1428results about "Diagnostics using light" patented technology

The invention provides a multifunctional health monitoring and early warning system based on radar and vision fusion, and relates to the technical field of intelligent early warning, and the system comprises a feedback receiving module which receives a radar feedback signal and an image feedback signal; the monitoring identification module traverses the target monitoring area to determine monitoring data, and carries out monitoring identification; the abnormity judgment module is used for carrying out fusion analysis on the first monitoring data set and the second monitoring data set, carrying out bidirectional abnormity judgment and generating an early warning instruction; and the risk analysis module uploads the abnormal judgment result to a cloud platform for risk analysis, and sends early warning information to a remote terminal according to the risk level. According to the invention, the technical problem that the accuracy of early warning information is affected due to high false alarm rate caused by environmental infection due to dependence on a single sensor in health monitoring in the prior art can be solved, risk assessment is carried out by combining radar and visual fusion, the accuracy of anomaly recognition is improved, and the accuracy of early warning information is improved. And the early warning real-time performance and accuracy are improved.

The invention provides a PPG optical sleep and respiration monitoring method and system based on a gravity sensing intelligent ring, and the method comprises the steps: synchronously collecting a PPG signal and a three-dimensional gravity acceleration signal of a hand through the intelligent ring, carrying out the adaptive filtering of the PPG signal, extracting a time-domain pulse wave and a frequency-domain heart rate variability feature, and carrying out the detection of the PPG signal. Body movement intensity and body position change frequency are calculated according to the gravitational acceleration signals, and body movement feature vectors are generated. And then fusing the pulse wave characteristics, the heart rate variability characteristics and the body movement characteristics, constructing a multi-dimensional physiological parameter matrix, analyzing and dividing sleep stages based on spatial-temporal correlation of the matrix, and detecting abnormal respiratory rhythm. And finally, dynamically adjusting a judgment threshold value of the abnormal respiratory rhythm, and outputting a sleep quality evaluation result and a respiratory event alarm. According to the invention, the accuracy and real-time performance of sleep monitoring can be improved.

The invention relates to the technical field of medical equipment and intelligent learning, in particular to a critical patient vital sign dynamic monitoring and intelligent early warning system which comprises a multi-modal data acquisition module, a medical equipment linkage control module and an intelligent early warning decision module. A dual-channel Kalman filtering state updating model, a biological impedance monitoring model and an anti-interference and signal optimization model are established in the multi-modal data acquisition module, and multi-dimensional physiological monitoring information of the critical patient is obtained through the models; a physiological information standardization model and a safety control mechanism are arranged in the medical equipment linkage control module, and then multi-dimensional physiological monitoring information is cooperatively controlled to output multi-modal vital sign data; and the intelligent early warning decision module receives and analyzes the multi-modal vital sign data to obtain power spectral density, and predicts the disease development condition of the critical patient in combination with the depth prediction model and the multi-modal vital sign data. All the modules work cooperatively to achieve dynamic monitoring and intelligent early warning of vital signs of critical patients.

The invention relates to the technical field of human body health assessment, in particular to a health assessment method and system based on a 3D human body posture image model. The method comprises the following steps: acquiring human body annular scanning data; extracting a dynamic posture sequence of a vertical position, a forward bending position and a lateral bending position of the human body annular scanning data, and performing phase shift fringe projection on the human body annular scanning data according to the dynamic posture sequence to obtain human body three-dimensional point cloud data; a sagittal plane, a coronal plane and a cross section of the human body three-dimensional point cloud data are analyzed for orthogonal projection, and a human body multi-angle projection image group is generated; and identifying skeleton contour features of the human body multi-angle projection image group so as to establish a dynamic marking system of the anatomical mark points. Through high-precision dynamic acquisition and multi-dimensional space analysis, precision, standardization and individuation of 3D human body posture health assessment are realized, and the accuracy and comprehensiveness of human body posture health assessment are improved.

The invention belongs to the technical field of medical image assistance, and particularly relates to an auxiliary stereo imaging system and method for a complex chest trauma operation. By integrating multi-modal image data, real-time three-dimensional space capture, dynamic correction and risk score driven view adjustment, accurate assistance of the thymotomy operation process is achieved, the accuracy and safety of the operation are improved, the operation risk and operation difficulty are remarkably reduced, and the operation efficiency is improved. By acquiring and processing multi-modal image data in real time, a high-precision three-dimensional anatomical structure model containing thymus tissues, a blood vessel network and nerve distribution can be constructed, a visual surgical navigation view is provided for a doctor, meanwhile, a dynamic correction technology is adopted, thymus displacement parameters caused by thoracic cavity respiratory movement of a patient are monitored in real time, and the thoracic cavity of the patient can be accurately and accurately positioned. And on the basis, the real-time three-dimensional space data is dynamically adjusted, so that the spatial synchronization of the image data and the anatomical structure is ensured, and the navigation error caused by respiratory movement is effectively avoided.

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 invention belongs to the technical field of physiological parameter measurement and testing, and discloses a multi-mode optical non-contact health monitoring system and method. The system comprises a sensing measurement module, a data synchronous acquisition module, a micro-motion stabilization and correction module, a three-dimensional trajectory reconstruction module, a multi-modal data processing module and a physiological index calculation module. Optical imaging data are processed preferentially through a U-Net network and then fused with video micro-motion features, and the problem of multi-modal data coupling interference in a traditional method is solved. A mathematical mapping relation between blood flow parameters and physiological indexes is established through a diffusion optical spectrum mapping network, the segmented blood flow parameters are mapped to initial physiological indexes, and the time lag problem of single-modal data is solved through time sequence features extracted through a time sequence feature fusion network. And the weights of the time sequence characteristics and the preliminary physiological indexes are dynamically adjusted through a space-time attention mechanism, so that the robustness in a complex environment is enhanced, and the prediction precision and stability of a non-contact health monitoring system are improved.

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.

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 relates to the technical field of acupuncture, in particular to an intelligent acupuncture point precise positioning and stimulation regulation and control system based on combination of traditional Chinese medicine and western medicine, and the system firstly collects body surface acupuncture point information of a user, analyzes sensitivity and generates visual data. The information processing module fuses the data, generates acupoint comprehensive characteristics and marks a target acupoint. And the virtual visualization module generates a virtual acupoint image and marks a target acupoint. The needling instrument massage module formulates a needling instrument pushing scheme according to acupuncture point characteristics and needling instrument parameters, and the needling instrument pushing scheme is executed by the body surface needling instrument pushing device. Meanwhile, the electrical stimulation regulation and control module generates parameters, the electrical stimulation intensity, frequency and time are controlled, and the electric acupuncture massage device is combined with body surface acupuncture massage operation to execute treatment. According to the system, accurate acupoint positioning, personalized needle pushing scheme making and electrical stimulation regulation and control are achieved, the effect and safety of needle pushing treatment are improved, and innovative technical support is provided for modernization of traditional Chinese medicine.

The invention discloses a health monitoring system based on pulse signal multi-domain feature extraction and deep learning, and the system is characterized in that a multi-dimensional feature extraction module employs an FMCW radar and a sensor device to collect and obtain pulse signals, carries out the multi-level processing and feature extraction of the obtained pulse signals, and extracts features related to a health state; the feature association mapping module matches and associates the pulse signal features of different frequency bands with the health state; the state recognition module automatically recognizes and classifies pulse signal features and health states based on a deep learning model; and the health information feedback module analyzes the health state of the user according to the health state recognition result of the state recognition module, provides health feedback and generates corresponding personalized health guidance information. By using the system, comprehensive analysis of health conditions can be realized, continuous and highly-targeted health tracking and evaluation services can be provided, and modern personalized and intelligent traditional Chinese medicine health monitoring and management requirements can be met.

The invention relates to the technical field of medical instruments, and discloses intravenous infusion puncture angiography glasses equipment and an application method thereof. The equipment comprises an infrared imaging module, an image processing module, a data communication module, a correction early warning module and a display module. The infrared imaging module collects original blood vessel image data, the image processing module generates blood vessel positioning information, the data communication module interacts with a background server, the correction early warning module generates an early warning prompt according to a server instruction, and the display module displays related information in an overlapping mode. By accurately positioning the blood vessel and providing puncture guidance, the equipment improves the success rate of venous transfusion puncture, reduces the pain of a patient, improves the medical working efficiency, reduces the medical cost, guarantees the medical safety, and is suitable for various venous transfusion puncture scenes, especially patients with poor blood vessel conditions.

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 present invention provides an automatic epilepsy signal recognition system and method based on multimodal information fusion, wherein the electroencephalogram acquisition unit is used to acquire the EEG signal of the patient in real time; the video acquisition unit is used to synchronously acquire the monitoring video signal of the patient; the data preprocessing unit performs noise reduction processing according to the EEG signal and the monitoring video signal, and adds the EEG signal and the monitoring video signal to the epileptic seizure start and end timestamps for time series calibration to obtain epileptic electroencephalogram data and epileptic video data; the multi-scale convolutional network combines the time domain, frequency domain, time-frequency domain and time-space domain features of the epileptic electroencephalogram data through a deep learning feature extraction method to obtain epileptic electroencephalogram multi-feature vectors; the multi-level feature pyramid network processes the epileptic video data through a spatial pyramid pooling method to obtain epileptic video multi-feature vectors scale features; the human optical flow estimation module obtains the epilepsy video optical flow vector by capturing the epilepsy video data sequence; the multimodal feature fusion module adopts the multi-layer perceptron mapping method to fuse the epilepsy EEG multi-feature vector, the epilepsy video multi-scale features and the video optical flow vector to obtain the epilepsy fusion feature data; the epilepsy signal classification module identifies the epilepsy fusion feature data through a bidirectional long short-term memory network with a multiple attention mechanism to obtain the epilepsy signal category; the epilepsy data automatic recognition model optimization module adopts the cross entropy loss function to train the epilepsy signal automatic recognition model; the present invention makes full use of various data in the patient's epilepsy detection, adopts multimodal information of different dimensions to capture the spatiotemporal correlation of epilepsy signals, and improves the robustness of the system.

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 an intelligent ring with a physiological feature detection function and a physiological feature detection method. The smart ring comprises a pressure detector and an arithmetic processor. The pressure detector is used for detecting the value of pressure applied to the smart ring by the target object. The arithmetic processor is electrically connected with the pressure detector and used for comparing the pressure value with a preset condition and judging the behavior of the target object according to the comparison result of the pressure value and the preset condition so as to generate a corresponding operation instruction. The smart ring not only can utilize the pressure detector to correct the detection result of the light volume change tracing detector, but also can utilize the pressure detector to detect the degree of tightness, gestures or finger actions when a user wears the smart ring, and has diversified application changes.

The invention discloses an auxiliary stroke rehabilitation method and device based on a multi-source physiological signal wearable device. The method comprises the steps that the pulse waveform variation rate and sublingual vein feature parameters of a subject are monitored through a self-powered five-hidden monitoring wrist strap; dynamically evaluating the internal organ imbalance index of the subject according to the pulse waveform variation rate and the sublingual vein characteristic parameters; generating personalized rehabilitation adjustment parameters according to the internal organ imbalance index of the subject; synchronously acquiring an electroencephalogram signal of a subject and a surface electromyogram signal of a target muscle, and generating an acupoint activation time sequence map of preset hand actions; the acupoint activation time sequence map is optimized according to the personalized rehabilitation adjustment parameters so as to adjust acupoint selection, stimulation intensity and stimulation time sequence; and presenting a built-in foot yangming stomach meridian virtual acupuncture scene to the subject through the VR guide instrument. According to the apoplexy rehabilitation method, electroencephalogram and myoelectricity cooperative decoding, personalized parameter adjustment, VR and a viscera imbalance traditional Chinese medicine diagnosis method are integrated into a rehabilitation scheme, and the apoplexy rehabilitation effect is improved.

The present disclosure provides a method and system for estimating the distance between an optical fiber end and a target. Treatments using laser and optical fiber technology require high precision to ensure that the laser is aimed at the correct target (stone, tissue, tumor, etc.) to achieve clinical objectives such as tissue ablation, coagulation, stone fragmentation, dust removal, etc. Therefore, it is important to know the distance between the target and the optical fiber end (distal end) from which the laser light is emitted, because laser treatment parameters such as energy, pulse width, laser power modulation, and / or repetition rate are often determined based on the distance from the tip of the optical fiber to the target.

The invention relates to the technical field of intelligent closestools, in particular to an intelligent closestool with human health monitoring analysis and early warning functions, which comprises an identity recognition module, a multi-source sensing module, an internet of things module, an intelligent analysis module and a user terminal module, the intelligent analysis module performs deep integration and analysis on the multi-modal time series data in the cloud database based on a large language model, generates a personalized health portrait, identifies a potential pathological phenomenon, triggers disease early warning, and constructs a health assessment model through a model fine tuning method in combination with a medical knowledge base; and outputting a personalized health analysis report and a health intervention suggestion. The multi-source sensing module is integrated, multi-dimensional health data can be collected in real time, the problems that in the prior art, the data collection dimension is single and means are limited are solved, the complex health state of the human body is comprehensively reflected, the situation that an existing analysis method is single is broken through, and the potential value of data is fully mined; and a complex scene with individual difference and coexistence of multiple disease species can be better handled.

A device for fluorescence-based imaging and monitoring of a target is provided. The device includes a light source emitting light for illuminating the target, the emitted light including at least one wavelength or wavelength band causing at least one biomarker associated with the target to fluoresce, and a light detector for detecting the fluorescence.

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 invention relates to the technical field of electroencephalogram monitoring, in particular to a non-contact electroencephalogram monitoring method and system, and the method comprises the steps: obtaining a corresponding head image of a target user at a current moment; determining a corresponding head pose of the target user at the current moment based on the head image corresponding to the current moment; based on the head pose at the current moment and the head pose corresponding to the previous moment, the pose of an electroencephalogram cap is determined, the electroencephalogram cap comprises a plurality of brain electrodes, and the brain electrodes monitor electroencephalogram data of the target user under the pose corresponding to the electroencephalogram cap. The problems that in the non-contact electroencephalogram collecting process, when the head moves, the collecting position deviates, and signals are not stable can be solved.

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 an intelligent baby crib monitoring system based on wireless transmission, and relates to the technical field of baby physiological monitoring, which comprises the following steps: dynamically distributing node communication time slots through wireless networking, adjusting the inclination angle of a laser array, activating a dual-band metamaterial antenna, synchronously collecting light intensity fluctuation signals and electrocardiosignals, and generating a mixed signal flow; carrying out physiological feature extraction on the mixed signal by adopting adaptive filtering and noise conjoint analysis, and generating a clean breathing waveform and an electrocardio waveform through wavelet decomposition and dynamic threshold suppression; respiration and heart rate abnormity is identified based on zero cross point detection and R wave energy verification, and a transmission priority is determined in combination with chaotic key generation and a Markov decision model. Through multi-band collaborative networking and dynamic time slot allocation, hardware-level synchronous acquisition of light intensity fluctuation signals and electrocardiosignals is realized, and signal synchronization errors are controlled in combination with metamaterial antenna dual-channel noise capture and differential common-mode rejection.

A device for fluorescence-based imaging and monitoring of a target is provided. The device includes a light source emitting light for illuminating the target, the emitted light including at least one wavelength or wavelength band causing at least one biomarker associated with the target to fluoresce, and a light detector for detecting the fluorescence.

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.

A device for fluorescence-based imaging and monitoring of a target is provided. The device includes a light source emitting light for illuminating the target, the emitted light including at least one wavelength or wavelength band causing at least one biomarker associated with the target to fluoresce, and a light detector for detecting the fluorescence.

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.