105 results about "Pain level" patented technology

The invention relates to a system for realizing pain scoring of an ICU (intensive care unit) patient. The system comprises a data acquisition module for acquiring a facial micro-expression video of the ICU patient in real time by using a high-definition camera; the micro-expression recognition module analyzes the collected micro-expression video image; the vital sign analysis module analyzes the collected vital sign data in real time; the pain scoring model is obtained by training through a machine learning algorithm, and the pain degree of the patient is quantitatively scored; and the result output module outputs the pain scoring result to the medical staff in real time. According to the system and method for achieving ICU patient pain scoring, the processor and the computer readable storage medium of the system and method, the objectivity and accuracy of pain scoring are improved through the artificial intelligence technology. By analyzing the combination of micro-expressions and vital signs, the sensitivity and specificity of pain assessment are improved. Real-time monitoring and continuous evaluation of pain scores are realized, and timely adjustment of a treatment scheme is facilitated.

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 intelligent medical treatment, in particular to an abdominal postoperative pain intelligent medical management system based on a JBI mode. The system comprises a platform application layer which is used for providing a user interface and an interaction mode, and medical staff and patients can access and use various services provided by a platform service layer; the platform service layer is used for monitoring and analyzing the pain level of a patient in real time, giving an alarm when the pain level is abnormal, and formulating and adjusting an analgesia strategy according to the pain level; and the evaluation feedback unit is used for formulating an evaluation index for the result of the platform service layer and optimizing the service according to the result of the index. The system not only records the pain score and accompanying symptoms of the patient, but also performs customized pain assessment according to different types of patients, so that the working efficiency of medical personnel is improved, the treatment compliance and satisfaction of the patient are enhanced, and finally the overall pain management level and the life quality of the patient are improved.

An interactive pain management system is provided comprising a laser treatment device and an application executing on the device that receives entry of identification information for a first patient and receives entry of a body region to receive treatment. The system also receives entry of a present pain level experienced in the body region, accesses stored information describing previously reported conditions of the first patient and based on the received entries and the accessed store information, determines a treatment regimen for the first patient. The laser treatment device executes the treatment regimen by manual administration by the first patient or a qualified technician. The system requests entry of the first patient's present condition, the condition comprising one of feeling worse, feeling better, and experiencing no change since an immediately previous treatment. Changes in the condition of the first patient are incorporated into the determination of the treatment regimen.

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 an AI-based personalized pain management system and method, and the system comprises a multi-dimensional data collection module which is used for collecting multi-dimensional monitoring data including user physiological state data, motion data and location information data, and bio-electricity signal sensing data in real time; the pain type identification module is used for performing feature extraction on the physiological state data and the bio-electricity signal sensing data and identifying the pain type and the pain level of the user in combination with basic information of the user; the pain trend prediction module is used for performing time sequence analysis based on the pain type, the pain level and the multi-dimensional monitoring data, and predicting the pain trend of the user in a future preset time period; and the feedback evaluation module is used for outputting a pain management evaluation report of the user according to the pain type, the pain level and the pain trend. According to the invention, accurate monitoring, assessment and intervention of pain can be realized, so that pain management is more intelligent and personalized, and help is provided for improving the life quality of a user.

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

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 image recognition, in particular to a patient post-anesthesia pain auxiliary recognition method based on a near-infrared technology, and the method comprises the steps: obtaining near-infrared images of a surgical site of a patient to be detected after anesthesia at a historical moment and a current moment; determining a temperature rise coefficient, a temperature fluctuation factor and a postoperative pain factor corresponding to each pixel point in the near-infrared image; determining a pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the difference among the temperature rise coefficients, the postoperative pain factors and the temperature rise coefficients corresponding to the pixel points at the same position; and determining a target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and the postoperative pain factor corresponding to each pixel point in the current near-infrared image. By analyzing the near-infrared image, the pain levels of different positions of the surgical site of the patient are quantified relatively objectively, so that the accuracy of pain recognition of the patient after anesthesia is improved.

Systems and methods are provided for evaluating pain for a user. A first pain-relevant parameter representing the user is monitored at an in-vivo sensing device over a defined period to produce a time series for the first pain-relevant parameter. A value for a second pain-relevant parameter for the user is obtained at first and second times in the defined period from the user via a portable computing device to provide respective first and second values for the second pain-relevant parameter. A value is assigned to the user via a predictive model according to the time series for the first pain-relevant parameter, the first value for the second pain-relevant parameter, and the second value for the second pain-relevant parameter.

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 embodiment of the invention provides an information processing method for a temporomandibular joint, which comprises the following steps: acquiring sensor information, determining six-degree-of-freedom motion information of the temporomandibular joint according to the sensor information, generating a three-dimensional motion track curve of the temporomandibular joint according to the six-degree-of-freedom motion information, and acquiring pain level information. According to the pain level information and the three-dimensional motion track curve, determining a lesion joint and obtaining a joint bouncing audio, according to the joint bouncing audio and the three-dimensional motion track curve, determining a bouncing type and obtaining muscle activity information, and according to the muscle activity information and the three-dimensional motion track curve, determining a muscle abnormal mode, and determining a diagnosis result of the temporomandibular joint based on the three-dimensional motion track information, the diseased joint, the bouncing type and the muscle abnormal mode. On the basis, the method and the device are used for fusing various information of the temporomandibular joint of the patient, and the accuracy of determining the cause of the temporomandibular joint disorder is improved through the motion-sound-myoelectricity combined characteristics.

The invention discloses a patient pain assessment method and system, and relates to the technical field of pain assessment. A patient pain assessment system comprises a data acquisition module, a self-assessment correction module, a physiological assessment correction module, an expression assessment correction module and a pain assessment module. According to the method, the personalized self-evaluation deviation P of the patient is calculated, and the score V of the visual simulation scale VAS filled by the patient is subjected to personalized correction, so that the subjective difference between individual patients can be effectively reduced, the final VAS score is closer to the actual pain level, the real pain state of the patient can be reflected more accurately, and the patient experience is improved. A doctor can make more accurate diagnosis, insufficient treatment or excessive treatment caused by misjudgment is avoided, and the assessment accuracy of the patient pain assessment method and system is 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.

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.

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 present invention proposes a pain visualization identification method and system based on multidimensional data analysis. The system uses EEG data, musculoskeletal ultrasound images and pain levels to construct a multidimensional pain visualization identification model. The constructed multidimensional pain visualization identification model generates the user's current pain level based on the real-time EEG data collected by the server and the musculoskeletal ultrasound images of a specific time period, and visualizes the user's pain area based on the user's current pain level. The present invention can meet the doctor's real-time monitoring of the patient's pain area, realizes an automated and intelligent pain identification effect, and can ensure that the doctor treats the patient in a targeted manner.

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.

The present application provides a pain relief system based on breathing detection, comprising: a breathing collection module, configured to collect audio samples of a user's breathing; a breathing detection module, configured to determine the user's breathing frequency based on the audio samples; a virtual reality module, configured to determine a virtual reality scene for pain management, wherein the determination of the virtual reality scene is related to the user's age, the pain level, and the user's real-time exhalation subsample and real-time inhalation subsample, and the determination of the pain level is related to the breathing frequency; the breathing collection module and the breathing detection module are arranged in a wearable device, and the virtual reality module is arranged in the virtual reality device.

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 present invention provides a method, device, server and storage medium for identifying pain levels, including: obtaining a first face image of a target object to be evaluated; performing pain evaluation on the target object based on the first face image through a pre-trained pain evaluation model to obtain the pain level of the target object; wherein the pain evaluation model is obtained by training multiple two-dimensional mapping images corresponding to a three-dimensional virtual pain expression, and the three-dimensional virtual pain expression is generated by a pre-trained data set generation network based on a second face image of the target object. The present invention can significantly improve the real-time performance of identifying pain levels.

The invention relates to the related technical field of training management, in particular to a headache relaxation training management system based on virtual reality, which comprises a data collection module for constructing a scene and collecting physiological data; the self-adaptive control module adjusts the scene according to indexes; the starting module starts training and enhances feedback under specific conditions; and the optimization management module analyzes the trend, strengthens the characteristics of the forward driving period, generates a prediction map and optimizes training, so that the technical problems that headache types, inducements, pain degrees and accompanying symptoms of users are different and the effectiveness of headache relaxation training is insufficient are solved, a virtual reality environment is constructed according to a potential pain mechanism of headache, and the user experience is improved. The technical effects of adaptively controlling and adjusting a visual focus guide path and sound field frequency distribution in a virtual environment scene, setting an attention mechanism to strengthen the characteristics of the attack proactive period, and generating a state prediction map in a time sliding window, so as to better adapt to the individual difference of users, and improve the pertinence and effectiveness of relaxation training are achieved.

The present invention relates to the field of image recognition technology, and particularly to a method for assisting in the recognition of postoperative pain of patients based on near-infrared technology. The method includes: obtaining near-infrared images of the surgical site of the patient to be detected after anesthesia at a historical moment and the current moment; determining the temperature increase coefficient, temperature fluctuation factor, and postoperative pain factor corresponding to each pixel point in the near-infrared image; determining the pain occurrence factor corresponding to each pixel point in the current near-infrared image according to the differences between the temperature increase coefficient, postoperative pain factor, and the temperature increase coefficients of the pixel points at the same position; and determining the target pain level corresponding to each pixel point in the current near-infrared image according to the pain occurrence factor and postoperative pain factor corresponding to each pixel point in the current near-infrared image. By analyzing the near-infrared image, the present invention relatively objectively quantifies the pain levels at different positions of the patient's surgical site, thereby improving the accuracy of the recognition of postoperative pain of patients.

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.