Nursing operation assessment device based on artificial intelligence and assessment system thereof

Abstract

The invention discloses a nursing operation assessment device based on artificial intelligence and an assessment system thereof, and relates to the technical field of nursing assessment, the nursing operation assessment device comprises a user management subsystem, a visual acquisition subsystem, a data processing and analysis subsystem, an assessment standard determination subsystem and an information feedback subsystem; the user management subsystem is used for inputting and auditing personal information of personnel participating in nursing operation examination, randomly arranging reference personnel by using the system and generating examination serial numbers so as to realize randomness of the reference personnel; meanwhile, the system can check the personal information of the reference personnel before formal examination, and the consistency of the reference personnel and the arrangement information of the system is kept. According to the nursing operation assessment device based on artificial intelligence and the assessment system thereof, it is avoided that a large amount of manpower and time are spent on assessment calculation of nursing personnel, time and labor are saved, the accuracy of all data can be guaranteed, and the assessment efficiency is greatly improved.

Description

Technical Field

[0001] The present invention relates to the technical field of nursing assessment, and in particular to a nursing operation assessment device and an assessment system based on artificial intelligence. Background Art

[0002] With the continuous progress and development of society, people's living standards are constantly improving. The medical and nursing industry faces tremendous social pressure, demanding an increasing number of practitioners with higher-quality work experience. Medical students, as the primary source of nursing personnel, can effectively alleviate the pressure on nursing practitioners.

[0003] At present, the review standards for nursing skills mainly rely on the subjective judgment of relatively experienced physicians to evaluate, compare and analyze nursing staff. The assessment process is not objective, and there is more or less luck in the assessment process. Its accuracy needs to be examined. Summary of the Invention

[0004] In view of the deficiencies in the prior art, the present invention provides an artificial intelligence-based nursing operation assessment device and an assessment system thereof, which solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above objectives, the present invention is implemented through the following technical solutions: a nursing operation assessment system based on artificial intelligence, including a user management subsystem, a visual acquisition subsystem, a data processing and analysis subsystem, an assessment standard determination subsystem, and an information feedback subsystem;

[0006] The user management subsystem is used to enter and review the personal information of participants in the nursing operation assessment. The system is used to randomly arrange the participants and generate examination serial numbers to achieve randomness of the participants. At the same time, the system will verify the personal information of the participants before the formal assessment to ensure consistency between the participants and the system arrangement information.

[0007] Personal information includes the reference person’s name, ID card, reference number, facial recognition capture, and fingerprint information;

[0008] The visual acquisition subsystem uses multi-angle cameras to capture and collect information about the examinee's behavioral characteristics during the nursing assessment process, and classifies the data according to assessment items and feature points, using this information as a scoring item for assessment evaluation. At the same time, audio recording equipment is used to synchronously capture the conversation and behavior during the nursing process, and match it with the examinee's behavior at the same time points.

[0009] The data processing and analysis subsystem is used to clean and remove impurities from the collected image and language data to maintain the clarity of the image and language information. It also analyzes the assessment data through data matching and classification operations. It determines whether the logic of the action characteristics is reasonable based on the candidate's body shape information, thereby determining whether the action characteristic data should be retained. At the same time, it determines whether the action characteristics are continuous based on the data integrity, thereby obtaining continuous and clear nursing assessment data;

[0010] The assessment standard determination subsystem establishes an assessment standard database that contains the original standard actions, nursing operation sequence, action detail standards, and language expression keywords for nursing operations. The system is used to compare the processed and analyzed individual nursing assessment data with the assessment standard database one by one, and determine whether the operation assessment standards are met based on the differences in the individual data comparison;

[0011] The information feedback subsystem classifies, reviews, and responds to the feedback audit and judgment information based on the assessment results obtained by the assessment standard judgment subsystem. It also searches and manually merges the feedback information; after verification is completed, a statistical report is generated.

[0012] Optionally, the user management subsystem includes an information collection module, an information verification module and a voice prompt module;

[0013] The information collection module is used to scan and retrieve the test information of candidates, including their identity information, dynamic facial features, and multi-finger fingerprint information, and to package the above data into sets for each individual and compare them with the candidate's registration information;

[0014] The information verification module compares and verifies the personal information collected by the information collection module with the current candidate information one by one. The process is as follows:

[0015] Compare the candidate's current personal information with the application information to see if they are consistent; if all are consistent, proceed to the next step of information comparison; if there is any inconsistency, locate the divergence point in the compared information and return to the previous step for re-comparison;

[0016] The facial information of the examinee is collected and compared with the examinee's application photo to determine whether the current examinee is the examinee himself. If the comparison is successful, the next step of information comparison is carried out. If there is any inconsistency in the comparison, the position of the divergent feature points is located, and the language prompt module is used to provide auxiliary prompts, followed by a second facial comparison until the facial information is consistent.

[0017] Collect the examinee's fingerprint information and compare it with the examinee's registration information. If the comparison is successful, the examination content will be carried out; if the comparison is unsuccessful, return to the previous step and collect and compare again;

[0018] The voice prompt module provides language assistance prompts to reference personnel when collecting each piece of information based on the type of information divergence points that appear in the comparison.

[0019] Optionally, the visual acquisition subsystem includes a motion capture module, a feature locking module and a visual device control module;

[0020] The motion capture module, based on the action content involved in the assessment, attaches sensors to the outside of the reference personnel and uses the inertial navigation sensor AHRS (attitude reference system) and IMU (inertial measurement unit) to measure the reference personnel's limb movement acceleration, direction and tilt angle during the nursing operation.

[0021] The feature locking module is used to capture and lock specific action details as a reference for the action, while avoiding movement differences between different body types and maintaining the accuracy of the assessment comparison;

[0022] The visual equipment control module determines the direction and acceleration of the movement based on the specific action points captured by the feature locking module and in combination with the motion capture module, controls the image acquisition device to follow the movement and keeps the image acquisition device in focus at all times during the nursing operation assessment process.

[0023] Optionally, the data processing and analysis subsystem includes video segmentation and key frame extraction;

[0024] First, each frame is divided into small blocks using block matching. Similarity is determined by comparing corresponding blocks between consecutive frames. Local image features are used to suppress the effects of noise and camera and object motion. To fully consider the motion and characteristics of the object and camera within the same shot, motion compensation is used to reduce the changes in the intra-shot frame difference caused by the motion of the object and camera.

[0025] Secondly, the gray-level co-occurrence matrix method is used to extract texture features. Contrast, texture consistency, pixel-to-grayscale correlation and entropy are selected as feature vectors in the gray-level co-occurrence matrix. The above four texture features are extracted in the four directions of 0°, 45°, 90° and 135° to form a 16-dimensional feature vector. At the same time, the clustering algorithm is used to classify the images in the image library, which can effectively obtain the visual content with significant changes in video shots.

[0026] Optionally, the assessment standard determination subsystem includes a data comparison module and an audit determination module;

[0027] The data comparison module compares the processed and analyzed action data with the original standard actions in the assessment standard database one by one, and records the differences in the comparison process;

[0028] The audit and judgment module scores the nursing operation assessment process according to the difference items and conducts a secondary evaluation of the difference items.

[0029] An artificial intelligence-based nursing operation assessment device includes an assessment operation platform located at the front end of an assessment machine, and a visual acquisition component is installed above the rear end of the assessment machine;

[0030] The assessment machine includes a shell assembly for support, the shell consists of a shell, a cover, a heat dissipation port and a support leg, the cover is located at the rear end of the shell, the heat dissipation port is opened on both sides of the shell, and the support leg is located at the bottom end of the shell.

[0031] Optionally, the assessment machine further comprises an input platform located at the front end of the housing assembly, and a display screen embedded in the front end of the housing assembly is provided above the input platform, and the display screen is tilted.

[0032] Optionally, the visual acquisition component is connected to the assessment machine through the cooperation between a clamp and a screw;

[0033] The visual acquisition component includes a vertically mounted telescopic support rod, and a rubber pad is provided between the telescopic support rod and the clamp.

[0034] Optionally, a horizontally arranged support is sleeved on the top end of the telescopic support rod, and the front end of the support is hinged to both sides of the image acquisition device, and the rotation range of the image acquisition device is 0-180°.

[0035] The present invention provides an artificial intelligence-based nursing operation assessment device and assessment system thereof, which have the following beneficial effects:

[0036] This artificial intelligence-based nursing operation assessment device and assessment system obtains assessment results by collecting action images of reference personnel during the nursing process, analyzing and processing the action process, and comparing the image information with the assessment standards one by one. Among them, the system can also capture the characteristics of the corresponding nursing assessment actions, thereby avoiding differences in the nursing operation process caused by the different body shapes of the reference personnel and maintaining objective evaluation standards for the reference personnel. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] Figure 1 It is a front view schematic diagram of the nursing operation assessment device in the invention;

[0038] Figure 2 It is a left side schematic diagram of the nursing operation assessment device in the invention;

[0039] Figure 3 It is a right side half-section schematic diagram of the nursing operation assessment device in the invention;

[0040] Figure 4This is a schematic side view of the adjusted nursing operation assessment device in the invention.

[0041] In the figure: 1. Assessment operation platform; 2. Assessment machine; 201. Housing assembly; 202. Input platform; 203. Display screen; 3. Visual acquisition assembly; 301. Telescopic support rod; 302. Support; 303. Image acquisition device. DETAILED DESCRIPTION

[0042] The technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments.

[0043] In the description of the present invention, unless otherwise specified, "plurality" means two or more; terms such as "upper," "lower," "left," "right," "inner," "outer," "front end," "rear end," "head," and "tail" indicate positions or relationships based on those shown in the accompanying drawings. These terms are intended solely to facilitate the description of the present invention and simplify the description. They do not indicate or imply that the devices or components referred to must have a specific orientation, be constructed, or operate in a specific orientation. Therefore, they should not be construed as limiting the present invention. Furthermore, terms such as "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0044] In the description of the present invention, it should be noted that, unless otherwise expressly specified or limited, the terms "connected" and "connection" should be understood in a broad sense. For example, they can refer to fixed connection, detachable connection, or integral connection; mechanical connection, electrical connection; direct connection, or indirect connection through an intermediary. Those skilled in the art will understand the specific meanings of the above terms in the present invention based on the specific circumstances.

[0045] Embodiment 1: The present invention provides a technical solution: a nursing operation assessment device and assessment system based on artificial intelligence, including a user management subsystem, a visual acquisition subsystem, a data processing and analysis subsystem, an assessment standard determination subsystem, and an information feedback subsystem;

[0046] The user management subsystem is used to enter and review the personal information of participants in the nursing operation assessment. The system is used to randomly arrange the participants and generate examination serial numbers to achieve randomness of the participants. At the same time, the system will verify the personal information of the participants before the formal assessment to ensure consistency between the participants and the system arrangement information.

[0047] Personal information includes the reference person’s name, ID card, reference number, facial recognition capture, and fingerprint information;

[0048] The user management subsystem includes an information collection module, an information verification module, and a voice prompt module;

[0049] The information collection module is used to scan and retrieve the test information of candidates, including their identity information, dynamic facial features, and multi-finger fingerprint information, and to package the above data into sets for each individual and compare them with the candidate's registration information;

[0050] The information verification module compares and verifies the personal information collected by the information collection module with the current candidate information one by one. The process is as follows:

[0051] Compare the candidate's current personal information with the application information to see if they are consistent; if all are consistent, proceed to the next step of information comparison; if there is any inconsistency, locate the divergence point in the compared information and return to the previous step for re-comparison;

[0052] The facial information of the examinee is collected and compared with the examinee's application photo to determine whether the current examinee is the examinee himself. If the comparison is successful, the next step of information comparison is carried out. If there is any inconsistency in the comparison, the position of the divergent feature points is located, and the language prompt module is used to provide auxiliary prompts, followed by a second facial comparison until the facial information is consistent.

[0053] Collect the examinee's fingerprint information and compare it with the examinee's registration information. If the comparison is successful, the examination content will be carried out; if the comparison is unsuccessful, return to the previous step and collect and compare again;

[0054] The voice prompt module provides language assistance prompts to reference personnel when collecting each piece of information according to the type of information divergence points found in the comparison;

[0055] The visual acquisition subsystem uses multi-angle cameras to capture and collect information about the examinee's behavioral characteristics during the nursing assessment process, and classifies the data according to assessment items and feature points, using this information as a scoring item for assessment evaluation. At the same time, audio recording equipment is used to synchronously capture the conversation and behavior during the nursing process, and match it with the examinee's behavior at the same time points.

[0056] The visual acquisition subsystem includes a motion capture module, a feature locking module, and a visual device control module;

[0057] The motion capture module, based on the action content involved in the assessment, attaches sensors to the outside of the reference personnel and uses the inertial navigation sensor AHRS (attitude reference system) and IMU (inertial measurement unit) to measure the reference personnel's limb movement acceleration, direction and tilt angle during the nursing operation.

[0058] The feature locking module is used to capture and lock specific action details as a reference for the action, while avoiding movement differences between different body types and maintaining the accuracy of the assessment comparison;

[0059] The visual device control module determines the direction and acceleration of the movement based on the specific action points captured by the feature locking module and in conjunction with the motion capture module, controls the image acquisition device to follow the movement and keeps the image acquisition device in focus at all times during the nursing operation assessment process;

[0060] The data processing and analysis subsystem is used to clean and remove impurities from the collected image and language data to maintain the clarity of the image and language information. It also analyzes the assessment data through data matching and classification operations. It determines whether the logic of the action characteristics is reasonable based on the candidate's body shape information, thereby determining whether the action characteristic data should be retained. At the same time, it determines whether the action characteristics are continuous based on the data integrity, thereby obtaining continuous and clear nursing assessment data;

[0061] The data processing and analysis subsystem includes video segmentation and key frame extraction;

[0062] First, each frame is divided into small blocks using block matching. Similarity is determined by comparing corresponding blocks between consecutive frames. Local image features are used to suppress the effects of noise and camera and object motion. To fully consider the motion and characteristics of the object and camera within the same shot, motion compensation is used to reduce the changes in the intra-shot frame difference caused by the motion of the object and camera.

[0063] Secondly, the gray-level co-occurrence matrix method is used to extract texture features. In the gray-level co-occurrence matrix, contrast, texture consistency, pixel-to-grayscale correlation, and entropy are selected as feature vectors. The above four texture features are extracted at four directions of 0°, 45°, 90°, and 135° to form a 16-dimensional feature vector. At the same time, a clustering algorithm is used to classify the images in the image library, which can effectively obtain the visual content of video shots with significant changes.

[0064] The assessment standard determination subsystem establishes an assessment standard database that contains the original standard actions, nursing operation sequence, action detail standards, and language expression keywords for nursing operations. The system is used to compare the processed and analyzed individual nursing assessment data with the assessment standard database one by one, and determine whether the operation assessment standards are met based on the differences in the individual data comparison;

[0065] The assessment standard determination subsystem includes a data comparison module and an audit determination module;

[0066] The data comparison module compares the processed and analyzed action data with the original standard actions in the assessment standard database one by one, and records the differences in the comparison process;

[0067] The review and judgment module scores the nursing operation assessment process according to the difference items and conducts a secondary evaluation of the difference items;

[0068] The information feedback subsystem classifies, reviews, and responds to the feedback audit and judgment information based on the assessment results obtained by the assessment standard judgment subsystem. It also searches and manually merges the feedback information; after verification is completed, a statistical report is generated.

[0069] Example 2: Please refer to Figures 1 to 4 , a nursing operation assessment device based on artificial intelligence, including an assessment operation platform 1, the assessment operation platform 1 is located at the front end of an assessment machine 2, and a visual acquisition component 3 is installed above the rear end of the assessment machine 2;

[0070] The testing machine 2 includes a housing assembly 201 for support. The housing 201 is composed of a shell, a cover, a heat dissipation vent, and a support leg. The cover is located at the rear end of the shell. The heat dissipation vent is opened on both sides of the shell. The support leg is located at the bottom end of the shell.

[0071] The assessment machine 2 further includes an input platform 202 located at the front end of the housing assembly 201. Above the input platform 202 is a display screen 203 embedded in the front end of the housing assembly 201. The display screen 203 is tilted.

[0072] The visual acquisition component 3 is connected to the assessment machine 2 through the cooperation between the clamp and the screw;

[0073] The visual acquisition component 3 includes a vertically mounted telescopic support rod 301, and a rubber pad is provided between the telescopic support rod 301 and the clamp;

[0074] The top end of the telescopic support rod 301 is sleeved with a horizontally arranged support 302 , and the front end of the support 302 is hinged to both sides of the image acquisition device 303 . The rotation range of the image acquisition device 303 is 0-180°.

[0075] The above description is only a preferred specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any technician familiar with the technical field, within the technical scope disclosed by the present invention, who makes equivalent replacements or changes based on the technical solution and inventive concept of the present invention, should be covered by the scope of protection of the present invention.

Claims

1. The artificial intelligence-based nursing operation assessment system is characterized by: It includes user management subsystem, visual acquisition subsystem, data processing and analysis subsystem, assessment standard determination subsystem, and information feedback subsystem; The user management subsystem is used to enter and review the personal information of participants in the nursing operation assessment. The system is used to randomly arrange the participants and generate examination serial numbers to achieve randomness of the participants. At the same time, the system will verify the personal information of the participants before the formal assessment to ensure consistency between the participants and the system arrangement information. Personal information includes the reference person’s name, ID card, reference number, facial recognition capture, and fingerprint information; The visual acquisition subsystem uses multi-angle cameras to capture and collect information about the examinee's behavioral characteristics during the nursing assessment process, and classifies the data according to assessment items and feature points, using this information as a scoring item for assessment evaluation. At the same time, audio recording equipment is used to synchronously capture the conversation and behavior during the nursing process, and match it with the examinee's behavior at the same time points. The data processing and analysis subsystem is used to clean and remove impurities from the collected image and language data to maintain the clarity of the image and language information. It also analyzes the assessment data through data matching and classification operations. It determines whether the logic of the action characteristics is reasonable based on the candidate's body shape information, thereby determining whether the action characteristic data should be retained. At the same time, it determines whether the action characteristics are continuous based on the data integrity, thereby obtaining continuous and clear nursing assessment data; The assessment standard determination subsystem establishes an assessment standard database that contains the original standard actions, nursing operation sequence, action detail standards, and language expression keywords for nursing operations. The system is used to compare the processed and analyzed individual nursing assessment data with the assessment standard database one by one, and determine whether the operation assessment standards are met based on the differences in the individual data comparison; The information feedback subsystem classifies, reviews, and responds to the feedback based on the assessment results obtained by the assessment criteria subsystem. It also searches and manually merges the feedback information. After the verification is completed, a statistical report will be generated.

2. The artificial intelligence-based nursing operation assessment system according to claim 1 is characterized in that: The user management subsystem includes an information collection module, an information verification module and a voice prompt module; The information collection module is used to scan and retrieve the test information of candidates, including their identity information, dynamic facial features, and multi-finger fingerprint information, and to package the above data into sets for each individual and compare them with the candidate's registration information; The information verification module compares and verifies the personal information collected by the information collection module with the current candidate information one by one. The process is as follows: Compare the candidate's current personal information with the application information to see if they are consistent; if all are consistent, proceed to the next step of information comparison; if there is any inconsistency, locate the divergence point in the compared information and return to the previous step for re-comparison; The facial information of the examinee is collected and compared with the examinee's application photo to determine whether the current examinee is the examinee himself. If the comparison is successful, the next step of information comparison is carried out. If there is any inconsistency in the comparison, the position of the divergent feature points is located, and the language prompt module is used to provide auxiliary prompts, followed by a second facial comparison until the facial information is consistent. Collect the examinee's fingerprint information and compare it with the examinee's registration information. If the comparison is successful, the examination content will be carried out; if the comparison is unsuccessful, return to the previous step and collect and compare again; The voice prompt module provides language assistance prompts to reference personnel when collecting each piece of information based on the type of information divergence points that appear in the comparison.

3. The artificial intelligence-based nursing operation assessment system according to claim 1 is characterized in that: The visual acquisition subsystem includes a motion capture module, a feature locking module and a visual device control module; The motion capture module, based on the action content involved in the assessment, attaches sensors to the outside of the reference personnel and uses the inertial navigation sensor AHRS (attitude reference system) and IMU (inertial measurement unit) to measure the reference personnel's limb movement acceleration, direction and tilt angle during the nursing operation. The feature locking module is used to capture and lock specific action details as a reference for the action, while avoiding movement differences between different body types and maintaining the accuracy of the assessment comparison; The visual equipment control module determines the direction and acceleration of the movement based on the specific action points captured by the feature locking module and in combination with the motion capture module, controls the image acquisition device to follow the movement and keeps the image acquisition device in focus at all times during the nursing operation assessment process.

4. The artificial intelligence-based nursing operation assessment system according to claim 1 is characterized in that: The data processing and analysis subsystem includes video segmentation and key frame extraction; First, each frame is divided into small blocks using block matching. Similarity is determined by comparing corresponding blocks between consecutive frames. Local image features are used to suppress the effects of noise and camera and object motion. To fully consider the motion and characteristics of the object and camera within the same shot, motion compensation is used to reduce the changes in the intra-shot frame difference caused by the motion of the object and camera. Secondly, the gray-level co-occurrence matrix method is used to extract texture features. Contrast, texture consistency, pixel-to-grayscale correlation and entropy are selected as feature vectors in the gray-level co-occurrence matrix. The above four texture features are extracted in the four directions of 0°, 45°, 90° and 135° to form a 16-dimensional feature vector. At the same time, the clustering algorithm is used to classify the images in the image library, which can effectively obtain the visual content with significant changes in video shots.

5. The artificial intelligence-based nursing operation assessment system according to claim 1 is characterized in that: The assessment standard determination subsystem includes a data comparison module and an audit determination module; The data comparison module compares the processed and analyzed action data with the original standard actions in the assessment standard database one by one, and records the differences in the comparison process; The audit and judgment module scores the nursing operation assessment process according to the difference items and conducts a secondary evaluation of the difference items.

6. An artificial intelligence-based nursing operation assessment device for use in any one of claims 1 to 5, comprising an assessment operation platform (1), characterized in that: The assessment operation platform (1) is located at the front end of the assessment machine (2), and a visual acquisition component (3) is installed above the rear end of the assessment machine (2); The testing machine (2) comprises a housing assembly (201) for supporting the test, wherein the housing (201) is composed of a shell, a cover plate, a heat dissipation port and supporting legs, wherein the cover plate is located at the rear end of the shell, the heat dissipation port is opened on both sides of the shell, and the supporting legs are located at the bottom end of the shell.

7. The artificial intelligence-based nursing operation assessment device and assessment system according to claim 6, characterized in that: The assessment machine (2) further comprises an input platform (202) located at the front end of the housing component (201), and a display screen (203) embedded in the front end of the housing component (201) is provided above the input platform (202), wherein the display screen (203) is tilted.

8. The artificial intelligence-based nursing operation assessment device and assessment system according to claim 6, characterized in that: The visual acquisition component (3) is connected to the assessment machine (2) through the cooperation between the clamp and the screw; The visual acquisition component (3) comprises a vertically mounted telescopic support rod (301), wherein a rubber pad is provided between the telescopic support rod (301) and the clamp.

9. The artificial intelligence-based nursing operation assessment device and assessment system according to claim 6, characterized in that: The top end of the telescopic support rod (301) is sleeved with a horizontally arranged support (302), the front end of the support (302) is hinged to both sides of the image acquisition device (303), and the rotation range of the image acquisition device (303) is 0-180 degrees.

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