12927results about "Blood characterising devices" patented technology

A blood glucose sensing system includes a plurality of physiological sensors. The system can estimate blood glucose based on discrete invasive blood glucose estimates from a blood sample, discrete noninvasive blood glucose estimates derived from optical sensors, and continuously-calculated blood glucose estimates derived from a nonlinear state-space model of glucose and insulin reactions within a human body. The state-space model has user-entered values corresponding to their insulin and meal intake. The user's blood glucose is estimated from a combination of the discrete invasive blood glucose estimates, the discrete noninvasive blood glucose estimates and the continuously-calculated blood glucose estimate.

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.

Aspects of the current subject matter are directed to a wearable analyte monitoring device with an integrated applicator for deployment of a microneedle array-based sensor into a skin surface of a subject. The microneedle array is retained within a housing body of the wearable analyte monitoring device in a first, loaded configuration. Upon actuation, a biasing element accelerates the microneedle array into the skin of the subject, transitioning the microneedle array to a second, deployed configuration in which the microneedle array protrudes through a distal opening of the housing body and enabling sensing by the microneedle array of one or more target analytes in dermal interstitial fluid of the subject.

Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

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.

An applicator for an analyte monitoring device may include an actuatable housing having a body defining a cavity therein and having a distal opening and a side opening. A cuff and a shuttle are received within the cavity and are separately translatable relative to the housing body. A base may removable engage the housing body at the distal opening. The housing body, the cuff, the shuttle, and / or the base may be engaged with one another with one or more releasable coupling features. The base may be removed from an engagement with the housing body, causing the cuff and the shuttle to be aligned and positioned in a configuration in which the analyte monitoring device, held by the shuttle, is ready for insertion into the skin.

Systems and methods are disclosed for determining individual-specific blood flow characteristics. One method includes acquiring, for each of a plurality of individuals, individual-specific anatomic data and blood flow characteristics of at least part of the individual's vascular system; executing a machine learning algorithm on the individual-specific anatomic data and blood flow characteristics for each of the plurality of individuals; relating, based on the executed machine learning algorithm, each individual's individual-specific anatomic data to functional estimates of blood flow characteristics; acquiring, for an individual and individual-specific anatomic data of at least part of the individual's vascular system; and for at least one point in the individual's individual-specific anatomic data, determining a blood flow characteristic of the individual, using relations from the step of relating individual-specific anatomic data to functional estimates of blood flow characteristics.

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.

The invention relates to the technical field of auxiliary treatment of traumatic patients, in particular to a construction method of an auxiliary treatment system based on traumatic patients. The method comprises the following steps: carrying out multi-modal physiological data acquisition and physiological data preprocessing on a trauma patient through hospital examination equipment to obtain multi-modal feature fusion data; carrying out physiological feature vector generation on the multi-modal feature fusion data to obtain a physiological feature vector of the trauma patient; carrying out physiological state coding on the physiological feature vector of the trauma patient to obtain physiological state coding data; acquiring trauma medical knowledge data; and according to the trauma medicine knowledge data and the physiological state coding data, carrying out physiological state and knowledge graph association to obtain physiological state and knowledge graph association data. According to the invention, through real-time adjustment of the patient processing sequence and automatic generation of the personalized resource allocation auxiliary processing scheme according to the patient features, the medical resource utilization efficiency is improved.

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 critical patient nursing intelligent monitoring system and method, and relates to the technical field of intelligent monitoring. Collecting physiological data and activity data of a patient in real time by using a multi-sensor system, and establishing an initial health file of the patient; according to the collected physiological data and activity data, daily activities and behavior modes of the patient are analyzed, and an individualized health baseline is established; monitoring physiological data and activity data of the patient in real time, identifying an abnormal trend, and generating an early warning signal; preferentially distributing nursing measures according to the state of the patient; the nursing measure effect is evaluated, and if the expected nursing effect is not achieved, the monitoring strategy is readjusted; according to the invention, the health state of the patient is comprehensively monitored and accurately analyzed in real time, so that abnormity can be found and intervened in time when the abnormity occurs, the patient can be nursed and treated in time, waste of nursing resources and nursing errors are reduced, and the treatment effect and the nursing quality of the critical patient are improved.

The invention relates to the technical field of health assessment, in particular to a big data-based breathing health assessment system, which comprises a breathing parameter analysis module, an interaction relationship assessment module, a breathing anomaly detection module and a breathing condition assessment module. According to the method, accurate extraction of breathing parameters and evaluation of breathing periodic changes are achieved by analyzing thoracic pressure waveform data, the breathing mode of a user is accurately recognized, physiological parameters are collected, the mutual relation between the physiological parameters and the breathing mode is analyzed, the influence of various physiological factors on breathing health is revealed, the dynamic monitoring capacity on the breathing health is enhanced, and the user experience is improved. According to the method, by combining detection and identification of abnormal breathing parameters, identification and classification of abnormal breathing reasons are achieved, medical professionals are helped to make treatment plans and health management strategies, diagnosis efficiency is improved, the misdiagnosis rate is reduced, and powerful support is provided for management of various chronic diseases by combining continuous trend analysis.

The invention discloses a self-adaptive music intervention system based on multi-modal physiological feedback, and belongs to the technical field of monitoring and intervention of emotional states, the self-adaptive music intervention system comprises a multi-modal emotional state evaluation module and a self-adaptive music intervention module, the multi-modal emotional state evaluation module acquires multi-modal physiological information of a user, and the self-adaptive music intervention module performs music intervention on the multi-modal emotional state evaluation module; feature extraction and fusion are carried out on the multi-modal data, and the emotional state is evaluated according to the fused data; and the adaptive music intervention module generates music features matched with the current emotion according to the evaluated emotion state, the music features and preference data of the user, and selects music according to the music features to intervene the user. By the adoption of the method, the wearable forehead lobe three-lead electroencephalogram equipment and the intelligent bracelet are combined, physiological signals of the central nervous system and the autonomic nervous system are collected and analyzed in real time, music intervention is automatically adjusted, a personalized emotion adjusting scheme free of side effects is provided, and a closed-loop mental state regulation and control mechanism is formed.

Systems, devices, and methods for neurostimulation using a combination of implantable and external devices to treat pain are disclosed.

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 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.

Systems and methods for integrating a continuous glucose sensor 12, including a receiver 14, a medicament delivery device 16, a controller module, and optionally a single point glucose monitor 18 are provided. Integration may be manual, semi-automated and / or fully automated.

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.

The invention relates to a digital acupuncture dynamic control optimization method. According to the technical scheme, the method comprises the steps that real-time physiological data, including the heart rate, the oxyhemoglobin saturation, the skin temperature and the muscle tension, of a patient are collected through a wearable device and a sensor; performing cleaning, denoising and feature extraction on the multi-modal physiological data by using a data preprocessing algorithm; a personalized digital acupuncture model is established, and the physical condition and acupoint sensitivity of a patient are dynamically evaluated; adjusting acupuncture output parameters, control frequency, pulse width and current intensity according to real-time feedback of a patient by adopting a self-adaptive fuzzy control algorithm through the obtained personalized digital acupuncture model; multi-channel biofeedback equipment including EEG, EMG and ECG is integrated, and instant response of a patient is monitored; through a closed-loop control algorithm, biological feedback data are input into the control model in real time, and dynamic adjustment of acupuncture and moxibustion parameters is controlled; a deep reinforcement learning algorithm is introduced into acupuncture therapy.

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.

A mobile edge computing system, a communication network and a method of using a mobile edge computing device. The mobile edge computing system includes a communication module that establishes signal communication over numerous wireless communication protocols at least one of which uses a low power wide area network protocol. A logic device is made up of a distributed set of processing units including both a central processing unit and one or more processors configured for performing machine learning operations the latter of which include one or more of a graphical processor unit and a tensor processing unit. When the system receives event data that has been acquired by one or more of the communication module and a sensor, the system executes a trained machine learning model and conveys, using the low power wide area network protocol, an output that has been produced by the trained machine learning model.

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.