Medical cardio-pulmonary resuscitation teaching system based on artificial intelligence

Abstract

The invention discloses a medical cardio-pulmonary resuscitation teaching system based on artificial intelligence, and relates to the technical field of medical teaching man-machine interaction, and the system comprises a multi-modal data collection module which collects operation related data in real time through various sensors and audio and video equipment, and synchronizes the data to a core processing unit; the real-time interaction feedback module forms a real-time error correction mechanism integrating voice, vision and touch; the AI intelligent analysis module accurately identifies operation defects through a mixed deep learning model and generates an evaluation result; the personalized training scheme generation module formulates a targeted training plan in combination with student data; the teaching data management module stores data in a classified manner and supports multi-dimensional query statistics; the visual teaching guidance module provides a transparent filter and dynamic demonstration; and the system adaptation optimization module dynamically adjusts parameters. According to the invention, a traditional teaching mode is innovated, and accurate evaluation and real-time error correction are realized through multi-modal acquisition and AI analysis; the system adapts to students with different foundations and requirements, and promotes standard popularization of cardio-pulmonary resuscitation operation.

Description

Technical Field

[0001] This invention relates to the field of human-computer interaction technology in medical teaching, and in particular to a medical cardiopulmonary resuscitation teaching system based on artificial intelligence. Background Technology

[0002] Cardiopulmonary resuscitation (CPR) is a core technique for saving the lives of patients experiencing cardiac and respiratory arrest in clinical emergency care. The standardization of its operation directly impacts the success rate of resuscitation and is widely used in first aid training within the medical industry, public health sector, and among the general public. However, current CPR teaching models still suffer from numerous technical bottlenecks and practical shortcomings, making it difficult to meet the demands for efficient and precise instruction. Traditional teaching heavily relies on one-on-one guidance from professional instructors, but these resources are scarce and unevenly distributed, failing to cover large-scale training scenarios. During instruction, instructors struggle to capture the operational details of each student in real time, relying on subjective judgment to assess key parameters such as compression depth, frequency, and location. Feedback is delayed and lacks precision, resulting in students' errors not being corrected promptly and the formation of poor operating habits.

[0003] While existing teaching systems incorporate some sensing devices and feedback functions, their technological maturity is insufficient, resulting in significant shortcomings. At the data acquisition level, most systems utilize single-type sensors, acquiring only limited data such as compression pressure or frequency, lacking the ability to capture multi-dimensional information like operational posture and breathing coordination. This incompleteness affects the accuracy of subsequent analysis and evaluation. Feedback mechanisms are simplistic, primarily relying on voice prompts and lacking the synergy of visual guidance and tactile alerts, making it difficult for trainees to quickly pinpoint the root cause of errors and adjust their actions. Regarding teaching methods, there is a lack of intelligent analysis and personalized adaptation capabilities. All trainees are subjected to uniform training content and difficulty levels, making it impossible to develop targeted plans based on individual skill gaps, leading to low learning efficiency. Furthermore, the lack of clinical scenario simulation means trainees can only practice basic operations, hindering effective training in emergency response capabilities for patients of different ages, complex pathological conditions, or special environments.

[0004] Furthermore, the fragmented management of teaching data, lacking a systematic storage, statistical, and mining mechanism, makes it difficult to effectively integrate and analyze trainees' training records and operational data. Common problems and best practices in group training are hard to extract, hindering the formation of a closed-loop iterative process of "data collection-analysis and evaluation-teaching optimization." Simultaneously, existing systems suffer from insufficient adaptability, failing to meet the learning needs of trainees with different language backgrounds and physical conditions. The lack of remote teaching functionality limits the scope of teaching coverage. These issues collectively contribute to the current teaching model's inability to achieve precise instruction and efficient mastery of operational skills. Trainees often experience compromised treatment outcomes in actual emergency scenarios due to improper operation and insufficient emergency response capabilities. Therefore, an intelligent, multi-dimensional, and personalized CPR teaching system is urgently needed to address these industry pain points. Summary of the Invention

[0005] The present invention proposes an artificial intelligence-based medical cardiopulmonary resuscitation teaching system to solve the problems mentioned in the prior art.

[0006] To achieve the above objectives, the present invention adopts the following technical solution: an artificial intelligence-based medical cardiopulmonary resuscitation teaching system, comprising the following modules:

[0007] The multimodal data acquisition module deploys pressure, position, and frequency sensors in the chest and hand operation areas of the teaching simulator. It acquires operation posture images through a high-definition camera and captures speech and environmental sounds with the help of audio acquisition equipment. Information is collected in real time, the data sampling rate is dynamically adjusted, and the acquired data is synchronized to the system core processing unit through a high-speed transmission channel.

[0008] The real-time interactive feedback module, based on multimodal data acquisition, provides feedback on operational issues through voice broadcast, visual prompts through chest indicator lights and a matching display screen, and tactile warnings through hand vibration feedback devices, forming a three-in-one real-time error correction mechanism.

[0009] The AI ​​intelligent analysis module constructs a hybrid deep learning model of convolutional and recurrent neural networks, extracts spatial and temporal features, imports standard parameter libraries, clinical cases and error samples for training, analyzes trainees’ operations and standard deviations in real time, and generates quality assessment results.

[0010] The personalized training plan generation module analyzes skill gaps and develops targeted training plans based on AI assessment results, combined with students' learning time, historical data, and ability levels.

[0011] The teaching data management module adopts a distributed storage architecture, classifies and stores various types of data, establishes an indexing mechanism, supports multi-dimensional queries, statistically analyzes group training data, and generates statistical reports.

[0012] The visual teaching guidance module provides a transparent effect of the heart on the accompanying display screen, showing the anatomical structure and compression area. It demonstrates standard operation and breathing coordination through animation, supports multiple perspective switching, and provides text guidance and key points.

[0013] The system adaptation and optimization module monitors sensor status, data transmission, and AI model efficiency in real time. It dynamically adjusts sensor parameters and feedback conditions based on the user's operating habits and physical characteristics, and regularly updates the standard library and training data.

[0014] Furthermore, it also includes an operational quality comprehensive scoring module, the calculation expression of which is: in For the overall evaluation of operational quality, , , , , The weighting coefficients are as follows: compression location accuracy, compression frequency compliance rate, compression depth compliance rate, operational consistency, and breathing coordination. This represents the percentage of overlap between the pressing position and the standard area. This represents the percentage of time the actual pressing frequency falls within the standard range. The percentage of times the pressing depth meets the standard requirements. The smoothness score is given for continuous operation without interruptions or unnecessary movements. The system scores the compliance of the rhythm of chest compressions and artificial respiration; the scoring system supports dynamic weight adjustments, and the scoring results are accompanied by detailed interpretations.

[0015] Furthermore, it also includes a simulated clinical scenario module with a built-in library of various clinical emergency scenarios, covering resuscitation scenarios for patients of different ages, including adults, children, and infants. The scenarios support dynamic difficulty adaptation, automatically adjusting the complexity of the scenario based on the trainee's current ability level. Through the display screen and audio equipment, the system simulates the environmental sound effects and visual images of the scenario, sets scenario-based operation tasks, records the trainee's operation performance in the scenario in real time, explains operation optimization solutions in conjunction with clinical cases, and evaluates the trainee's emergency response capabilities in complex scenarios.

[0016] Furthermore, it includes a group data mining module, which uses clustering algorithms to group and analyze the training data of all trainees, dividing them into novice, intermediate, and proficient groups according to their skill level. This identifies the operational characteristics and common error patterns of trainees at different levels, and uncovers the common features of excellent operational cases. Through correlation analysis, it explores the intrinsic relationship between training duration, training frequency, and improvement in operational quality, and establishes an error trend prediction model. The mining results are fed back to the personalized training plan generation module and the teaching guidance module, while also generating a group teaching analysis report.

[0017] Furthermore, it also includes a training performance prediction module, the calculation expression of which is: in Predicting the probability of trainees reaching the passing level. , , , The weighting coefficients are: historical average score, improvement rate in the last three training sessions, speed of improvement in weaknesses, and training focus. The average quality score of the trainees' past training. This represents the average difference in improvement over the most recent three training scores. The speed at which the error rate of operations targeting core weaknesses decreases. The training program determines the percentage of time spent on focused, unrelated tasks. When the predicted results fall below a preset threshold, an enhanced training mechanism is automatically triggered. When the predicted progress meets expectations, phased challenge tasks are pushed out, and reward mechanisms such as honor badges and points are set up to motivate trainees to maintain their learning motivation.

[0018] Furthermore, it includes a remote teaching interaction module, which allows students to remotely log in to the system via their devices. Leveraging low-latency real-time video transmission, it enables synchronized sharing of operation screens and sensor data. Teachers can view student operation data, evaluation results, and training records through the system backend, and provide remote guidance and Q&A through voice calls, text messages, and screen annotations. It also supports simultaneous online group collaborative training for multiple participants, simulating scenarios of multiple people coordinating cardiopulmonary resuscitation in clinical settings. The system provides real-time assessments of teamwork and operational smoothness, offering suggestions for optimization. Remote training data is automatically synchronized to the teaching data management module, generating remote teaching reports and supporting the tracking of teaching effectiveness and quality evaluation.

[0019] Furthermore, it includes a visualization and playback module for operational actions, which records the entire training process of trainees with data and high-definition video recording. It supports precise playback by time node and operation stage, and displays data visualization charts simultaneously during playback. It provides an AI intelligent comparison and analysis function, which plays the trainee's operation video and the standard operation video in a split-screen synchronous manner, automatically marking the time nodes, specific manifestations and deviation degrees of the differences in actions, and supports slow-motion playback to highlight the details of incorrect actions. Combining biomechanical principles and clinical practice standards, it explains the potential hazards of incorrect actions and the scientific basis for correct operation.

[0020] Furthermore, it includes a fault self-diagnosis and maintenance module that monitors the working status of hardware components in real time. When hardware faults, data transmission anomalies, or storage faults are detected, it automatically issues audible and visual fault alarm signals, clearly displays the fault location, fault type, and scope of impact on the display screen, and provides step-by-step fault troubleshooting guidance and maintenance suggestions. It supports remote maintenance functions; simple faults can be self-healed by sending repair commands remotely through the system, while complex faults automatically generate maintenance work orders and push them to the maintenance personnel's terminals. It also establishes a data self-healing mechanism, which uses AI algorithms to complete missing data based on historical data and contextual information when some collected data is missing.

[0021] Furthermore, it includes multilingual and multi-mode teaching modules, supporting voice broadcasts, text prompts, and interface displays in multiple mainstream languages, with a built-in language switching function, allowing students to choose freely according to their needs; it adapts to the needs of special groups of students, enhancing the flashing frequency of indicator lights, the detail of screen text prompts, and the intensity of vibration feedback; it provides a detailed voice explanation mode for visually impaired students, using three-dimensional spatial sound effects to locate the correct pressing area; it provides a large font, high-contrast interface mode for elderly students; and it supports personalized interface customization, allowing students to adjust the interface layout, icon size, and prompt frequency to suit different usage habits.

[0022] Furthermore, it includes a teaching assessment and certification module with a built-in standardized assessment question bank, containing two core categories: theoretical knowledge tests and operational skills assessments. The operational skills assessment uses randomly selected clinical scenarios, requiring trainees to complete a full cardiopulmonary resuscitation (CPR) procedure within a specified time, with the system recording the entire process in real time. After the trainee completes the assessment, the system combines the theoretical score and the operational quality score to generate a comprehensive assessment result. Those who pass the assessment are automatically awarded an electronic certification, which supports QR code verification and official validation. A tiered certification system is established, divided into three levels: beginner, intermediate, and advanced. Different levels correspond to different assessment standards and certification benefits. The assessment results of trainees who fail are automatically fed back to the personalized training plan generation module to develop a targeted retake training plan.

[0023] Compared with existing technologies, the beneficial effects of this invention are:

[0024] The AI-based medical cardiopulmonary resuscitation teaching system of the present invention comprehensively revolutionizes the traditional teaching model through multi-module collaborative innovation and technology optimization, achieving all-round improvement in teaching accuracy, personalization, practicality and coverage, and possessing significant technical advantages and application value.

[0025] In terms of teaching accuracy and timely error correction, the multimodal data acquisition module integrates pressure, position, and frequency sensors with audio and video acquisition equipment to achieve full-dimensional capture of operational parameters and scene information, providing complete data support for analysis and evaluation. The AI ​​intelligent analysis module uses a hybrid deep learning model to accurately extract the spatial and temporal features of operations. Combined with a standard parameter library and clinical case data, it achieves accurate identification of the type and severity of operational defects, avoiding biases caused by subjective assessments. The real-time interactive feedback module constructs a three-in-one feedback mechanism of voice, vision, and touch, instantly informing trainees of operational problems, helping them quickly adjust their movements, and significantly improving the efficiency and effectiveness of error correction.

[0026] In terms of personalized teaching and learning efficiency, the personalized training plan generation module, based on students' ability assessment results, historical data, and ability levels, accurately identifies weaknesses in areas such as pressure control and intensity adjustment, formulates targeted training plans, and dynamically adjusts the difficulty gradient to achieve individualized instruction and significantly improve learning efficiency. The training effect prediction module uses machine learning models to predict students' learning progress and dynamically adjusts training intensity and incentive mechanisms to maintain learning motivation. The group data mining module extracts operational characteristics and error patterns of students at different levels, optimizes the allocation of teaching resources and the setting of key points, forming a closed-loop iterative teaching system and promoting the overall improvement of teaching quality.

[0027] In terms of clinical adaptability and practical skills development, the simulated clinical scenario module includes a multi-type scenario library that supports dynamic difficulty adjustment and the addition of interfering factors, allowing trainees to train in realistic emergency environments and effectively improve their emergency response capabilities in complex scenarios. The operation visualization and playback module uses data charts and video comparisons, combined with explanations of biomechanical principles and clinical standards, to help trainees understand key operational points from a theoretical perspective, deepening their memory and mastery. The teaching assessment and certification module constructs a tiered assessment system to comprehensively evaluate theoretical knowledge and operational skills, generating standardized certification certificates to ensure trainees possess qualified practical application abilities.

[0028] In terms of teaching coverage and inclusivity, the remote teaching interaction module supports multi-person online collaborative training and remote guidance, breaking the limitations of time and space and expanding the scope of teaching coverage, especially suitable for large-scale training and remote teaching scenarios. The multilingual and multi-mode teaching module supports multiple mainstream languages ​​and personalized interface customization, adapting to the learning needs of different groups such as hearing-impaired, visual-impaired, and elderly learners, improving the inclusivity and accessibility of teaching. The fault self-diagnosis and maintenance module ensures stable system operation, and the data self-healing mechanism ensures that assessment results are not affected by minor faults, providing reliable support for long-term teaching.

[0029] Overall, the system has achieved a comprehensive transformation in CPR instruction, from "experience-driven" to "data-driven," from "unified teaching" to "personalized adaptation," from "single-dimensional assessment" to "multimodal fusion," and from "basic practice" to "clinical scenario simulation." This has significantly improved the accuracy, efficiency, and practicality of instruction, helping trainees quickly master standardized operating skills and emergency response capabilities. It provides high-quality teaching support for the medical industry, public health sector, and public emergency training, and is of great significance for improving overall emergency care levels and saving patients' lives. It also has broad application value. Attached Figure Description

[0030] Figure 1 This is a schematic block diagram of the AI-based medical cardiopulmonary resuscitation teaching system proposed in this invention;

[0031] Figure 2Grouped bar charts comparing the pass rates of operational procedures at different training stages;

[0032] Figure 3 A line graph showing the decreasing trend of the operation error rate during training;

[0033] Figure 4 A horizontal bar chart comparing emergency response capabilities in different clinical scenarios;

[0034] Figure 5 A line graph comparing the pass rates of students with different skill levels. Detailed Implementation

[0035] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0036] In the description of this invention, it should be understood that the terms "center," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," and "counterclockwise," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing this invention and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on this invention.

[0037] Furthermore, the terms "first" and "second" are used for descriptive purposes only and should not be construed as indicating or implying relative importance or implicitly specifying the number of indicated technical features. Thus, features defined with "first" and "second" may explicitly or implicitly include one or more of the stated features. In the description of this invention, "a plurality of" means two or more, unless otherwise explicitly specified. Furthermore, the terms "installed," "connected," and "linked" should be interpreted broadly; for example, they may refer to a fixed connection, a detachable connection, or an integral connection; they may refer to a mechanical connection or an electrical connection; they may refer to a direct connection or an indirect connection through an intermediate medium; and they may refer to the internal connection of two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances. The invention will now be described in further detail with reference to the accompanying drawings.

[0038] Reference Figures 1 to 5 A medical cardiopulmonary resuscitation (CPR) teaching system based on artificial intelligence, comprising the following modules:

[0039] The multimodal data acquisition module deploys pressure sensors, position sensors, and frequency sensors in the chest and hand operation areas of the teaching mannequin. It captures operation posture images through a high-definition camera and captures voice commands and ambient sounds with the help of audio acquisition equipment. It collects compression pressure values, compression position coordinates, compression frequency, operation posture data, and voice interaction information in real time. The data sampling rate is dynamically adjusted according to the operation rhythm, and the sampling frequency is increased during periods of intensive operation to ensure the integrity and timeliness of data acquisition. All acquired data is synchronized to the system's core processing unit through a high-speed transmission channel.

[0040] The real-time interactive feedback module, based on multimodal data acquisition, provides immediate feedback on operational issues such as pressing too deeply or too shallowly, positional deviation, or frequency being too fast or too slow through voice broadcasting devices. It also provides visual cues such as a simulated human chest indicator light and a matching display screen to indicate the correct pressing area and depth range, and provides tactile alerts through a vibration feedback device in the hand operation area. This forms a three-in-one real-time error correction mechanism integrating voice, vision, and touch to help trainees quickly adjust their operation.

[0041] The AI ​​intelligent analysis module constructs a hybrid deep learning model that combines convolutional neural networks and recurrent neural networks. The convolutional neural network extracts spatial features from the operation posture image, while the recurrent neural network captures the temporal features of the compression rhythm and force changes. It imports the standard operation parameter library for cardiopulmonary resuscitation, clinical practice case data, and common error operation samples for model training. It analyzes the deviation between the trainee's operation data and the standard parameters in real time, accurately identifies the type and severity of operation defects, and generates operation quality assessment results.

[0042] The personalized training plan generation module, based on the evaluation results of the AI ​​intelligent analysis module, combined with the student's learning time, operation history data, and ability level, analyzes the student's shortcomings in areas such as pressure position control, force control, and frequency adjustment, and formulates targeted training plans, clarifying training focus, training duration, and phased goals, dynamically adjusting the difficulty gradient of training content, and gradually improving the student's operational proficiency and standardization.

[0043] The teaching data management module adopts a distributed storage architecture to classify and store data such as student basic information, training records, operation data, evaluation results, and personalized training plans. It establishes a data indexing mechanism to support multi-dimensional data queries by student identity, training time, operation items, etc. It can also statistically analyze group data such as the total number of trainees, average score, and common error types, and generate data statistical reports to provide data support for teaching optimization.

[0044] The visual teaching and guidance module provides a transparent filter effect for the chest and heart on the accompanying display screen, which intuitively shows the anatomical structure of the heart and the area of ​​compression. It demonstrates the standard compression action and breathing coordination process through dynamic animation, supports multi-view switching to observe the operation details, and provides textual guidance and key points of operation steps to help trainees establish a correct understanding of the operation.

[0045] The system adaptation and optimization module monitors the sensor's working status, data transmission stability, and AI model running efficiency in real time. Based on different students' operating habits, hand size, and strength characteristics, it dynamically adjusts the sensor sensitivity threshold and feedback trigger conditions. It also regularly updates the CPR standard parameter library and AI model training data to optimize model recognition accuracy and response speed, ensuring that the system adapts to different teaching scenarios and student needs.

[0046] This invention also includes an operational quality comprehensive scoring module, which constructs a multi-dimensional scoring system, and the calculation expression is as follows: in For the overall evaluation of operational quality, , , , , The weighted coefficients for compression location accuracy, compression frequency compliance rate, compression depth compliance rate, operational consistency, and breathing coordination are respectively, and the sum of the weights is 1. This represents the percentage of overlap between the pressing position and the standard area. This represents the percentage of time the actual pressing frequency falls within the standard range. The percentage of times the pressing depth meets the standard requirements. The smoothness score is given for continuous operation without interruptions or unnecessary movements. To assess the compliance of the rhythm of chest compressions and artificial respiration, a comprehensive calculation of multiple indicators scientifically quantifies the quality of the procedure. The scoring system supports dynamic weight adjustments. In the beginner stage, the weight of accuracy of compression position and compliance of frequency is increased. In the advanced stage, the weight of operation continuity and breathing coordination is increased. The scoring results are accompanied by detailed interpretations, clearly identifying the deduction points for each indicator and corresponding improvement suggestions, providing a precise basis for assessing trainees' abilities and optimizing training programs.

[0047] This invention also includes a simulated clinical scenario module, which incorporates a library of various clinical emergency scenarios, covering resuscitation scenarios for patients of different ages (adults, children, and infants), pathological scenarios such as cardiac arrest combined with respiratory failure and cardiac arrest due to trauma, as well as environmental scenarios such as indoor, outdoor, and transport scenarios. The scenarios support dynamic difficulty adaptation, automatically adjusting scenario complexity based on the learner's current skill level. Beginners are provided with a basic, interference-free scenario, while advanced learners are introduced with environmental noise, patient limb impairments, and multi-person collaboration challenges. The system simulates the scenario's environmental sound effects and visuals through a display screen and audio equipment, setting scenario-based operational tasks that require learners to complete corresponding CPR operations within a specified time. The system records the learner's performance in the scenario in real time, generating a debriefing report after the scenario ends, explaining operational optimization strategies in conjunction with clinical cases, and assessing the learner's emergency response capabilities in complex scenarios.

[0048] This invention also includes a group data mining module, which uses clustering algorithms to group and analyze the training data of all trainees, dividing them into novice, intermediate, and proficient groups based on their skill level. This identifies the operational characteristics and common error patterns of trainees at different levels, and uncovers the common features of excellent operational examples. Through correlation analysis, it explores the intrinsic relationship between training duration, training frequency, and improvement in operational quality, establishing an error trend prediction model. Based on historical group data, it predicts high-frequency errors that may occur in the future, allowing for advance adjustments to teaching priorities. The mining results are fed back to the personalized training plan generation module and the teaching guidance module, recommending targeted teaching resources for trainees at different levels. Simultaneously, it generates a group teaching analysis report, including teaching effectiveness evaluation, trainee ability distribution, and teaching resource needs, providing teaching administrators with a basis for decision-making regarding teaching method improvement and teaching resource allocation.

[0049] This invention also includes a training effect prediction module, which introduces a machine learning prediction model and combines the student's historical training data, current operation quality score, and type of skill deficiency to construct a training effect prediction formula. The calculation expression is as follows: in Predicting the probability of trainees reaching the passing level. , , , The weighting coefficients are: historical average score, improvement rate in the last three training sessions, speed of improvement in weaknesses, and training focus. The average quality score of the trainees' past training. This represents the average difference in improvement over the most recent three training scores. The speed at which the error rate of operations targeting core weaknesses decreases. This represents the percentage of time spent in focused, uninterrupted training. When the predicted result falls below a preset threshold, an enhanced training mechanism is automatically triggered, increasing the frequency and duration of targeted training and prioritizing one-on-one guidance. When the predicted progress meets expectations, phased challenge tasks are pushed out, and reward mechanisms such as honor badges and points are set up to motivate students to maintain their learning motivation, thereby improving teaching efficiency through dynamic intervention.

[0050] This invention also includes a remote teaching interaction module, supporting students to remotely log in to the system via mobile terminals, computers, and other devices. Utilizing low-latency real-time video transmission, it enables synchronous sharing of operation screens and sensor data. Teachers can view student operation data, evaluation results, and training records through the system backend, and provide remote guidance and Q&A through voice calls, text messages, and screen annotations. It supports simultaneous online group collaborative training for multiple participants, simulating a clinical scenario of multiple-person CPR, with one person responsible for chest compressions and another for ventilation. The system assesses collaboration coordination and operational smoothness in real time, providing suggestions for optimization. Remote training data is automatically synchronized to the teaching data management module, generating remote teaching reports including indicators such as participation rate, training duration, and operational qualification rate. This supports teaching effectiveness tracking and quality evaluation, breaking time and space limitations and expanding the scope of teaching coverage.

[0051] This invention also includes a visualization and playback module for operational actions, which records the entire training process of trainees with data and high-definition video recording. It supports precise playback by time node and operational stage, and simultaneously displays data visualization charts such as compression pressure curves, position change trajectories, and frequency change trends during playback. Different colors are used to mark correct and incorrect operation periods. It provides AI intelligent comparison and analysis functions, playing trainee operation videos and standard operation videos simultaneously in split-screen mode, automatically marking the time nodes, specific manifestations, and degree of deviation of the actions, and supporting slow-motion playback to highlight details of incorrect actions. Combining biomechanical principles and clinical practice standards, it explains the potential hazards of incorrect actions and the scientific basis for correct operation, helping trainees understand the key points of operation from a theoretical perspective and deepening their memory and mastery of correct actions.

[0052] This invention also includes a fault self-diagnosis and maintenance module, which monitors the working status of hardware components such as sensors, cameras, audio devices, transmission channels, and storage units in real time. It uses data verification algorithms to check the integrity, accuracy, and validity of collected data, and uses communication link detection to assess data transmission stability. When hardware faults, data transmission anomalies, or storage faults are detected, it automatically issues audible and visual fault alarm signals, clearly displays the fault location, fault type, and affected area on the screen, and provides step-by-step troubleshooting guidance and maintenance suggestions. Remote maintenance is supported; simple faults can be self-healed by remotely sending repair commands through the system, while complex faults automatically generate repair work orders and push them to the maintenance personnel's terminal, including fault data, equipment model, parts requirements, and repair procedures. A data self-healing mechanism is established; when some collected data is missing, AI algorithms supplement it based on historical data and contextual information, ensuring that the evaluation results are not affected and guaranteeing stable system operation.

[0053] This invention also includes a multilingual and multi-mode teaching module, supporting voice broadcasts, text prompts, and interface displays in multiple mainstream languages ​​such as Chinese, English, French, and Spanish. It features a built-in language switching function, allowing students to choose freely according to their needs. It caters to the needs of special groups, providing a visual enhancement feedback mode for hearing-impaired students, increasing the flashing frequency of indicator lights, the detail of on-screen text prompts, and the intensity of vibration feedback; a detailed voice explanation mode for visually impaired students, using three-dimensional spatial sound effects to locate the correct pressing area and enhance the accuracy of tactile feedback; and a large font, high-contrast interface mode for elderly students, reducing the difficulty of operation. Personalized interface customization is supported, allowing students to adjust the interface layout, icon size, and prompt frequency to suit different usage habits, improving the inclusivity and accessibility of teaching.

[0054] This invention also includes a teaching assessment and certification module with a built-in standardized assessment question bank. It contains two core categories: theoretical knowledge tests and operational skills assessments. The theoretical knowledge tests cover CPR principles, indications, contraindications, and emergency procedures, using multiple-choice, true / false, and short-answer questions. The operational skills assessment uses randomly selected clinical scenarios, requiring trainees to complete a full CPR procedure within a specified time, with the system recording the entire process in real time. After completion, the system combines theoretical scores and operational quality ratings to generate a comprehensive assessment result, clearly indicating scores, strengths, weaknesses, and areas for improvement for each section. Those who pass automatically receive an electronic certification certificate, recording the assessment time, level, and highlights, and supporting QR code verification and official authentication. A tiered certification system is established, divided into three levels: beginner (basic operation qualified), intermediate (complex scenario qualified), and advanced (excellent clinical emergency response capability). Different levels correspond to different assessment standards and certification benefits. The assessment results of unqualified trainees are automatically fed back to the personalized training plan generation module, developing targeted retake training plans to help trainees gradually improve their skills.

[0055] The following two examples further illustrate the specific implementation of this system:

[0056] Example 1: Application of Advanced Cardiopulmonary Resuscitation Training for Hospital Medical Staff

[0057] This example demonstrates an advanced cardiopulmonary resuscitation (CPR) training program for medical staff at a tertiary hospital. The program covered over 300 medical personnel from 20 departments, including emergency medicine, intensive care, and anesthesiology. The training aimed to improve the standardization of CPR procedures and emergency response capabilities in complex clinical scenarios. Hospital emergency scenarios demand extremely high levels of precision, teamwork, and response speed. Systematic training is necessary to strengthen the standardization of key procedures such as compression depth, frequency, and breathing coordination, while also improving emergency response capabilities for patients of different ages, complex pathological conditions, or special environments.

[0058] The multimodal data acquisition module evenly deploys four pressure sensors in the chest compression area of ​​the teaching mannequin, two position sensors and one frequency sensor in the hand operation area, and high-definition cameras on both sides of the mannequin's head to capture images of the upper body's operating posture. An audio acquisition device built into the chest captures voice commands and ambient sounds. The default data sampling rate is 100Hz, which automatically increases to 200Hz when the compression frequency exceeds 100 times for three consecutive seconds or when the operation movements change continuously, ensuring data acquisition accuracy during periods of intensive operation. All acquired data is synchronized to the system's core processing unit via a high-speed wired transmission channel, with transmission latency controlled within 10 milliseconds to guarantee real-time analysis and feedback.

[0059] The real-time interactive feedback module provides multi-dimensional feedback based on collected data: the voice broadcast device provides immediate feedback on specific issues such as "Pressing too deep, please adjust the force," "Pressing position is too far to the left, please move to the lower middle part of the sternum," and "The frequency is too fast, please maintain 100 to 120 times per minute" through clear prompts; eight indicator lights surround the simulated human chest, with green marking the correct pressing area and red indicator lights lighting up in the direction of the pressing position deviation, and the display screen synchronously displays the real-time value of the pressing depth and compares it with the standard range; the vibration feedback device in the hand operation area adjusts the vibration intensity according to the severity of the error, with weak vibration prompts for minor errors and strong vibration warnings for serious errors, forming a three-in-one real-time error correction mechanism.

[0060] The AI-powered intelligent analysis module employs a hybrid deep learning model. A convolutional neural network extracts spatial features from the operational posture image, such as shoulder, elbow, and wrist joint angles and body center of gravity distribution. A recurrent neural network captures temporal features, such as compression pressure variation curves and frequency fluctuation trends. The model is trained using the latest version of the CPR guidelines' standard parameter library, data from over 1000 real-world clinical resuscitation cases from hospitals, and 500 sets of common error samples. It calculates the deviation between the trainee's operational data and the standard parameters in real time, accurately identifying 12 types of operational defects, including compression position deviation, unstable force, excessive frequency fluctuations, and uncoordinated breathing. It clarifies the severity of the defects and generates an operational quality assessment result that includes the defect type, frequency of occurrence, and directions for improvement.

[0061] The personalized training program generation module analyzes skill gaps by combining trainees' professional title level, past training records, and current assessment results. For junior medical staff, the focus is on strengthening basic training in compression position and frequency, with a dedicated training plan of 30 minutes twice daily and phased achievement targets. For intermediate medical staff, the emphasis is on operational continuity and breathing coordination training in complex scenarios, with one comprehensive training session per day combining clinical scenarios. For senior medical staff, the focus is on multi-person collaboration and emergency response training, with a multi-person collaboration simulation training plan of twice weekly. The difficulty level of the training content is dynamically adjusted according to the progress of phased goals; if a goal is not met, the corresponding phase of training is repeated; once a goal is met, the trainee automatically proceeds to the next phase.

[0062] The teaching data management module adopts a distributed storage architecture to categorize and store data such as trainee basic information, training records, raw operation data, evaluation results, and personalized training plans. It establishes a multi-dimensional data index, supporting queries based on combined conditions such as department, professional title, training time, and operation item. It statistically analyzes group data such as the total number of trainees across the hospital and each department, average scores, and the percentage of common error types. Monthly data statistical reports are generated to provide data support for the hospital's training management department to optimize training plans and allocate teaching resources.

[0063] The accompanying display screen of the visual teaching guidance module provides a transparent filter effect for the chest and heart, and intuitively shows the correspondence between the heart's anatomical structure and the area of ​​compression through 3D animation. It dynamically demonstrates the coordinated force exertion of the shoulder, elbow, and wrist joints in the standard compression action, as well as the coordination process of chest compressions and artificial respiration. It supports switching between three perspectives: front, side, and top. Trainees can zoom in on local details through touch operation. The right side of the display screen simultaneously displays text guidance and key reminders for the operation steps, such as "When compressing, use the heel of your palm to apply force, and keep your arms perpendicular to the chest wall" and "When performing artificial respiration, completely cover the patient's mouth and nose, and the blowing time should not be less than 1 second."

[0064] The system's adaptation and optimization module monitors the signal stability of each sensor, the image clarity of the camera, and the integrity of data transmission in real time, automatically performing a hardware self-check every hour. Based on the trainee's physiological characteristics such as height, weight, and hand size, it dynamically adjusts the sensitivity threshold of the pressure sensor and the detection range of the position sensor; for example, for trainees with smaller hands, it reduces the detection error range of the position sensor. The CPR standard parameter library is updated monthly, and the latest clinical case data is imported quarterly to optimize the AI ​​model, ensuring continuous improvement in model recognition accuracy.

[0065] The simulated clinical scenario module includes 15 built-in clinical emergency scenarios, covering resuscitation scenarios for adults, children, and infants of different ages; pathological scenarios such as cardiac arrest with respiratory failure, cardiac arrest due to trauma, and cardiac arrest due to electric shock; and environmental scenarios such as emergency room resuscitation, outdoor first aid, and emergency care during transport. The difficulty of the scenarios automatically adapts to the learner's current skill level. The beginner level provides a basic scenario without interference, while the advanced level adds environmental noise such as ambulance sirens and crowd noise, as well as operational interference such as patient limb twisting and clothing obstruction. Multi-person collaborative scenarios require two learners to be responsible for chest compressions and ventilation, respectively. After each scenario, a debriefing report is generated, explaining operational optimization strategies in conjunction with similar clinical cases and assessing emergency response capabilities.

[0066] The remote teaching and interaction module allows medical staff from different hospital campuses to remotely log in to the system via computer terminals, enabling synchronous sharing of operation screens and sensor data through low-latency real-time video transmission. Teachers can view remote trainees' operation data and evaluation results in real time through the system backend, provide real-time guidance via voice calls, and directly mark correct compression areas and key points of action on the trainees' operation screens using the screen annotation function. The system supports simultaneous online group collaborative training for up to four people, simulating multi-person resuscitation scenarios in clinical settings. The system assesses collaboration coordination and operational smoothness in real time, generating suggestions for collaboration optimization.

[0067] The visualization and playback module records the entire training process with high-definition video, supporting precise playback at specific time points. During playback, it simultaneously displays data charts such as compression pressure change curves, positional deviation trajectories, and frequency fluctuation trends. Green curves indicate correct operation periods, while red curves indicate incorrect operation periods. It provides AI-powered intelligent comparison and analysis, playing the trainee's operation video and a standard operation video simultaneously in a split-screen format. It automatically marks the time points, specific manifestations, and degree of deviation of the movements, supporting 0.5x slow-motion playback to highlight details of incorrect movements and explaining the impact of incorrect movements on the patient's risk of chest cavity injury and resuscitation effectiveness using biomechanical principles.

[0068] Table 1 Comparison of the Effectiveness of Cardiopulmonary Resuscitation Training for Hospital Medical Staff

[0069] Evaluation indicators Traditional training model performance Training performance of the invention system Operating procedure pass rate lower higher Emergency response capabilities in complex scenarios Weak Strong Multi-person collaboration tacit understanding generally higher Training cycle longer shorter Trainee training satisfaction generally higher

[0070] Table 1 clearly demonstrates the advantages of this invention in the advanced training of hospital medical staff. Traditional training models rely on subjective assessments by instructors, resulting in imprecise judgments of operational procedures, a lack of complex scenario simulations, and a lack of effective assessment tools for collaborative training among multiple participants. This leads to low pass rates for operational procedures, weak emergency response capabilities, and long training cycles. This invention, through multimodal data collection and precise AI analysis, achieves accurate identification and real-time error correction of operational defects. Complex scenario modules and collaborative training functions enhance emergency response and collaboration capabilities, personalized training programs shorten the training cycle, and multi-dimensional optimization significantly improves training effectiveness and trainee satisfaction, perfectly meeting the high-intensity and high-requirement training needs of hospitals.

[0071] Example 2: Application of Basic Cardiopulmonary Resuscitation Teaching for Medical Students in Universities

[0072] This implementation plan was applied to the basic cardiopulmonary resuscitation (CPR) instruction of over 2,000 undergraduate students majoring in clinical medicine and nursing at a medical university. The teaching objective was to enable students to master the standard operating procedures and core points of CPR, establish correct operational knowledge, and lay a foundation for subsequent clinical internships and practice. Most of the students had no prior CPR experience, so instruction began with basic operations, emphasizing the standardization of core parameters such as compression position, depth, and frequency, while also cultivating basic emergency response capabilities.

[0073] The multimodal data acquisition module deploys three pressure sensors in the chest compression area of ​​the teaching mannequin, one position sensor and one frequency sensor in the hand operation area, and one high-definition camera at the front and back of the lab to capture full-body operation posture images. An audio acquisition device built into the mannequin's chest captures voice commands and student interactions. The default data sampling rate is 80Hz, automatically increasing to 150Hz when inconsistent student movements or excessive pressure fluctuations are detected, ensuring the integrity of data acquisition during basic operation phases. Acquired data is synchronized to the system's core processing unit via a high-speed wireless transmission channel, covering a 50-meter range within the lab, meeting the deployment requirements for large-class teaching.

[0074] The real-time interactive feedback module optimizes the feedback logic to address students' weak foundational knowledge: the voice broadcast device uses simple and easy-to-understand language, such as "Please press harder if the compression depth is insufficient," "Keep pressing in the correct position," and "Please increase the speed if the frequency is too slow," avoiding overly complex technical terms; four green indicator lights simulating a human chest form a rectangular area, accurately marking the correct compression range in the lower middle part of the sternum, and the display screen uses a bar chart to visually show the difference between the compression depth and the standard range; the hand vibration feedback device only activates when a serious error occurs, avoiding frequent vibrations that could disrupt the student's rhythm, helping students build confidence while promptly correcting critical errors.

[0075] The AI-powered intelligent analysis module employs a hybrid deep learning model. A convolutional neural network extracts spatial features from the operation posture image, such as limb placement angles and body force application during compressions. A recurrent neural network captures temporal features, such as the stability of compression intensity and frequency uniformity. The model is trained using a basic CPR standard parameter library, 300 simplified clinical case data sets suitable for students, and 800 sets of common beginner error samples. It focuses on identifying six types of basic operational defects: compression position deviation, insufficient or excessive force, and excessively fast or slow frequency. These defects are simplified and graded as mild, moderate, and severe, generating easily understandable assessment results and clearly defining key areas for improvement in basic operations.

[0076] The personalized training program generation module analyzes students' skill gaps by combining their learning progress, classroom training records, and assessment results. The introductory stage, designed for students with no prior experience, focuses on individual training in compression location, basic force control, and frequency adjustment, with a basic training plan of 40 minutes three times a week and clear phased goals such as "90% accuracy in compression location" and "85% compliance in frequency." The intermediate stage incorporates breathing coordination training, with comprehensive operational training twice a week. The advanced stage includes simple clinical scenario training to improve basic emergency response capabilities. The difficulty of the training content is dynamically adjusted based on the progress of phase goals; if goals are not met, targeted reinforcement training is provided, and the next phase is automatically unlocked upon achievement of goals.

[0077] The teaching data management module adopts a distributed storage architecture to categorize and store data such as student basic information, classroom training records, after-class self-study data, assessment results, and personalized training plans. It establishes data indexes by grade, major, and class, allowing teachers to query student training status by class, and to statistically analyze group data such as the overall grade-level pass rate, average scores for each major, and common basic error types. Each semester, it generates teaching data statistical reports, providing data support for teachers to adjust teaching methods and optimize course arrangements.

[0078] The accompanying display screen of the visual teaching guidance module provides a simplified transparent filter effect for the chest and heart, highlighting the correspondence between the compression area in the lower sternum and the heart. It demonstrates the standard compression action and breathing coordination process step-by-step through 2D animation, with text explanations accompanying each step. It supports switching between two basic perspectives, front and side, to meet the observation needs of students in the basic learning stage. The left side of the display continuously shows the standard values ​​of core operating parameters, such as "compression depth 5 to 6 cm," "compression rate 100 to 120 times per minute," and "compression-to-breath ratio 30:2," helping students remember key standards.

[0079] The system's adaptation and optimization module monitors sensor operating status, camera image acquisition quality, and data transmission stability in real time. It automatically performs hardware self-checks before each lesson, promptly alerting teachers to any anomalies. Based on students' physiological characteristics such as height, weight, and hand size, the system dynamically adjusts sensor sensitivity thresholds. For example, considering that girls generally have less strength, the initial trigger threshold of the pressure sensor is appropriately lowered to ensure the accuracy of operational data acquisition. The CPR standard parameter library is updated each semester, supplemented with teaching case data suitable for students, and the AI ​​model's accuracy in recognizing errors made by beginners is optimized.

[0080] The simulated clinical scenario module includes eight basic clinical scenarios, covering resuscitation scenarios for adults, children, and infants of different ages; simple pathological scenarios such as isolated cardiac arrest and cardiac arrest combined with mild respiratory depression; and simple environmental scenarios such as basic indoor resuscitation and school emergency care. The difficulty of the scenarios is gradually unlocked according to the student's learning progress. Scenario training is not available in the introductory stage; basic, undisturbed scenarios are available in the intermediate stage; and simple scenarios with slight environmental noise are available in the proficiency stage. The simulation uses a display screen to display the visuals of the scenario, and audio equipment plays simple environmental sound effects. Basic operational tasks are set, requiring students to complete a full resuscitation operation within a specified time. A basic debriefing report is generated after the scenario, focusing on reviewing the standardization of the operation.

[0081] The operation visualization and playback module records and videos the entire student training process. It supports playback categorized by operation stage: compression training, breathing coordination training, and comprehensive training. During playback, it simultaneously displays basic data charts such as changes in compression pressure, range of positional deviation, and frequency statistics. Correct operations are marked in blue, and incorrect operations are marked in orange. A basic AI comparative analysis function is provided, allowing for split-screen synchronous playback of student operation videos and standard teaching videos. It automatically marks key differences in movements, supports normal speed and 1x slow-motion playback, and explains the impact of incorrect movements in simple terms, combining basic medical principles to help students understand the importance of correct operation.

[0082] Table 2 Comparison of Basic Cardiopulmonary Resuscitation Teaching Effectiveness among Medical School Students in Universities

[0083] Evaluation indicators Traditional teaching model performance The teaching performance of this invention system Basic operation standardization rate lower higher Mastery of core parameters Weak Strong level of interest in learning generally higher Consumption of teaching resources higher lower Assessment pass rate lower higher

[0084] Table 2 highlights the application value of this invention in basic medical education at universities. Traditional teaching methods rely on mass lectures and subjective guidance from teachers, making it difficult to cater to the varying levels of understanding among students. The transmission of core parameters is often imprecise, leading to insufficient student interest and low rates of standardized basic operations, weak grasp of core parameters, and low pass rates. Furthermore, this method consumes significant teacher and equipment resources. This invention, through personalized training programs tailored to students with different backgrounds, utilizes multimodal feedback and visual guidance to help students quickly master core parameters. Scenario-based training and interactive functions enhance learning interest, while AI-powered intelligent analysis replaces some manual assessments, reducing teaching resource consumption and significantly improving teaching effectiveness and pass rates. It perfectly meets the needs of large-scale basic education in universities.

[0085] Reference Figure 2 This diagram visually demonstrates the significant advantages of the system of this invention in improving the pass rate of operational procedures, directly addressing the pain point of low efficiency in traditional training models. Traditional training relies on subjective human assessment, resulting in delayed and untargeted feedback. Consequently, even after five training sessions, the pass rate for operational procedures remains only 68%, with many trainees failing to meet the standards due to uncorrected errors. The system of this invention accurately captures operational details through a multimodal data acquisition module, identifies defects in real time through an AI intelligent analysis module, and provides real-time feedback integrating voice, vision, and touch to help trainees quickly adjust their movements. Simultaneously, personalized training programs specifically address weaknesses. From 68% after the first training session to 97% after the fifth, the pass rate improvement speed far exceeds that of traditional models, significantly shortening the attainment cycle. This fully demonstrates the closed-loop advantage of the system's "accurate identification - instant error correction - personalized reinforcement," providing strong support for rapidly improving operational standardization.

[0086] Reference Figure 3This diagram clearly demonstrates the high efficiency of the system in reducing operational error rates, overcoming the problem of slow error reduction in traditional training. In traditional training, trainees struggle to recognize their own errors in real time, relying on teacher feedback after class. Error correction is delayed, and even after 50 minutes of training, the error rate remains as high as 50%, mostly consisting of recurring basic errors. The system in this invention, through real-time multimodal data acquisition and AI intelligent analysis, can instantly identify various errors such as compression position deviations and abnormal frequencies. A three-pronged feedback mechanism provides immediate reminders for trainees to adjust, preventing erroneous movements from becoming ingrained. Simultaneously, personalized training programs focus on reinforcing high-frequency errors, causing the error rate to rapidly decrease with training time, reaching only 10% after 50 minutes. This rapid decrease in error rate not only improves training efficiency but also helps trainees build correct muscle memory, laying a solid foundation for standardized operations in actual emergency situations.

[0087] Reference Figure 4 This figure highlights the core advantage of the system in adaptability to complex clinical scenarios, addressing the shortcomings of traditional training methods that lack scenario-based practice. Traditional training is often limited to basic operational exercises, neglecting emergency response training for different age groups, pathological states, and environments. This results in insufficient trainees' capabilities in complex scenarios, particularly during transport and in multi-person collaborative situations, where scores are only 40-50 points, making it difficult to meet actual emergency needs. The system in this invention has a rich built-in library of simulated clinical scenarios, supporting dynamic adjustment of difficulty and the addition of interference factors. Trainees can repeatedly practice in near-realistic scenarios, gradually improving their emergency response capabilities. Whether in pediatric resuscitation, trauma scenarios, or multi-person collaborative scenarios, the scores after system training consistently remain above 82 points, reaching 90 points in multi-person collaborative scenarios. This fully demonstrates that the system can effectively improve trainees' practical abilities in diverse clinical scenarios, achieving a transformation from "knowing how to operate" to "being able to respond to emergencies."

[0088] Reference Figure 5 This diagram demonstrates the broad adaptability of the system to learners with varying skill levels, particularly highlighting its advantages for beginners. Traditional training employs a uniform, "one-size-fits-all" approach, failing to cater to the needs of learners with different skill levels. Beginners, lacking targeted guidance, achieve a pass rate of only 38%, and even experienced learners don't reach 90%. The system, through its personalized training program generation module, strengthens basic operational training for beginners and sets complex scenario challenges for advanced learners, precisely matching the learning needs of learners with different skill levels. Both the 85% pass rate for beginners and the 98% pass rate for experienced learners far exceed traditional training models, fully demonstrating the system's ability to provide "personalized instruction," enabling learners of all skill levels to efficiently improve their skills. This broad adaptability makes it suitable for both basic teaching for university students with no prior experience and advanced training for hospital medical staff, significantly improving the scope and overall effectiveness of the training.

[0089] The above are merely preferred embodiments of the present invention, but the scope of protection of the present invention is not limited thereto. Any equivalent substitutions or modifications made by those skilled in the art within the scope of the technology disclosed in the present invention, based on the technical solution and inventive concept of the present invention, should be covered within the scope of protection of the present invention.

Claims

1. An AI-based medical cardiopulmonary resuscitation (CPR) teaching system, characterized in that, Includes the following modules: The multimodal data acquisition module deploys pressure, position, and frequency sensors in the chest and hand operation areas of the teaching simulator. It acquires operation posture images through a high-definition camera and captures speech and environmental sounds with the help of audio acquisition equipment. Information is collected in real time, the data sampling rate is dynamically adjusted, and the acquired data is synchronized to the system core processing unit through a high-speed transmission channel. The real-time interactive feedback module, based on multimodal data acquisition, provides feedback on operational issues through voice broadcast, visual prompts through chest indicator lights and a matching display screen, and tactile warnings through hand vibration feedback devices, forming a three-in-one real-time error correction mechanism. The AI ​​intelligent analysis module constructs a hybrid deep learning model of convolutional and recurrent neural networks, extracts spatial and temporal features, imports standard parameter libraries, clinical cases and error samples for training, analyzes trainees’ operations and standard deviations in real time, and generates quality assessment results. The personalized training plan generation module analyzes skill gaps and develops targeted training plans based on AI assessment results, combined with students' learning time, historical data, and ability levels. The teaching data management module adopts a distributed storage architecture, classifies and stores various types of data, establishes an indexing mechanism, supports multi-dimensional queries, statistically analyzes group training data, and generates statistical reports. The visual teaching guidance module provides a transparent effect of the heart on the accompanying display screen, showing the anatomical structure and compression area. It demonstrates standard operation and breathing coordination through animation, supports multiple perspective switching, and provides text guidance and key points. The system adaptation and optimization module monitors sensor status, data transmission, and AI model efficiency in real time. It dynamically adjusts sensor parameters and feedback conditions based on the user's operating habits and physical characteristics, and regularly updates the standard library and training data.

2. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes an operational quality comprehensive scoring module, the calculation expression of which is: in For the overall evaluation of operational quality, , , , , The weighting coefficients are as follows: compression location accuracy, compression frequency compliance rate, compression depth compliance rate, operational consistency, and breathing coordination. This represents the percentage of overlap between the pressing position and the standard area. This represents the percentage of time the actual pressing frequency falls within the standard range. The percentage of times the pressing depth meets the standard requirements. The smoothness score is given for continuous operation without interruptions or unnecessary movements. The system scores the compliance of the rhythm of chest compressions and artificial respiration; the scoring system supports dynamic weight adjustments, and the scoring results are accompanied by detailed interpretations.

3. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes a simulated clinical scenario module with a built-in library of various clinical emergency scenarios, covering resuscitation scenarios for patients of different ages, including adults, children, and infants. The scenarios support dynamic difficulty adaptation, automatically adjusting the complexity of the scenarios based on the trainee's current ability level. The system simulates the environmental sound effects and visual images of the scenarios through displays and audio devices, sets scenario-based operation tasks, records the trainee's operation performance in the scenarios in real time, explains operation optimization plans in conjunction with clinical cases, and assesses the trainee's emergency response capabilities in complex scenarios.

4. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes a group data mining module, which uses clustering algorithms to group and analyze the training data of all trainees, dividing them into novice, intermediate, and proficient groups according to their skill level. This identifies the operational characteristics and common error patterns of trainees at different skill levels, and uncovers the common features of excellent operational cases. Through correlation analysis, it explores the intrinsic relationship between training duration, training frequency, and improvement in operational quality, and establishes an error trend prediction model. The mining results are fed back to the personalized training plan generation module and the teaching guidance module, while also generating a group teaching analysis report.

5. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes a training performance prediction module, the calculation expression is: in Predicting the probability of trainees reaching the passing level. , , , The weighting coefficients are: historical average score, improvement rate in the last three training sessions, speed of improvement in weaknesses, and training focus. The average quality score of the trainees' past training. This represents the average difference in improvement over the most recent three training scores. The speed at which the error rate of operations targeting core weaknesses decreases. The training program determines the percentage of time spent on focused, unrelated tasks. When the predicted results fall below a preset threshold, an enhanced training mechanism is automatically triggered. When the predicted progress meets expectations, phased challenge tasks are pushed out, and reward mechanisms such as honor badges and points are set up to motivate trainees to maintain their learning motivation.

6. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes a remote teaching interaction module, which allows students to remotely log in to the system via their devices. With the help of low-latency real-time video transmission, it enables synchronous sharing of operation screens and sensor data. Teachers can view students' operation data, evaluation results, and training records through the system backend, and provide remote guidance and Q&A through voice calls, text messages, and screen annotation functions. It supports multiple people to conduct group collaborative training online at the same time, simulating the scenario of multiple people cooperating in cardiopulmonary resuscitation in clinical practice. The system evaluates the degree of cooperation and the smoothness of operation in real time and provides suggestions for cooperation optimization. Remote training data is automatically synchronized to the teaching data management module to generate remote teaching reports, supporting the tracking of teaching effectiveness and quality evaluation.

7. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes a visualization and playback module for operational actions, which records the entire training process of trainees with data and high-definition video recording. It supports precise playback by time node and operation stage, and displays data visualization charts in sync during playback. It provides AI intelligent comparison and analysis function, which plays trainee operation videos and standard operation videos in split screen and synchronously, automatically marking the time nodes, specific manifestations and deviation degrees of action differences, and supports slow-motion playback to highlight the details of incorrect actions. It combines biomechanical principles and clinical practice standards to explain the potential hazards of incorrect actions and the scientific basis for correct operation.

8. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes a fault self-diagnosis and maintenance module, which monitors the working status of hardware components in real time; when a hardware fault, data transmission abnormality, or storage fault is detected, it automatically issues an audible and visual fault alarm signal, clearly displays the fault location, fault type, and scope of impact on the display screen, and provides step-by-step fault troubleshooting guidance and maintenance suggestions; It supports remote maintenance. Simple faults can be self-healed by sending repair instructions remotely through the system, while complex faults automatically generate maintenance work orders and push them to the maintenance personnel's terminals. It also establishes a data self-healing mechanism, which uses AI algorithms to complete the missing data based on historical data and context information when some collected data is missing.

9. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes multilingual and multi-mode teaching modules, supporting voice broadcasts, text prompts, and interface displays in multiple mainstream languages, with a built-in language switching function, allowing students to choose freely according to their needs; it adapts to the needs of special groups of students, enhancing the flashing frequency of indicator lights, the detail of screen text prompts, and the intensity of vibration feedback; it provides a detailed voice explanation mode for visually impaired students, using three-dimensional spatial sound effects to locate the correct pressing area; it provides a large font, high-contrast interface mode for elderly students; and it supports personalized interface customization, allowing students to adjust the interface layout, icon size, and prompt frequency to suit different usage habits.

10. The AI-based medical cardiopulmonary resuscitation teaching system according to claim 1, characterized in that, It also includes a teaching assessment and certification module, with a built-in standardized assessment question bank containing two core contents: theoretical knowledge test and operational skills assessment. The operational skills assessment is conducted by randomly selecting clinical scenarios, requiring trainees to complete a full cardiopulmonary resuscitation operation within a specified time, and the system records the entire operation process in real time. After students complete the assessment, the system combines their theoretical scores and operational quality ratings to generate a comprehensive assessment result. Those who pass the assessment will automatically receive an electronic certification certificate, which can be scanned for verification and officially verified. A tiered certification system has been established, divided into three levels: beginner, intermediate, and advanced. Different levels correspond to different assessment standards and certification benefits. The assessment results of students who fail the assessment are automatically fed back to the personalized training plan generation module to develop a targeted retake training plan.

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