154 results about "Photoplethysmogram" patented technology

The invention relates to a cardiovascular disease risk prediction system, which comprises a user terminal module, an edge computing module, a cloud intelligent module, an application service module and a system support module, wherein the user terminal module comprises an intelligent sensing equipment cluster unit and a mobile terminal data entry unit; and the intelligent sensing equipment cluster unit adopts a millimeter wave radar-PPG fusion sensor, monitors subcutaneous 0.5 mm depth arterial vibration through a 60GHz radar array, is aligned with a photoplethysmogram signal in a space-time manner, and performs user data acquisition based on a surface plasma resonance chip. According to the cardiovascular disease risk prediction method and system, real-time signal processing is realized by adopting JetsonAGX through a heterogeneous calculation architecture, medical image accelerated calculation is performed by using NVIDIAClara, non-out-of-domain joint modeling of data of hospitals is supported through federal learning, privacy protection reasoning based on homomorphic encryption is realized, and full-process closed-loop management is realized by adopting a layered modular architecture. And the accuracy of cardiovascular disease risk prediction is improved.

The invention relates to the technical field of blood pressure measurement, in particular to a cuff-free blood pressure continuous monitoring method based on ICG, PPG and ECG multi-mode physiological signals. According to the method, deep mining and fusion are carried out based on multi-modal physiological signals such as a chest impedance signal ICG, a photoelectric volume pulse wave signal PPG and an electrocardiosignal ECG of a tested object synchronously collected by multi-modal hemodynamics monitoring equipment, and a richer multi-modal feature matrix related to blood pressure changes is constructed; after deep learning fusion training is carried out on the constructed cuff-free blood pressure prediction model, prediction output of a cuff-free blood pressure prediction result can be directly carried out according to ICG, PPG and ECG multi-mode physiological signals of a to-be-detected tested object by means of the cuff-free blood pressure prediction model, the generalization ability and monitoring precision of continuous monitoring of the cuff-free blood pressure can be effectively improved, and the accuracy of continuous monitoring of the cuff-free blood pressure can be improved. The continuous, non-invasive and high-precision blood pressure monitoring requirements are met, and the clinical application value is more remarkable.

The invention relates to the technical field of vital sign measurement, in particular to a wearable underwater vital sign and environment monitoring system, which comprises a physiological signal acquisition module, a photoelectric volume pulse wave sensor, a bioelectrical impedance electrode, a piezoelectric sensor, a signal processing module, a signal processing module and a signal processing module, a wrist photoelectric volume pulse wave signal, a chest bioelectrical impedance signal and a neck piezoelectric signal are collected. According to the method, signal reconstruction is carried out based on energy proportion calculation, an electrocardiogram R wave peak value point position and an R-R interval time sequence are extracted, variability indexes are generated through frequency domain decomposition and frequency band energy statistics, time domain and frequency domain index values are calculated, weight distribution is carried out, and therefore comprehensive monitoring on the underwater physical sign state is achieved. The signal acquisition precision is improved by means of a multi-source signal acquisition mode, noise interference is eliminated through signal track matrix decomposition operation, and the signal reconstruction quality is ensured through feature vector energy proportion calculation.

A multi-sensor remote patient monitoring platform combines multiple technologies on a patient's own device to dramatically reduce the number of devices a patient needs while also reducing device distribution logistics on health care providers. Continuous temperature monitors can be used to collect frequent temperature data over large periods of time. Additionally, a rPPG device can obtain at least one vital sign data by a remote photoplethysmogram (rPPG) comprising a plethysmogram that is optically obtained via a camera by detection of blood volume changes in the microvascular bed of tissue of the patient. The temperature data and vital sign data can be input into a remote server-side computer system that can analyze the received data for indication of a fever, and provide an alert to the patient or a clinician when the result of the analysis indicates the fever. Such alerts can be clinically beneficial for disease states in which fever can be fatal, particularly for those fevers having fast onset.

A wearable electronic device and method for the same are disclosed. The wearable device includes a memory, housing, photoplethysmogram (PPG) sensor, display and processor. The processor implements the method, including: receiving a PPG signal via the PPG sensor, detecting blood pressure values for a plurality of consecutive time periods based on characteristic information detected from the received PPG signal, in response to detecting termination of an exercise event, generating an exercise parameter determination model based on blood pressure characteristic information generated from the detected blood pressure values after the detected termination, and control the display to display information generated from the exercise parameter determination model.

Methods, systems, and devices for cardiovascular health metric determination are described. A system may be configured to receive a photoplethysmogram (PPG) signal representative of a pulse waveform for a user. The pulse waveform may include a first local maximum, a downward slope following the first local maximum, and a curved feature representative of a transition from a systolic phase to a diastolic phase of a cardiac cycle. Additionally, the system may extract one or more morphological features from the pulse waveform and compare the one or more morphological features with one or more features from a plurality of baseline PPG signal morphologies associated with a plurality of chronological ages. The system may determine a cardiovascular health metric that indicates a cardiovascular health of the user relative to a chronological age of the user and cause a graphical user interface to display an indication of the cardiovascular health metric.

The invention discloses a real-time multi-mode physiological signal analysis method for myocardial ischemia, and belongs to the technical field of data analys.The technical scheme includes that electrocardiogram, photoelectric volume pulse waves and thoracic impedance signals are synchronously acquired, and phase difference caused by motion displacement is compensated in real time by the aid of a triaxial accelerometer; constructing a multi-dimensional quality evaluation matrix containing an electrocardiogram signal-to-noise ratio, a photoelectric volume pulse wave perfusion index and a chest impedance variance, and jointly judging whether signal reconstruction is triggered or not based on a dynamic threshold value and accelerometer data; a multi-channel signal block which is judged to be low in quality through the quality evaluation matrix and comprises the electrocardiogram and photoelectric volume pulse waves and chest impedance synchronized with the electrocardiogram is input into a generative adversarial network with electrophysiological constraints to be reconstructed, and a reconstructed high-quality electrocardiogram signal is output; and myocardial ischemia key indexes are extracted from the reconstructed signals, and multi-level risk early warning is carried out. The real-time multi-modal physiological signal analysis method for myocardial ischemia has the beneficial effect that the real-time multi-modal physiological signal analysis method for myocardial ischemia is provided.

The invention relates to a continuous noninvasive physiological parameter detection method and device, and belongs to the technical field of physiological parameter detection. According to the method, on the basis of photoelectric volume pulse wave detection data under multiple wavelengths, absorbance related characteristic parameters and pulse wave related characteristic parameters are extracted, a hemoglobin concentration detection result is obtained through calculation of a hemoglobin concentration calculation model, and the absorbance related characteristic parameters comprise the adjacent surface difference, the relative difference, the adjacent surface ratio, the relative ratio and the progressive surface ratio. Extracting based on a normalized absorbance fluctuation amplitude curve; the pulse wave characteristic parameters comprise rising branch maximum slope, rising time, falling time, dicrotic wave slope, dicrotic amplitude ratio, rising branch time ratio, low-high frequency energy ratio and harmonic component ratio, and extraction is carried out based on normalized pulse waves. The detection device for implementing the method comprises a probe, a data processing circuit and the like. The accuracy of the noninvasive hemoglobin concentration detection result can be effectively improved.

The invention discloses a diabetes detection system based on multi-sensor pulse characteristics, and relates to the field of medical signal processing, and the diabetes detection system comprises a pulse wave data acquisition module which uses a pressure sensor and a photoelectric sensor to collect wrist pressure pulse waves and fingertip photoelectric volume pulse waves of a user; the preprocessing module is used for obtaining usable single-cycle pulse wave data; the signal fusion module is used for converting the pressure pulse wave signal and the photoelectric volume pulse wave signal into two-dimensional image signals through continuous wavelet transformation so as to reserve time-frequency domain information, and processing two two-dimensional images by using non-subsampled shear wave transformation and a pulse coupling neural network; the feature extraction and calculation module is used for extracting pulse wave image features through a ResNet network and finally performing classification through a random forest; according to the detection system, the fusion effect is improved under the condition that the calculated amount is not increased, the effect of noninvasive detection of diabetes by using multiple sensors is achieved, and the detection accuracy is improved.

The invention discloses a method for reconstructing electrocardiosignals through photoelectric volume pulse waves based on deep learning, and aims at solving the problems that a traditional ECG collection mode is complex, an electrode is uncomfortable to wear, and an existing signal mapping method is low in precision. PPG signals are collected through a portable device and subjected to denoising and normalization processing, a generative adversarial network is constructed, a generator maps the PPG signals into ECG signals, and a discriminator evaluates the authenticity of the generated signals. The generator loss function is combined with the mean square error and the adversarial loss, and the discriminator loss function optimizes the identification degree of the signal; a multi-scale loss function of a time domain and a frequency domain is introduced, the consistency of the generated signals in dynamic characteristics and spectral characteristics is improved, and finally, the generated ECG signals are transmitted back to the portable equipment to be displayed. Real-time signal generation can be achieved on portable equipment such as an intelligent bracelet and a watch, the generated ECG signals have high fidelity, PQRST waveforms can be accurately restored, and the method is suitable for heart rate and heart rhythm variability analysis and abnormal heart rhythm detection.

A computing system, a computer implemented method, and a wearable computing device to detect pulselessness in a wearer of the wearable computing device while minimizing the risk of false positives is provided. A series of passive, semi-active, and / or active algorithms utilizing time domain analysis, gate-based analysis, and / or a machine-learned model can be implemented to analyze motion and photoplethysmogram sensor data to verify if a pulselessness event associated with the wearer has occurred and to alert a third party of such an event.

An electronic fitness device comprises a housing, a first optical transmitter, a second optical transmitter, an optical receiver, and a processor. The housing is positioned partly in contact with a user's skin. The first optical transmitter emits a first optical signal. The second optical transmitter emits a second optical signal. The optical receiver receives the optical signals and generates a first photoplethysmogram (PPG) signal resulting from the received first optical signal and a second PPG signal resulting from the received second optical signal. The processor receives the PPG signals, determines a pressure metric value based on a characteristic of a waveform of the first PPG signal and a characteristic of a waveform of the second PPG signal, and determines a pressure compensation factor based on the determined pressure metric value, the first wavelength corresponding to the first optical signal and the second wavelength corresponding to the second optical signal.

The invention relates to the technical field of biomedical signal processing, in particular to an electrocardiogram reconstruction method and system based on machine learning, and the method comprises the following steps: carrying out iterative initialization; respectively carrying out data preprocessing on the PPG signal and the ECG signal; based on the convolutional neural network and a Transform encoder, constructing a signal conversion model; inputting the preprocessed data into a signal conversion model, carrying out model training, and outputting a predicted electrocardiogram signal; calculating a loss function between the predicted electrocardiogram signal and the target real signal, and optimizing the model parameters based on the loss function; when the number of iterations is smaller than a threshold value, continuously updating model parameters according to a gradient descent method and circularly training, and evaluating model performance and calculating performance indexes in a verification stage; after iteration is completed, the trained network parameters are stored, and reconstructed electrocardiogram signals are output. The electrocardiogram reconstruction method provided by the invention aims to solve the technical problem of accurately reconstructing the electrocardiogram through the photoplethysmogram.

An apparatus for estimating blood pressure may include: a photoplethysmogram (PPG) sensor configured to measure a PPG signal from a user; and a processor configured to: extract a cardiovascular feature from the PPG signal; calculate a first variation in the extracted cardiovascular feature compared to a reference cardiovascular feature measured at a reference time; determine a measurement posture of the PPG signal based on a time interval between two waveform components of the PPG signal; in response to the measurement posture corresponding to the reference posture, estimate the blood pressure based on the first variation; and in response to the measurement posture not corresponding to the reference posture, obtain a second variation by correcting the first variation; and estimate blood pressure based on the second variation.

The invention discloses an immersive cinema dynamic content adaptation control method based on multi-modal fusion, and the method comprises the steps: collecting at least one non-contact physiological signal of a target audience seat area in real time, the physiological signal comprising a respiratory wave signal collected by a millimeter wave radar or a photoplethysmography signal collected by a near-infrared photoelectric sensor; performing time-frequency analysis on the physiological signal, and extracting a physiological wake-up characterization value in a current time window; the physiological wake-up characterization value comprises a heart rate variability change rate and a heart rate deviation value; constructing a dynamic damping adjustment model, and calculating a virtual damping coefficient of the seat movement system at the current moment based on the physiological wake-up characterization value; and the virtual damping coefficient is converted into an execution instruction of a seat motion controller, and damping force output of the motion platform is adjusted in real time. The technical problem that the pre-programmed motion feedback is disjointed with the real-time physiological and psychological state of the audience can be solved, the experience adaptability and comfort of the cinema seat are improved, and the system safety is improved.

The invention discloses a pressure monitoring method and device based on wearable equipment and electronic equipment. The method comprises the following steps: acquiring current photoelectric plethysmography (PPG) signals corresponding to a plurality of channels collected by wearable equipment; based on the current PPG signals corresponding to the multiple channels, candidate pulse wave sequences corresponding to the multiple channels are determined, and the candidate pulse wave sequences comprise multiple pulse waves of the corresponding channels; determining a target pulse wave sequence from the candidate pulse wave sequences corresponding to the multiple channels based on the pulse wave continuity evaluation results and the pulse wave quality evaluation results corresponding to the multiple channels; performing feature extraction on the target pulse wave sequence to obtain current heart rate variability features; and determining a current pressure monitoring result according to the current heart rate variability characteristic. According to the invention, the technical problem of low pressure monitoring accuracy and reliability of wearable equipment in a dynamic environment in the prior art is solved.

The invention discloses a remote photoplethysmography (rPPG) signal extraction method based on a 3D convolutional neural network (3D CNN), and belongs to the technical field of image processing and physiological signal detection. In order to solve the problem that space-time information utilization is insufficient in a complex scene (illumination change and motion interference) in a traditional method, space-time feature fusion is carried out through 3D CNN, and feature expression is enhanced in combination with an encoder-decoder structure and an attention mechanism. The method comprises the following steps: extracting continuous time difference features of a face video, generating space-time fusion features through a 3D CNN, enhancing the space-time fusion features through a codec, and outputting rPPG signals. A self-supervised training method is innovatively proposed, and the generalization ability of the model is improved by adopting data enhancement, time period alignment and positive and negative sample comparison. Experiments show that according to the method, the heart rate estimation error is reduced to be within 1 BPM, the signal-to-noise ratio is increased to 1.8 dB, and the motion robustness is superior to that of a traditional algorithm. The method is suitable for non-contact heart rate monitoring, respiratory rate detection and other scenes, has the characteristics of high precision and strong anti-interference, and provides a reliable technical scheme for intelligent health monitoring.

The invention discloses a space behavior recognition access control system based on UWB and PPG fusion, and mainly relates to the technical field of intelligent security and protection. Comprising a UWB positioning unit used for acquiring spatial position data of a user in an access control area through an ultra wide band technology; the PPG acquisition unit is used for acquiring physiological signals of a user through a photoelectric volume pulse wave technology; the data fusion unit is used for performing multi-source data fusion on the spatial position data and the physiological signal to generate a fusion feature vector; the behavior recognition unit is used for recognizing a behavior mode of the user through a mode recognition algorithm based on the fused feature vector; and the access control unit is used for outputting a control instruction according to the identified behavior mode so as to operate the opening, closing or alarming of access control equipment. The access control system has the beneficial effects that non-contact accurate identification of spatial behaviors is realized through multi-source data fusion, and the identification accuracy, the anti-interference capability and the user experience of the access control system are effectively improved.

An electronic device comprises a housing configured to contact a user's skin, an optical transmitter array, an optical receiver, a memory element and a processing element. The optical transmitter array outputs a plurality of optical signals that pass through the user's skin, each of the plurality of optical signals having one of three wavelengths. The optical receiver receives the optical signals and generates corresponding photoplethysmogram (PPG) signals. The memory element stores absorption constants for deoxyhemoglobin blood content, oxyhemoglobin blood content and glycated hemoglobin blood content, each absorption constant associated with one of the first wavelength, the second wavelength or the third wavelength. The processing element determines a glycated hemoglobin blood level for the user based on a ratio of a determined glycated hemoglobin blood content to a sum of determined deoxyhemoglobin blood content, a determined oxyhemoglobin blood content and a determined glycated hemoglobin blood content.

The present invention relates a multimodal emotion recognition system with edge AI accelerator. The system employs a Fuzzy Long-Short Recurrent Convolutional Network (Fuzzy LRCN) for multiple emotion classifications. The algorithm strategically utilizes only eight channels of electroencephalogram (EEG) signals and combines electrocardiogram (ECG) and photoplethysmogram (PPG) through a fuzzification engine to improve emotion recognition accuracy with minimal hardware resource consumption. The goal is to optimize hardware resource utilization, reduce system power consumption, and maximize emotion recognition accuracy. The fuzzy algorithm provides a lightweight multimodal AI model to address the issue of increased computational load caused by recognizing emotions from multiple signals.

The invention discloses a method for classifying various arrhythmia signals, belongs to the technical field of arrhythmia signal classification methods, and the accurate classification of arrhythmia has important significance for disease diagnosis and clinical treatment. However, the traditional detection method is often interfered by various factors such as signal non-stationarity, feature non-specificity, artifacts and noise caused by arrhythmia when processing various arrhythmia, and the classification precision and efficiency are difficult to meet the requirements. Therefore, the invention provides an improved power normalized cepstrum coefficient (SPNCC) method based on singular spectrum analysis (SSA), which is used for extracting key features from a photoplethysmogram (PPG) signal and reducing the influence of noise, and constructing a deep learning classification model in combination with a convolutional neural network and a bidirectional long short-term memory network (CNN-BiLSTM). Therefore, accurate identification of various arrhythmias can be realized.

Methods, systems, and devices for optical signal measurement are described. A wearable electronic device may activate a first combination of optical sensors, the first combination of optical sensors including a set of transmitter sensors and a set of receiver sensors. In some cases, one or more optical sensor of the first combination of optical sensors may be positioned under a protrusion on an inner surface of the wearable electronic device. The device may measure, at the set of receiver sensors at a first time, one or more signals from the set of transmitter sensors, determine a signal quality metric associated with the one or more signals, and select a second combination of optical sensors for use at a second time based on the signal quality metric.

The invention provides an equipment control method and device, and electronic equipment, and is applied to terminal equipment, and the method comprises the steps: transmitting a first light ray according to a target current; collecting second light rays reflected by the first light rays through the outside; evaluating the signal quality of a photoplethysmography (PPG) signal corresponding to the second light; if the currently evaluated signal quality is less than or equal to a preset threshold value, adjusting the target current; re-executing the signal evaluation operation; and if the currently evaluated signal quality is less than or equal to the preset threshold value and the adjusted target current is equal to the preset maximum current value, adjusting a signal acquisition parameter so as to execute the light acquisition operation according to the adjusted signal acquisition parameter. According to the method provided by the embodiment of the invention, the precision of the PPG signal acquired by the terminal equipment can be improved.

The invention relates to a hub configured to wirelessly connect to a plurality of wearable devices of one or more users, each wearable device being attachable to a part of a body for collecting health-related data, wherein the hub comprises a photoplethysmogram, PPG, sensor, and wherein an optical window of the PPG sensor is arranged on the hub, wherein a user-identification sensor is arranged at the PPG sensor, the user-identification sensor being configured to authenticate a user identity.

Various improved methods and systems for detecting blood pressure characteristics are described herein, including algorithms, structures, architectures, and implementation details thereof. In some cases, photoplethysmography (PPG) data may be obtained, for example in a continuous or real time manner. A temporal period of PPG data can be provided to a multi-stage machine learning model having a unique architecture as described herein. A segmentation or masking stage generates multiple modified PPG data partitions, based on attributes of the PPG data. A feature network stage generates feature data corresponding to points in time during the temporal period in such partitions. A decision stage assesses whether a blood pressure characteristic is present.

The present invention relates to the technical field of medical monitoring, and particularly to a wrist signal acquisition and processing method and system based on biological tissue analysis. The system is used for: designing a wrist-worn device integrated with a photoplethysmogram sensor, an electrocardiogram sensor, and a galvanic skin response sensor, and optimizing the position and structure design to generate an optimized device for wrist multi-signal acquisition; using the optimized device for wrist multi-signal acquisition to collect PPG signals, ECG signals, and GSR signals corresponding to wrist biological tissues in real time, and performing high-frequency drift preprocessing and biological tissue feature analysis to obtain a wrist biological tissue signal feature set; performing feature redundancy and dimensionality reduction processing on the wrist biological tissue signal feature set, and performing wrist physiological state evaluation and analysis to obtain a wrist biological tissue state evaluation result. The present invention can achieve accurate acquisition and analysis of wrist signals, thereby providing reliable data support for personalized analysis.

A wearable device for determining a photoplethysmogram, the wearable device comprising at least a first light source arranged to provide light to at least one entry point on the body of a proband when the wearable device is used by the proband. The wearable device further comprises at least a first light detector arranged to detect light received from the light source through the body of the proband. The wearable device further comprises a control device arranged to calculate a photoplethysmogram based on the detected light. The wearable device further comprises at least a first detector optical waveguide and a second detector optical waveguide arranged to detect light at at least two different positions on the body of the proband and feed the light to the at least first light detector when the wearable device is used by the proband.

The invention belongs to an evaluation signal generation method, and aims to solve the technical problems that non-contact detection or non-invasive detection including an existing video photoelectric volume pulse wave detection technology lacks an effective signal evaluation method and evaluation device, and direct and accurate measurement is difficult to perform by depending on known signals. The invention provides a signal generation method for video photoelectric volume pulse wave detection and evaluation and a related device, which are used for respectively generating known ambient light signals, skin color signals and blood flow signals which are very important for video photoelectric volume pulse wave detection. Photoelectric volume pulse wave characteristic wavelength information is generated from the generated blood flow signals according to photoelectric volume pulse wave signal characteristics; then generating a visible light signal which can be displayed by a color LED display device according to a colored light superposition principle by visible light parts in the obtained ambient light signal, the skin color signal and the blood flow signal; and the characteristic wavelength signal and the visible light display signal are displayed in a time-sharing manner.

The utility model relates to full-time blood pressure detection equipment which comprises photoelectric volume pulse wave equipment and pressure pulse wave equipment, the photoelectric volume pulse wave equipment is used for collecting the blood pressure measurement value of the wrist or the finger of a user, and the pressure pulse wave equipment is used for collecting the blood pressure measurement value of the brachial artery of the user. The photoelectric volume pulse wave device is connected with the pressure pulse wave device through a magnet or a buckle. The pressure pulse wave device is a cuff sphygmomanometer, and the photoelectric volume pulse wave device is one of a ring, a bracelet and a watch. The photoelectric volume pulse wave equipment and the pressure pulse wave equipment are combined together in a magnetic attraction or buckling mode, so that a user can conveniently select any one of the photoelectric volume pulse wave equipment and the pressure pulse wave equipment to measure and implement blood pressure at any time, and the problem that the photoelectric volume pulse wave equipment is lost due to the small size is solved.

A photoplethysmogram (PPG) sensor includes: a pixel array that collects light; a pixel sampler that converts the light collected by the pixel array into a plurality of pixel data; a valid area determiner that determines a valid area and an invalid area of the pixel array based on the pixel data; a power controller that is operable to cut off power supply to the invalid area of the pixel array; and a PPG data generator that generates PPG data according to the pixel data corresponding to the valid area in the pixel data.