54 results about "Pain score" patented technology

The invention discloses an intelligent orthopedic rehabilitation self-adaptive evaluation method and device based on multi-modal physiological feedback. The method comprises the steps that multi-modal physiological data including electromyographic signals, knee joint three-dimensional motion trails, plantar pressure distribution and cognitive input degree of a subject are synchronously collected; constructing a three-dimensional baseline vector according to the MMT muscle force test, knee joint ROM measurement and VAS pain score of the subject; inputting the multi-modal physiological data and the three-dimensional baseline vector into a personalized orthopedic rehabilitation model, and outputting a knee joint function index prediction value and personalized rehabilitation adjustment parameters; dynamically adjusting electrical stimulation parameters according to rehabilitation adjustment parameters output by the personalized orthopedic rehabilitation model; if the cognitive load index exceeds the early warning line, the electrical stimulation intensity is reduced; otherwise, activating the hippocampus body wave stimulation mode. Adjustment is carried out according to the real-time state of the subject, blindness and subjectivity in the rehabilitation process are avoided, and the rehabilitation efficiency and success rate are improved.

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

The invention provides a nerve electrical stimulation method, device and system based on electroencephalogram signals, and the method comprises the steps: obtaining pain related features in real time, the pain related features comprising electroencephalogram index features and heart rate variability features; generating a comprehensive pain score value in real time based on the pain related characteristics, and generating an electrical stimulation parameter in real time based on a target difference value between the comprehensive pain score value and the baseline score value; the vagus nerve of the patient is electrically stimulated based on the electrical stimulation parameters. The problem that in the prior art, due to immobilization of electrical stimulation parameters, dynamic adjustment cannot be conducted according to real-time neural activity of a patient, and therefore the treatment effect is affected is solved.

The invention provides an exercise load multi-objective optimization method based on an anti-gravity rehabilitation treadmill. The exercise load multi-objective optimization method comprises the steps of obtaining gait kinematics data, plantar pressure distribution data, electromyographic signal data and pain scores of lower limbs of a patient; based on the data, a gait asymmetry index and a myoelectricity activation sequence consistency index are screened out, the gait asymmetry index and the myoelectricity activation sequence consistency index are combined with pain scores to serve as key features, and a function grading model is constructed to obtain function grades; establishing a multi-objective optimization problem by taking minimization of a pain score, maximization of joint activity and electromyographic activation sequence consistency and minimization of muscle fatigue as objectives and taking non-overrun of articular cartilage contact stress as a constraint; an NSGA-III algorithm is adopted to solve the problem, and a Pareto optimal solution set of the weight reduction proportion and the speed of the treadmill is obtained; through a PPO reinforcement learning strategy network, taking the real-time state of a patient as input, and selecting and dynamically adjusting treadmill parameters in a Pareto solution set; and issuing the adjusted parameters to a treadmill execution mechanism in real time.

The invention discloses an intelligent control method for an analgesia pump based on deep learning. The intelligent control method comprises the following steps: S1, acquiring multi-modal original signals of a postoperative patient and synchronously normalizing the multi-modal original signals; s2, removing artifacts and noise in the synchronous data, and performing interpolation compensation on missing data; s3, carrying out modal reliability weight distribution on the sensing sequence based on a Hemma optimization algorithm; s4, inputting the node set into a PainNet relation statistical network to generate a dynamic graph topological structure; s5, predicting the pain score of the patient in the future, and generating a low-dose high-frequency micro-pulse administration instruction; s6, according to the circadian rhythm of the patient, dynamically adjusting the edge weight and the administration interval of the PainNet network; and S7, when the pain score fluctuation exceeds the limit, triggering an emergency mode, quickly converging to the most conservative parameter combination, and outputting a safety locking instruction. According to the invention, the control accuracy and safety of postoperative analgesia are improved.

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 discloses a training mode determination method and system for a lower limb exoskeleton rehabilitation robot. The method comprises the steps that angle data, joint torque data and plantar pressure data when a patient executes multiple target actions are collected; according to the angle data and the joint torque data, calculating to obtain a joint motion range, and according to the angle data and the plantar pressure data, calculating a joint output torque; acquiring a pain score of the patient, and calculating a training mode score according to the joint motion range, the joint output torque and the pain score of the patient; and according to the training mode score, determining a training mode of the lower limb exoskeleton rehabilitation robot, wherein the training mode comprises a passive mode, an auxiliary mode and an anti-resistance mode. The motion range of the hip, knee and ankle joints can be accurately quantified, the joint motion range can be dynamically evaluated, the optimal training mode is dynamically recommended in combination with the training score calculated in real time, therefore, the rehabilitation efficiency can be improved, and the method can be widely applied to the technical field of rehabilitation training.

The invention relates to the technical field of rehabilitation treatment, and discloses a lumbar vertebra rehabilitation exercise adjusting method and system based on HAPA and a storage medium. The method comprises the following steps: setting an HAPA psychological scale electronic questionnaire on a patient terminal, collecting answer data, and standardizing to obtain an HAPA psychological index; meanwhile, the waist three-axis angular speed, the myoelectricity root-mean-square value and the voice pain score are collected, and the exercise tolerance index is obtained. And inputting the HAPA psychological index and the exercise tolerance index into an exercise intensity mapping model to obtain an exercise intensity grade, and calling out corresponding parameters from a rehabilitation action library to form a basic training scheme. The exercise intensity grade, the exercise tolerance index and the corresponding threshold value are judged, the exercise parameters of the basic training scheme are correspondingly adjusted, the adjusted parameters and the patient completion degree are transmitted to a cloud end, and model self-learning is achieved. Personalized, intelligent and self-adaptive adjustment of lumbar vertebra rehabilitation training is achieved, and the training tolerance and the rehabilitation effect of a patient are improved.

The invention relates to the technical field of medical instruments, in particular to a magnetic control pulse type pelvic floor nerve muscle stimulator and a parameter optimization system thereof. Comprising a host, a dynamic layered pulse waveform generation technology module, a multi-target reinforcement learning parameter optimization module, a multi-modal biofeedback fusion and VR enhancement module, and a flexible wearable stimulation array and ultrasonic targeting module. The system has the advantages that real-time data and deep learning are achieved, and personalized precise treatment is achieved, in the actual use process, the dynamic layered pulse waveform generation technology module predicts the requirements of muscles in the next stage through a deep learning model on the basis of pelvic floor electromyographic signals, pressure data and infrared thermal imaging which are collected in real time, and the treatment accuracy is improved. Dynamically generating a composite pulse waveform; the multi-objective reinforcement learning parameter optimization module constructs a'state-action-reward 'reinforcement learning framework, and takes patient treatment feedback, muscle force improvement, pain score and treatment compliance as reward signals.

The invention discloses a breast cancer postoperative upper limb function rehabilitation assisting method and system, and the method achieves the precise rehabilitation through multi-dimensional data collection and intelligent scheme generation: collecting the joint movement range, muscle strength index, lymphedema data, VAS pain score and Borg fatigue index of the upper limb of a patient, and carrying out the fusion to generate an upper limb function feature set; dividing the rehabilitation process into four stages, and generating a rehabilitation target corresponding to each stage by means of a preset mapping mechanism based on the feature set; generating a candidate rehabilitation scheme by applying a rehabilitation scheme generation model based on machine learning according to the staged rehabilitation target; through a prediction model based on multi-dimensional evaluation, rehabilitation prediction scores are generated in combination with the upper limb function feature set and the candidate rehabilitation schemes, and the candidate scheme with the highest score is selected as the optimal rehabilitation scheme. Personalized and dynamic management of upper limb rehabilitation after the breast cancer operation is achieved, and adaptability and effectiveness of a rehabilitation scheme are improved.

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 invention discloses a multi-parameter accelerated rehabilitation evaluation method for stomach cancer postoperative ERAS management, and belongs to the technical field of medical data processing, and the method comprises the steps: obtaining stomach cancer postoperative multi-source data of a patient, carrying out the preprocessing and fusion analysis of the stomach cancer postoperative multi-source data of the patient, and forming stomach cancer postoperative fusion data of the patient; according to the multi-parameter accelerated rehabilitation evaluation model, analyzing and identifying the gastric cancer postoperative fusion data of the patient, determining a gastric cancer postoperative multi-parameter accelerated rehabilitation evaluation result of the patient, and realizing dynamic risk evaluation of gastric cancer postoperative rehabilitation of the patient. The method solves the problem that the existing method is high in subjectivity and difficult to comprehensively consider multi-dimensional data at the same time, so that the gastric cancer postoperative rehabilitation management effect of the patient is poor. The method does not have a quantitative standard for judging whether the patient reaches the standard or not, multi-dimensional data such as laboratory indexes, vital signs, pain scores and activity amount can be comprehensively considered at the same time, and the gastric cancer postoperative rehabilitation management effect of the patient can be improved.

A computer-based system and method for generating a pain score of a subject using one or more sensory signals extracted from an AV signal of the subject. The AV signal may comprise one or more sensory signals including a face sensory signal, a body sensory signal and an audio sensory and wherein one or more of the sensory signals is missing from the AV signal.

Embodiments are described herein for training machine learning models using digital twin outputs, including for purposes of pain quantification and estimation. In various embodiments, simulation parameter(s) may be applied to a digital twin created for a subject. The digital twin may simulate an anatomical structure of the subject, and the simulation parameter(s) may cause the digital twin to generate digital twin output that simulates behavior of the anatomical structure in response to the one or more simulation parameters. Data indicative of the digital twin output may be applied as input across a pain estimation machine learning model to generate pain estimation output. Based on the pain estimation output, a quantification of pain, e.g., an estimated pain score, may be rendered using an output device and / or used to select a treatment plan.

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.

To provide a method for predicting breakthrough pain in a cancer patient using an artificial intelligence model that processes time-series data collected from the cancer patient.SOLUTION: A method for predicting breakthrough pain in a subject comprises the steps of: receiving, by an analysis device, pain score data collected from a subject for a predetermined time; pre-processing, by the analysis device, the pain score data; and inputting, by the analysis device, the pre-processed pain score data into a pre-trained deep learning model and predicting whether or not breakthrough pain will be generated in the subject at a future time point based on a predicted value output by the deep learning model.SELECTED DRAWING: Figure 1

Proposed is a prediction method for breakthrough pain of a subject, the method including receiving pain score data collected from a subject for a predetermined time by means of an analysis apparatus, preprocessing the pain score data by means of the analysis apparatus, and inputting the preprocessed pain score data into a deep learning model trained in advance and predicting whether breakthrough pain will be generated in the subject in a point in time in the future on the basis of a prediction value output from the deep learning model by means of the analysis apparatus.

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

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

A pain-management system (300) configured to: receive a target-pain-score (314) that represents a level of pain that the user considers acceptable during a defined period of time: receive one or more target-input-parameters (304) which represent properties and / or activities of a user during the same defined period of time, wherein the one or more target-input-parameters (314) are a subset of a full list of input-parameters that are available: receive one or more settings (315) that represent one or more input-parameters that are to be increased or decreased: use a neural network that has been trained for the individual user to determine one or more calculated-user-parameters (316) based on the target-pain-score (314) and the target-input-parameters (304), wherein at least one of the calculated-user-parameters (316) is set based on the settings (315); and present the one or more calculated-user-parameters (316) using a user interface.

This document discusses, among other things, systems and methods for managing pain of a subject. A system includes a first sensor circuit to sense a first signal indicative of a functional state of the subject, a second sensor circuit to sense a second signal different from the first signal, and a controller circuit. The controller circuit may determine an operating mode of the second sensor circuit according to the sensed first signal, trigger the second senor circuit to sense the second signal under the determined operating mode, and generate a pain score using at least the second signal sensed under the determined operating mode. The pain score may be output to a patient or used for closed-loop control of a pain therapy.

A system including a range of motion, quality of sleep, overall, and control modules. The range of motion module, prior to a procedure being performed on a patient, determines a first range of motion score of the patient based on a first signal generated by a sensor. The quality of sleep module, prior to the procedure being performed on the patient, determines a first quality of sleep score or a first pain score based on the first signal. The overall module determines a combined score based on the first range of motion score and the first quality of sleep score or the first pain score. The control module compares the combined score to a predetermined threshold and predicts an outcome of the procedure based on the comparison. The control module, based on the combined score, determines whether to perform the procedure, adjust the procedure or refrain from performing the procedure.

The invention relates to the technical field of data analysis, and discloses a pain sensing method and system based on multi-dimensional data analysis, and the method comprises the steps: generating a first pain score based on an existing pain scoring method; the method includes introducing the limb features to generate a second pain score, generating a predicted pain score based on the diagnostic features, and performing a pain intervention based on the predicted pain score and the second pain score. The system corresponds to the method. According to the method, limb features are introduced, and the authenticity of pain characterization is enhanced; by adjusting the weight of the multi-modal data, the data dependence on the face and audio features is reduced, and the evaluation efficiency is improved; a prediction model is constructed in combination with diagnosis data, combination of clinical experience and data analysis is realized, and evaluation reliability is improved; and confidence difference value judgment and dynamic correction are carried out, so that the problem of conflict between multi-modal data and diagnosis results is solved, dynamic adaptive adjustment of pain scores is realized, and the pertinence and effectiveness of a pain intervention scheme are effectively improved.

An end to end process is disclosed that includes an installation of a prosthetic component, a prosthetic joint, or repair a musculoskeletal system. The pathway of care comprises a pre-operative pathway of care, an intra-operative pathway of care, and a post-operative pathway of care. An application downloaded to a computer drives the process. The application can include integrated PROMS, pain scores, patient Q&A, surveys, a calendar of events (such as physical therapy visits), and contact to surgeon, doctor, or healthcare provider. The pathway of care is an end to end process utilizing wearable devices, implantable devices, sensorized equipment, and sensorized tools to generate measurement data that supports a pre-operative plan, surgery, and post-operative rehabilitation. The application couples to the devices disclosed herein above to engage the patient one or more tasks such that sensors on the devices generate measurement data. The process provides better patient care while allowing the surgeon, doctor, or healthcare provider to handle more patients.

The present invention provides a method and system for analyzing the pain state of liver cancer patients, and relates to the technical field of pain state analysis. The system includes a data acquisition module, a local analysis module, a pain local evolution analysis module, and a pain assessment analysis module. The present invention constructs a multi-dimensional pain abnormality feature matrix by integrating the liver function indicators, dynamic physiological parameters, and pain score data of postoperative patients, adopts a density clustering algorithm based on time and space constraints to mine the pain evolution law of the patient group, determines multiple local time period evolution patterns, quantifies the correlation between the time period evolution pattern and the pain state to generate multiple group pain evolution laws, and realizes hierarchical strategy modeling in combination with the individual pain resistance level. It integrates the common laws of the group and the individual resistance differences to generate an adaptive pain assessment strategy, realizes dynamic matching and early warning of the patient's pain development trend, and can provide accurate pain management solutions for clinicians.

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.

This invention discloses a method for generating graded administration recommendations for labor analgesia based on pain scores, vital signs, and adverse reactions. The method acquires pain scores, vital signs, adverse reaction grades, and analgesia pump log data. After time alignment, it determines the level of analgesia need and safety risk. Based on a preset decision matrix, it generates recommendations for increasing, decreasing, maintaining, pausing, or requiring manual review. The recommendations are then constrained by time, maximum dosage, step size, and minimum adjustment interval before being output and logged. This method can be implemented independently by a physician.

A system for monitoring a joint of a patient includes multiple sensors to be disposed near a joint and to measure or observe actions or physical quantities associated with the joint; and at least one communications module coupled to the sensors to receive data from the sensors and to transmit sensor information to an external device. The system also includes a patient device and clinician device which can be used to, for example, monitor and display information obtained from the sensors, determine range of motion measurements from the sensor data, show progress in physical therapy, take a photograph or video of the site on the patient, obtain a pain score, and include friends for providing encouragement during physical therapy.