CN113033387A - Intelligent assessment method and system for automatically identifying chronic pain degree of old people - Google Patents
Intelligent assessment method and system for automatically identifying chronic pain degree of old peopleDownload PDFInfo
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Abstract
Description
Technical Field
The invention relates to the technical field of medical image processing, in particular to an intelligent evaluation method for automatically identifying chronic pain degree of old people and an intelligent evaluation system for automatically identifying chronic pain degree of old people.
Background
Studies have shown that the incidence of chronic pain in the elderly is 25-50%, with 45-80% of those with significant pain symptoms requiring long-term treatment and care. The presence of chronic pain seriously affects the quality of life of the elderly, and even serious ones can cause changes in psychological and mental states, leading to the occurrence of depression, anxiety and even a feeling of boredom. However, many elderly chronic pain are not adequately treated, mainly due to:
(1) most elderly people consider pain as a natural phenomenon accompanying the increase of age, or do not take a doctor because of fear that pain indicates the presence of serious disease, or intentionally avoid using pain in describing symptoms.
(2) Since the elderly commonly have the condition of coexistence of various diseases, the pain part, the pain nature and the pain intensity cannot be clearly described.
(3) The reduction in sensory and cognitive function in the elderly impairs their ability to perceive and express pain.
Disclosure of Invention
In order to overcome the defects of the prior art, the invention aims to solve the technical problem of providing an intelligent evaluation method for automatically identifying the chronic pain degree of the old people, which can quantify the chronic pain degree of the old people, is not limited by the subjectivity of external human factors, obtains an accurate evaluation result, and has the characteristics of objectivity, accuracy and easiness in operation.
The technical scheme of the invention is as follows: the intelligent evaluation method for automatically identifying the chronic pain degree of the old comprises the following steps:
(1) inputting the temperature, pulse, respiration and blood pressure data of the old;
(2) video recording of facial expressions: acquiring a dynamic image sequence including facial expressions of the elderly;
(3) preprocessing of the image: capturing a key frame static image according to the dynamic image sequence, simultaneously processing the light brightness and the background, and comparing captured key parameters with a large database;
(4) face region detection and tracking: detecting a face area from an original picture or video sample for subsequent feature extraction and classification;
(5) expression feature extraction: extracting expression features of the preprocessed expression images by using a corresponding feature extraction algorithm;
(6) classification and identification: and identifying the analysis processing model obtained by the collected sample by using a pain classification algorithm and the vital signs to obtain a comprehensive analysis conclusion of the pain level.
According to the method, the chronic pain degree of the old people is identified and divided into large data comparison in a video sequence and a static image, comprehensive analysis is carried out aiming at specific facial expressions and by combining various information sources, and the analysis result of the pain degree is automatically obtained by utilizing the operation characteristics of an intelligent system, so that the chronic pain degree of the old people can be quantified, the method is not limited by the subjectiveness of external human factors, the accurate evaluation result is obtained, and the method has the characteristics of objectivity, accuracy and easiness in operation.
Also provided is an intelligent evaluation system for automatically identifying the degree of chronic pain in an elderly person, comprising:
the information acquisition module is used for acquiring the body temperature, pulse, respiration and blood pressure data of the old;
the image acquisition module acquires a dynamic image sequence comprising facial expressions of the elderly;
the image preprocessing module is used for capturing a key frame static image according to the dynamic image sequence, processing the light brightness and the background at the same time, and capturing key parameters to compare with the large database;
the detection and tracking module detects the face area from the original picture or video sample for subsequent feature extraction and classification;
the feature extraction module is used for extracting the expression features of the preprocessed expression images by using a corresponding feature extraction algorithm;
and the classification identification module is used for identifying the analysis processing model obtained by the collected sample by using a pain classification algorithm and the vital signs to obtain a comprehensive analysis conclusion of the pain level.
Drawings
Fig. 1 is a diagram illustrating a pain level in an intelligent evaluation method for automatically recognizing the degree of chronic pain in an elderly person according to the present invention.
Fig. 2 is a flowchart of an intelligent evaluation method for automatically recognizing the degree of chronic pain in an elderly person according to the present invention.
Detailed Description
Modern medicine believes that chronic pain is always accompanied by emotional reactions, which involve complex psychophysiological processes including emotions, cognition, motivation, and physiological components. The system identifies the chronic pain degree of the old into large data comparison in a video sequence and a static image, comprehensively analyzes specific facial expressions and various information sources in a fusion mode, and automatically obtains an analysis result of the pain degree by utilizing the operation characteristics of an intelligent system.
As shown in fig. 2, the intelligent evaluation method for automatically identifying the chronic pain level of the elderly comprises the following steps:
(1) inputting the temperature, pulse, respiration and blood pressure data of the old;
(2) video recording of facial expressions: acquiring a dynamic image sequence including facial expressions of the elderly;
(3) preprocessing of the image: capturing a key frame static image according to the dynamic image sequence, simultaneously processing the light brightness and the background, and comparing captured key parameters with a large database;
(4) face region detection and tracking: detecting a face area from an original picture or video sample for subsequent feature extraction and classification;
(5) expression feature extraction: extracting expression features of the preprocessed expression images by using a corresponding feature extraction algorithm;
(6) classification and identification: and identifying the analysis processing model obtained by the collected sample by using a pain classification algorithm and the vital signs to obtain a comprehensive analysis conclusion of the pain level.
According to the method, the chronic pain degree of the old people is identified and divided into large data comparison in a video sequence and a static image, comprehensive analysis is carried out aiming at specific facial expressions and by combining various information sources, and the analysis result of the pain degree is automatically obtained by utilizing the operation characteristics of an intelligent system, so that the chronic pain degree of the old people can be quantified, the method is not limited by the subjectiveness of external human factors, the accurate evaluation result is obtained, and the method has the characteristics of objectivity, accuracy and easiness in operation.
Preferably, in the step (2), a video camera and a camera are used for acquiring a dynamic image sequence including facial expressions of the elderly.
Preferably, in the step (4), if a video sequence requiring real-time performance is processed, face detection and face tracking are used.
It will be understood by those skilled in the art that all or part of the steps in the method of the above embodiments may be implemented by hardware instructions related to a program, the program may be stored in a computer-readable storage medium, and when executed, the program includes the steps of the method of the above embodiments, and the storage medium may be: ROM/RAM, magnetic disks, optical disks, memory cards, and the like. Therefore, in accordance with the method of the present invention, the present invention also includes an intelligent assessment system for automatically identifying chronic pain levels in the elderly, which is generally expressed in terms of functional modules corresponding to the steps of the method. The system comprises:
the information acquisition module is used for acquiring the body temperature, pulse, respiration and blood pressure data of the old;
the image acquisition module acquires a dynamic image sequence comprising facial expressions of the elderly; (facial expression of the patient is shot by the evaluation system, a video segment of 2-5 seconds is recorded, and the peak expression frame in the video segment is automatically extracted by the analysis processing module to be used as a corresponding static expression image)
The image preprocessing module is used for capturing a key frame static image according to the dynamic image sequence, processing the light brightness and the background at the same time, and capturing key parameters to compare with the large database;
the detection and tracking module detects the face area from the original picture or video sample for subsequent feature extraction and classification;
the feature extraction module is used for extracting the expression features of the preprocessed expression images by using a corresponding feature extraction algorithm;
the classification identification module is used for identifying the analysis processing model obtained by the collected sample by using a pain classification algorithm and the vital signs to obtain a comprehensive analysis conclusion of the pain level, and the system emphasizes the blood vessel region part, so that the imbalance problem caused by a large number of background pixels is reduced; and the multi-task attention fusion module is used for processing long-distance information dependence in and among tasks and promoting network learning.
As shown in FIG. 1, the elderly were first allowed to express their pain levels in the order of 0 to 10, with 0 indicating no pain and 10 indicating the most severe pain. Secondly, the medical staff selects the pain level of the old people by an expression method, and the two types of pain assessment are combined to judge the pain assessment level.
Preferably, the image acquisition module extracts static expression images of 5 time periods from a color video segment with a resolution of 720 × 480 or more, and these images form a database.
Preferably, the image preprocessing module, taking the identification in the still image, aligns the eyes in all images mainly by rotation and size transformation, clips the original image to a size of 100 × 120, and further clips out an elliptical face image. The method is suitable for a face detection method, the obtained original video contains background information, the region of the face information is extracted, and relevant preprocessing such as a DeepID algorithm, an MTCNN algorithm, an Adaboost algorithm and the like is carried out on the region. Feature extraction can be processed by SIFT features, PCA principal component analysis, local binary patterns and the like.
Preferably, the classification and identification module analyzes the preprocessed image information by using a deep learning model, and the deep learning model uses a convolutional neural network; in the initial stage, a checking and verifying method is adopted, 2000 samples are trained, and the average accuracy rate is calculated; in order to improve the recognition rate of the linear kernel support vector machine, a Neural network simultaneous operation and optimization (NNSOA) is adopted in the later stage to combine with depth information to establish a three-dimensional human face for pain expression grading evaluation.
The characteristic face method in the analysis module refers to that the face of a user is treated as a whole, a set of facial images of static expression images is used for generating a two-dimensional gray image to generate biological characteristics, and a plurality of characteristic point data of corresponding characteristic faces are reserved through preprocessing. A facial space database was built with 2000 painful elderly eigenfaces. And when a new face image exists, a group of weights are calculated according to the comparison of the new feature points and the face space database, and the closest feature face is selected.
Preferably, the classification recognition module calculates a feature face: let the face image i (x, y) be a two-dimensional N x N array of (8-bit) intensity values, with one image being considered as N2Vector of dimensions, so that an N × N face image is considered to be N2Vector of dimensions, or equivalent to N2A point in dimensional space; finding out vectors which can reflect the distribution of the face image in the whole image space, wherein the vectors define the subspace of the face image, the subspace is called face space, and the length of each vector is N2N × N images are described, being a linear combination of facial spatial databases; these vectors are the feature vectors of the variance matrix corresponding to the face images in the face space database.
Preferably, the system further comprises an evaluation module, which is connected with the elderly life sign and analysis module, and the evaluation module carries out result evaluation on the result obtained by the analysis module; the patient's pain level was assessed using an assessment method corresponding to facial expression + digital pain level. As shown in FIG. 1, the elderly were first allowed to express their pain levels in the order of 0 to 10, with 0 indicating no pain and 10 indicating the most severe pain. Secondly, the medical staff selects the pain level of the old people by an expression method, and the two types of pain assessment are combined to judge the pain assessment level.
Preferably, in order to prevent the pressure and conflict of the server when a large number of users access the system simultaneously, the system optimizes a cache mechanism and an asynchronous processing mechanism, video or image data uploaded by the users are automatically encrypted and stored on a background server, and then the data can be further utilized to optimize and adjust big data, so that the identification precision of the system is higher, and the evaluation result is more accurate.
The above description is only a preferred embodiment of the present invention, and is not intended to limit the present invention in any way, and all simple modifications, equivalent variations and modifications made to the above embodiment according to the technical spirit of the present invention still belong to the protection scope of the technical solution of the present invention.
Claims (10)
1. An intelligent assessment method for automatically identifying the chronic pain degree of the old is characterized in that: which comprises the following steps:
(1) inputting the temperature, pulse, respiration and blood pressure data of the old;
(2) video recording of facial expressions: acquiring a dynamic image sequence including facial expressions of the elderly;
(3) preprocessing of the image: capturing a key frame static image according to the dynamic image sequence, simultaneously processing the light brightness and the background, and comparing captured key parameters with a large database;
(4) face region detection and tracking: detecting a face area from an original picture or video sample for subsequent feature extraction and classification;
(5) expression feature extraction: extracting expression features of the preprocessed expression images by using a corresponding feature extraction algorithm;
(6) classification and identification: and identifying the analysis processing model obtained by the collected sample by using a pain classification algorithm and the vital signs to obtain a comprehensive analysis conclusion of the pain level.
2. The intelligent evaluation method for automatically identifying the degree of chronic pain in the elderly according to claim 1, wherein: in the step (2), a video camera and a camera are used for acquiring a dynamic image sequence comprising facial expressions of the elderly.
3. The intelligent evaluation method for automatically identifying the degree of chronic pain in the elderly according to claim 2, wherein: in the step (4), if a video sequence requiring real-time property is processed, face detection and face tracking are used.
4. The utility model provides an automatic discernment old person chronic pain degree's intelligent evaluation system which characterized in that: it includes:
the information acquisition module is used for acquiring the body temperature, pulse, respiration and blood pressure data of the old;
the image acquisition module acquires a dynamic image sequence comprising facial expressions of the elderly;
the image preprocessing module is used for capturing a key frame static image according to the dynamic image sequence, processing the light brightness and the background at the same time, and capturing key parameters to compare with the large database;
the detection and tracking module detects the face area from the original picture or video sample for subsequent feature extraction and classification;
the feature extraction module is used for extracting the expression features of the preprocessed expression images by using a corresponding feature extraction algorithm;
and the classification identification module is used for identifying the analysis processing model obtained by the collected sample by using a pain classification algorithm and the vital signs to obtain a comprehensive analysis conclusion of the pain level.
5. The intelligent evaluation system for automatically identifying chronic pain level of elderly people according to claim 4, wherein: the image acquisition module extracts static expression images of 5 time periods from color video bands with the resolution of 720 multiplied by 480 or more, and the images form a database.
6. The intelligent evaluation system for automatically identifying chronic pain levels in the elderly according to claim 5, wherein: the image preprocessing module adopts identification in a static image, aligns eyes in all images mainly through rotation and size transformation, clips an original image into a size of 100 x 120, and further clips an elliptical face image.
7. The intelligent evaluation system for automatically identifying chronic pain levels in the elderly of claim 6, wherein: the classification identification module analyzes the preprocessed image information by adopting a deep learning model, and the deep learning model adopts a convolutional neural network; in the initial stage, a checking and verifying method is adopted, 2000 samples are trained, and the average accuracy rate is calculated; and in the later stage, a neural network same algorithm is adopted to combine with depth information to establish a three-dimensional human face shape for pain expression grading evaluation.
8. The intelligent evaluation system for automatically identifying chronic pain levels in the elderly according to claim 7, wherein: the classification identification module calculates the characteristic face: let the face image i (x, y) be a two-dimensional N x N array of (8-bit) intensity values, with one image being considered as N2Vector of dimensions, so that an N × N face image is considered to be N2Vector of dimensions, or equivalent to N2A point in dimensional space; finding out vectors which can reflect the distribution of the face image in the whole image space, wherein the vectors define the subspace of the face image, the subspace is called face space, and the length of each vector is N2N × N images are described, being a linear combination of facial spatial databases; these vectors are the feature vectors of the variance matrix corresponding to the face images in the face space database.
9. The intelligent evaluation system for automatically identifying chronic pain levels in the elderly according to claim 8, wherein: the system also comprises an evaluation module which is connected with the life characteristics of the elderly and the analysis module, and the evaluation module carries out result evaluation on the result obtained by the analysis module; the patient's pain level was assessed using an assessment method corresponding to facial expression + digital pain level.
10. The intelligent evaluation system for automatically identifying chronic pain levels in the elderly of claim 9, wherein: the system optimizes a cache mechanism and an asynchronous processing mechanism, and automatically encrypts and stores video or image data uploaded by a user to a background server.
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