Peripheral blood glucose monitoring tactile feedback training method and system

Abstract

The invention relates to the technical field of medical training simulation, and provides a peripheral blood glucose monitoring tactile feedback training method and system, and the method comprises the steps: presetting a tactile primitive library comprising a plurality of tactile primitives; acquiring real-time operation information of the trainee in peripheral blood glucose monitoring training; according to the real-time operation information, simulation organization state information interacting with student operation is determined; selecting a corresponding number of tactile primitives from a tactile primitive library according to the real-time operation information and the simulated tissue state information; obtaining a quantized value of the real-time operation information; the performance characteristics of the selected tactile elements are adjusted in real time according to the quantized values, all the tactile elements after real-time adjustment are combined, and tactile element information is generated; and generating a composite continuous tactile feedback signal corresponding to the operation of the student based on the tactile primitive information so as to help the student to establish operation pre-judgment and adjustment capabilities and achieve the training purpose. The method has the advantages of helping students to establish operation pre-judgment and adjustment capabilities and improving the training effect.

Description

Technical Field

[0001] This invention relates to the technical field of medical training simulation, specifically to a method and system for tactile feedback training of peripheral blood glucose monitoring. Background Technology

[0002] Within medical education institutions, medical and nursing students typically transition from theoretical learning to simulated practice when mastering the fundamental clinical procedure of capillary blood glucose monitoring. Basic simulation tools, such as silicone finger molds, help students familiarize themselves with the basic process: selecting the blood collection site, disinfection, holding the needle, puncture, blood collection, and pressure application. Students can practice repeatedly on these tools until they can complete the entire sequence of actions.

[0003] In clinical practice, trainees may encounter subtle situations that are not frequently described in textbooks but actually affect the feel of manipulation. Trainees may find it difficult to clearly experience the differences in immediate tissue response caused by their different manipulation behaviors, ultimately limiting their ability to develop operational prediction, fine-tuning skills, and overall grasp of the entire operation process based on continuous tactile perception.

[0004] To address the aforementioned issues, existing technologies urgently need improvement. Summary of the Invention

[0005] The purpose of this application is to provide a tactile feedback training method and system for peripheral blood glucose monitoring, which has the advantage of generating a composite continuous tactile feedback signal corresponding to the trainee's operation based on the trainee's real-time operation and simulated tissue state, thereby helping the trainee to establish operation prediction and adjustment capabilities and improve training effect.

[0006] This application provides a tactile feedback training method for peripheral blood glucose monitoring, the technical solution of which is as follows:

[0007] include:

[0008] A tactile primitive library containing several tactile primitives is pre-set; each tactile primitive represents a tactile event generated by the interaction between the trainee's operation and the simulated tissue during peripheral blood glucose monitoring training;

[0009] Obtain real-time operational information of trainees during peripheral blood glucose monitoring training;

[0010] Based on real-time operation information, determine the simulated organizational status information that interacts with the trainees' operations;

[0011] Based on real-time operation information and simulated tissue status information, select the corresponding number of tactile primitives from the tactile primitive library;

[0012] Obtain quantified values ​​of real-time operation information;

[0013] The performance characteristics of the selected tactile primitives are adjusted in real time according to the quantization value. All the tactile primitives that have been adjusted in real time are combined to generate tactile primitive information.

[0014] Based on tactile primitive information, a composite continuous tactile feedback signal corresponding to the trainee's operation is generated to help the trainee develop the ability to predict and adjust operations, thereby achieving the training objective.

[0015] The above scheme can generate composite continuous tactile feedback signals corresponding to the trainee's operation based on the trainee's real-time operation and simulated organizational state, helping the trainee to develop the ability to predict and adjust operations, and improve training effectiveness.

[0016] Furthermore, this application also proposes a step of adjusting the performance characteristics of selected tactile primitives in real time with quantized values, and combining all the tactile primitives after real-time adjustment to generate tactile primitive information, including:

[0017] The performance characteristics of the selected tactile primitives are adjusted in real time based on the quantization value;

[0018] Identify the preset operation scenario stage corresponding to real-time operation information;

[0019] From all the tactile primitives that have been adjusted in real time, the corresponding specific tactile features are identified according to the preset operation scenario stage;

[0020] Adjusting the relative perceptual intensity or presentation order of specific tactile features, and combining all the tactile primitives after the relative perceptual intensity or presentation order has been adjusted, generates tactile primitive information.

[0021] The above scheme can identify specific tactile features according to the stage of the operation scenario and adjust their relative perception intensity or presentation order, so that the tactile feedback is more in line with the perception focus in the actual operation process.

[0022] Furthermore, this application also proposes a step for adjusting the relative perceived intensity or presentation order of specific tactile features, including:

[0023] Obtain the student's historical operation data or the student's preset classification parameters;

[0024] Based on the student's historical operation data or the student's preset classification parameters, determine the personalized adjustment parameters for the student;

[0025] Parameters can be adjusted according to the individual needs of each student, adjusting the relative perceived intensity or presentation order of specific tactile features.

[0026] The above approach allows for personalized adjustments based on students' historical data or classification parameters, making tactile feedback more aligned with students' learning characteristics and needs.

[0027] Furthermore, this application also proposes that the steps for determining personalized adjustment parameters for students based on their historical operation data or preset classification parameters include:

[0028] Obtain the preset type of the specific tactile feature to be adjusted;

[0029] Based on the preset type, extract the historical response information of the student corresponding to the preset type from the student's historical operation data, or extract the student classification information corresponding to the preset type from the student's preset classification parameters.

[0030] Based on the student's historical response information or student classification information, and the preset adjustment rules associated with the preset type, determine the student's personalized adjustment parameters.

[0031] The above approach allows for more accurate determination of personalized adjustment parameters based on the type of specific tactile features, combined with the student's historical response or classification information and preset rules.

[0032] Furthermore, this application also proposes a step for determining personalized adjustment parameters for trainees based on their historical response information or trainee classification information, and preset adjustment rules associated with preset types, including:

[0033] Obtain data on the trainee's training process or data on the evaluation of the trainee's training effectiveness;

[0034] Based on student training process data or student training effect evaluation data, evaluate the adjustment effect of preset adjustment rules associated with preset types and generate adjustment effect evaluation results;

[0035] If the adjustment effect evaluation result indicates that the preset optimization conditions are not met, the preset adjustment rules will be updated based on the adjustment effect evaluation result.

[0036] Based on the student's historical response information or student classification information, as well as the updated preset adjustment rules, determine the student's personalized adjustment parameters.

[0037] The above approach allows for dynamic optimization and adjustment of rules based on the training process or performance evaluation results, further enhancing the effectiveness of personalized adjustments.

[0038] Furthermore, this application also proposes that the steps for updating the preset adjustment rules based on the adjustment effect evaluation results include:

[0039] Based on the evaluation results of the adjustment effects, the deviation between the preset adjustment rules and the preset performance targets is determined;

[0040] Based on the deviation between the preset adjustment rules and the preset performance targets, and the preset rule correction lookup table, determine the corresponding modification operation;

[0041] Perform the modification operation to update the preset adjustment rules.

[0042] The above scheme enables automated updating and optimization of adjustment rules by evaluating the deviation between the adjustment effect and the performance target, and by making modifications based on the rule correction lookup table.

[0043] Furthermore, this application also proposes a step for determining the deviation between the preset adjustment rules and the preset performance targets based on the results of the adjustment effect evaluation, including:

[0044] Identify key performance indicators corresponding to peripheral blood glucose monitoring training from preset performance goals;

[0045] Based on key performance indicators, determine the corresponding evaluation data in the evaluation results of the adjustment effect;

[0046] Based on the comparison results between the preset target values ​​of key performance indicators and the evaluation data, the deviation between the preset adjustment rules and the preset performance targets is determined.

[0047] The above approach can identify key performance indicators and compare them with evaluation data to quantify the deviation between the adjustment effect and the performance target, thus providing an accurate basis for rule updates.

[0048] Furthermore, this application also proposes a step for determining the corresponding modification operation based on the deviation between the preset adjustment rules and the preset performance target, and a preset rule correction lookup table, including:

[0049] Based on the deviation between the preset adjustment rules and the preset performance targets, the characteristics of the deviation are identified;

[0050] Read the preset type corresponding to the specific tactile feature to be adjusted and the preset operation scenario stage corresponding to the real-time operation information;

[0051] Based on the deviation characteristics, preset type, preset operation scenario stage, and preset rule correction lookup table, the modification operation is determined.

[0052] The above approach combines deviation characteristics, tactile feature types, and operational scenario stages to determine more refined modification operations through a lookup table, thereby improving the targeted nature of rule correction.

[0053] Furthermore, this application also proposes that, based on deviation characteristics, preset types, preset operational scenario stages, and a preset rule correction lookup table, the steps for determining the modification operation include:

[0054] Identify and correct the output of the lookup table according to preset rules;

[0055] If the output indicates that the combination of deviation characteristics, preset type, and preset operation scenario stage is covered by the preset rule correction logic, and the output contains only one modification operation suggestion, then the modification operation suggestion will be used as the modification operation.

[0056] If the output indicates a combination of deviation characteristics, preset type, and preset operation scenario stage that is not covered by the preset rule correction lookup table, and the output contains at least two modification operation suggestions, then it is organized into a candidate modification operation set.

[0057] Based on preset types, preset operation scenario stages, and preset evaluation rules, evaluate the potential impact of each modification operation suggestion in the candidate modification operation set on specific tactile features;

[0058] Select the modification suggestion with the best evaluation results as the modification action.

[0059] The above approach can handle various situations in the lookup table output, including uncovered or multiple suggestions. By evaluating and selecting the optimal modification operation, the robustness and effectiveness of rule correction can be ensured.

[0060] A tactile feedback training system for peripheral blood glucose monitoring, the technical solution of which is as follows:

[0061] include:

[0062] The tactile primitive library preset module is used to preset a tactile primitive library containing several tactile primitives; each tactile primitive represents a tactile event generated by the interaction between the trainee's operation and the simulated tissue in peripheral blood glucose monitoring training;

[0063] The real-time operation acquisition module is used to acquire the trainees' real-time operation information during peripheral blood glucose monitoring training.

[0064] The simulation information determination module is used to determine the simulated organizational status information of the interaction between the trainee and the trainee based on real-time operation information;

[0065] The tactile primitive selection module is used to select a corresponding number of tactile primitives from the tactile primitive library based on real-time operation information and simulated tissue state information.

[0066] The quantization value acquisition module is used to acquire the quantization values ​​of real-time operation information;

[0067] The primitive information generation module is used to adjust the performance characteristics of the selected tactile primitives in real time according to the quantization value, and combine all the tactile primitives after real-time adjustment to generate tactile primitive information.

[0068] The feedback signal generation module is used to generate composite continuous tactile feedback signals corresponding to the trainee's operation based on tactile primitive information, so as to help the trainee establish the ability to predict and adjust operations and achieve the training purpose.

[0069] The above scheme provides a system for implementing the above method, providing hardware and software support for haptic feedback training.

[0070] As can be seen from the above, the tactile feedback training method and system for peripheral blood glucose monitoring provided in this application decomposes complex tactile sensations into primitives and selects and adjusts them according to real-time operation and tissue state, and then generates composite continuous signals. It can provide continuous and variable tactile feedback closely related to the student's subtle operations. It effectively solves the problem in the prior art that it is difficult to calculate and generate a clear and distinguishable continuous tactile sensation closely related to each subtle operation of the student based on the student's immediate operation and simulated tissue characteristics. It is also difficult to generate tactile prompts about the pressing effect based on the pressure and position applied by the student at the end of the operation. This makes it difficult for the student to clearly experience the differences in the immediate tissue response caused by different control behaviors. Ultimately, it limits the student's ability to establish operation prediction, fine adjustment ability based on continuous tactile process perception, and overall grasp of the entire operation process. It has the advantage of being able to generate composite continuous tactile feedback signals corresponding to the student's operation based on the student's real-time operation and simulated tissue state, helping the student to establish operation prediction and adjustment ability and improve training effect. Attached Figure Description

[0071] Figure 1 This is a flowchart of a method for tactile feedback training for peripheral blood glucose monitoring in one embodiment of the present invention;

[0072] Figure 2 This is one of the flowcharts of a tactile feedback training method for peripheral blood glucose monitoring according to another embodiment of the present invention;

[0073] Figure 3 This is a second flowchart of a method for tactile feedback training for peripheral blood glucose monitoring, as described in another embodiment of the present invention.

[0074] Figure 4 This is the third flowchart of a method for tactile feedback training for peripheral blood glucose monitoring in another embodiment of the present invention;

[0075] Figure 5 This is the fourth flowchart of a method for tactile feedback training for peripheral blood glucose monitoring in another embodiment of the present invention;

[0076] Figure 6 This is the fifth flowchart of a method for tactile feedback training for peripheral blood glucose monitoring in another embodiment of the present invention;

[0077] Figure 7 This is the sixth flowchart of a method for tactile feedback training for peripheral blood glucose monitoring in another embodiment of the present invention;

[0078] Figure 8 This is the seventh flowchart of a method for tactile feedback training for peripheral blood glucose monitoring in another embodiment of the present invention;

[0079] Figure 9 This is the eighth flowchart of a method for tactile feedback training for peripheral blood glucose monitoring in another embodiment of the present invention;

[0080] Figure 10 This is a system block diagram of a peripheral blood glucose monitoring tactile feedback training system according to another embodiment of the present invention;

[0081] Explanation of reference numerals in the attached figures:

[0082] 1. Peripheral blood glucose monitoring tactile feedback training system; 11. Tactile primitive library preset module; 12. Real-time operation acquisition module; 13. Simulation information determination module; 14. Tactile primitive selection module; 15. Quantitative value acquisition module; 16. Primitive information generation module; 17. Feedback signal generation module. Detailed Implementation

[0083] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0084] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0085] Traditional peripheral blood glucose monitoring training methods typically rely on simulated manipulatives with fixed mechanical properties, such as silicone finger molds, during simulated practice. While these manipulatives help trainees familiarize themselves with basic procedures, their limitations become apparent when trainees need to transition from simply completing the procedure to precise manipulation. The elasticity of real human skin, the porosity of subcutaneous tissue, and the depth and resilience of blood vessels vary from person to person and from location to location. These combinations of characteristics affect the feel during puncture. Existing simulation methods struggle to provide tactile feedback that dynamically changes with the trainee's movements, making it difficult for trainees to develop an accurate ability to identify the feel of puncture under different combinations of tissue characteristics through simulated practice. Furthermore, the trainee's subtle movements when operating the lancet—such as the initial pressure of the needle tip against the skin before insertion, maintaining the angle during insertion, controlling the speed of puncture, and even the slight probing movements that may occur when locating blood vessels under the skin—all trigger a series of subtle tactile events in real tissue. If the training system cannot provide perceptible feedback to trainees regarding the subtle tissue interactions directly triggered by their specific operational behaviors, trainees will find it difficult to recognize the direct consequences of their improper actions and thus struggle to proactively correct them. At a deeper level, high-quality training is not only about getting the needle to "hit the target," but also about cultivating their ability to "anticipate" and "fine-tune" during the puncture process. This requires trainees to perceive the immediate state of the needle tip within the tissue through continuous, subtle tactile changes transmitted back from the needle tip.

[0086] For example, suppose a trainee is practicing finger puncture in a peripheral blood glucose monitoring training simulator. The simulator is configured to simulate specific thicknesses, elasticities, and viscosity characteristics of the skin, subcutaneous layer, and vascular layer. The trainee inserts the needle at a certain angle and speed. With current technology, the simulator might only provide a simple vibration or resistance change as feedback for "puncture" when the needle tip reaches the preset "vascular depth." However, the initial pressure of the needle tip against the skin, minute changes in the insertion angle, speed control, and the changes in resistance and layering felt during the subcutaneous layer—these continuous tactile information closely related to the trainee's specific actions—often cannot be accurately captured and fed back by existing systems. If the trainee inserts the needle at an off-center angle, or at too high a speed causing the tissue to be pushed aside instead of penetrated smoothly, or performs unnecessary probing movements in the subcutaneous layer, these non-standard operations may not produce tactile feedback that matches real-world conditions in existing simulators. Trainees therefore find it difficult to judge whether their operation is accurate or whether it has caused unnecessary damage or discomfort to the tissue through tactile perception, and they are also unable to adjust subsequent operations based on real-time tactile feedback.

[0087] In response, this application proposes a tactile feedback training method for peripheral blood glucose monitoring, combining... Figure 1 As shown, it includes:

[0088] S1, a preset tactile primitive library containing several tactile primitives; each tactile primitive represents a tactile event generated by the interaction between the trainee's operation and the simulated tissue in peripheral blood glucose monitoring training;

[0089] S2, obtains real-time operational information of trainees during peripheral blood glucose monitoring training;

[0090] S3, based on real-time operation information, determines the simulated organizational status information for interaction with trainees;

[0091] S4. Select the corresponding number of tactile primitives from the tactile primitive library based on real-time operation information and simulated tissue state information;

[0092] S5, obtain the quantified value of real-time operation information;

[0093] S6, adjust the performance characteristics of the selected tactile primitives in real time according to the quantization value, combine all the tactile primitives after real-time adjustment, and generate tactile primitive information.

[0094] S7 generates composite continuous tactile feedback signals corresponding to the trainee's operations based on tactile primitive information, in order to help trainees develop operational prediction and adjustment capabilities and achieve training objectives.

[0095] The tactile primitive library refers to a pre-stored collection containing various tactile elements, which can be implemented using a database, file system, or memory structure. For example, it can store preset vibration waveform data, force feedback curve parameters, or tactile event descriptors, primarily providing the basic materials for constructing tactile feedback signals. A tactile primitive is an abstract representation of a discrete tactile event generated by the interaction between a trainee's operation and simulated tissue in peripheral blood glucose monitoring training. It can be represented by a preset vibration pattern, a specific force feedback curve, or an event combining sound and vibration, primarily simulating the tactile sensation generated at a specific moment of operation or interaction with a specific tissue. Real-time operation information refers to the instantaneous operation data collected by the operating device (e.g., a simulated lancing device) during the training process. It can be represented by pressure data collected by a force sensor, displacement data collected by a position sensor, or velocity data collected by a velocity sensor, primarily capturing the trainee's actual operational behavior. Simulated tissue state information refers to the instantaneous physical state or characteristic parameters of the simulated tissue interacting with the trainee in the simulation training system. It can be represented by the elastic coefficient of simulated skin, the viscosity coefficient of simulated subcutaneous tissue, or the toughness parameters of simulated blood vessel walls, primarily for... The tactile feedback mechanism reflects the physical characteristics of the simulated tissue in response to the student's actions; quantified values ​​refer to parameters obtained after numerical processing of real-time operational information, which can be represented by the magnitude of the operational force, the speed of needle insertion, or the deviation of the angle. Its main purpose is to provide a basis for finely adjusting tactile feedback based on the student's operational behavior; performance characteristics refer to the adjustable attributes of the tactile primitives when presented to the student, which can be represented by the amplitude, frequency, and duration of vibration, or the magnitude and rate of change of force feedback. Its main purpose is to enable the presentation effect of the tactile primitives to dynamically change according to the student's specific operations; tactile primitive information refers to the information obtained through actual... The set or sequence of tactile primitives after adjustment and combination can be represented by a data structure containing multiple adjusted tactile primitive parameters or a time axis describing the sequence of tactile events. It is mainly used to carry the data used to generate the final composite tactile feedback signal. The composite continuous tactile feedback signal refers to a signal generated based on tactile primitive information that can be presented to the learner through a tactile feedback device and simulates the continuous tactile sensation in the real operation process. It can be represented by a vibration signal, force feedback signal or a combination of both that varies with time. It is mainly used to provide learners with a realistic and dynamic tactile experience.

[0096] This application's solution provides foundational material for simulating tactile events generated by various operations and tissue interactions by pre-setting a tactile primitive library containing multiple tactile primitives. When a trainee is training on peripheral blood glucose monitoring, the system acquires the trainee's operational information in real time and determines the immediate state of the simulated tissue based on this information. Based on the trainee's real-time operational information and the current state of the simulated tissue, the system selects one or more tactile primitives from the tactile primitive library that match the current situation. Simultaneously, the system acquires quantitative values ​​of the trainee's real-time operational information, such as the force and speed of the operation. Because of these quantitative values, the system can adjust the performance characteristics of the selected tactile primitives in real time, making the tactile feedback dynamic rather than static or preset, adapting to subtle differences in the trainee's actions. After real-time adjustment, these adjusted tactile primitives are combined to generate tactile primitive information, which contains the details of the tactile feedback expected at the current operational stage. Finally, based on the generated tactile primitive information, the system generates a composite continuous tactile feedback signal corresponding to the trainee's operation and presents it to the trainee through a tactile feedback device. This complex, continuous feedback signal can simulate the dynamic and subtle tactile sensations of a needle tip traversing different tissue layers, encountering resistance, and breaking through tissue during real-world operations. This helps trainees perceive the immediate impact of their actions on the tissue and understand the tactile differences resulting from different techniques. In this way, trainees can more effectively develop tactile-based predictive abilities and the capacity for fine-tuning during operations, ultimately improving training effectiveness.

[0097] In some preferred embodiments, a pre-defined tactile primitive library can store various predefined vibration modes and force feedback curves. Each mode or curve corresponds to a typical operational event, such as "skin puncture sensation," "blood vessel wall breakthrough sensation," and "tissue traction sensation." Real-time operational information can be acquired through force and motion sensors mounted on the simulated swabbing pen, such as the pressure applied by the pen tip, the speed and angle of needle insertion. Simulated tissue state information can be calculated by a simplified biomechanical model, which calculates the deformation or internal state of the simulated tissue under force in real time based on pre-defined tissue parameters (such as skin elasticity and subcutaneous tissue density) and the user's operational input. Based on the real-time operational information (such as current depth and force) and the simulated tissue state (such as current puncture layer and tissue hardness), the corresponding tactile primitive is selected from the tactile primitive library, for example, selecting the "skin puncture sensation" primitive when penetrating the simulated skin layer. Quantitative values ​​of the real-time operational information are obtained, such as the current needle insertion speed of V. Based on the quantized value V, the performance characteristics of the selected "skin puncture sensation" primitive are adjusted in real time. For example, the vibration amplitude and duration are adjusted according to the speed V; the faster the speed, the shorter and larger the vibration may be. The adjusted tactile primitive (e.g., the "skin puncture sensation" vibration pattern after adjusting amplitude and duration) is combined with other simultaneously occurring or sequentially related tactile primitives to generate tactile primitive information. Based on this information, a composite continuous vibration and / or force feedback signal is generated by connecting to a tactile feedback actuator (e.g., a linear resonant actuator or force feedback motor) connected to the simulated lancing device, simulating the real feeling of a needle tip penetrating the skin.

[0098] Optional, combined Figure 2 As shown, S6 adjusts the performance characteristics of the selected tactile primitives in real time according to the quantization value, and combines all the tactile primitives that have completed real-time adjustment to generate tactile primitive information. The steps include:

[0099] S61, adjusts the performance characteristics of the selected tactile primitives in real time according to the quantization value;

[0100] S62, identify the preset operation scenario stage corresponding to the real-time operation information;

[0101] S63, from all the tactile primitives that have been adjusted in real time, identify the corresponding specific tactile features according to the preset operation scenario stage;

[0102] S64, adjust the relative perception intensity or presentation order of specific tactile features, combine all the tactile primitives after the relative perception intensity or presentation order has been adjusted, and generate tactile primitive information.

[0103] Real-time operational information refers to the specific operational data generated by trainees during peripheral blood glucose monitoring training, such as the force applied by the needle tip, the speed, angle, and path of movement. This data can be acquired through sensor collection, image recognition analysis, and other methods. Pre-defined operational scenario stages refer to the division of the entire peripheral blood glucose monitoring operation process into several discrete or continuous stages based on technical requirements or teaching objectives. These stages could include skin contact, puncture breakthrough, subcutaneous exploration, blood collection, and pressure hemostasis stages. The purpose is to link tactile feedback with the specific operational step the trainee is in. Specific tactile features refer to the types of touch that are closely related to the key operations or tissue interactions in a given pre-defined operational scenario stage and that trainees need to focus on perceiving or distinguishing. For example, in the puncture breakthrough stage, a specific tactile feature could be the "plop" sensation of breaking through the skin or blood vessel wall; in the subcutaneous exploration stage, a specific tactile feature could be the elasticity or sliding sensation of the needle tip touching the blood vessel wall. The purpose is to highlight key tactile information and guide trainees' attention. Relative perceptual intensity refers to the perceived salience or amplitude of a specific tactile feature relative to other tactile features in the final generated tactile primitive information. This can be achieved by adjusting parameters such as the amplitude, frequency, or duration of the corresponding tactile primitive. The purpose is to influence the learner's perceptual priority and clarity by enhancing or weakening the intensity of a specific tactile feature. Presentation order refers to the order in which multiple specific tactile features appear or how they are superimposed on the timeline. This can be achieved by adjusting parameters such as the playback delay, superimposition method, or duration of the corresponding tactile primitive. The purpose is to simulate the sequence of tactile events or complex sensations in real-world operations, improving the realism of tactile feedback.

[0104] In some preferred embodiments, this application is implemented as follows. For example, when a trainee is training on peripheral blood glucose monitoring, the system can continuously acquire the trainee's real-time operation information, such as the force applied by the needle tip, the speed and depth of needle insertion, etc. Simultaneously, the system presets multiple operation scenario stages, such as "skin contact stage," "skin puncture stage," "subcutaneous exploration stage," "vascular puncture stage," and "pressure hemostasis stage." The system can identify the current preset operation scenario stage of the trainee based on parameters such as needle tip depth and force change rate in the real-time operation information. For example, when the needle tip depth is less than the preset skin thickness and the force gradually increases, it can be identified as the "skin contact stage"; when the needle tip depth reaches near the preset skin thickness and the force suddenly decreases, it can be identified as the "skin puncture stage"; when the needle tip depth is within the preset subcutaneous tissue range and the force changes slightly, it can be identified as the "subcutaneous exploration stage." After identifying the current operation scenario stage, the system can identify the specific tactile features corresponding to that stage from the tactile primitives that have been preliminarily adjusted according to quantification values. For example, in the "skin puncture stage," a specific tactile feature could simulate the sensation of skin penetration; in the "subcutaneous exploration stage," a specific tactile feature could simulate the sensation of subcutaneous tissue resistance or blood vessel wall. Subsequently, the system can adjust the relative perceived intensity or presentation order of these specific tactile features as needed. For instance, in the "skin puncture stage," the relative perceived intensity of the simulated skin penetration sensation can be enhanced to make it more prominent; in the "subcutaneous exploration stage," if blood vessels are present in the simulated tissue setting and the student's operation approaches the blood vessels, the system can adjust the presentation order of the simulated blood vessel wall sensation, making it appear after the simulated change in subcutaneous tissue resistance, or dynamically adjust the superposition method and intensity ratio of these two sensations based on the student's exploration actions. After completing these contextualized adjustments, all tactile primitives are combined to generate the final tactile primitive information, which is used to drive the tactile feedback device to produce the corresponding tactile sensation.

[0105] Optional, combined Figure 3 As shown, the step of adjusting the relative perceptual intensity or presentation order of specific tactile features in step S64 includes:

[0106] S641, obtain the student's historical operation data or the student's preset classification parameters;

[0107] S642 determines personalized adjustment parameters for students based on their historical operation data or preset classification parameters.

[0108] S643 allows for personalized parameter adjustments based on the student's individual needs, allowing for adjustments to the relative perceived intensity or presentation order of specific tactile features.

[0109] Among them, the student's historical operation data refers to the sequence of student's operational behaviors, operation parameters, error types, number of repetitions, completion time, and evaluation indicators generated by the system in each training session, which are recorded by the system. The purpose is to provide an objective basis for the student's operation habits, skill level, and learning progress. The student's preset classification parameters refer to the classification or labeling of the student in advance based on non-operational factors such as the student's medical background, learning style, and cognitive ability assessment results. The purpose is to provide a basis for personalized adjustment based on prior knowledge when there is a lack of sufficient historical operation data. The student's personalized adjustment parameters refer to the set of values ​​or rules calculated or obtained by looking up tables based on the analysis of the student's historical operation data or student's preset classification parameters. These are used to specifically adjust the performance characteristics (relative perception intensity or presentation order) of specific tactile features. For example, they can be the gain coefficient, delay time, priority weight, etc. of a certain tactile feature. The purpose is to transform the student's individual differences into executable tactile feedback adjustment instructions.

[0110] In some preferred embodiments, this application is implemented as follows. For example, consider a trainee whose historical operation data shows a tendency to deviate from the angle during rapid needle insertion. The system first acquires the trainee's historical operation data and analyzes it to find that the angle deviation frequency is high during high-speed needle insertion. Based on preset adjustment rules associated with this operation mode, the system determines personalized adjustment parameters. For example, the rules stipulate that for such trainees, the perceived intensity of specific tactile features related to "tissue traction" and "vascular wall sliding" should be enhanced, and the timing of these negative feedbacks may be brought forward. During training, when the trainee inserts the needle rapidly and deviates from the angle, the system identifies the two specific tactile features of "tissue traction" and "vascular wall sliding," and enhances the relative perceived intensity of these features according to the previously determined personalized adjustment parameters, potentially making them appear earlier or more prominently than other features. In this way, the trainee can more clearly perceive the tissue reaction caused by their improper operation (rapid needle insertion and angle deviation), thereby being guided to correct it.

[0111] Optional, combined Figure 4 As shown, the steps S642 takes to determine personalized adjustment parameters for students based on their historical operation data or preset classification parameters include:

[0112] S6421, Obtain the preset type of the specific tactile feature to be adjusted;

[0113] S6422, based on the preset type, extract the student's historical response information corresponding to the preset type from the student's historical operation data, or extract the student's classification information corresponding to the preset type from the student's preset classification parameters.

[0114] S6423, based on the student's historical response information or student classification information, and the preset adjustment rules associated with the preset type, determine the student's personalized adjustment parameters.

[0115] Among these, the preset types of specific tactile features refer to the categories of tactile features identified during training that require personalized adjustments. These can be implemented using predefined tactile event classification labels, such as "puncture resistance" or "vascular breakthrough sensation," with the aim of classifying and managing tactile feedback for targeted adjustments. Trainee historical operation data refers to various operation records generated by trainees during previous training sessions. This can be achieved by recording data such as force, speed, angle, duration, and repetition count during the trainee's operations, providing raw information about the trainee's operational behavior. Trainee preset classification parameters refer to parameters set for trainees before or during training that reflect their individual characteristics. These can be implemented using information such as the trainee's experience level, learning style, and assessment results of specific skill mastery, providing static individual characteristic information about the trainee. Trainee historical response information refers to data extracted from trainee historical operation data that is related to a specific tactile feature type and reflects the trainee's reaction or performance to that tactile feature feedback. This can be achieved by analyzing whether the trainee pauses or exerts force when encountering a certain resistance. The system utilizes behavioral data such as degree mutation and repeated attempts to obtain dynamic individual performance of learners in specific tactile situations. Learner classification information refers to the characteristic information of learners under a specific tactile feature type extracted from the learner's preset classification parameters. This information can be obtained by filtering relevant information from parameters such as learner experience level and learning style according to preset types. Its purpose is to obtain the learner's static individual characteristics in specific tactile situations. Preset adjustment rules refer to the set of rules that are pre-set to map learner's historical response information or classification information to personalized adjustment parameters. These rules can be implemented using lookup tables, conditional judgment logic, or mapping relationships derived from statistical analysis and machine learning models. Their purpose is to provide the logical basis for determining personalized adjustment parameters. Learner personalized adjustment parameters refer to the specific values ​​or instructions calculated or determined according to learner's individual information and preset adjustment rules to adjust the relative perception intensity or presentation order of specific tactile features. These parameters can be implemented using adjustment ratios, offsets, or specific adjustment instructions. Their purpose is to provide a specific basis for personalized adjustment of specific tactile features.

[0116] In some preferred embodiments, a specific example is given below. Assume the preset type of the specific tactile feature to be adjusted is "puncture resistance." The system first acquires this preset type. Next, based on this type, the system searches the student's historical operational data for relevant data on overcoming resistance during previous simulated puncture training, such as average force and force variation curves, to extract the student's historical response information, such as whether the student exhibited excessive force or hesitation when encountering resistance. Alternatively, if the student lacks sufficient historical data, the system extracts classification information related to "puncture resistance" from the student's preset classification parameters, such as the student being labeled as a "beginner" and having "weak perception of force feedback." Then, the system determines the student's personalized adjustment parameters based on the extracted student historical response information or student classification information, and the preset adjustment rules associated with the "puncture resistance" type. For example, if the student's historical response information shows excessive force, the preset adjustment rule might be "reduce the resistance feedback intensity when applying excessive force," and the determined personalized adjustment parameter might be "reduce the puncture resistance feedback intensity by 10%." If a student's classification information indicates that they have a weak perception of force feedback, the preset adjustment rule might be "increase the resistance change gradient when the perception of force feedback is weak." The determined personalized adjustment parameter might then be "increase the puncture resistance change gradient by 15%." These personalized adjustment parameters will then be used to adjust the relative perceived intensity or presentation order of the specific tactile feature of "puncture resistance."

[0117] Optional, combined Figure 5 As shown, S6423 determines the personalized adjustment parameters for students based on their historical response information or student classification information, and the preset adjustment rules associated with the preset type. The steps include:

[0118] A1, Obtain data on the student's training process or data on the evaluation of the student's training effect;

[0119] A2, based on student training process data or student training effect evaluation data, evaluate the adjustment effect of preset adjustment rules associated with preset types and generate adjustment effect evaluation results;

[0120] A3. If the adjustment effect evaluation result indicates that the preset optimization conditions are not met, then the preset adjustment rules are updated based on the adjustment effect evaluation result.

[0121] A4 determines the personalized adjustment parameters for each student based on their historical response information or student classification information, as well as the updated preset adjustment rules.

[0122] Among them, trainee training process data refers to the operational data, physiological data, or system interaction data recorded in real time during trainees' peripheral blood glucose monitoring training, such as operation duration, operation force curve, needle insertion angle changes, operation path, heart rate, skin conductance, etc. Trainee training effect evaluation data refers to the quantitative evaluation results of trainees' operational skills, knowledge mastery, or error rate after training or at a specific training stage, such as puncture success rate, bleeding volume, patient pain score, and operation step accuracy score, etc., which can be obtained through sensor collection, system log recording, or manual scoring, and its purpose is to provide objective evidence for evaluating the actual effect of preset adjustment rules. Preset type refers to the label that classifies specific tactile features, such as "puncture breakthrough sensation," "tissue traction sensation," "vascular wall sensation," etc., and its purpose is to associate adjustment rules with specific tactile feedback types. Preset adjustment rules refer to a series of logical or mapping relationships pre-set during the initialization of the training system or based on experience to guide how to determine personalized adjustment parameters based on trainee information. For example, a rule might stipulate that "if the trainee's historical operating force is too large, and the current operating situation is the puncture stage, then the relative perceived intensity of the puncture breakthrough sensation will be reduced by 20%." Its purpose is to provide an initial basis for personalized adjustments. Adjustment effect evaluation results refer to a quantitative or qualitative evaluation of the performance of the currently applied preset adjustment rules in improving trainee training effectiveness, based on trainee training process data or trainee training effect evaluation data. For example, an evaluation result might indicate that "the current rule caused a 5% decrease in the trainee's puncture success rate" or "the trainee's accuracy in perceiving the puncture breakthrough sensation has not improved." Its purpose is to determine whether the current rule is effective and whether optimization is needed. Preset optimization conditions refer to the standards or thresholds used to determine whether the adjustment effect of the preset adjustment rules has reached the expected goal. For example, "the trainee's puncture success rate increases by more than 10%" or "the trainee's error rate decreases to below 5%" can be set as optimization conditions. Their purpose is to determine when rule updates need to be triggered.

[0123] In some preferred embodiments, the student training process data can specifically be records of operational parameters such as the maximum force applied by the student during the puncture, the needle insertion speed, and the angle deviation. The student training effect evaluation data can specifically be indicators such as the average puncture depth, the number of times blood vessels were successfully avoided, and the simulated bleeding volume after the student completes a set of training tasks. When evaluating the adjustment effect of preset adjustment rules associated with preset types, the student training effect evaluation data before and after applying the rule can be compared, or the student data applying the rule can be compared with the control group data not applying the rule, generating a percentage or score representing the improvement or decrease in effect as the adjustment effect evaluation result. The preset optimization condition can be set to an effect improvement percentage greater than a certain threshold, such as greater than 5%. If the evaluation result shows that the effect improvement does not reach 5%, it is considered that the preset optimization condition is not met. When updating the preset adjustment rules based on the adjustment effect evaluation result, the corresponding modification suggestions can be found in a preset rule correction strategy library according to the degree of effect decline or the gap that is not met. For example, if the evaluation result shows that reducing the intensity of puncture breakthrough leads to insufficient puncture depth, the rule update operation can be to reduce the proportion of intensity reduction or increase the relative intensity of puncture resistance tactile sensation. The updated preset adjustment rules were then used to calculate the personalized adjustment parameters for that student or similar students.

[0124] Optional, combined Figure 6 As shown, step A3, which involves updating the preset adjustment rules based on the adjustment effect evaluation results, includes the following steps:

[0125] A31. Based on the evaluation results of the adjustment effect, determine the deviation between the preset adjustment rules and the preset performance target;

[0126] A32, based on the deviation between the preset adjustment rules and the preset performance target, and the preset rule correction lookup table, determine the corresponding modification operation;

[0127] A33, perform the modification operation to update the preset adjustment rules.

[0128] The adjustment effect evaluation result refers to the quantitative or qualitative evaluation of the effect of the currently used preset adjustment rules in actual training. This evaluation can be generated using student training process data, student training effect evaluation data, student subjective feedback, or expert evaluation. The preset adjustment rules refer to the set of rules or algorithmic models used to determine personalized adjustment parameters for students based on their historical operation data or preset classification parameters. These rules can be implemented using lookup tables, decision trees, regression models, neural networks, or other machine learning models. The preset performance target refers to the ideal or expected standard set for the personalized haptic feedback training effect. This target can be based on preset target values ​​for key performance indicators, student satisfaction thresholds, and training time reduction. The deviation refers to the difference between the effect of the preset adjustment rule in actual application and the preset performance target. It can be quantified or represented by numerical difference, percentage difference, grade difference, or qualitative description. The rule correction lookup table is a set of preset correspondences used to map the deviation between the preset adjustment rule and the preset performance target to specific modification operations. It can be implemented using two-dimensional or multi-dimensional lookup tables, rule sets, or rule-based reasoning systems. The modification operation refers to the specific instructions or steps to modify the preset adjustment rule. It can be represented by adjusting the parameter values ​​in the rule, modifying the logical structure of the rule, adding or deleting rule entries, or switching to different rule models.

[0129] In some preferred embodiments, the system can continuously monitor the trainee's recognition of the tactile feedback of the simulated "breakthrough sensation" generated by penetrating the vessel wall during simulated vascular puncture. For example, by comparing the trainee's operational data (such as changes in puncture depth and speed) with the actual "breakthrough" event feedback from the simulated tissue model, or by combining the trainee's subjective feedback questionnaire after training, an evaluation result is generated of the preset adjustment rules used to adjust the relative perceived intensity of the "breakthrough sensation" tactile feature. Suppose the evaluation result shows that the trainee's accuracy in recognizing the "breakthrough sensation" does not meet the preset performance target (e.g., the preset target is 90% accuracy, and the actual evaluation result is 80%). The system will calculate this deviation. Then, the system will consult a preset rule correction lookup table. This lookup table presets rule modification suggestions corresponding to different deviation situations, specific tactile feature types (here, "breakthrough sensation"), and operational scenarios (here, "vascular puncture"). Based on the found suggestions, the system determines the modification operation to be performed; for example, the modification operation might be "increasing the parameter value in the current rule used to enhance the vibration amplitude of the breakthrough sensation." The system then executes this modification operation to update the preset adjustment rules. For example, if the original rule increases the breakthrough vibration amplitude by 10% based on the learner's historical performance, the updated rule might increase it to 15% or 20% to provide more significant breakthrough feedback to such learners in subsequent training, thereby helping them improve their recognition accuracy.

[0130] Optional, combined Figure 7 As shown in Figure A31, the steps for determining the deviation between the preset adjustment rules and the preset performance targets based on the adjustment effect evaluation results include:

[0131] A311 identifies key performance indicators corresponding to peripheral blood glucose monitoring training from preset performance goals;

[0132] A312, Based on key performance indicators, determine the corresponding evaluation data in the adjustment effect evaluation results;

[0133] A313, based on the comparison results of preset target values ​​of key performance indicators and evaluation data, determines the deviation between preset adjustment rules and preset performance targets.

[0134] Among them, the preset efficacy goals refer to the set of standards for trainee performance or training effect that the training system expects to achieve. These can include multiple measurement dimensions, such as operational accuracy, operational efficiency, and trainee perception. Key efficacy indicators refer to specific measurement dimensions that have a decisive impact or high correlation with the training effect of peripheral blood glucose monitoring within the preset efficacy goals. These can be identified through expert experience, historical data analysis, or by ranking the importance of training goals. The adjustment effect evaluation results refer to the measurement and summary of the effect of the current preset adjustment rules in actual training. These can include various operational data, physiological response data, and subjective feedback data of trainees during the training process. Evaluation data refers to the quantitative information in the adjustment effect evaluation results that is directly related to specific efficacy indicators. For example, if the key efficacy indicator is the puncture success rate, the evaluation data is the actual success rate value. The preset target values ​​refer to the performance level or values ​​that trainees are expected to achieve in advance for each key efficacy indicator. These values ​​can be set based on clinical standards, teaching objectives, or historical best performance. Bias refers to the degree of difference between the evaluation data and the preset target values, which can be expressed as numerical difference, percentage difference, or a multi-dimensional vector.

[0135] In some preferred embodiments, the preset performance targets of the training system may include: puncture success rate, pain score, operation time, bleeding volume, and tissue damage degree. Through analysis, the key performance indicators can be determined as: puncture success rate and pain score. The evaluation results of a training batch's adjustment effect may show: puncture success rate 85%, pain score 4, operation time 25s, bleeding volume 0.05ml, and slight tissue damage degree. Based on the key performance indicators, the evaluation data can be determined as: puncture success rate 85%, pain score 4. The preset target values ​​can be set as: puncture success rate target value 90%, pain score target value 3. Based on the comparison results, the puncture success rate deviation can be calculated as 85% - 90% = -5%, and the pain score deviation can be calculated as 4 - 3 = +1. Therefore, the deviation between the preset adjustment rules and the preset performance targets can be determined as a 5% negative deviation in puncture success rate and a 1-unit positive deviation in pain score.

[0136] Optional, combined Figure 8 As shown, A32 determines the corresponding modification steps based on the deviation between the preset adjustment rules and the preset performance target, as well as the preset rule correction lookup table.

[0137] A321 identifies deviation characteristics based on the deviation between preset adjustment rules and preset performance targets;

[0138] A322, reads the preset type corresponding to the specific tactile feature to be adjusted and the preset operation scenario stage corresponding to the real-time operation information;

[0139] A323 determines the modification operation based on the deviation characteristics, preset type, preset operation scenario stage, and preset rule correction lookup table.

[0140] Among them, deviation characteristics refer to the specific manifestations or types of deviations between preset adjustment rules and preset performance targets. These can be achieved through analysis, classification, or pattern recognition of evaluation data, aiming to quantify or categorize abstract deviations to provide input for subsequent rule modifications. Preset types refer to the categories of specific tactile features to be adjusted within the system's preset classification system. These can be classified based on the physical attributes, physiological significance, or operational stage relevance of the tactile features, aiming to distinguish different types of tactile features, as different types may require different adjustment strategies. Preset operational scenario stages refer to specific time periods or operational segments within the peripheral blood glucose monitoring training process where real-time operational information is located. These stages can be divided based on operational sequences or key events, aiming to distinguish different operational scenarios. The key points for haptic feedback needs and rule adjustments during the operation phase are as follows: A pre-built rule correction lookup table is a pre-constructed data structure used to store the mapping relationship between combinations of deviation features, preset types, and preset operation scenarios and corresponding modification operations. This lookup table can be a multidimensional array, hash table, or database. Its purpose is to provide a structured, quickly searchable rule correction guide, mapping multidimensional inputs to specific modification instructions. Modification operations refer to specific modification instructions or actions performed on the preset adjustment rules. These can be adding or subtracting a parameter value in the rule, modifying the rule's logical conditions, or adjusting haptic feature characteristics (such as intensity, duration, and timing). The purpose is to make targeted corrections to the rules based on the identified problems and the current context to optimize the haptic feedback effect.

[0141] In some preferred embodiments, for example, suppose the system assessment finds that trainees generally have insufficient perception of the tactile feature of skin puncture resistance during the initial needle insertion, leading to excessively fast needle insertion speed. The system first identifies deviation features based on the discrepancy between preset adjustment rules and preset performance targets, such as insufficient perception of a specific tactile feature. Next, the system reads the preset type corresponding to the specific tactile feature to be adjusted, for example, the preset type for skin puncture resistance could be tissue resistance, and reads the preset operation scenario stage corresponding to the real-time operation information, such as the initial needle insertion stage. Then, based on the identified deviation feature of insufficient perception of a specific tactile feature, the preset type of tissue resistance, and the preset operation scenario stage of the initial needle insertion stage, the system queries a preset rule correction lookup table. The lookup table contains preset entries; for example, when the deviation feature is insufficient perception of a specific tactile feature, the preset type is tissue resistance, and the preset operation scenario stage is the initial needle insertion stage, the corresponding modification suggestion is to increase the initial intensity of the tactile feature signal of that type. Based on the lookup table results, the system determines that the modification operation is to increase the initial intensity of the skin puncture resistance tactile signal. The system then performs the modification operation, updating the adjustment parameters in the preset adjustment rules regarding the tactile characteristics of tissue resistance in the initial stage of needle insertion, for example, by increasing the parameter value by a preset step size.

[0142] Optional, combined Figure 9 As shown, A323 determines the modification operation steps based on deviation characteristics, preset type, preset operation scenario stage, and preset rule correction lookup table, including:

[0143] A3231, identifies and corrects the output of the lookup table according to preset rules;

[0144] A3232, if the output situation represents a combination of deviation characteristics, preset type and preset operation scenario stage that is covered by preset rule correction logic, and the output situation contains only one modification operation suggestion, then the modification operation suggestion will be used as the modification operation.

[0145] A3233, if the combination of deviation characteristics, preset type and preset operation scenario stage in the output situation is not covered by the preset rule correction lookup table, and the output situation contains at least two modification operation suggestions, then it is organized into a candidate modification operation set;

[0146] A3234, based on preset type, preset operation scenario stage and preset evaluation rules, evaluate the potential impact of each modification operation suggestion in the candidate modification operation set on specific tactile features;

[0147] A3235, select the modification suggestion with the best evaluation result as the modification operation.

[0148] The output of the preset rule correction lookup table refers to the result status obtained after consulting the table. It indicates whether the consulted combination has a corresponding entry and the number of suggested modification operations contained in that entry. The preset rule correction logic refers to the mapping relationship or judgment criteria used to construct the preset rule correction lookup table, defining the output that a specific input combination should correspond to. The suggested modification operation refers to one or more recommended rule modification schemes that the preset rule correction lookup table may provide for a specific input combination; these can be specific adjustment instructions for the preset adjustment rules. The candidate modification operation set is a collection of multiple suggested modification operations output by the lookup table or multiple possible suggested modification operations generated based on other logic when the preset rule correction lookup table cannot directly provide a unique modification operation. Its purpose is to provide alternatives for subsequent evaluation and selection. The preset evaluation rules refer to the standards and methods used to measure the impact of different suggested modification operations on specific tactile features. These rules may include evaluation indicators and weights for tactile perception intensity, presentation sequence, and student acceptance. Among them, specific tactile features refer to tactile feedback elements that are closely related to the trainee's operational behavior and need to be focused on or adjusted in specific operational scenarios during peripheral blood glucose monitoring training. These can be tactile representations of simulated tissue characteristics or operational interaction effects.

[0149] In some preferred embodiments, specifically, assuming that during a training session, the system detects a deviation between the trainee's performance on "vascular breakthrough sensation" (preset type) during the "mid-needle insertion" stage (preset operation scenario stage) and a preset performance target, for example, the trainee fails to accurately perceive the instantaneous tactile sensation of a blood vessel being punctured. The system determines a deviation characteristic based on the deviation, such as "insufficient perception of vascular breakthrough sensation." The system consults a preset rule correction lookup table, inputting "insufficient perception of vascular breakthrough sensation" (deviation characteristic), "vascular breakthrough sensation" (preset type), and "mid-needle insertion" (preset operation scenario stage). Assuming the lookup table output indicates that this combination is not directly covered, but based on associated entries or fuzzy matching, the lookup table outputs two modification operation suggestions: suggestion A is "increase the relative perceived intensity of vascular breakthrough sensation," and suggestion B is "advance the presentation order of vascular breakthrough sensation." The system organizes suggestion A and suggestion B into a candidate modification operation set. Then, the system evaluates the potential impact of suggestion A and suggestion B on the trainee's perception of "vascular breakthrough sensation" based on the preset type "vascular breakthrough sensation," the preset operation scenario stage "mid-needle insertion," and preset evaluation rules. The preset evaluation rules may stipulate that, during the "mid-needle insertion" phase, the accuracy of the timing is more important than the intensity of the "vascular breakthrough sensation," or, considering the student's historical data (e.g., the student is more sensitive to changes in timing), the evaluation might conclude that suggestion B, "prioritizing the presentation order of the vascular breakthrough sensation," yields a better result than suggestion A. The system ultimately selects suggestion B as the modification operation to update the preset adjustment rules associated with the "vascular breakthrough sensation."

[0150] A tactile feedback training system for peripheral blood glucose monitoring, used to perform tactile feedback training for peripheral blood glucose monitoring, combined with Figure 10 As shown, the peripheral blood glucose monitoring tactile feedback training system 1 includes:

[0151] The tactile primitive library preset module 11 is used to preset a tactile primitive library containing several tactile primitives; each tactile primitive represents a tactile event generated by the interaction between the trainee's operation and the simulated tissue in peripheral blood glucose monitoring training;

[0152] The real-time operation acquisition module 12 is used to acquire the real-time operation information of trainees during peripheral blood glucose monitoring training.

[0153] The simulation information determination module 13 is used to determine the simulated organizational status information of the interaction between the trainee and the trainee based on the real-time operation information.

[0154] The tactile primitive selection module 14 is used to select a corresponding number of tactile primitives from the tactile primitive library based on real-time operation information and simulated tissue state information;

[0155] The quantization value acquisition module 15 is used to acquire the quantization value of real-time operation information;

[0156] The primitive information generation module 16 is used to adjust the performance characteristics of the selected tactile primitives in real time according to the quantization value, and combine all the tactile primitives after real-time adjustment to generate tactile primitive information.

[0157] The feedback signal generation module 17 is used to generate a composite continuous tactile feedback signal corresponding to the student's operation based on tactile primitive information, so as to help the student establish the ability to predict and adjust operations and achieve the training objective.

[0158] The tactile primitive library refers to a pre-defined collection of various basic tactile units, which can be implemented using a database or file structure. A tactile primitive is a basic unit that constitutes complex tactile feedback, representing specific tactile sensations such as force, vibration, and texture. A tactile event refers to a perceptible or simulated tactile change generated during the interaction between the trainee and the simulated tissue, which can include puncture resistance, tissue layering, and a sense of breakthrough. Real-time operational information refers to the time-varying motion data generated by the trainee during training, which can include needle tip position, speed, magnitude and direction of force, etc. Simulated tissue state information refers to the instantaneous physical characteristics or response state of simulated human tissue under the trainee's manipulation, which can include the deformation, hardness, elasticity, and viscosity of the simulated tissue. Quantified values ​​refer to the conversion of real-time operational information into a calculable numerical representation, which can be the numericalization of operational parameters such as force, speed, and displacement. Performance characteristics refer to the adjustable attributes of tactile primitives during presentation, which can include the intensity, duration, frequency, and waveform of the tactile primitives. Tactile primitive information refers to the adjusted and combined set of tactile primitives used to guide the generation of the final tactile feedback signal; it can be a data structure or a signal description. Composite continuous tactile feedback signal refers to a signal composed of multiple tactile primitives, changing in real time with the student's operation, and presented by the tactile feedback device; it can be an electrical signal or a mechanical drive signal.

[0159] In some preferred embodiments, the tactile primitive library preset module can be a database file stored in computer memory or on a hard disk, containing tactile waveform data corresponding to different tactile events. The real-time operation acquisition module can be a sensor array connected to a simulated lancet or training device, used to collect data such as force, position, and speed of the trainee's operations. The simulation information determination module can be a physical simulation engine running on a processing unit, which calculates the deformation, force distribution, and other state information of the simulated tissue in real time based on the trainee's operation data and preset tissue parameters. The tactile primitive selection module can be a lookup table or rule engine, which searches for and selects the corresponding tactile waveform ID or reference from the tactile primitive library based on real-time operation information and simulated tissue state information. The quantization value acquisition module can be a data processing unit that converts the raw operation data collected by the sensors into physically meaningful quantized values. The primitive information generation module can be a signal processing unit that adjusts the amplitude or duration of the selected tactile primitive waveform according to the quantized values, and combines multiple adjusted waveforms into a composite waveform sequence in chronological order or by superposition. The feedback signal generation module can be a drive circuit connected to the haptic feedback device, which converts the generated composite waveform sequence into a drive signal to control the haptic feedback device to generate corresponding force or vibration.

[0160] The above description is merely an embodiment of this application and is not intended to limit the scope of protection of this application. Various modifications and variations can be made to this application by those skilled in the art. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the scope of protection of this application.

Claims

1. A method for tactile feedback training in peripheral blood glucose monitoring, characterized in that, include: A tactile primitive library containing several tactile primitives is preset; each tactile primitive represents a tactile event generated by the interaction between a trainee's operation and the simulated tissue during peripheral blood glucose monitoring training; Obtain real-time operational information of trainees during peripheral blood glucose monitoring training; Based on the real-time operation information, determine the simulated organizational status information that interacts with the trainee's operations; Based on the real-time operation information and the simulated tissue state information, select a corresponding number of tactile primitives from the tactile primitive library; Obtain the quantized value of the real-time operation information; The performance characteristics of the selected tactile primitives are adjusted in real time according to the quantization value, and all the tactile primitives that have been adjusted in real time are combined to generate tactile primitive information. Based on the tactile primitive information, a composite continuous tactile feedback signal corresponding to the student's operation is generated to help the student develop the ability to predict and adjust operations, thereby achieving the training objective.

2. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 1, characterized in that, The step of adjusting the performance characteristics of the selected tactile primitives in real time according to the quantization value, and combining all the tactile primitives after real-time adjustment to generate tactile primitive information includes: The performance characteristics of the selected tactile primitive are adjusted in real time according to the quantized value; Identify the preset operation scenario stage corresponding to the real-time operation information; From the tactile primitives that have all been adjusted in real time, the corresponding specific tactile features are identified according to the preset operation scenario stage; Adjust the relative perception intensity or presentation order of the specific tactile features, and combine all the tactile primitives after the relative perception intensity or presentation order has been adjusted to generate tactile primitive information.

3. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 2, characterized in that, The step of adjusting the relative perceived intensity or presentation order of the specific tactile features includes: Obtain the student's historical operation data or the student's preset classification parameters; Based on the student's historical operation data or the student's preset classification parameters, determine the student's personalized adjustment parameters; Based on the student's personalized adjustment parameters, the relative perceived intensity or presentation order of the specific tactile features is adjusted.

4. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 3, characterized in that, The step of determining the personalized adjustment parameters for students based on their historical operation data or preset classification parameters includes: Obtain the preset type of the specific tactile feature to be adjusted; Based on the preset type, extract the student's historical response information corresponding to the preset type from the student's historical operation data, or extract the student's classification information corresponding to the preset type from the student's preset classification parameters; Based on the student's historical response information or student classification information, and the preset adjustment rules associated with the preset type, the student's personalized adjustment parameters are determined.

5. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 4, characterized in that, The step of determining the personalized adjustment parameters for students based on the student's historical response information or student classification information, and the preset adjustment rules associated with the preset type, includes: Obtain data on the trainee's training process or data on the evaluation of the trainee's training effectiveness; Based on the student training process data or student training effect evaluation data, evaluate the adjustment effect of the preset adjustment rules associated with the preset type, and generate adjustment effect evaluation results; If the adjustment effect evaluation result indicates that the preset optimization conditions are not met, then the preset adjustment rule is updated based on the adjustment effect evaluation result; Based on the student's historical response information or student classification information, and the updated preset adjustment rules, determine the student's personalized adjustment parameters.

6. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 5, characterized in that, The step of updating the preset adjustment rules based on the adjustment effect evaluation results includes: Based on the evaluation results of the adjustment effect, the deviation between the preset adjustment rule and the preset performance target is determined; Based on the deviation between the preset adjustment rules and the preset performance target, and the preset rule correction lookup table, the corresponding modification operation is determined; Perform the modification operation to update the preset adjustment rules.

7. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 6, characterized in that, The step of determining the deviation between the preset adjustment rule and the preset performance target based on the adjustment effect evaluation results includes: Identify key performance indicators corresponding to peripheral blood glucose monitoring training from preset performance goals; Based on the key performance indicators, determine the corresponding evaluation data in the evaluation results of the adjustment effect; Based on the comparison results between the preset target values ​​of the key performance indicators and the evaluation data, the deviation between the preset adjustment rules and the preset performance targets is determined.

8. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 6, characterized in that, The step of determining the corresponding modification operation based on the deviation between the preset adjustment rule and the preset performance target, and the preset rule correction lookup table, includes: Based on the deviation between the preset adjustment rules and the preset performance target, the deviation characteristics are identified; Read the preset type corresponding to the specific tactile feature to be adjusted and the preset operation scenario stage corresponding to the real-time operation information; Based on the deviation characteristics, the preset type and preset operation scenario stage, and the preset rule correction lookup table, the modification operation is determined.

9. The method for tactile feedback training in peripheral blood glucose monitoring according to claim 8, characterized in that, The step of determining the modification operation based on the deviation characteristics, the preset type, the preset operation scenario stage, and the preset rule correction lookup table includes: Identify and correct the output of the lookup table according to preset rules; If the output indicates that the combination of the deviation feature, the preset type, and the preset operation scenario stage is covered by the preset rule correction logic, and the output contains only one modification operation suggestion, then the modification operation suggestion is taken as the modification operation. If the output indicates that the combination of the deviation feature, the preset type, and the preset operation scenario stage is not covered by the preset rule correction lookup table, and the output contains at least two modification operation suggestions, then it is organized into a candidate modification operation set. Based on the preset type, the preset operation scenario stage, and the preset evaluation rules, evaluate the potential impact of each modification operation suggestion in the candidate modification operation set on the specific tactile feature; Select the modification suggestion that yields the best evaluation results as the modification operation.

10. A tactile feedback training system for peripheral blood glucose monitoring, used to perform tactile feedback training for peripheral blood glucose monitoring, characterized in that, include: The tactile primitive library preset module is used to preset a tactile primitive library containing several tactile primitives; Each of the aforementioned tactile primitives represents a tactile event generated by a trainee's operation and interaction with simulated tissues during peripheral blood glucose monitoring training; The real-time operation acquisition module is used to acquire the trainees' real-time operation information during peripheral blood glucose monitoring training. The simulation information determination module is used to determine the simulated organizational status information of the interaction between the trainee and the trainee based on the real-time operation information. The tactile primitive selection module is used to select a corresponding number of tactile primitives from the tactile primitive library based on the real-time operation information and the simulated tissue state information; The quantization value acquisition module is used to acquire the quantization value of the real-time operation information; The primitive information generation module is used to adjust the performance characteristics of the selected tactile primitives in real time according to the quantization value, and combine all the tactile primitives after real-time adjustment to generate tactile primitive information. The feedback signal generation module is used to generate a composite continuous tactile feedback signal corresponding to the student's operation based on the tactile primitive information, so as to help the student establish the ability to predict and adjust operations and achieve the training objective.

Images