79 results about "Pain assessment" patented technology

An assessment apparatus and method for objective pain assessment are provided, wherein the apparatus includes a processor comprising a frequency-domain transformation module, a frequency-band segmentation module, a first assessment submodule, and a second assessment submodule. The frequency-domain transformation module generates a global time-frequency feature matrix, which is segmented by the frequency-band segmentation module in a frequency domain into five frequency bands associated with pain perception. The first assessment submodule extracts last time step features and an adjacency matrix from the time-frequency feature matrix and generates a first feature vector representing global association patterns among electrodes used for acquiring EEG signals. The second assessment submodule generates a second feature vector with local spatiotemporal dynamic features of EEG signals, concatenates it with the first feature vector to form a fused feature vector, computes its class probability distribution, normalizes it, and generates an objective pain quantification indicator corresponding to the EEG signals.

The invention discloses a whole-process patient self-control analgesia management platform and method based on artificial intelligence, and relates to the related field of analgesia management, and the platform comprises a multi-source data interaction module which is used for building a time sequence standard data set; the information configuration module is used for starting the self-control analgesia pump; the AI pain evaluation module is used for generating a pain scoring result; and the identification early warning module is used for generating graded early warning. The technical problems of insufficient safety and unstable effect in the analgesia management process due to dependence on a fixed dose threshold and lagged artificial pain assessment in traditional patient self-control analgesia management are solved, the whole-process pain management platform based on artificial intelligence is constructed, multi-source data can be analyzed in real time, and the pain management efficiency is improved. And the pain state of the patient is dynamically evaluated, and the parameters of the analgesia pump are automatically adjusted, so that the technical effects of personalized analgesia management and safety and effectiveness in the analgesia process are realized.

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 discloses a health monitoring and pain assessment system based on multi-modal data fusion, and relates to the technical field of health assessment. The system comprises data acquisition, preprocessing, feature fusion and other modules. Multi-modal data are collected and frequency is adaptively adjusted, various data are processed by using a specific technology during preprocessing, a feature fusion module innovates a fusion mechanism, and an evaluation module uses a reinforcement learning model and comprises a health monitoring module, a user interaction module and a data storage module, so that comprehensive functions are guaranteed. According to the method, the evaluation accuracy is improved through multi-modal collection, data association is uniquely fused and mined, personalized services are realized through self-adaptive adjustment, data security is guaranteed through the block chain technology, and medical health development is powerfully promoted.

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 discloses a self-adaptive control method of an analgesia infusion system based on multi-modal feedback, and relates to the field of analgesia infusion automation control, and the method comprises the steps: inputting a feature vector into a multi-modal fusion decision-making layer, and calculating a comprehensive pain index PI by adopting a confidence-weighted self-adaptive fusion algorithm; taking the comprehensive pain index PI, the drug cumulant and the physiological stability index as state input, and inputting the state input into a self-adaptive controller based on reinforcement learning; the adaptive controller outputs a basic infusion rate adjustment amount, a pulse dose adjustment amount, and a locking time adjustment amount. According to the invention, through multi-source signal acquisition and confidence coefficient weighted adaptive fusion, the accuracy and robustness of pain assessment are improved; the hierarchical reinforcement learning controller realizes personalized dose optimization on the premise of safety priority, and gives consideration to both analgesic effect and physiological stability; and the safety monitoring module is combined with multi-modal cross validation and pharmacokinetic prediction, so that the safety risk is effectively reduced.

The invention relates to the technical field of facial dynamic video image pain assessment, in particular to a facial dynamic video image pain assessment method based on a dynamic fusion module, and the method comprises the steps: collecting data through a multi-modal sensor, and generating a three-dimensional feature mapping map; activating an adaptive weight adjustment module to generate a configuration parameter set; executing micro-expression feature extraction and dynamic fusion operation to output a high-precision feature vector; inputting a grading evaluation model to complete pain degree quantitative grading. According to the method, facial micro-expression changes can be accurately captured, feature distortion can be dynamically recovered, nonlinear feature components can be mined, feature extraction integrity and evaluation accuracy can be improved, meanwhile, the feature capture capability in a complex environment can be enhanced through a controlled gradient optimization dynamic fusion technology, and the calculation efficiency can be optimized.

The invention provides a pain assessment method, and the method comprises the steps: obtaining target myoelectricity data and target scale assessment data of a target object, the target myoelectricity data being myoelectricity data obtained by carrying out the myoelectricity signal sampling of a specified sampling part under a specified action, the target scale evaluation data is pain evaluation data obtained by performing pain grade evaluation on the target object according to a preset pain scale; and based on the target myoelectricity data, correcting the target scale evaluation data to obtain a final pain evaluation result. According to the method, the myoelectricity signal sampling is performed on the specified sampling part under the specified action, the scale evaluation data obtained by adopting the pain scale are corrected by utilizing the myoelectricity data obtained by sampling, and the myoelectricity data reflect the real pain response of the target object, so that the corrected pain evaluation result is more accurate, and the accuracy of the pain evaluation result is improved. And the accuracy of pain assessment is improved.

The invention is suitable for the field of pain assessment, and particularly provides a pain threshold estimation method and system based on multi-modal data fusion, and the method comprises the steps: constructing a cross-modal weak feature enhancement fusion network, carrying out the processing of collected multi-modal data, and extracting physiological weak features and behavior weak features; introducing a time-varying attention Transform model, dynamically allocating weights for the processed physiological weak features, behavior weak features and environment auxiliary data by using a pain dynamic tag, and outputting cross-modal fusion features; constructing a double-branch pain threshold estimation model, wherein the model comprises a supervised estimation branch and an unsupervised estimation branch; and introducing a pain feature element library, carrying out online calibration on parameters of the double-branch model in combination with a meta learning algorithm, and outputting a real-time pain threshold value and a short-term pain threshold value change trend of the target object. According to the embodiment of the invention, the clinical operability of pain assessment is remarkably improved, and ineffective intervention caused by inaccurate assessment is reduced.

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 the technical field of medical technology, in particular to a method for evaluating and adjusting pain in maternal and child anesthesia, which comprises the following steps of: constructing a physiological signal acquisition subsystem, a pretreatment subsystem, a self-adaptive evaluation subsystem and a closed-loop analgesia control subsystem to realize continuous, objective and specific evaluation of pain states of maternal and child patients under anesthesia; and the analgesic medicine is accurately and automatically adjusted. The continuous, objective and specific pain assessment can be realized, the analgesic medicine can be accurately and automatically adjusted, and the safety and comfort of maternal and child anesthesia are improved.

The invention relates to the technical field of medical auxiliary evaluation, in particular to a perioperative pain evaluation system for a brain tumor surgery patient, which comprises a surgery pain data acquisition module for acquiring an HRV signal, an intracranial pressure waveform, a subjective VAS score and pain part data of the patient; the feature extraction module processes the data through abnormal value detection and sliding window filtering, extracts HRV and ICP features in combination with time domain analysis, and outputs standardized data after integrating the HRV and ICP features with subjective feedback features; the AI pain quantification module is used for generating a pain risk probability value based on an AI model constructed by an XGBoost algorithm, triggering three-level early warning and associating pain properties and causes of a patient; the intervention pushing module triggers a multi-terminal prompt and pushes an intervention scheme, and a patient performs graphic interaction and feedback through a mobile phone terminal; and the management and tracing module is used for associating single-time whole-process data, recording an intervention effect and automatically upgrading an unexpected scheme. Therefore, the problems that in the prior art, intracranial pressure and pain coupling is not quantified, intervention lags and the like are solved.

The present application relates to the technical field of medical image processing, in particular to a pain assessment system for neonatal facial expression image analysis, comprising a data acquisition module, a facial expression manifold construction module, a curvature field analysis module, a spatiotemporal feature hierarchical analysis module and a pain assessment and alarm module, the facial expression manifold construction module maps discrete facial feature points to a Riemann manifold space to construct an individualized expression manifold model; the curvature field analysis module calculates a facial expression dynamic curvature field and extracts micro-motion curvature features; the spatiotemporal feature hierarchical analysis module performs multi-scale decomposition on the curvature features to identify pain-related micro-motion units; the pain assessment and alarm module calculates a pain score and performs early warning, the present application significantly improves micro-expression capture sensitivity, is robust to changes in shooting conditions, improves the ability to distinguish different types of pain, realizes objective quantitative assessment and multi-level early warning of the pain state of neonates, and provides reliable technical support for clinical pain management.

The invention belongs to the technical field of pain assessment, and particularly relates to a multi-modal real-time intelligent pain assessment auxiliary system based on deep learning. A multi-modal data acquisition device and a two-stage consistency reasoning device; wherein the multi-modal data acquisition device is used for synchronously acquiring a face video and a near-field voice under a unified time reference, and segmenting a time axis to form a unified time slice sequence so as to output the unified time slice sequence and an anchor point set which are aligned; and the two-stage consistency reasoning device is used for executing short window scoring on each time slice to obtain a pain score and uncertainty under the constraint of the aligned unified time slice sequence and the anchor point set, completing candidate fragment confirmation on a score timeline and outputting the real-time pain score, the confidence coefficient and the starting and ending time of the confirmed pain fragment. According to the method, the evaluation stability and reliability are improved while real-time response is ensured; and by combining a double-threshold rule and anchor point neighborhood consistency verification, non-pain interference is effectively eliminated, and a pain fragment is accurately confirmed.

The application provides a non-contact pain monitoring mattress and system based on multi-modal physiological signal fusion, the mattress body comprises, from bottom to top, a bottom support layer, a signal shielding layer, a sensing core layer and a breathable skin-friendly layer, the system comprises the non-contact pain monitoring mattress, a low-noise charge amplifier, a fiber demodulator, an edge computing gateway and a cloud analysis center, the data after signal conditioning is transmitted to the edge computing gateway and uploaded to the cloud analysis center, and the cloud analysis center performs quantitative scoring through a tumor-specific pain model.The application has the advantages that no electrode or wearable device needs to be pasted, the skin irritation and psychological burden of traditional monitoring are completely eliminated, the patient compliance is improved, the limitation of subjective evaluation is broken through, an objective pain evaluation method is provided for patients in the late stage / aphasia, multi-modal signal fusion monitoring is used to effectively distinguish pain from ordinary emotional fluctuations, the degree of pain is fed back in real time, the doctor is assisted in dynamically adjusting the dose of analgesic drugs, and the risk of drug overdose and deficiency is reduced.

The present application relates to the technical field of medical image analysis, in particular to a patient post-anesthesia pain grading evaluation method based on artificial intelligence. The method acquires multi-time body posture images in a complete respiratory cycle, constructs a trunk reference system, defines the end of the limb as an analysis point and a preset defense position as a target point; constructs a limb scheduling consumption matrix based on the distance between the analysis point and the target point, calculates the limb wandering confusion degree combined with the element distribution, and fuses to obtain the defense intention focus degree reflecting the order of whole body movement; the consumption matrix is subjected to geometric cost matching to obtain a limb scheduling total consumption index, and the focus degree is combined to generate a defense interference index; finally, the grading evaluation coefficient is determined according to the numerical distribution and stability of the index in the complete respiratory cycle, thereby objectively distinguishing the pain state and the wake-up period agitation state, and significantly improving the automation level and reliability of post-anesthesia pain evaluation.

The invention discloses an electromyographic signal acquisition system and device for standard pose pain assessment and discrimination, and relates to the field of electromyographic acquisition, and the system comprises a pose detection module which is used for discriminating whether the pose of a person to be acquired is a specific standard pose; the electromyographic signal acquisition module is used for selecting a proper acquisition channel to perform signal acquisition according to the pose of the acquired person; according to the electromyographic signal acquisition system and device for standard pose pain assessment and discrimination, the pose of a person to be acquired is detected in real time by using an external camera and a wearable gyroscope sensor array, and is compared with specific standard pose model data in a pose comparison system; whether the posture of the collected person is a specific standard posture or not is judged through a clear and efficient method; the specific standard pose is designed according to a pain principle and a scientific experimental scheme, so that the electromyographic signal corresponding to the specific standard pose of the collected person can be used as an important index for pain assessment and treatment effect judgment.

This invention discloses a method and system for pain threshold determination based on multimodal automatic laser stimulation and animal behavior analysis. The method includes: using an RVC-Pose convolutional neural network model to detect key points on the animal's foot and outputting the foot's spatial coordinates in real time; positioning a laser spot on the foot and outputting continuously adjustable laser stimulation according to a preset gradient; extracting facial key points and micro-expression features using a Light-Face facial recognition algorithm, and / or reconstructing the animal's three-dimensional skeleton using a multi-view geometric reconstruction algorithm and extracting pain-related behavioral features; inputting the multimodal behavioral data into a multi-parameter fusion judgment model to automatically identify pain responses, adaptively adjusting the stimulation intensity under gradient stimulation mode, terminating stimulation and recording the pain threshold when an effective pain response is detected. This invention achieves fully automatic closed-loop control for pain threshold determination, solving the problems of inaccurate positioning, imprecise stimulation, and subjective judgment in existing technologies, significantly improving the objectivity and accuracy of pain assessment.

1. The name of the design product: dial gauge (real-time pain). 2. The use of the design product: for real-time pain assessment, specifically for assessing dial gauge. 3. The design points of the design product: in the combination of shape and pattern. 4. The picture or photo that best indicates the design points: reference diagram in use. 5. Omit the front and back views; the left and right views are symmetrical, omit the left view.

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.

An assessment apparatus and method for objective pain assessment are provided, wherein the apparatus includes a processor comprising a frequency-domain transformation module, a frequency-band segmentation module, a first assessment submodule, and a second assessment submodule. The frequency-domain transformation module generates a global time-frequency feature matrix, which is segmented by the frequency-band segmentation module in a frequency domain into five frequency bands associated with pain perception. The first assessment submodule extracts last time step features and an adjacency matrix from the time-frequency feature matrix and generates a first feature vector representing global association patterns among electrodes used for acquiring EEG signals. The second assessment submodule generates a second feature vector with local spatiotemporal dynamic features of EEG signals, concatenates it with the first feature vector to form a fused feature vector, computes its class probability distribution, normalizes it, and generates an objective pain quantification indicator corresponding to the EEG signals.

The invention relates to the technical field of electroencephalogram signal evaluation, in particular to a pain evaluation model, method and system based on electroencephalogram signals and comparative learning. Wherein the model comprises a lightweight channel attention network and a comparative learning interaction layer which are in cascade connection; the lightweight channel attention network serves as a backbone network and is used for receiving the preprocessed standardized electroencephalogram signal fragments and outputting potential representation vectors of electroencephalogram signals; the comparative learning interaction layer is used for pre-training to obtain a pre-training weight, and comprises a hierarchical soft comparison module, a time dynamic comparison module and a depolarization clustering comparison module which are connected to the output end of the lightweight channel attention network in parallel; the lightweight channel attention network adjusts parameters based on a pre-training weight, and outputs a pain assessment result through a classifier. By adopting the evaluation model, the unlabeled data can be effectively utilized, the individual difference can be overcome, and the method has the characteristic of accurately capturing the dynamic change of the pain time sequence.

The invention relates to the field of machine vision, in particular to a medical care monitoring method based on computer vision, and the method comprises the steps: constructing a body recognition model, collecting a prone position image of a to-be-assessed patient as a to-be-assessed image, and carrying out the posture recognition of the to-be-assessed image based on the body recognition model. Performing pain assessment based on the identified posture; according to the scheme, body position monitoring of the patient is achieved based on the principle of image recognition, computer vision and the deep learning technology, the model is trained based on the deep learning technology, the accuracy of recognizing the body position of the patient is further guaranteed, influences caused by environmental factors are reduced, and the medical care monitoring accuracy is improved based on the medical care monitoring method. The body position monitoring of the patient can be efficiently and accurately realized, the monitoring effect is ensured, and the labor cost is reduced.

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 application discloses a kind of massage and whole body vibration training synergistic effect treatment chronic low back pain system, including patient information registration and management module, chronic pain assessment module, treatment effect assessment module, data analysis and feedback module, vibration training control terminal and massage treatment and guidance module;Through guiding patient to complete standardization waist action and record each posture under the pain score, determine main pain area in combination with Pareto analysis method, and utilize the score data in multiple treatments, construct mean and standard deviation model, and then realize the normal distribution fitting of pain change and curative effect determination;Massage and vibration training are linked through system, data intercommunication, realize the standardization of treatment process, intelligent and personalized intervention, effectively improve the rehabilitation efficiency of chronic low back pain patient.The application has the advantages of complete structure, scientific evaluation, precise intervention, etc., and is suitable for the popularization and application in the field of rehabilitation medicine and clinical physical therapy.

The present invention relates to the field of pain assessment technology, specifically a pediatric pain assessment system, including a muscle group rhythm recognition module, a behavioral linkage screening module, a periodic fluctuation extraction module, a behavioral-physiological difference module, and a pain grading judgment module. In the present invention, a linkage offset recognition path is constructed through the angular relationship between facial muscle groups and the start-up timing, the multi-muscle coordination imbalance manifestation is identified, and a regional-level dynamic behavior judgment basis is formed. The response overlap of multiple behavioral characteristics in the same time period is used to establish a behavioral coordination discrimination mechanism, exclude path signals that do not form effective coordination, enhance the effectiveness of behavioral characteristics, introduce a continuous trend structure of peak time in rhythmic changes, construct a periodic response mutation monitoring channel, improve the recognition sensitivity of short-term fluctuation concentrated segments, extract response mismatch segments based on the trigger time misalignment state between behavioral and physiological responses, and locate pain judgment key points in the timing overlap area.

The Intelligent Pain Management Patch System (IPMPS) is a wearable medical device designed to deliver personalized and continuous pain relief for palliative care patients. The system integrates advanced sensor technology, a smart drug delivery system, and AI-driven pain assessment. The patch continuously monitors patient physiological parameters, assesses pain levels in real-time using AI, and administers precise doses of analgesic medication. The system includes a user interface for healthcare providers and patients, facilitating remote monitoring and adjustments. The IPMPS offers a non-invasive, personalized approach to managing chronic pain, reducing the need for oral medications and minimizing side effects.

Compared with the prior art, the pain assessment reference tool has the advantages that the plate-shaped body is a regular plate body and is convenient to carry, related pain assessment information is recorded on the plate-shaped body according to areas, medical staff can conveniently refer to the plate-shaped body to carry out pain assessment, the identification plate is detachably connected with the plate-shaped body, and therefore the pain assessment reference tool is convenient to carry out. Different pain standard information can be recorded on the identification plate according to departments; comprising a rectangular plate-shaped body, a grade judgment area and an evaluation standard area are sequentially arranged on the first face of the plate-shaped body in the length direction, the grade judgment area is used for setting grade judgment marks, the evaluation standard area comprises at least one identification plate detachably connected with the plate-shaped body, and the identification plate is used for setting evaluation standard information.

The application relates to the technical field of electroencephalogram signal evaluation, in particular to a pain evaluation model, method and system based on electroencephalogram signals and contrast learning; wherein the model comprises a cascade-connected lightweight channel attention network and a contrast learning interaction layer; the lightweight channel attention network serves as a backbone network and is used for receiving a preprocessed standardized electroencephalogram signal segment and outputting a potential representation vector of the electroencephalogram signal; the contrast learning interaction layer is used for obtaining pretraining weights through pretraining and comprises a hierarchical soft contrast module, a time dynamic contrast module and a debiased clustering contrast module which are connected in parallel to an output end of the lightweight channel attention network; the lightweight channel attention network adjusts parameters based on the pretraining weights and outputs a pain evaluation result through a classifier. The evaluation model can effectively utilize unannotated data and overcome individual differences, and has the characteristics of accurately capturing the time sequence dynamic change of pain.