed information in video presented on an outward display of a wearable device. For example, a process may include generating a video signal depicting a current appearance of a face portion. Changes in an attribute of the face portion in the video signal over time may correspond to a current heart rate of a user wearing the wearable electronic device. The process may further include embedding data into the video signal by altering the attribute of the face portion in the video signal over time such that the changes in the attribute of the face portion in the video signal over time correspond to both the current heart rate and the data. The process may further include presenting the video signal depicting the current appearance of the face portion on an outward-facing display of the wearable electronic device.

The invention discloses a near-infrared light controllable irradiator for astroganglion stimulation, which comprises a physiological signal acquisition module used for acquiring a near-infrared light sensing signal of a carotid artery area in real time; the signal processing module is used for performing variational mode decomposition on the near-infrared sensing signal and extracting a heart rate component; the autonomic nerve analysis module is used for receiving the heart rate component and calculating the excitement ratio of sympathetic nerves to parasympathetic nerves; a deep learning model is arranged in the control signal generation module, and an optical parameter control signal is generated according to the heart rate cycle characteristics and the excitement ratio; and the light adjusting module is used for receiving the light parameter control signal so as to control the emission of near-infrared light. According to the invention, the output of the infrared light can be adjusted according to the physiological signal of the human body, intelligent adjustment is realized, the near-infrared light stimulation better fits the individual condition, and the pertinence and effectiveness of the stimulation are effectively improved.

The invention relates to the technical field of biological radar signal processing, in particular to an FMCW radar-based heart beat interval estimation method, which comprises the following steps of: S1, converting a chest vibration echo phase time sequence obtained by irradiating a chest area of a monitored object by an FMCW radar into an acceleration time sequence by using a second-order time derivative; s2, dynamically mapping the center frequency and the wavelet order in a preset frequency analysis interval, constructing a self-adaptive wavelet dictionary, then executing multi-order wavelet time domain convolution operation on the acceleration time sequence by using the dictionary, and aggregating time-frequency energy distribution to generate a time-frequency energy diagram; and S3, performing a deconvolution operation reconstruction strategy based on an energy-guided multi-stage time-frequency feature screening technology and wavelet function conjugation, generating an approximate time domain signal of heart beat vibration, and completing heart beat information inversion. According to the method, the accuracy and robustness of IBI extraction can be effectively improved under the condition of low signal-to-noise ratio, so that stable monitoring of light and moderate HRV (heart rate variability) is supported.

A system and method for distinguishing the type of seizures in a human patient, such as an epileptic seizure (ES), or a functional or dissociative seizure (FDS). The system and method use a diagnostic analytical platform that gets heart rate variability (HRV) analytical metrics from a ECG and uses an analytical diagnostic algorithm to determine if an ES or FDS has occurred in the patient. The diagnostic analytical platform can create a model for distinguishing that a predetermined type of seizure has occurred from the HRV analytical metrics.

The invention discloses a VR emotion quantitative management method and system based on physical and psychological interaction, and relates to the field of psychotherapy, and the method comprises the steps: collecting the heart rate variability data, the electrodermal response data and the EEG alpha wave power data of a user in a resting state, and obtaining the EEG alpha wave power data of the user based on the heart rate variability data and the electrodermal response data; generating color, density and physical boundary parameters of the initial emotion energy field; and rendering a dynamic emotion energy field in the VR environment according to the initial emotion energy field parameters, and detecting the distance change between the user and the dynamic emotion energy field through a field density growth algorithm. According to the method, heart rate variability, electrodermal response and EEG alpha wave data are fused by adopting an S-shaped curve function to generate emotion energy field parameters, accurate mapping of physiological states and virtual environments is established, emotion recognition sensitivity is improved, treatment efficiency is improved, field density attenuation rate is dynamically controlled innovatively through parasympathetic nerve activation degree, and the treatment effect is improved. And the synergistic effect of behavior intervention and nerve regulation is realized.