54 results about "Pain level" patented technology

The invention provides a pain assessment system and method based on multi-modal physiological signals. The system comprises a multi-modal signal acquisition module, a signal preprocessing module, a multi-modal feature extraction module, a deep fusion analysis module, an individualized calibration module and a result output and early warning module. By synchronously collecting and analyzing multi-dimensional data such as facial expressions, sound features, physiological signs and behavior responses and combining deep learning and multi-modal information fusion technologies, objective quantitative evaluation and real-time monitoring of the pain degree are achieved, and accurate decision support is provided for clinical pain management.

The invention relates to a pain assessment method and system based on a visual large language model. The method comprises the following steps: S100, synchronously collecting video data of the face and limbs of a target object; s200, performing space-time sampling processing on the video data to obtain image data; s300, generating a multi-step guide instruction sequence based on a preset pain assessment dimension; s400, inputting the image data and the instruction sequence into a visual big language model, and gradually reasoning the visual big language model according to a preset pain assessment dimension; and S500, the visual big language model sequentially generates a scoring basis, a scoring result and a comprehensive pain grade of each dimension according to the reasoning result of each dimension. Through the arrangement, the technical problem of objective assessment of the pain degree in the medical operation process is solved.

The invention relates to the technical field of medical monitoring, and particularly discloses a pain degree assessment method and system based on multi-modal sensing and wearable equipment. The method comprises the following steps: acquiring physiological signals and limb movement data of a patient to form a multi-dimensional pain feature vector; a dynamic weight distribution algorithm is adopted, and physiological and limb action modal weight coefficients are adjusted in real time based on each modal data prediction confidence coefficient; a multi-modal classifier is utilized, features are fused through a cross-modal attention mechanism, and 0-10 levels of pain quantized values are output; and intelligent regulation and control of analgesia parameters are realized. The system comprises a multi-modal sensing module, a feature extraction module, a dynamic weight distribution module, a multi-modal classification module and an analgesia equipment control module. The wearable device integrates a sensor and a processing unit to realize data acquisition and analysis. According to the invention, the dynamic, precise and intelligent pain assessment is realized, the technical problems of strong subjectivity and poor real-time performance of the traditional assessment method are solved, and reliable technical support is provided for clinical analgesia management.

The invention relates to the field of medical auxiliary diagnosis, in particular to an intelligent child pain assessment system based on AI facial expression analysis, a facial detection module obtains a child facial image, and performs three-stage cascade detection by using a multi-task cascade convolutional neural network model to obtain facial key point pixel coordinate information; the key point positioning module carries out noise filtering processing on the coordinate information to obtain more accurate face key point coordinate information, the information is transmitted to the key point thermodynamic diagram representation module, and the key point thermodynamic diagram representation module generates a face key point thermodynamic diagram representing the degree of pains of children in combination with the face key point coordinate information and a thermodynamic diagram mechanism. The child pain intelligent evaluation module evaluates the degree of pain of the child based on the thermodynamic diagrams and obtains an evaluation result, and finally, the pain evaluation result visualization module visualizes the evaluation result, and the accuracy of pain characterization is improved through high-precision face detection and key point thermodynamic diagram technology, so that the accuracy of pain characterization is improved. And an effective method is provided for evaluating the pains of children.

The invention relates to a pain level identification method and system based on a multi-modal large language model, electronic equipment and a readable storage medium, and belongs to the technical field of medical data processing. The method comprises the following steps: encoding an input facial expression image into visual feature representation by adopting a visual encoder ViT model; coding clinical text data of the patient by using a text coder built in the large language model; using a modal alignment module to align and splice the visual features and the text features to generate fusion features; and processing the fusion features through a Q-Lora fine-tuned large language model, and outputting and mapping the fusion features into probability distribution of each pain level. According to the method, the facial expression and the clinical text information of the patient are accurately aligned in the semantic space, the ability of the model to understand the relation between complex data is remarkably improved, the method has higher recognition accuracy for complex or fuzzy pain expression, and the robustness for illumination variation, language missing and other situations is also remarkably improved.

The invention provides a pain discrimination method based on a time sequence and multiple modes. The pain discrimination method comprises the steps that conventional physiological indexes, behavior characteristic indexes and potential auxiliary indexes are collected; preprocessing the multi-modal data, including abnormal value processing, individual normalization and time alignment, and constructing a time sequence data block; establishing an individual baseline, dynamically calibrating a baseline threshold value every 7 days, and calibrating a blood glucose baseline once every 3 days for a diabetic patient; physiological features, behavior features and time sequence features of the time sequence data blocks are extracted through an AI model, multi-modal features are fused through an attention mechanism, and full-level pain is recognized through multi-modal index synchronous verification, multi-classification probability calculation and grading threshold judgment; and outputting a clinical intervention strategy corresponding to the pain level, and dynamically updating the model parameters and the baseline threshold based on clinical intervention feedback and new crowd data, so that various crowds can be covered, the hierarchical intervention strategy is output, the intervention delay is shortened, and the clinical practicability and accuracy of pain management are improved.

The invention relates to the technical field of medical informatics, in particular to a pain management-oriented physiological signal real-time monitoring and intervention system, which comprises a determination module for determining the abnormal tendency of a stimulation window updated in real time at each updating moment to obtain an abnormal tendency sequence; the determination module is also used for determining similar fluctuation segments in the abnormal tendency sequence; the determination module is also used for determining an abnormal process factor of the physiological sensing signal at the current moment based on the abnormal tendency difference value and the length of each similar fluctuation section; the determination module is also used for determining a trigger factor for evaluating the abnormal pain state at the current moment according to the abnormal process factors of each physiological sensing signal at the current moment and the previous moment, and performing normalization processing on the trigger factor to obtain a normalized trigger factor; and the intervention module is used for carrying out visualization and intervention on the pain degree of the target user based on the normalized trigger factor. According to the invention, the physiological signal abnormity identification accuracy and sensitivity are improved.

The application relates to a pain real-time discrimination and treatment effect evaluation system based on EEG microstate monitoring. The system comprises the following modules: a microstate extraction and analysis module which extracts GFP peak topography from EEG signal data, optimizes a template map, calculates the average duration of each microstate category and the like, and outputs a microstate feature parameter set; a pain real-time discrimination module which inputs the microstate feature parameter set into a deep learning model for detection to obtain a detection result containing a pain level; and a treatment effect evaluation module which obtains and compares the microstate feature parameter set before and after treatment, obtains a quantitative treatment effect index, and generates a report containing the index, analysis results and a microstate change trend chart. The system can monitor the pain state of a patient in real time through EEG signal microstate, and can provide objective evaluation indexes about the treatment effect for a clinician by quantifying the dynamic change of the EEG microstate of the patient before and after treatment, so that the scientificity and accuracy of treatment effect monitoring are improved.

A system and method for visualizing a position of a needle inside a patient's body for use in providing regional anesthesia and a system and method for providing electro anesthesia during surgery, post surgery and during rehabilitation. The method and system use EMG / AMG information regarding activity of a muscle associated with a target nerve to provide a visualization of a position of a needle or other device relative to the target nerve and to determine efficacy of the electro anesthesia. EMG / AMG information and feedback information such as EEG information may be used to provide automatic adjustment of a waveform provided for electro anesthesia to maintain a suitable pain level.

The application discloses a pain assessment model construction method and a non-contact continuous pain assessment method, and belongs to the field of image expression recognition. Through a non-contact mode, multi-modal time sequence data of a patient is collected, and after time-space synchronous processing to obtain a multi-modal frame package sequence, single-modal self-supervision pre-training is performed based on extracted single-modal time sequence data, and then a multi-modal projection layer is connected to obtain a front structure for cross-modal semantic alignment coding, so as to map output features of each mode to a large language model word vector dimension, and then a large language backbone model is connected to obtain an overall structure. The multi-modal time sequence data of the patient is input, fine-tuning training of the large language model is performed by indicating the overall structure to output a pain assessment result, a trained pain assessment model is obtained, and the trained pain assessment model is used for non-contact continuous pain assessment. The pain assessment result includes a pain level, a pain score, an interpretable heat map and the like. The application combines a large language model, so that the trained model can realize pain level division and interpretable continuous output.

This invention belongs to the field of rehabilitation training technology. It provides a method and system for analyzing upper limb rehabilitation training data, including the following steps: During the upper limb rehabilitation training process, static single-joint training parameter data and dynamic multi-joint collaborative training parameter data are collected in real time. The static single-joint training parameter data is then used to identify whether it meets the pain-free standard. This invention optimizes the problem of focusing only on single-joint data and ignoring multi-joint linkage and collaboration in upper limb rehabilitation training data analysis. It first identifies the static single-joint pain-free standard to eliminate basic interference, then filters and analyzes high-incidence combinations of multi-joint pain, and then reverse-engineers the final balance range where pain level meets the standard and range of motion is maximized, assessing the degree of achievement. Finally, it combines a safe expansion span to generate a stepped optimization range and predicts feasibility using a rehabilitation trend synergy index, achieving a shift from experience-driven to data-driven approaches.

The utility model discloses an assessment ruler for postoperative active pain, which relates to the technical field of pain assessment tools, and comprises an assessment ruler main body, one side of the assessment ruler main body is provided with an NRS pain score indication line, the surface of the NRS pain score indication line is provided with a plurality of first lamp bodies, and the first lamp bodies are provided with second lamp bodies. A plurality of first control buttons matched with the first lamp bodies are arranged above the NRS pain score indication line. According to the utility model, the positions of the first indicator and the second indicator are moved, so that the first control button and the second control button which correspond to each other are pressed, the first lamp body and the second lamp body which correspond to each other are lightened, and the voice output device synchronously broadcasts the contents of the NRS pain level and the FAS pain level which reach the positions; according to the design, the postoperative active pain assessment process of the patient can be quicker and more efficient, the patient can express own pain feeling more accurately, and assessment errors caused by unsmooth communication or understanding deviation are reduced.

The invention discloses a method for detecting the pain level in combination with neural activity and detection equipment, the detection equipment comprises a detection deck chair support, a buffer structure and a detection structure, the detection structure and the buffer structure are installed on the detection deck chair support, and the invention relates to the technical field of neuropathic pain detection. Through electroencephalogram spectrum analysis, electrocardio variability monitoring, pupil dynamic tracking and enphalin blood concentration detection, a pain related biomarker database is constructed, and compared with a traditional subjective scale, the objective index association degree is remarkably improved; the original arc-shaped guide rail buffer system and the rotary air bag fixing devices effectively absorb detection shaking, and are matched with the four pairs of rotary air bag fixing devices, so that the human engineering impact is attenuated under the low-frequency vibration working condition of below 10Hz.

The application discloses a pain pump administration control method and device, a pain pump and a storage medium. The method comprises the following steps: acquiring a corresponding relationship between a set index data range of a set associated part of a patient's operation site and a pain level, the set associated part being the brain or any body surface position within a set distance range of the operation site, and when the set associated part is the brain, the set index data is preset electroencephalogram index data of an electroencephalogram signal, and when the set associated part is any body surface position, the set index data is potential change amplitude data of an electromyogram signal; determining a current administration speed according to the detected set index data of the set associated part and the corresponding relationship between the set index data range and the pain level; and performing an infusion operation of the current patient according to the current administration speed. The application solves the problem that the existing pain level control pain pump administration is not objective enough, can determine the pain level based on the potential change, timely adjusts the administration, and can also issue an alarm when an abnormal situation occurs.

1. Name of this design product: Pain Assessment Scale (Naan Bread Pattern). 2. Purpose of this design: To assess the patient's pain level by using the pain rating values ​​on the card. 3. The key design features of this product are the combination of pattern and color. 4. The image or photograph that best illustrates the design's key features: the front view. 5. Other situations requiring explanation: Other explanation: Omitted views: Because this design product is a card-type product, the rear view, left view, right view, top view and bottom view do not have design features, so they are omitted.

The invention provides a method for obtaining a measure of user pain at home. The proposed concept is to measure the pain by utilizing sensor data obtained from force and / or motion sensors integrated in a personal care device, e.g. an oral care device, over the course of each of one or more personal care sessions of the user. Patterns in the sensor data, or changes in said patterns, can be used to infer pain information, such as an overall pain level, pain reaction events at certain points during the personal care session and / or at certain locations, or increased sensitivity at certain body areas. This provides a more objective and reliable estimation and monitoring of user oral pain that relying on patient (subjective) reporting of pain.

This application relates to the field of computer vision technology and discloses a method and system for detecting pain expressions. The method includes: acquiring a facial video stream, segmenting muscle regions and extracting texture features after facial key point localization and inter-frame alignment; calculating motion energy based on these features, extracting enhanced micro-expression temporal segments and multi-scale spatiotemporal features, constructing a muscle dynamics model and completing state estimation; generating an adaptive candidate spatiotemporal window set through spatiotemporal attention weighted fusion; performing temporal modeling for dynamic feature fusion, and selecting the optimal window by fusing micro-expression features; and outputting the final pain level after double consistency verification and loop closure optimization. This application overcomes the limitations of static images, accurately captures facial dynamics and temporal changes, strengthens feature correlation, and significantly improves detection accuracy.

The invention discloses a brain disease postoperative wearable pain monitoring device and system, and the device comprises a multi-mode physiological signal collection module which collects key physiological parameters in real time through an intelligent sensing system in the prior art; the signal processing and pain analysis module is used for preprocessing the signal and quantifying the pain level; the remote communication and cloud module is used for realizing secure data interaction between the terminal and a cloud platform through an encryption protocol; and the real-time alarm module is used for active early warning when the abnormal pain threshold exceeds the limit, and the auxiliary treatment and feedback module is used for providing a personalized pain relieving scheme according to a pain evaluation result. According to the method, activation of a pain-related brain region is represented through differential entropy, subtle changes in electroencephalogram signals are captured, the accuracy of pain feature extraction is improved, a fuzzy entropy value is extracted, the ability of analyzing signal complexity and change trend is provided, the high-definition camera and the flexible electrode collect the pain and vomiting frequency of a patient and gastric electrical signals, and the accuracy of pain feature extraction is improved. And subsequent information analysis and recovery assistance are facilitated.

The present application relates to the field of physical therapy equipment, especially to a kind of spinal step quantitative traction physiotherapy control system based on closed-loop force feedback, the system includes: data acquisition module, real-time traction force value, electromyographic signal data, displacement rate of change between traction bed movable bed plate and traction bed fixed bed body are collected;Traction bed drive module, according to the traction force control instruction output by self-learning fuzzy logic controller, execute traction action to traction bed body;Self-learning fuzzy logic controller, according to real-time traction force value, electromyographic signal data, displacement rate of change and the pain level signal feedback by the object to be traction, using step traction strategy, generates traction force control instruction, traction force control instruction is set to use the fuzzy parameter adjustment model pre-constructed based on neural network and fuzzy inference, dynamically generated by updating the shape parameter of membership function of first fuzzy logic rule.The present application effectively improves the self-adaptability and accuracy of traction force regulation.

The invention provides an intelligent patient monitoring system and method based on hemodialysis and electronic equipment, and is suitable for hospitals and community dialysis center scenes. The system comprises a data acquisition module used for acquiring vital signs to evaluate anxiety / pain levels; the data processing module is used for receiving sensor data and triggering real-time early warning for key indexes; the AI analysis module is used for establishing a patient individualized baseline model, dynamically identifying an abnormal mode, carrying out emotion analysis and predicting risks in combination with dialysis parameters and real-time data; and the feedback and decision module is used for pushing a third-level alarm, providing a visual interface, displaying patient historical data and AI inference logic, and assisting clinical decision. Vital signs and emotion data of the hemodialysis patient are collected in real time through the wearable device, graded alarm and solutions are provided after AI analysis, the system gives an alarm through the micro-expression and emotion change of the patient, and the treatment safety is remarkably improved.

The invention discloses a postoperative pain monitoring method based on a wearable flexible sensor, and relates to the technical field of postoperative pain monitoring. According to the method, a postoperative pain monitoring model mapping relation is constructed based on patient basic feature data in a patient information database, and a corresponding postoperative pain monitoring model is determined based on the postoperative pain monitoring model mapping relation; patient pain feature data of a postoperative patient is input into a postoperative pain monitoring model, pain level monitoring is performed, a postoperative pain monitoring model mapping relation is constructed by integrating historical patient physiological data and individual features, and a dual mechanism of rapid screening of a first classification model and accurate verification of a secondary multimode monitoring model is adopted. According to the invention, the method achieves the grading monitoring of the pain grade of a postoperative patient, enhances the adaptability to different patient groups based on the model adaptation of individual features, and solves the problems that the existing postoperative pain monitoring method is too limited in single-mode evaluation, and is insufficient in individual difference adaptability.

The present application relates to the technical field of medical informatics, and in particular to a physiological signal real-time monitoring and intervention system for pain management, which comprises: a determination module configured to determine the abnormal tendency of a real-time updated stimulation window at each update time to obtain an abnormal tendency sequence; the determination module is further configured to determine similar fluctuation segments in the abnormal tendency sequence; the determination module is further configured to determine an abnormal progress factor of the physiological sensing signal at the current time based on the abnormal tendency difference and the length of each similar fluctuation segment; the determination module is further configured to determine a trigger factor for evaluating the abnormal pain state at the current time according to the abnormal progress factors of each physiological sensing signal at the current time and the last time, and to normalize the trigger factor to obtain a normalized trigger factor; and an intervention module configured to visualize and intervene in the pain degree of a target user based on the normalized trigger factor. The present application improves the accuracy and sensitivity of physiological signal anomaly recognition.

The invention discloses a multi-mode pain perception objective quantitative evaluation system and application, and relates to the technical field of pain perception, and the system comprises a data acquisition module which is used for obtaining visual data, voice data, text data, a photoelectric volume pulse wave PPG signal and an electrodermal response EDA signal corresponding to a target user; the data processing module is used for determining a pain index of the target user according to the visual data, the voice data and the text data; performing amplitude differential processing on the PPG signal to generate a PPG amplitude variation delta PPG; performing slope integration processing on the EDA signal to generate an EDA accumulated variation delta EDA; determining an energy competition index according to the ratio of the delta PPG to the delta EDA; determining the type of the pain index according to the size of the energy competition index; and the decision output module is used for outputting the pain index and the type of the target user. According to the present invention, the positive real pain is distinguished through the ratio of the [delta] PPG to the [delta] EDA so as to improve the accuracy of the pain level evaluation of the patient.

The application discloses a postoperative children's pain recognition method based on an interpretable prototype, aiming to improve the interpretability of postoperative children's pain detection by comparing the similarity of facial feature positions. First, facial expression frames and sparse optical flow frames of each frame of picture are extracted from postoperative children's pain videos. Then, the feature vector of the facial feature position is extracted by using the ResNet convolution network, and the randomly initialized prototype vector is optimized by minimizing the loss function. Then, the similarity of the feature vector and the prototype vector is calculated, and the pain level recognition result of each frame of picture is obtained through the full connection network. Finally, the facial feature positions corresponding to the feature vector and the facial feature positions corresponding to the prototype vector are visually compared. The application improves the interpretability of the postoperative children's pain recognition process by showing the similarity of the facial feature positions on the facial expression and the sparse optical flow, which helps medical professionals better understand the automatic pain recognition result.

An apparatus includes a facial expression detection device and a computing device, the computing device being configured to receive facial pattern input from the facial expression detector, identify one or more micro-expressions in the facial pattern input, and predict one or more of pain intensity and pain source based on the identified one or more micro-expressions. Various aspects of the disclosed embodiments aim to identify pain sources non-contactly based on pain-induced micro-expressions using machine learning.

The invention discloses an objective pain quantification method and system based on a thalamus-cortex coupled neuron group model. Firstly, a thalamus-cortex coupled neuron group model is constructed, the model comprises a thalamus module and a cortex module, dynamic behaviors of neurons are described through a specific differential equation, and parameters are set. EEG signals are collected and preprocessed, model parameters are estimated through an optimization algorithm, objective quantitative indexes are defined according to the parameters and neuron activities, comprehensive pain indexes are calculated, pain is divided into different grades according to the indexes, and objective quantitative evaluation is achieved. According to the method, the complex state of the brain during pain perception can be comprehensively and deeply reflected, so that the pain degree is accurately quantified, and the inaccuracy and uncertainty of a traditional subjective evaluation method are effectively avoided; neural information related to pain can be more comprehensively captured through a multi-parameter fusion mode, so that a pain assessment result is more reliable and stable.

The invention relates to the technical field of wearable medical equipment, and discloses wearable pain management and rehabilitation equipment with dynamic impedance adaptation. Comprising a millimeter wave radar, a flexible multi-electrode array, a multi-frequency biological impedance detection module, a pain biomarker fusion processor, an sEMG sensor, a thermistor, a 9-axis IMU, a pain grade prediction model, a dynamic impedance adaptation engine, a nerve stimulation waveform generator and a cloud pain digital twin system, by arranging a millimeter-wave radar, a flexible multi-electrode array, a multi-frequency biological impedance detection module, a pain biomarker fusion processor, an sEMG sensor, a thermistor and a 9-axis IMU, comprehensive monitoring and management of the pain state of a user can be achieved, biomarkers related to pain are extracted through complex algorithm analysis, and therefore the pain state of the user can be comprehensively monitored and managed. And more accurate information is provided for pain management.

The invention discloses a deep learning-based facial micro-expression pain recognition and analysis method. The method comprises the following steps of S1, constructing an original data set; s2, preprocessing the obtained original data set; s3, based on the first-layer generative adversarial network and the preprocessed original data set, generating facial micro-expression images under different pain levels through a first-layer generator; s4, based on the second-layer generative adversarial network, extracting pain level features from the micro-expression image generated by the first-layer generative adversarial network and the real micro-expression data; s5, processing the extracted micro-expression features and pain grade scores through a fuzzy logic module; s6, based on a fuzzy logic reasoning result, performing defuzzification processing on the pain level; and S7, according to the real-time analysis result, continuously monitoring the pain state of the patient, and adjusting the sensitivity of the system to the micro-expression under different environmental conditions. According to the method, the facial micro-expression capture precision and the pain level identification accuracy in a complex environment are improved.

The application relates to a pain degree recognition method, device and system for a patient. The pain degree recognition method for a patient comprises the following steps: judging the data type of to-be-detected pain data; if the data type is a physiological signal type, adopting a multi-scale convolution mechanism, a compression-excitation residual network and a variable pressure encoder for feature extraction, and adopting a support vector machine to perform pain grading on the extracted signal features to obtain the pain degree of the patient; if the data type is an image type, adopting a double-branch feature extraction mechanism to perform multi-scale feature extraction on a facial expression image, alternately combining residual and attention mechanisms for feature fusion, and performing feature flattening and pain degree mapping on the fused feature map to obtain the pain degree of the patient. The pain degree recognition method for a patient has the advantages of high-precision pain grading based on physiological signals and high-accuracy pain grading based on images.

The invention relates to a pain real-time discrimination and treatment effect evaluation system based on EEG micro-state monitoring. The system comprises a micro-state extraction and analysis module which extracts a GFP peak topographic map from EEG signal data, optimizes the GFP peak topographic map to generate a template map, calculates parameters such as average duration of each micro-state category, and outputs a micro-state feature parameter set; the pain real-time judgment module inputs the micro-state characteristic parameter set into a deep learning model for detection, and a detection result containing a pain level is obtained; the treatment effect evaluation module obtains quantitative treatment effect indexes by obtaining and comparing micro-state characteristic parameter sets before and after treatment, and generates a report containing the indexes, analysis results and a micro-state change trend chart. According to the system, the pain state of a patient is monitored in real time through the EEG micro-state, objective evaluation indexes about the curative effect are provided for clinicians by quantifying the dynamic change of the EEG micro-state of the patient before and after treatment, and the scientificity and accuracy of treatment effect monitoring are improved.