105 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 relates to the technical field of pain assessment, in particular to a paediatric pain assessment system which comprises a muscle group rhythm recognition module, a behavior linkage screening module, a periodic fluctuation extraction module, a behavior physiological difference module and a pain grading judgment module. According to the method, a linkage offset identification path is constructed through an angle relation between facial muscle groups and a starting time sequence, multi-muscle cooperative imbalance performance is identified, a region-level dynamic behavior judgment basis is formed, and a behavior cooperative judgment mechanism is established by utilizing response overlapping conditions of multiple behavior characteristics in the same time period; path signals which do not form effective cooperation are eliminated, the effectiveness of behavior characteristics is enhanced, a continuous trend structure of peak time in rhythm change is introduced, a periodic response sudden change monitoring channel is constructed, the recognition sensitivity of a short-time fluctuation concentration section is improved, and a response mismatch section is extracted based on a trigger time dislocation state between behavior and physiological response, so that the recognition sensitivity of the short-time fluctuation concentration section is improved. And a pain judgment key point is positioned in the time sequence overlapping region.

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 discloses an interpretable multi-modal information system for postoperative chronic pain assessment, which is characterized by comprising a facial expression algorithm framework for acquiring dynamic change of a facial expression facial action unit (AU) and a physiological signal algorithm module for receiving physiological signals, and the multi-modal fusion framework is used for fusing the facial expression algorithm framework and the physiological signal algorithm module. The method has the effects of accuracy, individuation and interpretability.

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 discloses a cancer pain treatment pain assessment method and system based on intelligent data processing, and relates to the field of treatment assessment, and the method comprises the steps: 1, obtaining basic pathological data, medication records and real-time vital signs of a patient in real time through a hospital information system, extracting symptom features, and carrying out the calculation of the symptom features; identifying the self-contained pain text of the patient, extracting the pain event characteristics, and matching the symptom with the pain event characteristics to form a pain correlation factor matrix; 2, constructing a pain prediction model based on a time sequence convolutional network, and generating a multi-dimensional data acquisition trigger instruction; a mode of combining a time sequence convolutional network and a multi-head self-attention mechanism is adopted, a high-sensitivity acquisition window of a pain event is automatically identified, discontinuous event interference is inhibited through a time decay factor, and a high-weight time period is reserved by utilizing sparsification, so that a data acquisition time period with a high contribution value is automatically obtained; and the recognition precision of the key pain time period is remarkably improved.

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.

State of art techniques hardly address real-time detection of pain with sensor fusion approaches. A method and system herein provides intelligent sensor selection and fusion mechanism to detect the presence of pain and quantify pain level on edge devices in an effective and efficient manner, guided by information on pain origin, pathway and type extracted from input signals, without the requirement of any manual intervention or interference. The system provides an automation framework to accelerate the deployment of customized tiny models for wearable and edge devices, based on sensor availability and device compute capacity to create suitable model for the target device and task. The system takes accuracy, model size and latency as objectives, and sensor availability and computation resource constraints as targets for the automation framework enabling rapid generation and deployment of models on multiple edge devices (wearable devices), independent of the availability of a specific sensor.

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 invention discloses a system for treating chronic lumbago through the synergistic effect of massage and whole body vibration training. The system comprises a patient information registration and management module, a chronic pain evaluation module, a treatment effect evaluation module, a data analysis and feedback module, a vibration training control terminal and a massage treatment and guidance module. A patient is guided to complete standardized waist actions, pain scores under various postures are recorded, a main pain area is determined in combination with a Pareto analysis method, an average value and standard deviation model is constructed by using score data in multiple treatments, and normal distribution fitting and curative effect judgment of pain changes are further achieved. Massage and vibration training are linked through the system, data intercommunication is achieved, standardized, intelligent and personalized intervention in the treatment process is achieved, and the rehabilitation efficiency of the chronic lumbago patient is effectively improved. The system has the advantages of complete structure, scientific evaluation, accurate intervention and the like, and is suitable for popularization and application in the fields of rehabilitation medicine and clinical physical treatment.

Technology for cooperative learning pain assessment may include a sensed pain classifier to determine a sensed context and a pain assessment classifier to determine an inferred pain score. Disclosed technology may determine an uncertainty score based on the sensed context and the inferred pain score. Responsive to the uncertainty score meeting a first threshold condition, a manual label for the subject data may be used to update the sensed pain classifier or the pain assessment classifier. Responsive to the uncertainty score meeting a second threshold condition, a generated label for the subject data may be used to update the sensed pain classifier or the pain assessment classifier. The inferred pain score may be provided as an assessed pain result for the subject.

The invention belongs to the technical field of neurobiology and pain mechanism research, and discloses a full-automatic pain sense measuring system based on artificial intelligence integrated vital sign monitoring, which is integrated with multi-mode biological signal acquisition equipment, can synchronously record pain sense behaviors and physiological data (such as heart rate, respiratory rate, body temperature, excretion and the like) of experimental animals, and can be used for measuring the pain sense of the experimental animals. And the acquired data is analyzed and processed in real time by using an artificial intelligence algorithm to generate quantitative pain assessment indexes. Compared with a traditional pain assessment method depending on subjective observation, the system has the advantages that the efficiency and accuracy of data acquisition and analysis are remarkably improved, the human deviation can be reduced, and the assessment error rate is reduced to 5% or below. The method is particularly suitable for research of complex pain models and high-throughput drug screening, the preclinical research and development period can be remarkably shortened by 20%-30%, and the research and development cost is reduced. The invention has obvious market demand and economic benefit, and provides important technical support for the field of pain research.

The invention relates to the field of pain management and rehabilitation treatment, in particular to a painless ward construction method and system based on big data, and aims to improve pain management efficiency and fracture patient rehabilitation quality through big data and artificial intelligence means. By integrating various information technologies, an artificial intelligence algorithm and a big data analysis technology, the functions of encrypted storage of fracture patient information, multi-dimensional pain assessment, personalized analgesia scheme formulation, real-time monitoring and early warning, comprehensive assessment of recovery conditions and the like are realized. Meanwhile, the method also considers the prediction of the pain trend and the intelligent generation of the training plan, solves the problems that the making of most rehabilitation training plans at present still depends on the experience and judgment of professionals and is lack of scientificity and systematicness, and provides a brand new solution for pain management and rehabilitation treatment based on big data.

The invention discloses a visual pain assessment report system and method, and relates to the technical field of medical treatment, three groups of algorithm units are matched with one another to form a core architecture of the visual pain assessment report system, a basic pain value Pd is calculated by considering pain intensity Pi, pain time Pct and a sensitivity coefficient Sp, and the basic pain value Pd is calculated by taking the pain intensity Pi, the pain time Pct and the sensitivity coefficient Sp into consideration. The comprehensive pain value Ps is calculated by combining the real-time physiological data (such as heart rate and blood pressure) of the patient, more comprehensive and more detailed visual pain assessment is provided, and compared with subjective description singly depending on the patient, the pain assessment mode can reflect the actual pain condition of the patient more accurately, and the pain assessment accuracy is improved. The planned drug application amount Dp is calculated by comprehensively considering the basic drug application amount Db, the body weight We of the patient and the comprehensive pain value Ps through the planned drug application amount algorithm unit, it can be ensured that the patient receives proper drug treatment, the situation that the drug amount is insufficient or excessive is avoided, and therefore the treatment effect on the patient can be improved, and adverse reactions are reduced.

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.

The invention relates to the technical field of image analysis, in particular to an anesthesia post-operation pain assessment method in combination with facial expressions of a patient. An optical flow method is adopted to analyze pixel motion of a facial image, optical flow vectors and information points are obtained, and facial expression changes are quantitatively captured. In view of information point validity differences, facial feature points are screened to reduce data redundancy. And in consideration of the facial structure difference of the patient, the traction characteristics of the optical flow vector are analyzed, and the traction direction of the image block is determined. Pain expressions such as eye eyebrow closeness and the like can be represented by the traction direction. By analyzing the distribution confusion degree and difference in the traction direction, the facial pain index is determined, and accurate reference is provided for pain assessment. And finally, screening a data set based on the facial images of multiple patients and pain indexes, training a neural network, and establishing a mapping relation between facial expressions and pain degrees. According to the method, the pain assessment accuracy is improved, the assessment efficiency is also remarkably improved, and automatic pain assessment of the facial image of the patient to be tested is realized.

The invention discloses a rabbit lower limb pain assessment instrument, and relates to the technical field of pain assessment, the instrument comprises a bottom plate, and also comprises mounting plates fixedly arranged on two sides of the bottom plate, the inner side walls of the mounting plates are fixedly connected with a supporting plate, the supporting plate is of an L-shaped structure, and a limiting assembly is arranged above the supporting plate; a left balance and a right balance are arranged on the two sides of the bottom plate below the supporting plate respectively, and balance trays are arranged on the left balance and the right balance; the limiting assembly comprises mounting blocks fixedly arranged on the front side and the rear side of the supporting plate, limiting coamings are rotationally connected between the mounting blocks, a baffle is arranged between the limiting coamings, and the two sides of the baffle are slidably connected with the limiting coamings so that the size of the limiting space can be adjusted to adapt to the domestic rabbits of different body types; the rabbit lower limb pain assessment instrument has the remarkable advantages of being accurate in assessment, objective in quantification, easy and convenient to operate, low in cost, wide in applicability and the like, and a novel and effective assessment tool is provided for the field of pain research.

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.