56 results about "Pain rating" patented technology

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 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 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.

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 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 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 invention discloses a wearable intelligent sensing abdominal pain monitoring device and system, and belongs to the technical field of child medical health monitoring. The method comprises the steps that a monitoring range is constructed, symptom parameters are collected, and internal invisible symptom parameters and external invisible symptom parameters are generated; internal invisible symptom parameters are obtained, borborygmus core features are extracted, the intestinal tract abnormal reaction degree is obtained, and the intestinal tract abnormal reaction degree is subjected to score processing; according to related data acquired by the wearable intelligent sensing abdominal pain monitoring device, the device gets rid of dependence on subjective description of child patients completely on the basis of evaluation of physiological indexes such as borborygmus and abdominal muscle tension, is extremely high in adaptability to young children and children with language development retardation, can generate an evaluation report once in a short time, can dynamically track pain changes, and has a good application prospect. And the accuracy of pain grade judgment and disease cause pointing is improved through matching of a double-index fusion algorithm and a clinical database.

The invention discloses a pain detection and evaluation device and a use method thereof, and relates to the field of pain detection and evaluation, the pain detection and evaluation device comprises an examination bed, and a head mounting rack, an arm mounting rack, a waist mounting rack and a foot mounting rack which are arranged on the examination bed and are used for mounting a skin electric detection module, and the skin electric detection module comprises a mounting box; the transverse plate is arranged at the bottom of the inner wall of the mounting box, the clamping frame penetrates through the transverse plate, the n-shaped frame is arranged at the top of the clamping frame, an airflow drying device is clamped to the bottom of the clamping frame, and an electrode attaching device with the execution end penetrating through the n-shaped frame is arranged at the top of the n-shaped frame. The pain detection and evaluation device can automatically obtain the skin electricity information of the patient, can quickly obtain the skin electricity information of different parts of the body of the patient, and can automatically dry the skin electricity information obtaining parts, so that the change information of the skin electricity when the patient pains can be obtained during secondary detection, and the pain detection and evaluation efficiency is improved. Therefore, the pain level of the patient can be evaluated.

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.

The invention relates to the technical field of medical monitoring information processing, in particular to a controlled medicine injection monitoring system and armlet, and the system comprises a controlled medicine injection monitoring armlet and a cloud processing module; the control drug injection monitoring armlet comprises a monitoring module, an information transmission module and a drug injection module, the monitoring module is used for collecting vital sign information of a user; the cloud processing module is used for constructing a deep learning model and determining a corresponding pain level; and the drug injection module performs estimation according to a pharmacokinetic model and a pain level through an extended unscented Kalman filter system, establishes a control model for optimization to determine a minimum-cost drug injection real-time scheme and dynamically performs drug injection. According to the method, automatic analysis of the pain condition of the user is achieved through a multi-modal analysis method, the medication scheme is dynamically adjusted in real time according to the state of the user in the injection process, and personalized analgesia optimization within the safety boundary is achieved.

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.

1. Name of the design product: Turntable (Simple Pain Management Turntable). 2. Use of the design product: Turntable for hospital pain rating management. 3. Design key points of the design product: The combination of shape and pattern. 4. Picture or photograph that best shows the design key points: Front view. 5. The claimed design includes color. 6. This design product is a flat product, the left view is omitted; this design product is a flat product, the right view is omitted; this design product is a flat product, the top view is omitted; this design product is a flat product, the bottom view is omitted.

The invention relates to the technical field of pain state prediction, and discloses a method for constructing a patient pain state prediction model based on brain electric signals. The method comprises the following steps: acquiring an original data stream of brain electric signals of a patient through a multi-channel biosensor array, synchronously recording pain event labeling information, and generating a standardized signal sequence after preprocessing; extracting multi-modal features of time domain stability, frequency domain resonance and space domain connectivity, and constructing a feature-label mapping table; training the graph convolutional neural network model, and obtaining an initial prediction model in combination with a node embedding technology; dynamically adjusting parameters through an adaptive filtering algorithm to generate an optimization model; and in combination with the real-time electric signal flow and the physiological context information, outputting a pain level evaluation report, and automatically matching a clinical intervention protocol library to form a personalized scheme. According to the method, multi-modal features and a dynamic optimization mechanism are integrated, and prediction accuracy and real-time performance are improved.

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.

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

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

The invention discloses a tumor patient pain data processing system, and relates to the technical field of health data processing, and the system comprises a data collection module which is used for collecting target physiological data and behavior data; the physiological monitoring module is used for constructing a target heart rate anomaly index and a breathing anomaly index; the judging module is used for judging the target physiological state; the behavior analysis module is used for analyzing the voice signal tone fluctuation state and the voice signal strength abnormity and analyzing the target behavior state according to the analysis result of the voice signal tone fluctuation state and the analysis result of the voice signal strength abnormity in the management period; the pain evaluation module is used for dividing target pain levels and outputting the pain levels to the user; and the data analysis module is used for analyzing the abnormality of the target skin conductance level and adjusting the division process of the target pain level according to an analysis result. The accuracy of pain assessment of the tumor patient is improved.

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

The present invention discloses a pain management system, comprising: a pain detection device for releasing a stimulation current to a patient to cause the patient to suffer from pain so as to form pain tolerance physiological data; the cloud platform comprises a server, a patient end and a doctor end; the patient end comprises a self-checking module, the self-checking module comprises a plurality of self-checking units, and the self-checking units are used for a patient to input pain information at least comprising a pain position, pain time and pain intensity and performing pain grading based on a preset grading rule; and the doctor end outputs diagnosis and treatment suggestions to the patient end according to the pain level and the pain tolerance physiological data. The pain detection device is used for obtaining pain tolerance physiological data of the patient, so that medical staff can conveniently and reasonably arrange a pain management scheme according to the specific condition of the patient in cooperation with pain rating obtained through self-evaluation of the patient.

1. The name of the design product: pain score ruler. 2. The use of the design product: for pain score. 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: front view. 5. The design product is a thin product, omitting the left view; the design product is a thin product, omitting the right view; the design product is a thin product, omitting the top view; the design product is a thin product, omitting the bottom view; the design product is a thin product, omitting the perspective view.

The invention discloses a pain prediction and warning method and system based on a medical pain scoring ruler, and relates to the field of pain prediction and warning. Classification center data of collected pain feature data is optimized, and a classification tool is provided for subsequent classification of pain feature data of a pain patient to be predicted at the future moment; by constructing a plurality of mapping models, a mapping tool is provided for subsequent classified mapping of psychological state data, environmental factor data and physiological index data of a to-be-predicted pain patient at a future moment; whether primary warning and secondary warning are carried out or not is judged according to the predicted pain type data at the future moment, and when the primary warning is carried out, a patient is prompted to need drug treatment to relieve the pain at the future moment; when secondary warning is carried out, it is indicated that pain occurring at the future moment cannot be effectively relieved only through medicine treatment, and therefore the patient is prompted to take further measures to relieve the pain.

The invention provides a facial pain assessment method based on comparative learning, and mainly relates to the problem of pain assessment by extracting and fusing multi-modal features in deep learning. The method comprises the following steps: firstly, respectively extracting geometric information, facial appearance information and time sequence information formed by facial key points to obtain facial pain features in different modes; secondly, a new loss function is designed based on a supervised comparative learning method, the features of the three modals are used as positive samples to perform comparative learning with other samples, and face differences under different pain degrees are effectively distinguished; and finally, designing a self-adaptive weight updating method to jointly optimize the comparison learning loss and the mean square error loss, and better learning difference information of a predicted value and a real label. The method fully considers the difference of different pain levels, fully learns the facial features corresponding to different pain levels by introducing a comparative learning thought, and solves the problem of incomplete feature extraction in pain assessment.

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

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

The invention discloses a pain grade evaluation bracelet with a positioning function. Belongs to the technical field of bracelets, can realize objective and continuous evaluation of pain levels and intelligently associate pain data with geographic position information, and is simple in structure and convenient to use. Comprising a bracelet body and a wrist strap which are detachably connected. The bracelet main body comprises an upper cover, a middle frame and a bottom shell, a core circuit board is arranged in the bracelet main body, and the core circuit board is fixedly connected with the inner side wall of the middle frame; the bottom shell is provided with a battery compartment, and the battery is arranged in the battery compartment and electrically connected with the core circuit board; the multi-mode sensing module is fixedly connected with the inner side wall of the bottom shell, and a sensing area of the multi-mode sensing module directly faces a sensing window formed in the bottom shell; the positioning module is welded on the core circuit board; the middle frame is provided with a motor fixing groove, and the tactile feedback module is fixedly connected with the middle frame through the motor fixing groove.

This invention relates to the field of medical monitoring information processing technology, and in particular to a controlled drug injection monitoring system and armband. The system includes a controlled drug injection monitoring armband and a cloud processing module. The controlled drug injection monitoring armband includes a monitoring module, an information transmission module, and a drug injection module. The monitoring module collects the user's vital signs information. The cloud processing module constructs a deep learning model to determine the corresponding pain level. The drug injection module uses an extended unscented Kalman filter system to estimate the pain level based on a pharmacokinetic model and the pain level, establishes a control model for optimization, establishes a real-time drug injection plan that minimizes the cost, and dynamically administers the drug. This invention achieves automatic analysis of the user's pain status through multimodal analysis and dynamically adjusts the medication plan in real time based on the user's state during injection, realizing personalized analgesia optimization within safety boundaries.

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