71127results about "Sensors" patented technology

A system is disclosed for continuous and intermittent monitoring of physiological parameters. The system comprises at least one sensor coupled to a wearable device configured to measure physiological parameters of a user; a non-transitory data store storing data collected from the at least one sensor and computer-executable instructions; and a processor in communication with the at least one sensor and the non-transitory data store, wherein the computer-executable instructions, when executed by the processor, configure the processor to: continuously measure the physiological parameters by sampling the at least one sensor at a sampling rate to obtain repeated measurements at series of intervals configured to capture important physiological events; compare measurements of the physiological parameters to a threshold to determine a likelihood of a physiological event; and alert the user when an occurrence of the physiological event is likely.

A robotic surgical system includes a surgeon consol coupled to a patient consol, and the patient consol coupled to surgical instruments. A surgeon computer is coupled to or at the surgeon consol that is coupled to to one or more surgical instruments. A robotic surgery control system includes an artificial intelligence (AI) system with one or more deep learning algorithms. A feedback loop monitors and collects data from the one or more sensors. One or more cameras provide feedback to the robotic surgical system, and are configured to provide images of an anatomical object in at least a two dimensional (2D) arrangements of pixels / Deep learning algorithms of the AI system distinguish different anatomical objects from the images.

An electronic device can comprise physiological sensors that can generate sensor data, a device processor, an actuator, and a controller electrically connected with the device processor and the actuator. The controller can monitor the state of the actuator; communicate a notification signal to the device processor to cause the device processor to prepare for a privacy mode responsive to determining that the actuator is in a privacy state, delay implementing the privacy mode until one or more conditions have been satisfied to allow the device processor to prepare for the privacy mode pursuant to communicating the notification signal to the device processor; and implement the privacy mode to inhibit access to the sensor data responsive to satisfaction of the one or more conditions.

A system for monitoring hemodynamic status of a patient can include a transducer, an adapter and one or more monitor devices. The adapter may be in communication with the transducer and the one or more monitor devices. The adapter can be configured to receive and process data from the transducer such as unprocessed physiological data. The adapter can be configured to transmit data to the monitor device(s) such as processed and / or unprocessed physiological data. The adapter can be configured to generate, and transmit to the monitor devices(s), user interface data for rendering interactive graphical user interfaces to display information such as physiological information relating to a hemodynamic status of the patient. The adapter can be configured to receive and process, from the monitor device(s) user commands or instructions to control an operation of the system or its components.

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.

The invention relates to the field of sleep monitoring, in particular to a sleep monitoring visual analysis method and system based on a smart bracelet. The method comprises the following steps: extracting a sleep physiological signal of a wrist part of a user based on a smart bracelet; sliding time window analysis and multi-mode physiological state sensing are carried out, and a dynamic sleep state sensing map is constructed; performing transient mutation rhythm recognition on the dynamic sleep state sensing map, performing sleep stage evolution trend analysis, and constructing a sleep stage evolution model; the method comprises the following steps: collecting multi-dimensional parameters of a sleep environment, carrying out environment parameter change detection and environment disturbance-sleep stage change association identification, and obtaining an environment disturbance-sleep rhythm mutation association rule; environment disturbance intensity evaluation is carried out according to the association rule, and a real-time sleep state visual view is constructed. According to the method, accurate and high-readability sleep monitoring data visualization is realized, the overall sleep quality is improved, and the long-term health risk is reduced.

Improved novel 3D imaging and reconstruction methods for quantitative and accurate 3D imaging used in multiple industries, segments, and applications within each. Improvement includes methods and hardware configured to improve performance and lower radiation and improve accessibility. Using simplified system matrix and datasets of little scatter interference in tomographic image acquisition and reconstruction, in some cases, using time continuity and spatial continuity to generate parameter data for substances within VOI for a number of key applications in medical and non-medical fields.

The invention discloses a livestock health state real-time monitoring method and system based on the Internet of Things, and the method comprises the steps: collecting livestock biological data and environment parameters through a biological chip ear tag and an environment sensor, and carrying out the preprocessing, and obtaining a standardized multi-dimensional livestock data flow; in combination with image data provided by a video monitoring system, identifying livestock individuals and analyzing behavior features, and fusing biological data and visual data to generate livestock individualized feature vectors; constructing an individualized health baseline model in combination with historical health records; comparing the deviation degree between the current state and the health baseline through an anomaly detection algorithm, and identifying a potential health problem; early warning is performed according to the abnormity severity level, intervention suggestions are generated, early warning information is pushed to the breeding personnel through multiple channels, and response measures and results are recorded to form closed-loop management. According to the invention, accurate real-time monitoring of the health state of the livestock is realized, the accuracy and timeliness of anomaly detection are improved, the breeding risk is reduced, and the production efficiency of animal husbandry is improved.

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.

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 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 relates to the technical field of flight training evaluation, and discloses an electroencephalogram feature and physiological index fused flight training evaluation system. The system comprises a physiological signal acquisition module which synchronously captures multichannel electroencephalogram original signals and body surface physiological index data, and the body surface physiological index data comprises an electrocardiograph R-R interval sequence, respiratory wave frequency amplitude and galvanic skin response amplitude; the multi-modal fusion module is used for analyzing an electrocardiograph R-R interval sequence to generate a heart rate variability feature vector and establishing dynamic association mapping of an electroencephalogram entropy value and a physiological feature vector; the cognitive state modeling module is used for generating a cognitive load index according to the dynamic association mapping and constructing a cognitive stability quantization matrix; the self-adaptive feedback module is used for receiving related data and dynamically adjusting simulated flight scene parameters; and the evaluation output module is used for integrating the data to generate a comprehensive training evaluation report containing a neurophysiological coordination degree score and an operation accuracy rating. According to the system, comprehensive evaluation and dynamic training adjustment of the cognitive state of the pilot are realized.

The invention discloses an auxiliary scheme generation method and system for dysphagia rehabilitation, belongs to the technical field of medical rehabilitation intelligent evaluation, and is used for solving the problem of poor optimization of a swallowing training path. According to the method, myoelectricity, tongue pressure, throat images and acoustic signals in the swallowing process are collected through a multi-channel sensing device, a unified multi-mode swallowing behavior feature vector is constructed, obstacle stage recognition and functional grade judgment are achieved in combination with a pre-training model and historical data, and a structured evaluation result is output; matching training actions with high adaptation degree based on the knowledge graph, fusing muscle group matching degree, edge weight and function level factors to generate a personalized training path, and setting strength, frequency and rhythm parameters; in the training process, the motion and myoelectricity deviation is monitored in real time, a prompt signal is output, and the behavior completion degree is recorded; and the rehabilitation progress map is dynamically updated according to feedback, and if the deviation exceeds the limit, the path is automatically reconstructed and doctor intervention is synchronized, so that the rehabilitation efficiency and safety 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 bodily-fluid transfer device includes a housing, a pre-sample reservoir, and an actuator. The housing defines an inner volume between a substantially open proximal end portion and a distal end portion that includes a port couplable to a lumen-defining device. The pre-sample reservoir is fluidically couplable to the port to receive a first volume of bodily fluid. The actuator is at least partially disposed in the inner volume and has a proximal end portion that includes an engagement portion and a distal end portion that includes a sealing member. The engagement portion is configured to allow a user to selectively move the actuator between a first configuration such that bodily fluid can flow from the port to the pre-sample reservoir, and a second configuration such that bodily fluid can flow from the port to a sample reservoir defined at least in part by the sealing member and the housing.

The invention relates to the technical field of multi-modal data processing, and discloses a multi-modal data fusion processing method and system, and the method comprises the steps: collecting a multi-modal data set containing the activities of the old through an intelligent old-age care robot; constructing a timestamp calibration matrix based on the multi-modal data set, and carrying out dynamic weight correction on the timestamp calibration matrix by adopting a three-modal data envelope delay compensation algorithm to obtain a three-modal data stream with a synchronous time sequence; performing hierarchical feature extraction through a nested feature extractor to obtain a three-mode tumble feature vector group; performing vector fusion on the three-mode tumble feature vector group to obtain tumble fusion feature vectors; and inputting the tumble fusion feature vector into a multi-modal forward prediction model to perform tumble risk trajectory prediction, and outputting a tumble detection result, the method realizes prediction analysis of body posture changes in a key time window before the old man tumble, and improves the ability to distinguish a slow getting-up action and a real tumble behavior of the old man.

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.

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.

The invention discloses a time-space alignment fusion processing method and system for multi-source physiological signals, and relates to the technical field of data processing.The method comprises the steps that the multi-source physiological signals in the limb movement state are synchronously collected, and a heterogeneous time sequence data set is obtained; performing space-time alignment processing on the heterogeneous time sequence data set to generate a synchronous physiological signal matrix; performing signal quality evaluation based on the synchronous physiological signal matrix, constructing a weighted decision tree model, performing confidence fusion on the synchronous physiological signal matrix according to the weighted decision tree model, and generating multi-parameter joint monitoring data; and performing motion artifact suppression processing on the multi-parameter joint monitoring data, outputting a physiological parameter index set, and transmitting the physiological parameter index set to a first-aid equipment monitoring terminal. Therefore, the technical effects of eliminating signal distortion, improving monitoring data quality and ensuring first-aid monitoring precision are achieved.

The invention discloses a millimeter wave radar human body tumble detection method based on multi-feature fusion, and the method comprises the steps: obtaining an original point cloud frame from each radar, and carrying out the timestamp calibration and space coordinate system conversion; performing noise filtering, ground segmentation and human body point cloud extraction on each frame of point cloud; dividing the preprocessed point cloud into a static cluster and a dynamic cluster; filtering false dynamic clusters; extracting a residual dynamic cluster set, and identifying and tracking the human body dynamic clusters in continuous frames by adopting a tracking algorithm; extracting a corrected time sequence feature from the tracked human body cluster; inputting the time sequence characteristics into a pre-trained deep time sequence network, learning a falling time sequence dependency relationship, and outputting an abnormal index; and performing multi-source fusion with the abnormal index to obtain a comprehensive index to judge whether to trigger an alarm. The method can adapt to a complex home environment, improves the detection accuracy and real-time performance, and reduces the false alarm and missing alarm.

The invention relates to the field of heart rate monitoring, and discloses a millimeter wave radar breath and heart rate synchronous monitoring method and system, and the method comprises the steps: transmitting a linear frequency modulation continuous wave signal according to a millimeter wave radar, and collecting original echo data reflected by a target region; baseband signal demodulation and phase information extraction are carried out on the original echo data to obtain original phase time sequence data, and the original phase time sequence data comprise thoracic cavity micro-motion features; and according to a Butterworth band-pass filter, preprocessing the original phase time sequence data through human body physiological signal frequency band characteristics to obtain a breathing frequency band signal and a heart rate frequency band signal. According to the method, the apnea event triggering threshold value and the arrhythmia early warning index are updated in real time through Kalman filtering and extended Kalman filtering, so that the monitoring system can dynamically adjust the health parameters, which means that the monitoring system can be optimized in real time and the abnormal health event can be accurately responded in different physiological states.

An apparatus and method for static image of time series measured data to time series translation is disclosed. The apparatus comprises at least a processor configured to receive a static image of time series measured data, convert that static image from its initial domain to a usable time series within another user-selected domain, then to validate the conversion against a confidence threshold.

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.

The disclosure herein relates to systems, methods, and devices for medical image analysis, diagnosis, risk stratification, decision making and / or disease tracking. In some embodiments, the systems, devices, and methods described herein are configured to analyze non-invasive medical images of a subject to automatically and / or dynamically identify one or more features, such as plaque and vessels, and / or derive one or more quantified plaque parameters, such as radiodensity, radiodensity composition, volume, radiodensity heterogeneity, geometry, location, and / or the like. In some embodiments, the systems, devices, and methods described herein are further configured to generate one or more assessments of plaque-based diseases from raw medical images using one or more of the identified features and / or quantified parameters.

The invention belongs to the technical field of biological calculation, and discloses an intelligent music regulation and control system and method based on electroencephalogram signal multi-modal feature recognition, electroencephalogram data are obtained through anti-interference collection and adaptive filtering processing, multi-modal feature vectors are constructed based on physiological feature extraction and information fusion, and the multi-modal feature vectors are extracted and subjected to multi-modal feature recognition. And accurate recognition of the emotional state and cognitive load evaluation are realized. A music-emotion mapping network is constructed through music feature analysis and emotion semantic matching, a personalized music regulation and control strategy is formulated, music parameter dynamic adjustment and audio stream reconstruction are realized, and a music output stream is generated. A closed-loop optimization feedback mechanism is formed by adopting electroencephalogram feedback collection and adjustment effect evaluation, user data security grading and privacy protection processing are implemented, a security specification database is constructed, the effect of a music output stream is continuously evaluated, and a comprehensive evaluation report is generated. Immediate emotion regulation is provided for the user, a healthier emotion management mode is established for the user through long-term data accumulation, and psychological toughness is improved.