52365results about "Sensors" patented technology

A pulse oximetry system includes a wrist portion configured for placement on a wrist of a subject, the wrist portion having a first component and a second component configured to removably secure to one another. The wrist portion can include emitter(s) and detector(s) operably positioned by the wrist portion. In some implementations, the pulse oximetry system further includes a ring member configured to secure around the subject's finger and operably position emitter(s) and detector(s) and a cable connected to the wrist portion in electrical communication with the emitter(s) and the detector(s) of the ring member and configured to transmit the signal(s) from the detector(s) to the wrist portion. The system includes a battery and hardware processor(s) configured to receive and process signal(s) outputted by the detector(s) to determine physiological parameter(s) of the subject.

An apparatus and method for real-time assessment and mapping of quality of life indicators includes: selecting a set of quality of life dimensions, receiving objective quality of life indicators based on the quality of life dimensions, receiving subjective quality of life indicators based on the quality of life dimensions, performing a statistical analysis on the objective quality of life indicators and the subjective quality of life indicators, storing the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis in a database, and outputting the objective quality of life indicators, the subjective quality of life indicators, and the results of the statistical analysis.

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.

A noninvasive blood pressure monitor. The noninvasive blood pressure monitor may include an inflatable cuff, a pressure transducer, one or more air pumps, and a processor. The processor may control the air pump(s) so as to initiate inflation of the cuff. The processor may also identify an oscillometric signal in an output of the pressure transducer, and may determine an envelope of the oscillometric signal. The processor may also determine one or more characteristics of the envelope of the oscillometric signal, and may control the air pump(s) so as to stop inflation of the cuff based on the one or more characteristics of the envelope of the oscillometric signal.

The invention relates to the technical field of multi-modal data, provides a pilot competency dynamic evaluation method, system and device based on multi-modal data and a storage medium, and solves the problems of low accuracy of pilot competency evaluation and poor pertinence of training guidance. The method comprises the steps of collecting flight control data, physiological signal data, psychological assessment data, subjective scale data and international civil aviation organization core competency information, performing alignment fusion on multi-source data by adopting a time synchronization algorithm, and identifying an attention fixation mode based on a hidden Markov model. And constructing a workload index by fusing a subjective scale and a physiological entropy value, inputting the multi-modal features into a pre-trained competency assessment model, outputting three levels of psychological assessment indexes including a basic ability layer, a dynamic presentation layer and a risk early warning layer, and finally generating a personalized training report. According to the invention, the accuracy of pilot competency evaluation and the pertinence of training guidance are improved.

The invention discloses a multi-label electrocardiogram classification method based on self-supervised pre-training and multi-modal semantic alignment, which belongs to the technical field of artificial intelligence, and comprises the following steps: realizing self-supervised pre-training of unlabeled data through a single-modal contrast enhancement network, generating global and local contrast views by adopting a multi-scale random cutting strategy, and classifying the global and local contrast views in a multi-scale random cutting mode; in combination with a teacher-student network architecture, the potential invariance features of the ECG signals are learned while negative sample dependence is avoided, the problem of annotation data scarcity is effectively relieved, and the feature robustness is improved. A multi-modal fusion mechanism based on label semantic guidance is provided, a time domain signal and a frequency domain time-frequency graph are mapped to a unified semantic space through fine-grained semantic alignment, local feature enhancement and cross-modal complementary information fusion are realized by using a cross attention mechanism, and the problem of semantic difference caused by modal heterogeneity in a traditional method is overcome. A multi-label comparison loss function based on a disease co-occurrence relation is proposed, a category discrimination boundary is dynamically optimized by modeling a label co-occurrence probability, the feature separability of a tail category is improved while the head category discrimination ability is enhanced, and the problem of sample category imbalance in a multi-label scene is remarkably relieved.

A wearable system can include an electronic device configured to measure one or more physiological parameters of a patient and a wearable device configured to operably position the electronic device. The electronic device can have at least one light emitter and at least one light detector and be configured to measure at least a pulse oximetry measurement. The wearable device can have a main body with a cavity configured to position the electronic device, a securement portion connected to the main body and configured to secure the main body to the patient, and storage component configured to store patient identification data associated with the patient. When the electronic device and the wearable device are secured to one another, the patient identification data can be transferred from the wearable device to the electronic device.

A system configured to facilitate monitoring a patient when the patient transitions between environments. The system can have an in-room display terminal configured to display indicia of a health of the patient for electronically monitoring the health of the patient within a healthcare environment. The system can receive, via the in-room display terminal, a request to initiate monitoring the patient with the in-room display terminal at the healthcare environment; access historical physiological data associated with the patient and generated by a home monitoring device before the patient enters the healthcare environment; access real-time physiological data associated with the patient and originating from a physiological monitoring device coupled to the patient within the healthcare environment; generate one or more physiological parameters from the real-time physiological data and the historical physiological data; and cause the in-room display terminal to display indicia of the one or more physiological parameters.

A computer implemented method for governing risk actions by an artificial intelligence (AI) browser, by classifying a proposed action by the AI browser based on a large language model (LLM) as safe or risky based on AI weights or based on policy rules; initiating a step up authentication flow for a risk action; presenting an action summary and required capabilities to the user for approval; and enforcing user configured spend or scope limits on the risk action.

In one embodiment, a medical system includes a catheter configured to be inserted into a chamber of a heart of a living subject, and including multiple electrodes configured to capture electrical activity from electrical activation signals propagating in tissue of the chamber, a display, and processing circuitry configured to automatically select bipolar signals to be captured into an electro-anatomical map from respective electrode pairs of the multiple electrodes responsively to an alignment of the respective electrode pairs with a direction of propagation of the electrical activation signals, and render the electro-anatomical map to the display.

A wearable device configured to monitor physiological parameters of a wearer can include: a physiological parameter measurement sensor configured to monitor a plurality of physiological parameters; a hardware processor; a display in communication with the processor; and a band configured to secure the physiological parameter measurement sensor on a wrist of the wearer. In some implementations, the hardware processor is configured to: obtain a first plurality of signals from the physiological parameter measurement sensor when the band is secured on the wrist at a first tightness; determine a signal quality responsive to the first plurality of signals; and output an indication on the display to adjust tightness of the band with respect to the wrist from the first tightness to a second tightness based on the determined signal quality.

The invention relates to the technical field of health monitoring, and discloses a patient health monitoring method and system based on dynamic electrocardiogram analysis, and the method comprises the steps: synchronously obtaining multi-lead dynamic electrocardiogram data continuously collected by a patient within a preset duration, and related physiological parameters of exercise intensity, respiratory rate and body position change; performing dynamic self-adaptive preprocessing on the dynamic electrocardiogram data based on the associated physiological parameters to obtain standardized electrocardiosignals; extracting a multi-dimensional characteristic parameter set from the standardized electrocardiosignal, and sampling according to a preset time window to generate a characteristic parameter sequence with a timestamp; and inputting the characteristic parameter sequence into a dynamic optimization analysis model capable of iteratively updating parameters through real-time physiological parameter feedback, and performing graded evaluation on the health state of the patient to generate an evaluation result. The system corresponds to the method. By adopting the method and the system, the accuracy of dynamic electrocardiogram monitoring and the evaluation refinement level meet the dynamic monitoring requirement of cardiovascular health.

Disclosed herein is a sensor device for volumetric capnography. The sensor device is coupled to an adapter comprising a housing that encloses a measurement volume. The adapter is configured for gas flow along a first length axis, which aligns with a second length axis of the sensor device. The device includes an IR light source and an IR light detector, positioned on opposite sides of the housing. The IR light source emits IR light toward the detector across the measurement volume. The sensor device also includes a first ultrasonic transceiver in contact with the adapter and configured to emit ultrasound centered at a first position along the second length axis, directed partly along the axis and partly transversely. A second ultrasonic transceiver, also in contact with the adapter, emits ultrasound centered at a second position along the second length axis, directed partly opposite to the first direction and partly transversely.

Disclosed herein are systems and methods for monitoring at least one volatile breathing compound in respiratory gases of patients. A device includes a patient respiratory gas interface coupled via a connector to a gas sampling line. The line includes a first channel for conducting a predetermined flux of respiratory gases from the interface to an inlet of a sidestream gas monitor, which monitors carbon dioxide (CO2) content. A pump facilitates gas flow through the device and the monitor. An outlet of the monitor is coupled to a second channel for conveying gases away. A valve controls passage of respiratory gas from the monitor via the second channel by connecting or disconnecting the flux to / from at least one volatile breathing compound detector, which monitors compound content. A control unit monitors CO2 values and uses this as a signal to control the valve, enabling compound monitoring or bypass based on CO2 content.

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.

Disclosed is a system for detecting whether the breast cancer is benign or malignant based on multiple modalities, which comprises: an ultrasonomics model configured to extract breast tumor position information and breast tumor morphological features on the basis of an acquired grayscale breast ultrasound image, perform global interactive fusion on the breast tumor position information and breast tumor morphological feature information, and convert a Transformer encoder-based feature output into an output vector of the corresponding classification dimensions by means of a fully connected layer; a metabonomics model configured to obtain a metabolic fingerprint spectrogram and analyze the metabolic fingerprint spectrogram on the basis of a metabolic database and a predetermined significantly differential metabolite to obtain abnormal metabolic pathways, signal transduction pathways, and related biochemical reactions associated with breast cancer malignancy; and a detection module configured to perform joint modeling, based on the breast tumor position information, the breast tumor morphological features, and a peak value of the significantly differential metabolite of the breast cancer malignancy, to output a prediction result about whether the breast cancer is benign or malignant.

The invention relates to the technical field of medical rehabilitation, and discloses a lower limb weight-bearing gait rehabilitation training system which comprises a data acquisition module, a data processing and analysis module, a patient individualized modeling module, an intelligent decision and control module, a rehabilitation execution module and a man-machine interaction and medical information interface module which are in communication connection through a network. The data acquisition module is used for acquiring multi-modal data of a patient in real time, and the multi-modal data comprises static sign data, dynamic physiological parameters, kinematics and dynamics parameters and non-motion physiological and psychological state data; and the data processing and analysis module is used for carrying out preprocessing, feature extraction and deep analysis on the original data, and outputting a structured patient individualized feature vector and an evaluation result. According to the invention, a patient three-dimensional skeletal muscle digital twinborn model is constructed through the patient individualized modeling module, and in combination with a continuous learning intelligent model library, body sign differences of different patients can be accurately adapted.

The invention discloses an intelligent management system and method applied to a radio frequency energy output device, and belongs to the technical field of intelligent management of the radio frequency energy output device.An integrated sensor array is deployed on a radio frequency host and a hand tool electrode, and load voltage, output current, electrode temperature and tissue impedance are synchronously collected; a dynamic thermal impedance collaborative analysis model is constructed after time synchronization alignment, a three-dimensional thermal field simulation map is generated, and a thermal accumulation trend is predicted; a double-layer control framework is constructed, a first control layer generates a dynamic power adjustment rule base based on a preset treatment target and a safety threshold value and outputs an instruction, a second control layer receives the instruction and adjusts output parameters in real time, and a strategy is adjusted by combining a model prediction result in the execution process; meanwhile, the operation state is continuously monitored, a fault classification model is constructed for anomaly detection, a fault source is positioned, and a visual report is generated; and finally, constructing a mapping relation based on historical data, and generating an energy control scheme adapted to individualized requirements through iterative optimization.

The invention discloses an electrocardiogram classification system and method fusing multi-scale self-adaptive attention, and relates to the technical field of electrocardiogram classification.The electrocardiogram classification method comprises the steps that electrocardiogram data are collected through a data collection module, and a structured data set is output; the data preprocessing module is used for carrying out data preprocessing on the structured data set; the window data segmentation module obtains a standardized windowed electrocardiosignal; the feature extraction module takes the standardized windowed electrocardiosignals as input, performs three stages of multi-scale convolution feature extraction, liquid neural network dynamic modeling and self-adaptive attention mechanism enhancement, and outputs diagnosis results of nine types of heart diseases; and the training and optimizing module is used for optimizing the extraction and classification capability of the model on the electrocardiosignal features, so that the technical effects of full-process technical upgrading from data acquisition, preprocessing, feature extraction to model training optimization and high-precision electrocardiogram automatic classification are achieved.

The invention relates to the technical field of electroencephalogram signal processing, and discloses an electroencephalogram emotion recognition method and system based on a deep neural network. The method comprises the following steps: collecting and preprocessing a multi-channel EEG signal; constructing a graph data structure, extracting multi-domain features by taking electroencephalogram channels as nodes, and constructing a self-adaptive dynamic adjacency matrix; constructing a graph convolution long and short-term memory network, learning spatial features by GNN, and extracting time dependence by LSTM; enhancing emotion capture by using a multi-scale time-frequency feature fusion method in combination with STF and CWT; constructing global topological information of an FCN brain extraction region in combination with brain network features; and outputting alertness and other emotion indexes by means of the classification model. According to the method, graph structure learning and time sequence modeling are combined, EEG signal emotion recognition is optimized, and personalized adaptation and emotion recognition accuracy is improved.

NADP-dependent oxidoreductase compositions, and electrodes, sensors and systems that include the same. Analyte sensors include an electrode having a sensing layer disposed thereon, the sensing layer comprising a polymer and an enzyme composition distributed therein. The enzyme composition includes nicotinamide adenine dinucleotide phosphate (NAD(P)+) or derivative thereof, an NAD(P)+-dependent dehydrogenase; an NAD(P)H oxidoreductase; and an electron transfer agent comprising a transition metal complex.

NADP-dependent oxidoreductase compositions, and electrodes, sensors and systems that include the same. Analyte sensors include an electrode having a sensing layer disposed thereon, the sensing layer comprising a polymer and an enzyme composition distributed therein. The enzyme composition includes nicotinamide adenine dinucleotide phosphate (NAD(P)+) or derivative thereof, an NAD(P)+-dependent dehydrogenase; an NAD(P)H oxidoreductase; and an electron transfer agent comprising a transition metal complex.

The invention relates to the technical field of computer systems based on specific calculation models, and discloses a blood oxygen change monitoring algorithm fused with dynamic Bayesian modeling, which comprises the following steps: quantizing signal uncertainty by calculating entropy of pulse waveform harmonic energy distribution; and on the basis of the entropy value, driving a dynamic Bayesian network to carry out confidence coefficient evaluation and weighting processing on the blood oxygen estimation model, and finally outputting a decision pair containing a blood oxygen estimation value and the confidence coefficient thereof. According to the method, the signal uncertainty is converted into a computable entropy index, so that the system can autonomously distinguish real physiological changes and measurement noise, the problem of misjudgment caused by motion interference in traditional blood oxygen monitoring is avoided, meanwhile, non-inductive personalized calibration is achieved by utilizing the continuous learning ability of the Bayesian network, and the accuracy of the system is improved. And the reliability and the practicability of the medical wearable equipment are remarkably improved.

The invention discloses a multi-mode pet health monitoring and motion artifact elimination method and system based on a millimeter wave radar, and relates to the technical field of pet health monitoring, and the method comprises the following steps: S001, building a unified time baseline and an energy fingerprint auditing surface, constructing an energy distribution model from a chest to a tail, and taking the model as a reference for artifact evolution, identifying a signal spectrum coupling trend in a time-frequency domain; and S002, based on the energy distribution model, performing causal playback on continuous time sequence signals acquired by the millimeter-wave radar, extracting a pseudo-motion energy nucleus caused by tail or limb movement, and calibrating a phase anchor point and a space observation area of a respiratory signal. According to the method, multi-source physiological data are fused, pure respiratory signals are extracted through energy distribution modeling, causal playback, phase anchor point calibration and three-dimensional resampling, risk assessment is achieved based on physiological credibility tensor, and the accuracy and anti-interference capacity of health monitoring are improved by combining phase conjugate traction and a space-time regulation strategy.

The present disclosure provides a method and system for analyzing unipolar electrophysiological (EP) signals acquired by a multi-electrode catheter placed in a ventricle of a patient's heart. The method includes receiving unipolar EP signals, extracting unipolar far-field signals from the unipolar EP signals, and analyzing the extracted unipolar far-field signals to estimate the distribution of scar regions across a thickness of wall tissue of the ventricle. Using the estimated distribution, a unipolar far-field EP map showing the scar regions is generated. The method further includes displaying the unipolar far-field EP map including the scar regions to a user. The system comprises a display device and a processor configured to perform the steps of the method.

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.

A charging system can include a housing. The housing can include a controller. The housing also can include a power source configured to power the controller. The charging system also can include a wireless charger configured to transfer energy to the power source. The wireless charger can include a first indicator. The first indicator can be configured to provide a first non-flashing visual output at a first brightness level and indicative of a first charging status of the power source. The first indicator also can be configured to provide a second non-flashing visual output at a second brightness level indicative of a second charging status of the power source. Other embodiments are disclosed.

A method for using a local large language model (LLM) within a user's secure computing environment is disclosed. The LLM operates behind a firewall to prevent transmission of sensitive data, and utilizes an encrypted vector database and artificial intelligence techniques for content retrieval, response generation, and task anticipation. This system can be used on mobile, wearable, vehicle, or IoT devices and offers various services such as health monitoring, financial advice, automated communications handling, and personalized daily activity optimization. It also has the ability to detect fraud, fine-tune responses using augmented user data, assist in negotiations, identify personal interests, and provide health recommendations based on dietary and physical activity data.