8551results about "Blood characterising devices" patented technology

A system for remotely monitoring and managing health statuses of a plurality of users includes software instructions storable on a memory device usable by a computing device, the software instructions causing a hardware processor of the computing device to receive a plurality of sets of health-related information from a plurality of mobile computing devices of a plurality of users. The health-related information includes physiological information derived from wearable devices of the users indicative of an onset of symptoms associated with an infection, contact tracing data, and diagnosis data. The hardware processor determines exposure levels based on at least the contact tracing data, and determines user-specific risk states based on physiological information, diagnosis data, and exposure levels.

The invention provides a field intelligent injection pump automatic control method and system based on physiological parameter monitoring, and relates to the technical field of automatic control. The physiological parameter information is input into an intelligent decision module, a personalized drug administration scheme is generated based on a reinforcement learning algorithm in combination with a physiological function state report and a drug action mechanism knowledge base, and the personalized drug administration scheme comprises a drug ratio, an injection rate and an injection time sequence; the personalized administration scheme is sent to the intelligent injection module, and the injection step is executed; the energy management unit performs energy collection and storage through a flexible photovoltaic film and a phase change energy storage material, establishes a multi-objective optimization function based on drug storage conditions and environmental parameters, and performs intelligent distribution and adaptive adjustment on the power consumption of the injection pump system; the state of the injection pump pipeline is evaluated based on multi-mode sensing information, dynamic safety evaluation is conducted in combination with real-time physiological monitoring, and graded protection is triggered.

The invention discloses an intelligent exercise health assessment method and system based on multi-sensor fusion, and relates to the technical field of intelligent health assessment and monitoring, and the method comprises the steps: collecting multi-modal exercise data and physiological signals, and carrying out the preprocessing of the collected data; carrying out data fusion and extracting key motion and health features; constructing an intelligent exercise health evaluation model, and analyzing the health state and the exercise performance of the user; personalized health feedback is generated based on an evaluation result, and real-time early warning is provided; and dynamically updating parameters of the evaluation model, and performing model optimization according to a specific motion scene and health requirements of the user. According to the method, various sensor data can be effectively fused, accurate health scores and real-time early warning can be generated, the health requirements of the user in different exercise scenes are met, the accuracy, adaptability and intelligent level of evaluation are remarkably improved, meanwhile, health improvement suggestions with high pertinence are provided for the user, and the user experience is improved. The method has remarkable technical advantages and application value.

A sensor system for monitoring patients is provided. The sensor system includes a wireless charging dock, one or more patient sensors, and a processing module. The patient sensor is configured to collect patient physiological data and send the data to the processing module. The processing module wirelessly transmits the patient physiological data to a patient monitor system. The wireless charging dock is wirelessly and removably coupled to the processing module to wirelessly provide power for the processing module. The wireless charging dock is magnetically coupled to the processing module.

The invention provides a personalized rehabilitation training method and system based on state monitoring, and relates to the technical field of rehabilitation training. The method comprises the steps of obtaining myoelectricity amplitude, root mean square and frequency spectrum median, calculating change rate, monitoring muscle fiber mobilization, blood oxygen, lactic acid metabolism and heart rate variability, extracting joint angle change rate and inertia moment, and obtaining a training load reference value. According to the method, multiple physiological parameters including the electromyographic signals, the oxyhemoglobin saturation and the heart rate variability are monitored and analyzed in real time, the accurate training load reference value is established, the accuracy of neuromuscular coordination evaluation is improved, the change rate of the electromyographic signals of short and long time windows is compared, the training load is accurately adjusted, the fatigue risk is reduced, and the neuromuscular coordination evaluation accuracy is improved. By adjusting the motion trail of the rehabilitation robot in real time and optimizing the training rhythm, it is ensured that the training intensity is matched with the individual metabolic capacity, dynamic matching and personalized adjustment of the training plan are achieved, and the training safety and effect are effectively improved.

Methods and analyte sensors including at least a first working electrode having a first active area thereon, and performing a dip coating operation to deposit a bilayer membrane upon the first working electrode and the first active area. The bilayer may include an inner layer having a first membrane polymer and an outer layer having a second membrane polymer, the first membrane polymer and the second membrane polymer differing from one another. The dip coating operation may comprise one or more first dips in a first membrane formulation to form the inner layer of the bilayer membrane and one or more second dips in a second membrane formulation to form the outer layer of the bilayer membrane upon the inner layer.

Multiple enzymes may be present in one or more active areas of an electrochemical analyte sensor for detecting one or more different analytes. In particular, an analyte sensor may comprise a sensor tail configured for insertion into a tissue and one or more working electrodes having a glucose-responsive active area and an ethanol-responsive active area to detect glucose and ethanol in vivo.

The invention discloses a heart ultrasonic monitoring system and method based on machine learning, and relates to the technical field of heart ultrasonic monitoring. The method comprises the steps of obtaining multi-modal data, performing standardization processing on the multi-modal data, and then performing space-time alignment processing to obtain a space-time aligned multi-modal feature cube; performing primary judgment on the time-space aligned multi-modal feature cube to obtain a primary judgment result; if the initial judgment result is normal, outputting multi-modal characteristic data; if the initial judgment result is abnormal, performing feature fusion processing on the time-space aligned multi-modal feature cube based on a hierarchical feature extraction network to obtain a joint feature coding vector; and performing abnormal ultrasonic monitoring on the combined feature coding vector, and outputting a cardiac ultrasonic monitoring result, so that the time-space correlation features of the ultrasonic image, the electrocardiosignal, the blood pressure and the blood biochemical index can be effectively fused, an abnormal mode is automatically identified through machine learning, and the early warning capability and the diagnosis precision of heart diseases are improved.

The invention provides a psychological crisis multi-stage joint control method and system based on a psychological large model, and aims to realize real-time monitoring, accurate evaluation and intelligent intervention of psychological states through a multi-modal data fusion and deep learning technology. The system collects multi-source information such as texts, voices, videos, physiological signals and behavior data, performs cross-modal analysis by using models such as Transform, LSTM and CNN, constructs personalized psychological portraits, and analyzes and predicts the psychological state change trend in combination with a time sequence. According to the method, a psychological crisis dynamic grading model is adopted, the psychological state of a user is divided into a normal grade, a mild grade, a moderate grade and a severe grade, multi-grade intelligent intervention is provided based on different risk grades, and the multi-grade intelligent intervention comprises AI self-service adjustment, psychological counseling matching, social support enhancement, emergency medical intervention and the like. The psychological intervention strategy is optimized in combination with reinforcement learning, the intervention mode is dynamically adjusted according to user feedback, and individuation and adaptability are improved.

The dynamic monitoring system for the senile cardiac patient comprises a multi-mode sensor module, a data processing module, an analysis module and an alarm module. The multi-modal sensor module collects parameters to generate a multi-modal physiological signal; the data processing module generates a preprocessing signal; the analysis module performs real-time analysis on the pre-processed signal based on a preset elderly electrocardiogram characteristic model to generate a graded alarm instruction; the alarm module triggers corresponding local reminding, remote notification or first-aid linkage response according to the graded alarm instruction, wherein the first-aid linkage response automatically sends the patient position and key physiological data to a specified first-aid center through the first-aid communication module; wherein the self-adaptive adjustment of the multi-mode sensor module comprises the step of dynamically optimizing signal gain according to a skin contact state. According to the dynamic monitoring system for the senile cardiac patients, the problems that dynamic monitoring of the senile cardiac patients is poor in real-time performance, high in false alarm rate and delayed in first-aid response can be solved.

A sensor system for optical detection of multiple analytes is discussed. An example system is presented that includes a plurality of lenses configured to receive individual light signals from analyte-sensing chromophores disposed on an array when the analyte-sensing chromophores are illuminated by light while being exposed to different specific analytes within an interstitial fluid. The system further includes at least one volume phase grating (VPG) communicatively coupled to at least one of the plurality of lenses and a detector communicatively coupled to the at least one VPG so that light signals from the analyte-sensing chromophores are focused by the plurality of lenses to the at least one VPG and transmitted to the detector. The detector being operable to generate spectra with each spectrum corresponding to an individual light signal from a single analyte-sensing chromophore.

Systems and methods for monitoring glucose levels of a user are described. Data indicative of glucose levels of the subject is received. A count is assigned for each glucose episode based at least on an area under the curve of the each glucose episode in a dataset of time-correlated glucose data. A running sum of counts for each glucose episode in a time period is calculated. The running sum of the counts may be displayed in a numerator of a fraction and a target count goal in a denominator of the fraction. A progress indicator representative of the running sum of the counts relative to a target count goal for the time period may also be displayed. A length of a variable portion may be proportional to the running sum of the counts, and a total length of the progress indicator may be proportional to the target count goal.

The invention provides an intelligent cooperative control method and system for a multi-mode phototherapy device, and the method comprises the steps: obtaining physiological parameters of a plurality of users to form a data set, and constructing a spectrum feedback model in combination with historical phototherapy data and environment data; on the basis of the model and historical phototherapy target data, a multi-mode spectrum parameter model is generated through a preset neural network, and cooperative control over multi-light-source equipment is achieved; physiological parameter changes of a target user are monitored in real time, and monitoring data are sent to an edge computing node; the edge node dynamically adjusts a spectral parameter combination scheme according to the monitoring data and a safety threshold, and generates a treatment effect evaluation index; and establishing a user spectrum feedback feature library based on the evaluation indexes, and continuously optimizing a spectrum feedback model through incremental learning. According to the invention, intelligent cooperative control of the multi-mode phototherapy equipment can be realized, the energy utilization efficiency of the phototherapy equipment is improved, the spectrum feedback difference influence among different users is reduced, and the accuracy and safety of the phototherapy effect are enhanced.

The invention belongs to the field of cerebral apoplexy rehabilitation training, and provides a cerebral apoplexy upper limb dynamic rehabilitation method and system based on vagus nerve electrical stimulation, the excitement degree of a cortical spinal cord pathway of a patient is measured and evaluated by using motor evoked potential to obtain a motor function evaluation result, and an initial rehabilitation training task is set according to the motor function evaluation result; the training motion intention of the patient is determined by monitoring the electroencephalogram signal and the electromyographic signal of the patient, and percutaneous ear vagus nerve electrical stimulation is triggered; after percutaneous ear vagus nerve electrical stimulation, performing rehabilitation training analysis on basic clinical characteristics and multi-modal signals of the patient to determine intermuscular coordination, brain region connectivity and brain muscle coupling of the patient; the training condition of the patient is evaluated according to the rehabilitation training analysis result, and parameters of percutaneous ear vagus nerve electrical stimulation and training tasks are adjusted based on the evaluation result for iterative rehabilitation training; and stopping iteration until the rehabilitation training analysis result of the patient reaches a preset standard, and completing rehabilitation training.

Devices and methods to mitigate the erroneous signal imparted by physical and / or chemical process incident upon analyte-selective electrochemical sensors that are non-analyte-related in origin are disclosed herein. A sensing system featuring at least one of an analyte-selective sensor and at least one of an analyte-invariant sensor.

Systems and methods for monitoring glucose variability in a subject are described. Data indicative of glucose levels of the subject is received from a sensor control device. A first glucose variability metric of the subject in a first time period is determined. The first glucose variability metric may be compared to a threshold. A first indicator is displayed if the first glucose variability metric does not exceed the threshold and a second indicator is displayed if the first glucose variability metric exceeds the threshold. Additional glucose variability metrics may be determined for subsequent time periods according to a rolling window, and the indicators may be displayed real time or in a report. The glucose variability metric may be a measure of variability compared to a baseline, a difference between a maximum and minimum glucose level, or time in or out of a target range.

The invention provides a time sequence fusion and health state prediction method and system for multi-dimensional physiological data, and relates to the technical field of electrical digital data processing.The method includes the steps that high-precision sensing equipment and a parameter calibration model are adopted to calibrate data, time synchronization and error compensation are achieved through a weighted fusion algorithm, and the accuracy of physiological parameters is improved; meanwhile, a fatigue index and a pressure index are corrected in real time by using a closed-loop feedback mechanism, and a comprehensive state index is generated based on an evaluation result for further analysis and prediction; the change trend of the comprehensive state index is predicted through a machine learning technology, and a targeted dynamic rehabilitation scheme including training intensity optimization, diet adjustment, rest cycle planning and the like is provided in combination with physiological data of the user; and a timing feedback mechanism is established, so that the user can obtain health state change and adjustment suggestions in real time, thereby improving scientificity and timeliness of a rehabilitation scheme, remarkably optimizing the rehabilitation effect and efficiency, and realizing intelligent health management at the same time.

Described herein are variations of an analyte monitoring system, including an analyte monitoring device. For example, an analyte monitoring device may include an implantable microneedle array for use in measuring one or more analytes (e.g., glucose), such as in a continuous manner. The microneedle array may include, for example, at least one microneedle including a tapered distal portion having an insulated distal apex, and an electrode on a surface of the tapered distal portion located proximal to the insulated distal apex. At least some of the microneedles may be electrically isolated such that one or more electrodes is individually addressable.

A medical device controller operating in conjunction with a medical device determines one or more current versions of executable code associated with one or more processors in a medical device. Medical devices may include infusion pumps, other patient treatment devices as well as vital signs monitors. The medical device controller determines one or more current versions of executable code and configuration information associated with the one or more processors in the medical device. The medical device controller further determines which of the processors in the medical device require updated executable code, and which of the processors in the medical device require updated configuration information. The medical device controller distributes to the medical device as required at least one of the updated executable code and the updated configuration information. The medical device deploys the distributed updates, and activates the updates at a clinically appropriate time.

Glucose and ketones may be dysregulated singularly or concurrently in certain physiological conditions and may be advantageously assayed together using an analyte sensor capable of detecting both analytes. Certain analyte sensors capable of dual detection may comprise a first working electrode and a second working electrode, a ketones-responsive active area disposed upon a surface of the first working electrode, a glucose-responsive active area comprising a glucose-responsive enzyme disposed upon a surface of the second working electrode, a membrane having a first portion overcoating the ketones-responsive active area and a second portion overcoating the glucose-responsive active area, in which the first portion and the second portion have different compositions. The ketones-responsive active area comprises an enzyme system comprising at least two enzymes that are capable of acting in concert to facilitate detection of ketones.

Methods and analyte sensors including at least a first working electrode having a first active area thereon, and performing a dip coating operation to deposit a bilayer membrane upon the first working electrode and the first active area. The bilayer may include an inner layer having a first membrane polymer and an outer layer having a second membrane polymer, the first membrane polymer and the second membrane polymer differing from one another. The dip coating operation may comprise one or more first dips in a first membrane formulation to form the inner layer of the bilayer membrane and one or more second dips in a second membrane formulation to form the outer layer of the bilayer membrane upon the inner layer.

Methods and analyte sensors including at least a first working electrode having a first active area thereon, and performing a dip coating operation to deposit a bilayer membrane upon the first working electrode and the first active area. The bilayer may include an inner layer having a first membrane polymer and an outer layer having a second membrane polymer, the first membrane polymer and the second membrane polymer differing from one another. The dip coating operation may comprise one or more first dips in a first membrane formulation to form the inner layer of the bilayer membrane and one or more second dips in a second membrane formulation to form the outer layer of the bilayer membrane upon the inner layer.

Described herein are variations of an analyte monitoring system, including an analyte monitoring device. For example, an analyte monitoring device may include an implantable microneedle array for use in measuring one or more analytes (e.g., glucose), such as in a continuous manner. The microneedle array may include, for example, at least one microneedle including a tapered distal portion having an insulated distal apex, and an electrode on a surface of the tapered distal portion located proximal to the insulated distal apex. At least some of the microneedles may be electrically isolated such that one or more electrodes is individually addressable.

The invention discloses a head-mounted brain body workability assessment method based on a space-time dynamic fusion network, belongs to the technical field of artificial intelligence and human body signal acquisition, and solves the problems that an existing method cannot comprehensively reflect the comprehensive state of an individual, and interaction and potential risks among multiple factors are difficult to reveal. The method comprises the steps that physiological parameter signals are collected based on wearable equipment, and an attention and fatigue prediction result is output by an LSTM prediction model; according to the method, the graph structure data packet is analyzed and processed based on the pre-constructed space-time dynamic fusion network model, the working state signal characteristics of the plateau high-risk operating personnel can be accurately evaluated, the space-time graph neural network (STGNN) can capture the complex relation, the high-dimensional topological characteristics are extracted, and the high-risk working personnel can be accurately evaluated. The dynamic event-driven pulse neural network (deSNN) can convert the pulse sequence features into pulse sequence features so as to describe feature values more accurately, iterative optimization enables the pulse sequence features and the pulse sequence features to cooperate, and the accuracy and reliability of time-space dynamic fusion network model evaluation are improved.

The invention relates to a data fusion processing method and system for an intelligent sensor based on MEMS, and the method comprises the following steps: carrying out the non-stationary signal decomposition of a multi-modal physiological parameter time sequence data stream, and obtaining a key vital sign feature sequence; performing phase coupling degree calculation on the key vital sign feature sequence to obtain an inter-organ function correlation intensity matrix; performing high-dimensional feature space projection on the critical patient based on the inter-organ function association intensity matrix to obtain a pathological state feature map of the patient; performing nonlinear kinetic analysis on the pathological state characteristic spectrum of the patient to obtain a disease progress dynamic trajectory; and performing clinical decision support reasoning based on the disease progress dynamic trajectory to obtain personalized treatment suggestions, thereby solving the technical problem that the existing analysis method is often limited to observation of a single physiological parameter or simple statistical analysis and lacks the ability to understand the disease mechanism from the overall perspective.

The invention relates to the technical field of medical health information, in particular to a heart failure risk prediction method and system based on multi-source heterogeneous data fusion, and the prediction system comprises a data collection module, a data management module, a multi-modal feature extraction module and a dynamic risk prediction model module. An intervention strategy recommendation module; and a visualization and iterative optimization module. The prediction method is applied to the prediction system, patient health data is collected and obtained, a multi-source heterogeneous health database is constructed, physiological time sequence features, traditional Chinese medicine dialectical features and western medicine clinical features are extracted respectively to be subjected to structured coding processing, a multi-modal feature set is formed, the multi-modal feature set is divided into a training set, a verification set and a test set, and the training set, the verification set and the test set are combined. A Bayesian attention mechanism is introduced, dynamic prediction of the heart failure risk is achieved through the constructed depth time sequence model, an intervention scheme can be adjusted and formulated in the whole course of heart failure management, excessive medical treatment is reduced, meanwhile, disease progress is effectively restrained or delayed, and the life quality of a patient is improved.

The invention provides an early-stage automatic identification method and system for acute kidney injury related to cardiac surgery, and relates to the technical field of physiological parameter detection. The method comprises the following steps: acquiring average arterial pressure, cardiac blood discharge volume, central venous pressure and blood oxygen saturation, regularly recording and extracting data of adjacent time points, calculating a variation range, screening a mutation region, judging whether a reference range is exceeded or not, analyzing a multi-parameter synchronous variation condition, and marking a hemodynamics abnormal state. According to the method, abnormal changes of hemodynamics and urine metabolism are accurately recognized through real-time monitoring and multi-parameter analysis, the urine flow rate and the metabolite excretion rate are combined, misjudgment of a single index is avoided, the fineness of renal function change analysis is improved, the high-risk time period is accurately positioned through time cross comparison, the diagnosis accuracy is improved, and the diagnosis accuracy is improved. Trend analysis and risk grade division enable the change of the illness state to be more visual, and a real-time early warning system effectively monitors the fluctuation trend, optimizes clinical management and improves the timeliness of medical response.

The invention provides a blood glucose monitoring and early warning method. The blood glucose monitoring and early warning method comprises the steps of a, collecting a skin surface spectral signal I (lambda), b, collecting skin surface temperature T, humidity H, environment light intensity L and environment electromagnetic interference E parameters, c, preprocessing the spectral signal I (lambda), removing noise N (I) through wavelet transform, and d, calculating the blood glucose monitoring and early warning parameters. And the signal intensity is corrected through an ambient light intensity compensation model. According to the blood glucose monitoring and early warning method, a non-invasive acquisition mode combining near infrared spectrum and visible light spectrum is adopted, and a multi-modal regression model and a dynamic interference compensation technology are innovatively introduced, so that the problem that the precision of a sensor is influenced by skin temperature, humidity and environmental electromagnetic interference in the prior art is solved; the timeliness and the stability of signal purification are improved by utilizing preprocessing and optimization algorithms of spectral signals, such as wavelet transform, empirical mode decomposition and fast Fourier transform.

Described herein are variations of an analyte monitoring system, including an analyte monitoring device. For example, an analyte monitoring device may include an implantable microneedle array for use in measuring one or more analytes (e.g., glucose), such as in a continuous manner. Each microneedle of the microneedle array may include a microneedle body, an electrode material on the microneedle body, a biorecognition layer on the electrode material, a diffusion-limiting layer on the biorecognition layer, an interferent blocking agent, and / or an attachment enhancer between the biorecognition layer and the diffusion-limiting layer, where the interferent blocking agent and the attachment enhancer are configured to improve sensor sensitivity variability.

The invention discloses a multi-parameter dynamic intelligent judgment method for the safety state of an operator, and belongs to the technical field of operation safety monitoring. According to the method, by integrating an intelligent wearable device, a sensor and an eye movement tracking device, physiological parameters (such as heart rate, blood pressure, oxyhemoglobin saturation, electroencephalogram signals and the like), behavior parameters (such as action frequency, posture change and the like), psychological parameters (such as pressure level, fatigue degree and the like) and environmental parameters (such as temperature, humidity, noise and the like) of an operator are collected in real time; and a multi-dimensional monitoring system is constructed. Collected data is subjected to cleaning, standardization and feature extraction and then is input into a safety state judgment model based on a bidirectional long short-term memory network (BiLSTM), the real-time safety state of an operator is dynamically analyzed, and low-risk, medium-risk and high-risk three-level early warning results are output.