865results about "Diagnostics using light" patented technology

The invention belongs to the technical field of artificial intelligence and health monitoring, and particularly relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The emotion recognition precision is improved through space-time alignment analysis of facial micro-expressions and physiological signals, adaptive feedback is achieved in combination with cognitive load correlation modeling and a wearable multi-channel regulation and control terminal, cross-period emotion evolution prediction and group situation awareness are supported, a'awareness-decision-intervention 'complete closed loop is constructed, and the emotion recognition efficiency is improved. And the accuracy and initiative of emotion management in a complex environment are enhanced.

The embodiment of the invention provides an elderly emotion recognition method and device based on multi-modal perception, and the method and device achieve the optimization and enhancement of the signal quality through innovatively constructing a multi-modal data preprocessing mechanism and integrating the facial expression, voice and posture features. And designing a personalized feature mapping model based on historical emotion expression data, and establishing an adaptive feature fusion strategy for intelligent matching in combination with a cross-modal attention network. A hierarchical time sequence classification mechanism is introduced, dynamic modeling of the emotional development trend is realized through a long-short term memory network, and accurate prediction of the emotional state is supported. According to the method, the defects of the traditional technology in the aspects of multi-modal processing, personalized modeling, time sequence analysis and the like are effectively overcome, and the accuracy and reliability of sentiment recognition of the old people are remarkably improved.

The invention discloses a peripheral vein puncture blood vessel pressure monitoring system for image enhancement examination based on multi-modal fusion, and relates to the technical field of medical image assistion.The peripheral vein puncture blood vessel pressure monitoring system comprises the steps that image data, sensor data and personalized data of a patient are collected; generating an image feature vector, a sensor feature vector and a personalized feature vector based on the preprocessed multi-modal data, carrying out feature interaction on a multi-modal feature line, carrying out representation fusion by combining Transform with contrast learning, and generating a fusion representation vector; the maximum tolerance pressure, the extravasation risk level and the recommended injection flow rate range of the target puncture point under the current injection flow rate are predicted and evaluated in the blood vessel pressure resistance prediction model, injection suggestions and injection alarms of the target puncture point are generated, and injection parameters are automatically adjusted. According to the method, the personalized safety pressure threshold values of patients with different blood vessel conditions are accurately evaluated, and the safety and accuracy of peripheral vein puncture are remarkably improved.

The invention relates to the field of traditional Chinese medicine diagnosis, and discloses a traditional Chinese medicine intelligent diagnosis system based on multi-source data fusion and an AI technology, and the system comprises a tongue picture collection and classification unit which is used for collecting tongue picture data of a patient through imaging equipment, carrying out the feature extraction of the tongue picture data, and obtaining a tongue picture feature data set; performing tongue picture pathological mode classification on the tongue picture feature data set based on a deep convolutional neural network to obtain tongue diagnosis identification result data; and the face diagnosis feature correlation analysis unit is used for inputting the tongue diagnosis identification result data into a multi-modal data fusion engine and carrying out face diagnosis pathological feature correlation analysis based on a graph attention network to obtain face diagnosis identification result data. The deep semantic understanding is realized by introducing a plurality of data acquisition modes such as three-dimensional imaging, thermal imaging, micro-expression recognition, spectral information and pulse condition harmonic analysis and combining advanced models such as a graph neural network, U-Net and Bi-LSTM.

The invention relates to the technical field of medical elastography and material multi-scale mechanical measurement, in particular to an elastography method and system based on an instantaneous scattering displacement field, and the method comprises the steps: driving a passive / active method scattering displacement field excitation device through a frequency domain controllable sine wave signal, and generating a scattering displacement field on a target material, an instantaneous displacement field is obtained through high spatial resolution measurement, then sliding window local interception, two-dimensional autocorrelation calculation and nonlinear fitting based on the Rayleigh / shear wave spatial autocorrelation theory are performed on the single-frame instantaneous displacement field, shear wave velocity distribution is obtained, and then the viscoelasticity modulus of a target area is inverted to achieve comprehensive characterization of the viscoelasticity of the material. According to the scheme, the single-frame scattering displacement field is excited and collected, and the elasticity inversion is performed based on the instantaneous displacement field, so that the dependence of traditional elasticity imaging on high-frame-rate equipment is broken through, the material viscoelasticity evaluation can be efficiently completed at low cost, and the application prospect in the fields of clinical diagnosis and mechanical measurement is wide.

The invention discloses a digital imaging method of an ear-nose-throat examination endoscope, and relates to the technical field of medical treatment, and the method comprises the following steps: synchronously collecting a white light image, a photoacoustic signal, a stimulated Raman spectrum, pressure sensor data and a physiological signal of a target orifice through a multi-physical field probe; constructing a four-dimensional tensor bound with the anatomical features; based on a vocal cord vibration fundamental frequency harmonic characteristic optimization graph convolutional network, dynamically constructing an adjacent matrix and coupling cross-modal characteristics to generate a submucosal lesion enhanced image; a deformable convolutional network constrained by vocal cord biomechanics is adopted, and the shape of a convolution kernel is dynamically adjusted according to the relation between real-time strain and elastic modulus, so that motion artifacts caused by swallowing actions are inhibited; generating a curvature-driven asymmetric convolution kernel based on ear canal spiral geometry, and executing super-resolution reconstruction in combination with confrontation training of fractal constraint; and fusing the white light image gradient and the pressure gradient field, and outputting a three-dimensional lesion contour consistent with the anatomical structure.

The invention discloses a noise speckle real-time imaging method suitable for various complex scenes, and the method comprises the steps: firstly obtaining a speckle image of a sample target after a scattering medium through a camera, and carrying out the preprocessing of the speckle image; secondly, the principal component of the preprocessed speckle image is extracted by using VMD based on a row-column separation strategy; and then noise of the principal component is extracted through a main singular value, and an enhanced speckle image is obtained through differential operation with the speckle image. And finally, autocorrelation operation is carried out on the enhanced speckle image, the amplitude of a target is obtained through AM algorithm optimization, and speckle image reconstruction is realized by using a phase recovery algorithm. The method has better robustness and structure retention capability in an edge region and a high-noise scene, can overcome the influence of a strong light interference environment, and remarkably improves the local contrast of a speckle image.

The embodiment of the invention provides a detection module and wearable equipment. The detection module comprises a cover plate, a coherent light source and an image sensor, the cover plate is used for being arranged on the side wall of a shell of the wearable equipment, the cover plate is provided with an outer surface and an inner surface opposite to the outer surface, and the outer surface is exposed out of the side wall; the cover plate is provided with a first light-transmitting area and a second light-transmitting area; the coherent light source is arranged on one side of the inner surface of the cover plate and used for emitting coherent light penetrating through the first light-transmitting area. The image sensor is arranged on one side of the inner surface of the cover plate, and the image sensor is used for imaging the light penetrating through the second light-transmitting area. According to the embodiment of the invention, more functions can be integrated in a small-size product, namely the wearable equipment.

The invention discloses a respiration monitoring analysis and evaluation method based on an optical fiber sensor, and relates to the field of respiration monitoring analysis and evaluation.The respiration monitoring analysis and evaluation method comprises the steps that respiration signals and heart rate signals are collected, and through time alignment, motion compensation and reconstruction analysis, a respiration rate space-time coupling tensor is obtained, a phase difference matrix is constructed to generate an atlas; after decomposition of a Gaussian mixture model, calculating mutual information entropy and a coupling strength index, performing analysis to obtain a mutual information entropy matrix and a dynamic coupling strength curve, performing graph attention network processing, state reproduction and phase synchronization analysis to obtain a dynamic multi-domain joint tensor, and performing projection, reconstruction and characteristic decomposition to obtain parameters. The method comprises the steps of obtaining a mode density manifold graph and a dynamic physiological mode graph, constructing a Gaussian mixture-manifold embedding model, obtaining the Gaussian probability and two-dimensional manifold space coordinates of an abnormal prototype, carrying out corresponding early warning and signal emission, describing a complex physiological mode for respiration and heart rate signals, and carrying out accurate respiration state evaluation and abnormal early warning.

The present invention discloses a multi-wavelength tunable laser and sensing system, which relates to the field of lasers and includes: a setting module for presetting or selecting two wavelengths of laser beams in the laser; a camera module for collecting the user's target skin surface image for treatment in real time, identifying the tumor area and the healthy skin area in the image data, and identifying the current skin surface position of the laser beam. The present invention identifies the tumor area and the healthy skin area in the operation data by collecting the user's target skin surface image for treatment, further plans the laser treatment path based on the target skin surface image, and controls the laser in the process of moving according to the treatment path, in combination with the sensor to sense the tumor thickness, so as to achieve further intelligent control of the movement speed of the laser in the process of moving according to the treatment path, so that the tumor position on the user's skin surface can be treated more adaptively.

A ring-type wearable device is disclosed. The disclosed ring-type wearable device comprises: an outer ring member; an inner ring member separably inserted into the outer ring member; a sensor unit disposed in the outer ring member; and a control unit disposed in the outer ring member so as to be electrically connected to the sensor unit, wherein the sensor unit can maintain constant sensitivity in correspondence to the thickness of the inner ring member.

The present disclosure provides apparatus and methods to both infer stent expansion (vessel expansion or compliance) and probability of procedural success (vessel patency) from pretreatment diagnostic imaging at the point-of-care and also train an inference model to generate such an inference, thus allowing a physician to choose with greater certainty an optimal treatment tool and treatment protocol for treating a vessel of a patient.

A biometric wearable device (e.g. finger ring or smart watch) has optical sensors to measure body oxygenation level, hydration level, glucose level, heart rate, heart rate variability, and / or blood pressure. Light from light emitters is transmitted through body tissue and changes in the light are analyzed. The angles and / or vectors along which the light is transmitted through body tissue can be automatically changed by the device in order to scan different tissue regions and / or different tissue depths.

The invention discloses an optical and neural feedback multi-modal data analysis method for a nerve regulation target, and belongs to the technical field of cranial nerve treatment, and the method specifically comprises the steps: receiving a head movement instruction of a patient through an interactive interface, pausing transcranial magnetic stimulation after receiving the instruction, and switching to a head movement monitoring state; an infrared optical navigation device is combined with a magnetic resonance image to construct an individualized brain three-dimensional model, head position changes are tracked in real time, and autonomic nerve feedback signals are collected at the same time; synchronously aligning the head coordinates with the neural feedback signals to generate a multi-modal data set, and analyzing relevance to obtain real-time regulation and control parameters; the positioning and stimulation intensity of the transcranial magnetic stimulation coil are adaptively adjusted according to the parameters, and related information is displayed on a treatment interface; after the head of the patient finishes moving and is stable, a rapid re-calibration process is automatically triggered, high-precision monitoring is recovered, and magnetic stimulation output is reactivated; according to the invention, the treatment stability and adaptability are improved.

The invention discloses a limb volume measuring instrument, which relates to the field of limb volume measurement and comprises a supporting groove, a sliding rod, a pulley trolley, a measuring frame, an infrared measuring grating and host equipment, the host equipment comprises a server; the server is used for sending a scanning signal to the infrared measurement grating; the infrared measurement grating is used for scanning the limb to be measured under the driving of the pulley trolley after receiving the scanning signal to obtain an infrared light signal; the server is also used for reading a group of infrared light signals scanned by the infrared measurement grating at set time intervals and storing each group of infrared light signals according to a time sequence; after the infrared measurement grating completely scans the limb to be measured, the length and width data of the section of the corresponding limb to be measured are calculated according to each group of stored infrared light signals; and inputting the length and width data of the to-be-measured limb section corresponding to all the infrared light signals into the volume error correction model to obtain corrected length and width data. The limb volume can be simply, conveniently and accurately evaluated.

The invention relates to the technical field of medical monitoring and assistance, in particular to a sensor-based intelligent monitoring system for obstetrical and gynecological operations, which comprises a multi-source sensing module, a feature fusion module, a mode recognition module, a collaborative early warning module and a decision output module, wherein the multi-source sensing module is used for outputting an instrument movement track, a uterine cavity pressure waveform, a fetal electrocardiosignal and a surgical field video stream; the feature fusion module is used for generating a multi-dimensional feature matrix; the mode recognition module is used for outputting an instrument risk label and a fetus risk level; the collaborative early warning module is used for generating a multi-level early warning instruction containing the coordinates of the risk part; and the decision output module is used for forming a dynamically updated safe operation boundary diagram and displaying the safe operation boundary diagram on the surgical field video stream in an overlapping manner. According to the invention, through a multi-source sensing and collaborative early warning mechanism, synchronous monitoring and dynamic risk prompting of operation behaviors and physiological states in the obstetrics and gynecology surgery are realized, and the safety management and control capability in the surgery is improved.

Configurations are disclosed for a health system to be used in various healthcare applications, e.g., for patient diagnostics, monitoring, and / or therapy. The health system may comprise a light generation module to transmit light or an image to a user, one or more sensors to detect a physiological parameter of the user's body, including their eyes, and processing circuitry to analyze an input received in response to the presented images to determine one or more health conditions or defects.

The invention relates to the technical field of wearable health monitoring, and discloses a distributed wearable health monitoring system, which comprises a watch dial and a wearable main body of a watch chain, the distributed sensing module is arranged on the wearable main body and is used for collecting multi-source physiological signals; the data preprocessing and tensor construction module is electrically connected with the distributed sensing module and used for processing the multi-source physiological signals and constructing the multi-source physiological signals into a physiological state tensor structured tensor fusion engine, and the data preprocessing and tensor construction module is used for decomposing the physiological state tensor to extract one or more low-dimensional core features reflecting potential physiological laws. According to the invention, distributed sensing is carried out on the dial plate and the watch chain, and the multi-source signal is constructed into the physiological state tensor, so that the limitation of scattered information of traditional equipment is overcome, and comprehensive and deep monitoring of the health state of the user is realized. The structured data basis makes it possible to capture the coupling relation between different physiological systems, and therefore more accurate evaluation is achieved.

Methods for non-invasive or minimally invasive assessment of epithelial tissue structure are disclosed. Digital imaging and processing are used to identify cell locations. More specifically, an automated algorithm that may be used to identify epithelial tissue structure, and / or to specify the coordinates / locations of cells in the epithelial tissue structure, through non-invasive or minimally invasive imaging, and use of this information to extract values of epithelial structure related parameters are disclosed.

The invention belongs to the field of biomedical engineering, and particularly relates to a thermal imaging and visible light fused non-contact wild animal health monitoring method, which comprises the following steps of: processing a thermal imaging video frame by frame to identify a blood vessel dense region; mapping pixel coordinates of the blood vessel security level area to a corresponding position of the visible light image and taking the pixel coordinates as an ROI area; in the ROI area, calculating arithmetic mean values of R, G and B channels of all pixel points respectively, repeating the process at different time points t to obtain three light intensity curves R (t), G (t) and B (t) changing along with time, fusing the three light intensity curves to obtain a light intensity change curve I (t), performing heart rate calculation and blood pressure calculation according to the light intensity change curve I (t), and performing blood oxygen calculation by using R (t) and B (t); and monitoring the health state of the wild animal according to a calculation result. According to the invention, animal health non-damage monitoring is realized.

This invention relates to the field of optical imaging and detection technology, specifically providing a broadband snapshot-type polarization interferometry imaging detection system and method for breast tumors. A broadband light source illuminates a breast tumor tissue sample. The reflected signal light is collected and collimated by a pre-optical system, then sequentially passes through a first and a second ray shaping unit for two shearing and two polarization state transformations, resulting in two beams of linearly polarized light and two beams of circularly polarized light. These beams are imaged by an imaging lens to an area array detector to obtain a polarization interferogram, which is then demodulated to obtain all Stokes parameters of the breast tumor tissue sample. This invention achieves broadband snapshot imaging of breast tumors with all Stokes parameters without using an analyzer, doubling the light throughput compared to traditional polarization imaging instruments using analyzers. Furthermore, this invention has advantages such as compact structure, no moving parts, easy assembly and adjustment, and no need for image registration.

A method of monitoring a subject with an opto-physiological sensor system is described herein. The method comprises obtaining a model of the optical properties of at least one body tissue type to be monitored, wherein the model of the optical properties comprises a definition of static (DC) and dynamic (AC) components of transmitted optical power and a definition of a source-detector separation related to a normalised path length for an illumination source of the opto-physiological sensor: obtaining an indication of at least one physiological property of the subject from a wearable device worn by the subject: obtaining an indication of at least one physical variable from the wearable device worn by the subject; determining, using the opto-physiological model, how the at least one physical variable affects the at least one physiological property; and determining a corrected value for the physiological property based on the determination of how the at least one physical variable affects the at least one physiological property.

The invention provides a flexible wearable multi-physiological-parameter monitoring device, and relates to the technical field of wearable medical instruments.The garment body comprises a flexible conductive fiber layer, a sensing center layer and an elastic protection layer which are attached to the human body; the sensor module in the sensor central layer is connected with the control module, and the power supply module is connected with the sensor module and the control module; the sensor module collects bioelectricity signals of a human body and transmits the bioelectricity signals to the control module, and the control module analyzes and processes the bioelectricity signals to obtain physiological parameters; a sensor in the sensor module is made of a flexible electronic material, the sensor module comprises a body temperature acquisition module, a blood pressure and blood oxygen acquisition module and an electrocardio acquisition module, and the blood pressure and blood oxygen acquisition module acquires a blood pressure signal and a blood oxygen signal by utilizing a photoelectric volume pulse wave tracing method. The equipment is high in integration level, comprehensive and dynamic monitoring of human health indexes is achieved, the accuracy of physiological parameter reading and monitoring result obtaining is improved, the wearing comfort is improved, and the equipment is suitable for the outdoor environment.

The invention discloses a traditional Chinese medicine four diagnosis comprehensive diagnosis method and device integrated with AI analysis, and relates to the field of intelligent medical treatment, and the method comprises the steps: obtaining user identity information and basic physiological parameters at least comprising age, gender, height and weight, carrying out the correlation binding of the two, and constructing a user exclusive diagnosis file; a preset camera is combined with a color card correction mechanism to collect tongue surface, tongue bottom and face images of a user, the shooting angle and focusing parameters are automatically adjusted in the collection process, and ambient light interference is eliminated through the color card correction mechanism; according to the method, the traditional Chinese medicine syndrome element mapping matrix is converted into the syndrome score, the personalized health report containing multi-dimensional information is generated based on the analysis result, and the file is associated to support multi-mode acquisition, so that traditional Chinese medicine four-diagnosis data acquisition precision, analysis intelligence and diagnosis personalization are integrally realized, the traditional Chinese medicine four-diagnosis efficiency and accuracy are effectively improved, and the traditional Chinese medicine four-diagnosis efficiency and accuracy are improved. And comprehensive and adaptive health assessment is provided for the user.

The invention relates to a personalized sleep-aiding regulation and control method and system fusing multi-band characteristics and closed-loop feedback, and relates to the technical field of sleep regulation and control. According to the method, a cross-band coupling matrix is constructed, a nerve-physiological synergy index is calculated, environment-brain state interaction potential energy is calculated based on different band powers and environment sound pressure levels, and the nerve-physiological synergy index and the environment-brain state interaction potential energy are combined into a coupling state vector; solving the coupling state vector to judge the stability of the sleep state; on the basis, personalized sleep induction dynamic attractor intensity is calculated, and different sleep regulation and control strategies are generated according to the steady state / unsteady state and the corresponding attractor intensity. According to the invention, the unique sleep dynamics of the user can be understood and self-adaptively regulated.

The invention relates to a multi-terminal collaborative wound surface information intelligent supervision method and system and related equipment, and belongs to the technical field of wound surface information supervision, and the method comprises the steps that a service instruction triggered by a patient through a patient terminal is received, and the service instruction at least comprises a wound surface image; after the wound image is preprocessed, the processed wound image is output to a nurse end so that a nurse can recheck the wound image; and receiving a re-checking result fed back by the nurse terminal, and when the received re-checking result meets a preset doctor intervention condition, pushing the re-checking result to a doctor terminal for a doctor to know. The method has the effect of helping to improve the timeliness of doctor-patient communication.

The invention relates to the technical field of operator safety management, and discloses a non-contact physiological status rapid screening method and system and a medium, and the method comprises the steps: collecting an RGB three-channel space-time image sequence of a facial region of interest; carrying out illumination correction on the RGB three-channel space-time image sequence by adopting an adaptive gamma correction algorithm, carrying out end-to-end noise reduction processing on the illumination correction image sequence by utilizing a one-dimensional convolutional neural network auto-encoder noise reduction algorithm, and separating a remote photoelectric volume pulse wave tracing pulse signal; calculating physiological indexes of heart rate, heart rate variability and oxyhemoglobin saturation based on the remote photoplethysmography pulse signal; and performing grading judgment on the physiological indexes based on the physiological index grading standard, determining a final post suitability judgment grade in combination with the wooden barrel effect model, and generating a grading early warning signal. According to the method, accurate and rapid evaluation of the physiological state of the operator in a complex environment can be realized, the individual health degree can be comprehensively and objectively considered, and decision support is provided for reasonable distribution of posts and efficient configuration of personnel.

The present application belongs to the field of artificial intelligence and health monitoring technology, and specifically relates to an emotion recognition and intervention system based on facial micro-expression and physiological signal fusion. The disclosed system integrates multi-modal synchronous acquisition, dynamic feature fusion decoding and individualized closed-loop intervention functions. Through the spatio-temporal alignment analysis of facial micro-expression and physiological signals, the emotion recognition accuracy is improved. Combined with cognitive load correlation modeling and wearable multi-channel regulation terminals, adaptive feedback is realized, cross-period emotion evolution prediction and group situation awareness are supported, a complete closed loop of "perception-decision-intervention" is constructed, and the accuracy and initiative of emotion management in complex environments are enhanced.

The present disclosure provides a detection apparatus and an electronic device. In the detection apparatus, a first photodetector is disposed between two adjacent first light sources, and second light sources are located in a detection region enclosed by a plurality of first light sources and a plurality of first photodetectors. The first light source has a first distance H1 and a second distance H2 from at least two of all first photodetectors, and the second light source has a third distance H3 and a fourth distance H4 from at least two of all first photodetectors. The first light sources, the second light sources, and the first photodetectors are distributed in distribution regions of the detection apparatus. Each light source and one photodetector may form one PPG module, and an effective detection depth in skin may vary with a distance between the light source and the photodetector.