430 results about "Blood pressure level" patented technology

A computer-implemented method for operating an artificial intelligence voice agent system includes receiving voice input through communication channels; analyzing converted text through natural language processing (NLP) pipelines implementing intent recognition and sentiment analysis detecting emotional cues using a multimodal large language model (LLM); generating response content using machine learning models trained on domain-specific corpora; converting generated responses to synthetic speech through text-to-speech (TTS) engines; integrating with a customer relationship management (CRM) platforms or an enterprise resource planning (ERP) database; and implementing continuous learning by updating language understanding models using conversation logs, voice recognition parameters based on user feedback, and response generation patterns. One implementation is a computer-implemented system and method that operates a suite of intelligent interactive devices and platforms including an artificial intelligence voice agent, enhanced communication platforms, an intimacy companion system, and augmented / virtual reality eyeglasses. Further, one implementation includes AR / VR eyeglasses that project visual content onto interchangeable lenses or directly onto the user's retina via laser-based retinal projection, provide prescription adjustments, incorporate ear-mounted sensors for monitoring physiological parameters like heart rate, oxygen saturation, and blood pressure, and utilize wireless data transmission, onboard environmental sensing, and remote calibration, all designed to offer dynamically adaptive, secure, and context-aware interactions across communication, personal assistance, health monitoring, and immersive augmented or virtual reality environments.

The invention relates to the technical field of health early warning, in particular to a human health early warning method and system based on physical examination data analysis, which comprises the following steps: collecting physical examination data of a human body, analyzing index changes of blood pressure, blood sugar, cholesterol and body mass index of the human body, sorting the physical examination data of the human body through time sequence sorting, and generating a time sequence sorting result. According to the method, time sequence sorting and spatial dimension evaluation are performed on the physical examination data, so that the health risk can be accurately identified and the early warning level can be dynamically evaluated, the data analysis precision is improved, the risk prediction dimension is increased, early warning is more timely and accurate, and the accuracy of early warning is improved by performing layout evaluation on the physical examination data points in a finite dimension space. Potential health abnormalities can be identified on multiple levels, and multi-dimensional analysis provides a new way for identifying complex health problems, so that health management is more active and predictable, health monitoring and risk management are optimized, and the health condition of a patient is improved.

The invention relates to the technical field of medical early warning, and discloses a graded early warning system based on multi-parameter vital sign detection. According to the system, real-time physiological parameters such as the heart rate, the blood pressure, the oxyhemoglobin saturation and the body temperature of a patient are collected through vital sign monitoring equipment; and inputting the parameters into a feature extraction network, generating a multi-dimensional physiological feature vector, and constructing a dynamic risk assessment matrix containing physiological state change trends of different time windows according to the multi-dimensional physiological feature vector. Dividing risk grade intervals according to a preset grading early warning threshold value, adjusting the intervals by adopting a self-adaptive weight distribution strategy, and generating a comprehensive risk score; and when the score exceeds the preset early warning trigger line, activating a corresponding early warning response mechanism. The system can realize comprehensive dynamic assessment of the physiological status of the patient, is suitable for emergency treatment, intensive care and chronic disease nursing scenes, and meets the clinical health risk monitoring and early warning requirements.

The invention relates to the technical field of intelligent data management, in particular to a multi-source data fusion type intelligent data management system based on edge computing. The system comprises a multi-modal data acquisition unit which acquires multi-source heterogeneous data in real time; the edge preprocessing and enhancement unit integrates a long short-term memory network and a cross attention mechanism, and the lightweight anomaly detection unit deploys a structured pruning and quantized double-branch hybrid model at edge nodes, and performs real-time classification and anomaly scoring on multi-modal fusion feature tensors; and the grading early warning unit is used for receiving the fused comprehensive abnormal score, mapping the comprehensive abnormal score into a discrete alarm grade, and generating a standardized message body containing the grade, a timestamp and details. According to the design, through local tolerance self-adaption and LSH Hash accelerated matching, the coupling complexity of the electrocardio-blood pressure-blood glucose multiple signals is quantified, and the problem that the complex coupling relation among multiple physiological parameters is difficult to capture through a single signal is solved.

The invention discloses a disaster emergency rescue nursing skill training system based on virtual reality, and relates to the technical field of virtual reality. A high-simulation virtual disaster scene is constructed based on real data, diversified injury condition virtual objects matched with the scene are generated, a secondary disaster risk diffusion effect and emergency prompt information are overlaid in real time, physiological state changes such as blood pressure and respiration generated by nursing operation differences in the rescue process of wounded personnel are dynamically simulated, and the rescue effect of the wounded personnel is improved. The problems of single scene and fixed injury condition in traditional training are solved, real-time and multi-modal feedback of student operation is realized through accurate comparison and hierarchical identification of multi-dimensional operation characteristics, the defects of guidance lagging and general are overcome, a personalized and adaptive training scheme is generated according to a comprehensive evaluation result, the progress is dynamically adjusted, and the training efficiency is improved. And scene simulation, operation practice, accurate feedback and personalized improvement are integrated.

The invention relates to the technical field of sleep monitoring, in particular to a sleep monitoring method and system based on combination of electroencephalogram and millimeter wave radar, and aims to solve the problem that OSA judgment accuracy is insufficient due to complication. The method comprises the following steps: acquiring an electroencephalogram signal of a user based on a wireless EEG sensor, and acquiring a physiological signal of the user by using a millimeter wave radar; performing sleep staging analysis on the electroencephalogram signal, and detecting a micro-awakening event in combination with body movement data; the physiological signals are processed through a physiological signal processing model, physiological signal features of the user are extracted, and an apnea event, a hypopnea event, an arrhythmia event, a blood pressure fluctuation abnormal event and a periodic leg movement event of the user are monitored; calculating each OSA judgment index according to the event; and calculating OSA personalized scores according to the OSA judgment indexes, performing OSA severity grading, and generating an OSA severity grading report for assisting medical staff in diagnosis and treatment scheme formulation.

A method for determining physiological characteristics is provided. The method involves causing light to be emitted by one or more light emitters toward a tissue of a user. The method involves obtaining samples of transmitted and / or reflected light. The method involves determining photoplethysmography (PPG) data based on the obtained samples. The method involves providing the PPG data as an input to a trained machine learning model, wherein the trained machine learning model has been trained, using a training set that comprises matched samples of electrocardiogram (ECG) data and PPG data. The method involves providing a blood pressure of the user based on an output of the trained machine learning model.

The invention relates to the technical field of data processing, and provides an AI-based healthy diet nutrition management method and system, and the method comprises the steps: collecting basic health data of a user, carrying out the analysis, obtaining the basal metabolic rate of the user and the daily required calorie, carrying out the matching, obtaining a recommended diet scheme, carrying out the health evaluation of the basal metabolic rate, and carrying out the management of the basic metabolic rate. The method comprises the steps of obtaining a nutrition matching suggestion, matching the nutrition matching suggestion with daily required heat, obtaining a daily diet suggestion, screening recommended diet schemes, obtaining an initial diet suggestion, and adjusting the initial diet suggestion in real time based on the blood glucose level, the blood pressure level and the heart rate level of a user to obtain a target diet suggestion. By collecting the basic health data of the user, generating the diet scheme and monitoring the physical condition of the user in real time, daily diet suggestions are adjusted, and the problems that a traditional method depends on consciousness and accurate recording of the user, and data accuracy and real-time performance are insufficient are solved.

The invention discloses a six-minute walking test system and method, and the system comprises a motion collection device and a mobile device, and the motion collection device is connected with the mobile device through Bluetooth. The motion acquisition equipment comprises an electrocardio recorder, an oxyhemoglobin saturation instrument and a blood pressure instrument, and the electrocardio recorder is integrated with a motion sensor; the mobile equipment comprises a processor and a display screen serving as a display and input device, the display screen comprises a plurality of display areas, and the processor is used for executing the following operations: acquiring test data acquired by the motion acquisition equipment; according to the motion parameters, calculating motion data of the testee participating in the six-minute walking test; the test data and the motion data are displayed in different display areas in the form of charts or data. According to the embodiment of the invention, the motion sensor is integrated in the electrocardiogram recorder, so that the complexity of wearing equipment of the six-minute walking test system is simplified, and a test result can be directly displayed on mobile equipment.

The invention relates to the technical field of digital health, in particular to an intelligent health data early warning method and system.The method comprises the steps that multi-channel signals such as electrocardio signals, pulse oximeter signals and continuous blood pressure signals which are synchronously filtered are injected into an erbium-doped annular cavity through light modulation to generate delay chaos, and sparse binary vectors are obtained through frequency domain hash coding; the vector predicts a future cardiovascular state through a spiking neural network and a reversible ordinary differential equation network; inputting the continuous prediction frame into a reversible tensor flow network, and obtaining a risk vector through anti-fact disturbance; splicing risks, states, codes and contexts to calculate risk energy, and outputting personalized intervention actions by means of a diffusion generation model; and synchronously updating model parameters and coupling coefficients according to the rewards. According to the invention, millisecond-level prospective early warning, low-power-consumption operation and interpretable intervention are realized, and the method is suitable for long-term wearable health monitoring.

The invention provides a cardiovascular and cerebrovascular disease risk prediction method and system, and relates to the technical field of data processing.The method comprises the steps that real-time physiological parameters of a target user are obtained, and the real-time physiological parameters comprise the ambulatory blood pressure fluctuation rate, the serum lipoprotein level, the heart rate variability frequency domain index and the sleep apnea hypopnea index; the method comprises the following steps: preprocessing real-time physiological parameters, extracting instantaneous waveform features of blood pressure signals by adopting wavelet transform, and extracting features related to health conditions, including blood pressure level, blood fat level, blood sugar level, electrocardiogram abnormal indexes and cardiac ultrasound abnormal indexes, to form a feature vector set; according to the method, the real-time physiological parameters are acquired, the feature vector set is formed through preprocessing, the krill swarm algorithm is used for optimization, the three-level fusion model is constructed, finally, the cardiovascular and cerebrovascular disease risk degree of the target user is accurately evaluated, early warning information is output, and the accuracy and timeliness of cardiovascular and cerebrovascular disease risk prediction are effectively improved.

A device may obtain a heartbeat profile of a current subject, the heartbeat profile being based on photoplethysmography (PPG) data associated with a set of wavelength channels. The device may determine a global model including a plurality of classes, each associated with a subject identifier and a blood pressure level (BPL) identifier. The device may identify a class as a closest class to the current subject based on the heartbeat profile of the current subject. The device may identify a local subject associated with the closest class based on the subject identifier associated with the closest class. The device may generate a BPL estimation model for the current subject based on heartbeat profile data for the local subject. The device may determine a BPL estimation for the current subject based on the heartbeat profile of the current subject and using the BPL estimation model.

The invention belongs to the field of biomedical engineering, and particularly relates to a thermal imaging and visible light fused non-contact wild animal health monitoring method, which comprises the following steps of: processing a thermal imaging video frame by frame to identify a blood vessel dense region; mapping pixel coordinates of the blood vessel security level area to a corresponding position of the visible light image and taking the pixel coordinates as an ROI area; in the ROI area, calculating arithmetic mean values of R, G and B channels of all pixel points respectively, repeating the process at different time points t to obtain three light intensity curves R (t), G (t) and B (t) changing along with time, fusing the three light intensity curves to obtain a light intensity change curve I (t), performing heart rate calculation and blood pressure calculation according to the light intensity change curve I (t), and performing blood oxygen calculation by using R (t) and B (t); and monitoring the health state of the wild animal according to a calculation result. According to the invention, animal health non-damage monitoring is realized.

The invention discloses a wireless positioning blood pressure transducer real-time monitoring and analyzing method based on the Internet of Things. The method comprises the following steps that S1, invasive pressure measuring signals are collected, wirelessly transmitted and preprocessed; s2, a Transform invasive pressure measurement signal analysis model is constructed; s3, performing local adaptive optimization on the hyper-parameters by adopting a bat algorithm; s4, performing global optimization on the bat algorithm hyper-parameters by using a sparrow search algorithm; s5, performing multi-sensor positioning data acquisition and Kalman filtering fusion to obtain real-time positioning; s6, the blood pressure anomaly detection result and the positioning data are fused, and a combined health risk assessment report is formed; and S7, the remote early warning module transmits the joint evaluation report and the real-time positioning information. The system has the advantages that real-time monitoring and accurate positioning of the blood pressure transducer are achieved by integrating the Internet of Things, multi-sensor positioning and intelligent data analysis, and efficient and accurate data support is provided for remote medical treatment and intelligent health management.

The invention discloses a thermal environment and air quality self-adjusting intelligent emergency rescue cabin and method, and relates to the technical field of emergency rescue. The core of the method is that firstly, heat loss characteristics of the rescue cabin are learned through a simulated environment, and a standard loss rate is established; then, a target temperature interval is set, and based on comparison between the temperature loss rate monitored in real time and a standard value, hierarchical temperature control modes such as dormancy, low-power-consumption maintenance or dynamic balance are dynamically selected. Furthermore, by integrating vital sign monitoring, the target temperature can be dynamically adjusted according to parameters such as the core body temperature, blood oxygen and blood pressure of a patient, medical intervention modes such as active cooling and shock rewarming are started, and safe rewarming is achieved in the shock rewarming mode in the mode that a core area is heated through directional radiation. The system is also internally provided with a multi-rule priority arbitration mechanism. The cabin body comprises a sensor, an air conditioner, vital sign monitoring equipment and a directional heating plate. Unification of energy conservation, temperature stabilization and precise medical intervention is achieved, and the intelligent level and the safety guarantee capacity of emergency rescue are remarkably improved.

1. The name of the product of this design: Watch Blood Pressure Monitor (U16). 2. Purpose of this product: This product is designed to be worn on the wrist to monitor blood pressure. 3. The key point of the design of this product lies in its shape. 4. The picture or photo that best illustrates the design points: Stereoscopic drawing 1.

The invention discloses a hypertension nursing system based on a remote AI monitoring technology, and particularly relates to the technical field of telemedicine, the hypertension nursing system comprises a data acquisition module, a data fusion module, a risk assessment module, a dynamic intervention module and a cloud management module, and physiological data, behavior data and environmental data are acquired through heterogeneous sensors; the method comprises the following steps: preprocessing the data, transmitting the preprocessed data to a data fusion module and a cloud management module, carrying out alignment and feature extraction on heterogeneous data from different sources to obtain comprehensive risk assessment features, carrying out blood pressure fluctuation risk prediction through an LSTM model, aggregating multi-center cases through cloud federated learning, and generating personalized long-term risk scores. Performing risk level judgment according to the long-term health risk score of the patient, outputting a customized nursing scheme in combination with historical data of the patient, performing privacy protection and storage on the collected data, supporting consultation between the patient and a doctor, automatically generating a dialogue abstract, and updating the dialogue abstract to an electronic health file.

The present invention relates to methods of inhibiting the expression of an AGT gene in a subject, as well as methods for treating subjects having an AGT-associated disorder, e.g., hypertension, using RNAi agents, e.g., double stranded RNAi agents, targeting the AGT gene. The invention also relates to methods of decreasing blood pressure levels in a subject using such RNAi agents to inhibit expression of an AGT gene.

Described herein are sensors for measuring various parameters. The sensors may be used in systems, devices, and methods for controlling blood flow or for measuring (e.g., monitoring) blood pressure, pH, or analyte levels such as a lactate level, an oxygen level, and / or a carbon dioxide level. The sensors may include a membrane configured to generate a measurable response (e.g., a change in an electrical signal such as resistance, capacitance, current, voltage) when the membrane interacts with a parameter of interest. In some instances, the sensors may include a piezoresistive MEMS subassembly. In other instances, the sensors may include an electrochemical membrane.

The invention relates to a method for monitoring cerebral apoplexy, a device for monitoring cerebral apoplexy and a storage medium, and the method comprises the steps: collecting physiological parameters such as electroencephalogram (EEG), oxyhemoglobin saturation (SpO2), electrocardiogram (ECG) and blood pressure through wearable equipment or a family health monitoring system, and carrying out data preprocessing, feature extraction and anomaly detection; the EEG signals are processed by utilizing frequency domain characteristics and short-time energy analysis, and the cerebral apoplexy risk is identified by adopting a support vector machine (SVM) classification model. The system can analyze and detect abnormal conditions in real time, and sends out early warning information through a mobile phone App, a short message or a telephone to remind a user to see a doctor in time. The system comprises wearable equipment, a family health system, a hospital platform and a mobile health application, and is suitable for personal health monitoring and centralized monitoring of medical institutions. The real-time performance and accuracy of cerebral apoplexy monitoring are remarkably improved, the user experience is enhanced, the medical response speed is increased, and the method has wide application prospects and practical value.

A system for real-time prediction of heart disease and to support clinical decisions; the system includes: a patient data acquisition module configured to receive multimodal inputs, including physiological signals selected from electrocardiographic waveforms, blood pressure readings, heart rate readings, and oxygen saturation readings, as well as demographic and lifestyle information, including age, gender, cholesterol levels, smoking habits, and family medical history; a preprocessing engine configured to perform data cleansing, imputation of missing values, categorical coding, and feature scaling, so that the input data is normalized and converted into a format suitable for machine learning; a processing core comprising a multi-core CPU / GPU system-on-chip operationally coupled with a secure storage unit, wherein the processing core is configured to execute a variety of pre-trained machine learning models, including Naive Bayes, Random Forest, Logistic Regression and Decision Tree classifiers; a model evaluation unit configured to calculate validation metrics such as precision, recall, F1 score and area under the curve for each of the models and dynamically select the model with optimal performance to generate real-time predictions for the risk of heart disease; a display interface configured to present predictive results in the form of risk probability values, confidence indices, and actionable recommendations that are mapped to clinical treatment guidelines; and a communication interface configured to transmit emergency alerts based on high-risk predictions to remote caregivers, hospitals, and emergency response systems via wireless communication protocols such as WLAN, Bluetooth, and 4G / 5G cellular connections.

A sensor, for measuring an electrical signal, includes a compressible body portion defining a longitudinal axis and having a foldable wall extending from a first end of the body portion to a second end of the body portion. The body portion is movable from a decompressed state to a compressed state by moving the first end along the longitudinal axis relative to the second end. The wall includes fold lines formed therein such that, during movement of the body portion from the decompressed state to the compressed state, the wall is folded along the fold lines. The sensor further includes an electrode portion connected to the body portion and comprising an electrode for measuring the electrical signal.

The invention relates to the field of millimeter wave therapeutic apparatuses, in particular to a millimeter wave therapeutic apparatus and an adjusting method thereof. The heart rate, the blood pressure and the skin temperature of a patient in the treatment process are subjected to real-time monitoring and data time sequence analysis by adopting an artificial intelligence technology based on deep learning, time sequence change rules of various physiological parameters are mined respectively, and multi-modal data time sequence characteristic principal component fusion analysis is carried out on the multiple physiological parameters. The comprehensive description of the physiological state of the patient is obtained, and then self-adaptive adjustment of the running power of the millimeter wave therapeutic apparatus main body is carried out, so that real-time monitoring and personalized treatment of the physiological state of the patient can be realized, the performance of the millimeter wave therapeutic apparatus is effectively improved, and the treatment effect and the intelligent level of the equipment are improved.

The invention discloses an intelligent robot system integrating health monitoring and cleaning functions for old people, and belongs to the technical field of intelligent old people nursing auxiliary equipment. According to the system, through cooperative work of the health monitoring module, the cleaning module, the interaction module, the control module and the communication module, health monitoring, intelligent cleaning and convenient interaction of old people in a family scene are achieved. The health monitoring module integrates the functions of blood pressure detection, medicine taking reminding and fall detection, and supports multi-dimensional health data acquisition and cloud transmission; the cleaning module realizes an efficient cleaning task through dual-mode control and intelligent path planning; the interaction module adopts a voice recognition mode and a touch control operation mode and adapts to the use habits of the old. And the control module dynamically coordinates health monitoring and cleaning functions through a multi-modal data fusion algorithm and a priority scheduling strategy, and preferentially processes emergency health requirements. The communication module supports dual-network encryption transmission, and collaborative management of local equipment and a cloud is realized. The system breaks through the bottleneck of function isolation of traditional equipment, improves the life safety and convenience of old people through an intelligent algorithm and an aging-suitable design, and has the advantages of high integration level, high adaptability and remote management capability.

The invention relates to the field of intelligent control of Bluetooth earphones, and discloses an intelligent program control method and system for an intelligent Bluetooth earphone, and the method comprises the steps: collecting voice, intonation and physiological signals of a user in real time, and carrying out the dislocation marking of a data stream according to the time, the signal type and the collection source; dynamically matching a psychological state confidence score matrix with a music database and a voice dredging module in combination with voice emotion characteristics, heart rate variability, blood pressure fluctuation and historical psychological state modes and in combination with characteristic clustering density mutation, emotion entropy drift inflection points and multimode signal coupling anomaly analysis, and constructing an emotion-intervention coupling map; and dynamically reconstructing the sensing weight, the recommendation sequence and the playing strategy, and generating an intelligent control instruction set of the Bluetooth headset. The method has the advantage of improving the emotion compactness between the Bluetooth earphone equipment and the user.

Described herein are sensors for measuring various parameters. The sensors may be used in systems, devices, and methods for controlling blood flow or for measuring (e.g., monitoring) blood pressure, pH, or analyte levels such as a lactate level, an oxygen level, and / or a carbon dioxide level. The sensors may include a membrane configured to generate a measurable response (e.g., a change in an electrical signal such as resistance, capacitance, current, voltage) when the membrane interacts with a parameter of interest. In some instances, the sensors may include a piezoresistive MEMS subassembly. In other instances, the sensors may include an electrochemical membrane.

The invention belongs to the technical field of biological medicine, and particularly relates to a preparation method and application of plant polypeptide. The schisandra chinensis active polypeptide is prepared by carrying out enzymolysis reaction on schisandra chinensis protein. The schisandra chinensis active polypeptide can effectively inhibit the activity of angiotensin converting enzyme (ACE), so that the schisandra chinensis active polypeptide can be used as an ACE inhibitor to reduce blood pressure. The plant extract disclosed by the invention not only has the functions of high-efficiency antihypertensive effect, no toxicity, no side effect and the like, but also has the advantages of simplicity in preparation, easiness in absorption and the like, and avoids adverse effects of side effects of traditional chemical medicines on human bodies.

The utility model relates to an implantable wireless blood pressure monitoring device, which belongs to the technical field of physiological parameter measuring instruments, and comprises an upper shell, a lower shell, an adjusting plate, a circuit board, a sealing element and a sensor, the upper shell and the lower shell are fixedly connected to form a whole shell, the circuit board is arranged in the whole shell, a circuit element is arranged on the circuit board, and the sensor is arranged in the whole shell. The adjusting plate is located between the upper shell and the circuit board, the sensor is located outside the whole shell and is electrically connected with the whole shell through a first end of the transmission line, a limiting groove is formed in the upper shell, the sealing piece is arranged in the limiting groove, a sealing plate is arranged at the position, corresponding to the limiting groove, of the lower shell, and the sealing plate and the limiting groove are correspondingly attached to each other. The transmission line is used for wrapping and sealing the sealing piece, and the first end of the transmission line penetrates through the sealing piece and is located outside the whole shell. The length of the sensor in the device can be adjusted according to needs, so that the sensor can better adapt to the specific anatomical structure of a patient, and it is ensured that the sensor can accurately and stably monitor blood pressure.

The invention relates to a control method and system for a vital sign monitoring sickbed. A management and control system of a vital sign monitoring sickbed comprises a millimeter radar wave module, a body temperature detection module, a blood pressure monitoring module, a data processing module, a wireless communication module and an alarm module, and the data transmission end of the millimeter radar wave module is connected with the first input end of the data processing module; the output end of the body temperature detection module is connected with the second input end of the data processing module, the blood pressure monitoring module is connected with the third input end of the data processing module, the output end of the data processing module is connected with the input end of the wireless communication module, and the alarm module is connected with the output end of the data processing module. The method has the effect of improving the medical response efficiency.

The invention provides a non-contact in-vehicle person blood pressure detection method, which comprises the following steps: acquiring video data of in-vehicle persons according to a vehicle-mounted image acquisition module, and acquiring a video frame sequence containing a face region; positioning a face region in the video frame by adopting a face detection algorithm, and cutting a face image from the video frame; preprocessing the cut face image, including image normalization and de-noising operation, to obtain a preprocessed face image; a time sequence model is adopted to analyze dynamic changes among the multiple video frames, and time sequence characteristic signals related to blood pressure fluctuation are extracted; calculating systolic pressure and diastolic pressure values of the target object through a deep learning algorithm according to the extracted spatial feature signal and time sequence feature signal; and transmitting the calculated blood pressure index data to a cloud server for storage through the wireless communication module.