11 results about "Pain level" patented technology

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 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.

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.

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 application relates to the field of physical therapy equipment, especially to a kind of spinal step quantitative traction physiotherapy control system based on closed-loop force feedback, the system includes: data acquisition module, real-time traction force value, electromyographic signal data, displacement rate of change between traction bed movable bed plate and traction bed fixed bed body are collected;Traction bed drive module, according to the traction force control instruction output by self-learning fuzzy logic controller, execute traction action to traction bed body;Self-learning fuzzy logic controller, according to real-time traction force value, electromyographic signal data, displacement rate of change and the pain level signal feedback by the object to be traction, using step traction strategy, generates traction force control instruction, traction force control instruction is set to use the fuzzy parameter adjustment model pre-constructed based on neural network and fuzzy inference, dynamically generated by updating the shape parameter of membership function of first fuzzy logic rule.The present application effectively improves the self-adaptability and accuracy of traction force regulation.

An apparatus includes a facial expression detection device and a computing device, the computing device being configured to receive facial pattern input from the facial expression detector, identify one or more micro-expressions in the facial pattern input, and predict one or more of pain intensity and pain source based on the identified one or more micro-expressions. Various aspects of the disclosed embodiments aim to identify pain sources non-contactly based on pain-induced micro-expressions using machine learning.

This invention discloses a method and system for facial pain expression analysis based on spatiotemporal information. By sampling facial expression videos using a sliding window, several video segments are obtained, improving dataset utilization. Discrete cosine transform is applied to the video frames, followed by sawtooth scanning for enhancement, resulting in detailed feature enhancement maps that enhance detail information. Then, all video frames and their corresponding detailed feature enhancement maps from each sampled video segment are combined and input into a Transformer model. The Transformer model uses a concatenated cross-processing method to extract and fuse temporal and spatial features. Finally, the pain analysis result is obtained based on the pain level corresponding to each sampled video segment. This method effectively extracts feature information from facial expression videos, resulting in high accuracy in pain assessment.

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.

The present application belongs to the technical field of medical rehabilitation equipment, and discloses a kind of laryngeal postoperative intelligent monitoring rehabilitation auxiliary instrument and control method;Including obtaining the physiological data of the throat of patient throat;Four-channel tensor formed after processing physiological data of throat is input into physical information neural network model for analysis by constructing physical information neural network model, and the output of laryngeal entrance closure degree and pharyngeal wall tissue elastic modulus, determine the pain level of patient based on laryngeal entrance closure degree and pharyngeal wall tissue elastic modulus;Based on the analysis of mucous membrane dynamic displacement sequence data and pressure data, determine the swallowing mode characteristics of patient, and combine laryngeal entrance closure degree, pharyngeal wall tissue elastic modulus and pain level to carry out rehabilitation decision analysis, generate rehabilitation intervention instruction;Based on rehabilitation intervention instruction, execute dosing operation, and obtain the execution feedback record after completing dosing operation, adjust rehabilitation intervention instruction based on execution feedback record, significantly enhance the timeliness and effectiveness of treatment decision.

The embodiment of the application provides a method and device for determining a pain index caused by endometriosis, and relates to the field of medical devices and digital health technology. The method comprises: acquiring an RR interval sequence in real time through a photoplethysmogram sensor, acquiring a skin conductance signal sequence in real time through a skin galvanic activity sensor, and acquiring a pressure dynamic signal sequence in real time through a wearable pressure sensing unit; processing the RR interval sequence to obtain an HRV feature set, processing the skin conductance signal sequence to obtain an EDA feature set, and processing the pressure dynamic signal sequence to obtain a pressure dynamic feature set; time-synchronizing the HRV feature set, the EDA feature set and the pressure dynamic feature set, and then performing fusion processing to obtain a feature vector; and inputting the feature vector into a pre-trained pain index determination model to obtain a pain index reflecting the pain caused by endometriosis. The method can reflect the pain degree of a user when the user is in pain caused by endometriosis.

This invention provides a labor pain feedback gripping device, belonging to the field of medical device technology. It includes a gripping body; a soft area is provided on the gripping body, and a pressure sensing module is disposed within the soft area to detect the pressure applied to the soft area; a control center is used to assess the pain level based on the pressure detected by the pressure sensing module, and outputs the assessment result through a feedback output module. This invention provides an intelligent device integrating real-time monitoring, assessment, and feedback functions for labor pain, offering effective support for pain management during labor for medical personnel.