81668results about "Diagnostic recording/measuring" patented technology

A physiological patient monitor can include a front end conditioner that can receive analog sensor signal from a sensor responsive to a person's physiology. The patient monitor can further include an analog-to-digital converter that can convert analog sensor signal to a digital sensor information. The patient monitor can determine one or more patient specific physiological parameters. The patient monitor can further determine a physiological condition of a patient based on processing multiple physiological parameters. The patient monitor can generate a user interface that can included a visual representation of the patient's physiological condition.

A band configured to apply a force to a physiological sensor on a portion of a user's body can include an elastic segment, a first indicator, and a second indicator. The elastic segment can have a first end and a second end opposite the first end, and a first portion of the elastic segment can be secured to a second portion of the elastic segment to form a closed loop that can at least partially secure the physiological sensor to the portion of the user's body when in use. The first and second indicators can be spaced apart from one another and positioned along the elastic segment. The first indicator can be positioned closer to the first end than the second indicator. A relative distance between the first and second indicators can provide an indication of a desired stretch of the elastic segment.

A system for generating and detecting transient vapor nanobubbles in a fluid from a patient can include a micro-fluidic device to receive a flow of the fluid from the patient, a laser pulse source to provide a laser pulse to the flow of the fluid from a first side of the micro-fluidic device, a probe light source to provide a probe light to the flow from the first side of the micro-fluidic device, and a photodetector located at a second side of the micro-fluidic device opposite the first side. The photodetector can detect scattered, reflected, and / or deflected probe light and output a nanobubble signal including characteristics of optical scattering, reflecting, and / or deflecting by the transient vapor nanobubble.

A physiological patient monitoring system with a healthcare professional-facing interface is disclosed. The healthcare professional interface may only display by default the most critical non-confidential patient information. To access the full range of features in the system, clinicians can unlock the device. Once unlocked, clinicians can access all of a patient's treatment history, and all the data may be consolidated and presented intuitively.

This invention presents a multisensor-connected, AI-enabled telehealth system for assisting healthcare providers with differential diagnosis and patients with early health concern detection. The system comprises a multi-sensor medical device with at least seven sensors, a secure cloud-based platform, and an interactive telehealth module. The device preprocesses and securely transmits patient information to the cloud platform, where an ensemble of deep learning models analyzes the data to generate ranked potential diagnoses with likelihood scores. The telehealth module facilitates communication between providers, patients, and the cloud platform, presenting visualizations and receiving feedback. The system continuously updates and fine-tunes its models using incremental learning algorithms, adapting to new data while retaining previous knowledge. It also generates alerts for providers and patients when deviations from normal physiological patterns are detected, accompanied by explainable AI visualizations.

Systems and methods for improving reimbursement rates from payors for services or procedures provided by a medical care provider are disclosed herein. In some examples, reimbursement can be improved by improving the accuracy of medical coding of services and diagnosis codes through the use of machine learning techniques. In some examples, the system can suggest codes more likely to get reimbursed by a payor through analysis of patient data and / or insurance information. In some examples, the system can determine a likelihood of reimbursement through analysis of patient data and / or insurance information.

The present invention relates to the technical field of heart rate monitoring, discloses a heart rate monitoring method, wherein the heart rate monitoring method comprises: obtaining initial electrocardio signals of a user by a preset heart rate sensor, and acquiring initial acceleration energies of the user by a preset acceleration sensor; pre-treating the initial electrocardio signals, obtaining pretreated target electrocardio signals, and pre-treating the initial acceleration energies to obtain pretreated target acceleration energies, the present invention can be used to help doctors and users to know current heart rate conditions, make personalized and well-targeted health management measures and improve living quality.

A driving state monitoring and feedback method and system based on multi-modal human factors intelligent data analysis, and an edge computing terminal device. The method comprises: receiving multi-modal human factors data of a tested driver that is collected in real time (S110); pre-processing the multi-modal human factors data, wherein the pre-processing comprises denoising processing and data normalization processing (S120); sending the pre-processed multi-modal human factors data into a pre-trained first state recognition model, so as to obtain a real-time recognized driver state, wherein driver states include a normal state and abnormal states, and the types of the abnormal states include a plurality of states such as a fatigue state, a distracted state and an angry state (S130); and when it is recognized that the driver state is an abnormal state, generating, for different categories of abnormal states, driving state feedback instructions to a driving intervention system, such that the driving intervention system performs state feedback adjustments on the driver on the basis of the received driving state feedback instructions (S140). The method and system can recognize different driving states of a driver in real time and then perform processing on the basis of different driving states, thereby avoiding the occurrence of traffic accidents.

The invention discloses an intelligent nursing management system and method for a critical patient, and relates to the technical field of medical health monitoring, and the method comprises the steps: setting a multi-physiological signal collection channel, synchronously collecting multi-dimensional physiological signals of the patient, carrying out the preprocessing of the collected signals, and forming a standardized multi-modal physiological data flow; the preprocessed multi-modal physiological data are input into a deep learning model based on LSTM and a multi-head attention mechanism, intelligent fusion analysis of different physiological signals is achieved in combination with a dynamic weight adaptive algorithm, and a patient individualized physiological system coupling model is established; and based on an analysis result of the deep learning model, calculating an illness state risk assessment value of the patient in real time, and outputting a specific early warning level according to a set multi-level early warning threshold. According to the invention, the multi-stage and multi-dimensional early warning threshold system is constructed, so that the condition of the patient can be monitored and early warned in all directions. The monitoring quality of critically ill patients can be remarkably improved, and the missed judgment rate and the misjudgment rate are reduced.

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.

Various wearable health monitoring devices are described herein. Some implementations include one or more emitter chamber covers injection molded out of a first material comprising transparent glass, one or more detector chamber covers injection molded out of a second material comprising transparent glass, and a light barrier construct injection molded out of a third material over the one or more emitter chamber covers and the one or more detector chamber covers. The third material may be opaque and can comprise glass and an optically absorbing pigment. Some implementations include a circuit board, emitter(s) mounted to the circuit board and arranged within the emitter chamber cover(s), and detector(s) mounted to the circuit board and arranged within the detector chamber cover(s). The circuit board, emitter(s), detector(s), emitter chamber cover(s), and detector chamber cover(s) can form part of a sensor assembly connected to a housing of the wearable health monitoring devices.

The disclosed system for interacting with objects in an extended reality (XR) environment generated by an XR system may include (1) neuromuscular sensors configured to sense neuromuscular signals from a wrist of a user and (2) at least one computer processor programmed to (a) determine, based at least in part on the sensed neuromuscular signals, information relating to an interaction of the user with an object in the XR environment and (b) instruct the XR system to, based on the determined information relating to the interaction of the user with the object, augment the interaction of the user with the object in the XR environment. Other embodiments of this aspect include corresponding apparatus, and computer programs recorded on one or more computer storage devices, each configured to perform the actions of the methods.

A multi-modal articulation system translates human gestures into control commands for digital and automation devices. The system integrates a camera module, which captures facial and hand movements, with an image processing unit that extracts facial features, such as eyebrow movement, lip curvature, eyelid motion, and eye trajectory. These extracted features are analyzed in real time by an articulation recognition module that compares them against a database to recognize specific gestures. The system tracks and processes air-drawn hand gestures using a gesture trajectory tracking unit, which converts the motions into digital representations and classifies them using deep learning algorithms. The recognized gestures are converted into machine-readable control signals and executed on various connected devices via a relay control unit. The system incorporates adaptive algorithms to improve accuracy, account for environmental conditions, and stabilize hand tremors, providing a gesture-based control interface for applications ranging from smart home systems to multimedia devices.

A medical system includes an elongate instrument, a tracking system, and one or more processors. The tracking system is disposed along at least a portion of the elongate instrument. The one or more processors are coupled to the tracking system and are configured to: generate a plurality of linked bounded-surface elements corresponding to a set of connected passageways of a patient anatomy; receive shape information from the tracking system, the shape information indicating a shape of at least a portion of the elongate instrument when the portion is disposed in the set of connected passageways; register the shape information to the plurality of linked bounded-surface elements; and based on the registering, display a composite image including an instrument image of the elongate instrument displayed within a model of the set of connected passageways.

A blood pressure monitoring system, comprising: an exciter configured to produce an acoustic signal, the exciter being provided on a first substrate portion; a detector spaced apart from the exciter, the detector being configured to detect the acoustic signal and to produce an electrical output signal, the detector being provided on a second substrate portion that is mechanically or acoustically decoupled from the first substrate portion, wherein the detector comprises at least four detectors; and a processor configured to determine a blood pressure measurement from the electrical output signal.

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.

Various wearable health monitoring devices are described herein. Some implementations include a housing including a front side and a back side configured to face tissue of the wearer when the device is worn; and a sensor assembly positioned by the back side of the housing. The sensor assembly can include emitters and detectors positioned on a surface of a substrate and a frame positioned adjacent to the surface of the substrate and configured to inhibit transmission of optical radiation through the frame. The frame can comprise an emitter chamber housing the emitters; and one or more detector chambers housing the one or more detectors.

The invention discloses a cerebral apoplexy risk prediction method and system based on multi-modal data fusion. The method comprises the following steps: A1, a data acquisition and preprocessing step; a2, a feature embedding and multi-modal fusion step; a3, a self-attention mechanism optimization feature fusion step; a4, a multi-mode cooperative training step; and A5, dynamic prediction and real-time updating. By integrating medical image data, gene data, electronic health record (EHR) and biomarker data, the risk of cerebral apoplexy can be comprehensively evaluated, and the limitation of a single data source is solved. According to the method, independent feature extraction and embedding processing are carried out on various data through a deep learning model, deep information in each data mode can be captured, and more detailed and accurate risk assessment is provided.

The invention relates to a closed-loop multi-guide multi-mode time domain interference electrical stimulation system and method. The method comprises the steps that a software part determines stimulation parameters; the automatic electrode position adjusting device adjusts the spatial position of the multi-conductive-electrode device, and the multi-conductive-electrode device stimulates the position of a target spot; the current output precision and impedance monitoring module monitors the precision and impedance information of electrode output current, and the core main control and functional circuit module adjusts a control signal according to the precision and impedance information; when the multi-conducting-electrode device conducts stimulation, the electroencephalogram device synchronously collects electroencephalogram signals and uploads the electroencephalogram signals to the electroencephalogram analysis algorithm module of the software part, the electroencephalogram analysis algorithm module evaluates and feeds back the effectiveness and the effect of stimulation, and the time domain interference electrical stimulation function module and the target area dynamic adjustment algorithm module optimize stimulation parameters according to the effectiveness and the effect of stimulation. Through the arrangement, a real-time closed-loop feedback control system is formed, continuous monitoring, real-time optimization and adjustment of electrical stimulation parameters and dynamic adjustment of a stimulated brain target region are realized, and the accuracy and safety of current output are ensured.

The invention discloses a multifunctional magnetic stimulation treatment system and an application method thereof, relates to the technical field of medical equipment, and solves the defects of unstable curative effect and limited application range caused by individual difference in the existing system. The precision of the laser beam is automatically adjusted through a self-adaptive calibration algorithm, and it is ensured that a treatment area is accurately positioned; optimal stimulation parameters are calculated through a self-adaptive magnetic field depth optimization algorithm, and pertinence and effectiveness of treatment parameters are ensured; the treatment effect is predicted through a feedback mapping algorithm, and the treatment parameters are dynamically adjusted to ensure the safety and effectiveness of the treatment process; physiological data, symptom description and medical history information of a patient are comprehensively analyzed and recognized through the complex disease pattern recognition module, a detailed disease pattern recognition report is output, the application range of the system is expanded, and personalized disease prediction and risk assessment are provided; the applicability and the curative effect of an existing system are greatly improved.

The approach presented here relates to a determination appliance (100) for determining a viscosity of a fluid. The determination appliance (100) has at least one determination device (110) and a provisioning device (115). The determination device (110) is designed to determine the viscosity of the fluid and / or a rotational speed (ω) of a blade wheel (205) for conveying the fluid by using at least one detected volume flow of the fluid and a detected pressure difference of the fluid. The provisioning device (115) is designed to provide or send a viscosity signal (130) representing the viscosity determined by the determination device (110).

The invention relates to the technical field of skin disease diagnosis, and discloses an AI auxiliary decision support system for skin disease diagnosis, which integrates an image acquisition module, an image analysis module and a comprehensive analysis and diagnosis decision module. A high-definition image of the skin of a patient is obtained through high-precision skin photographing equipment, and a training data set is formed by combining basic information of the patient. A deep learning algorithm is utilized to train a skin disease recognition model and an illness state evaluation model, and accurate recognition of skin disease types and objective evaluation of severity are realized. And the comprehensive analysis module fuses output of the two models, constructs a fusion decision model and provides comprehensive diagnosis suggestions. And the diagnosis decision module is used for generating a disease type judgment and illness state evaluation report based on the trained model in combination with real-time image data so as to effectively assist doctors in making decisions. The accuracy, the efficiency and the popularization degree of skin disease diagnosis are improved, and complex and changeable diagnosis requirements in clinical practice are met.

The invention relates to the technical field of sleep aiding, in particular to a method and a system for extracting feature information of a sleep state monitoring model. The method comprises the following steps: carrying out multi-mode physiological signal acquisition on a detected object to obtain original sleep monitoring data; performing noise removal and signal enhancement processing on the original sleep monitoring data, and performing signal segmentation to obtain multi-channel sleep signal fragment data; performing feature extraction based on electroencephalogram, eye movement diagram, electromyogram, electrocardiogram and accelerometer signals on the multi-channel sleep signal fragment data to obtain multi-dimensional feature data; and performing ultrasonic feature and acoustic feature extraction on the multichannel sleep signal fragment data to generate acoustic feature data. According to the method, dynamic modeling and trend prediction are carried out on the conversion process of the sleep state, potential sleep disorder events can be accurately recognized, influence evaluation is carried out on the potential sleep disorder events through deep learning, and support is provided for personalized sleep diagnosis.

The invention relates to the technical field of medical data processing, in particular to an integrated rehabilitation training system based on motion capture and myoelectricity feedback, which comprises a motion data acquisition module, a motion evaluation and analysis module and a control module, and is specifically integrated on a training garment through inertial motion capture equipment and a surface myoelectricity sensor. The motion state and muscle activation condition of a patient are monitored in real time, actual motion data and actual electromyographic signal data of the patient are collected, and whether the motion state of the patient meets the standard or not is judged by comparing the data deviation condition between the actual motion of the patient and the standard motion. And according to the deviation analysis result, the electrical stimulation parameters can be dynamically adjusted through closed-loop control, so that personalized and precise spinal cord electrical stimulation rehabilitation training is realized.

A Magnetic Stimulation (MS) coil system 2 for connection to a current pulse generator comprising: at least one coil 4 winding for delivering a magnetic field to a target zone of a body; a first and se

A method for early diagnosis of Parkinson's disease based on multimodal deep learning is provided. Audio-visual data of a to-be-diagnosed subject while performing a speech task is acquired. The audio-visual data are preprocessed to extract a plurality of audio segments and a plurality of video segments. A face image sequence is extracted from each of the plurality of video segments. A Mel-spectrogram of each of the plurality of audio segments is calculated. The face image sequence and the Mel-spectrogram are input into a multimodal deep learning model to output a classification result for Parkinson's disease early diagnosis of the to-be-diagnosed subject. A system for early diagnosis of Parkinson's disease based on multimodal deep learning is also provided.

Provided in the present application are a real-time artifact processing and feature extraction method and system for an electroencephalogram signal. The method comprises: receiving in real time an electroencephalogram signal data stream collected by means of an electroencephalogram device; performing real-time segmentation on the electroencephalogram signal data stream by means of a sliding window; acquiring parameter information including the number of channels and the sampling rate of the electroencephalogram device, and on the basis of the parameter information, performing a suitable filtering processing step and artifact removal step on electroencephalogram signal data stream segments; and matching a feature extraction strategy for one or more pre-selected output indexes, and according to a matched feature extraction strategy, performing, from perspectives including a time domain, a frequency domain, a time frequency domain and / or nonlinear analysis, real-time extraction on the electroencephalogram signal data stream segments which have been subjected to the filtering processing step and the artifact removal step, so as to obtain a feature value meeting the output index. In the present application, corresponding feature values can be extracted by means of integration and automation for different types of electroencephalogram devices and different output indexes, thereby facilitating subsequent analysis processing.

The sperm collection device includes a core member having an insertion hole for a penis and an insertion space, and a container having an internal space in which the core member is accommodated. The container includes a cylindrical base unit having a base end open in an axial direction of the base unit and attached to an insertion-hole-side end of the core member, and a cylindrical movable unit having a base end open in an axial direction of the movable unit and a front end being closed, and attached rotatably around an axis of the movable unit with respect to the other end of the base unit. In an overlapping part in which the base end of the movable unit overlaps the other end of the base unit, an airflow path and an adjustment mechanism are provided.

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.