High-quality cardio-pulmonary resuscitation training device system

Abstract

The invention discloses a high-quality cardio-pulmonary resuscitation training device system, and relates to the technical field of first-aid instruments, and the system comprises a pressing device composed of a conductive film, a spring and a silica gel shell; the linkage acquisition module is integrated with an acceleration sensor, a displacement sensor, an angle sensor and a visual sensor; the analysis terminal is in communication connection with the pressing device and the linkage acquisition module and used for reading time sequence acquisition signals of the pressing device and the linkage acquisition module, linkage analysis of the time sequence acquisition signals is carried out through an intelligent calculation model arranged in the analysis terminal, and a linkage analysis result is established; and the feedback unit is used for receiving a linkage analysis result and generating feedback data, so that the technical effects of providing trunk posture judgment and guaranteeing training consistency and training quality are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of first aid equipment, and in particular to a high-quality cardiopulmonary resuscitation training device system. Background Art

[0002] High-quality CPR training devices can provide learners with real-time feedback and guidance, helping them master the correct compression force, frequency and posture, thereby improving the effectiveness of CPR.

[0003] Existing cardiopulmonary resuscitation training devices are mostly based on simple simulators, simulators with feedback, advanced simulators, or rely on external compression feedback devices. The simulator function only judges the quality of compression, but not the compression posture. In addition, the quality and equipment status of simulators of different models and different years of use also vary, resulting in inconsistency between the compression force and the quality of training. In summary, the existing technologies have technical problems such as lack of trunk posture judgment and poor training consistency. Summary of the invention

[0004] The present invention provides a high-quality cardiopulmonary resuscitation training device system to solve the technical problems of lack of trunk posture judgment and poor training consistency in the prior art, and achieve the technical effects of providing trunk posture judgment and ensuring training consistency and training quality.

[0005] The present invention provides a high-quality cardiopulmonary resuscitation training device system, comprising:

[0006] The pressing device is composed of a conductive film, a spring, and a silicone shell. The spring is used for pressing and rebounding the pressing device, and the conductive film is used for converting a pressure signal into an electrical signal for output.

[0007] A linkage acquisition module, wherein the linkage acquisition module integrates an acceleration sensor, a displacement sensor, an angle sensor, and a visual sensor. The acceleration sensor, the displacement sensor, and the angle sensor are arranged on the pressing device, and the visual sensor is arranged on the analysis terminal, for collecting the pressing posture of the trainee.

[0008] The analysis terminal is communicatively connected with the pressing device and the linkage acquisition module, and is used to read the timing acquisition signals of the pressing device and the linkage acquisition module, perform linkage analysis of the timing acquisition signals through the intelligent computing model built into the analysis terminal, and establish linkage analysis results.

[0009] A feedback unit is used to receive the linkage analysis result and generate feedback data.

[0010] In a feasible implementation, the analysis terminal is used to:

[0011] The timing acquisition signal is decomposed to obtain a pressing signal collected by the pressing device, wherein the pressing signal includes pressing depth, pressing frequency, and pressing rebound; the first calculation network of the intelligent calculation model is called to calculate the pressing signal and establish a first pressing score, as follows:

[0012]

[0013] Among them, Q represents the first pressing score, w D 、w F 、w R are the weight factors of compression depth, compression frequency and compression rebound, D min The minimum standard for compression depth, D max The highest standard for representing compression depth, D is the compression depth data, F is min The minimum standard for compression frequency, F max The highest standard for representing compression frequency, F represents compression frequency data, R min The minimum standard for characterizing the rebound depth, R max The highest standard for representing the rebound depth, R represents the rebound depth data; the first pressing score is fed back to the intelligent computing model, and after performing linkage analysis, a linkage analysis result is established.

[0014] In a feasible implementation, the analysis terminal is further used for:

[0015] The timing acquisition signal is decomposed to obtain a linkage signal collected by the linkage acquisition module, wherein the linkage signal includes acceleration data, displacement data, angle data, and visual data; the linkage signal is synchronized to a second computing network of the intelligent computing model, and a press score is calculated through the second computing network to establish a second press score; after jointly analyzing the first press score and the second press score, a linkage analysis result is established.

[0016] In a feasible implementation, the analysis terminal is further used for:

[0017] The second computing network is used to perform a compression stability evaluation based on acceleration data, and a compression stability evaluation result is established as follows:

[0018]

[0019] Among them, σ a Characterizes the standard deviation of the acceleration signal, represents the mean value of acceleration, N is the total amount of acceleration data, i represents any acceleration data, a i Represents the i-th acceleration data, V aCharacterizing the acceleration volatility, establishing a pressing stability evaluation result according to the standard deviation and the acceleration volatility; and establishing a second pressing score according to the pressing stability evaluation result.

[0020] In a feasible implementation, the analysis terminal is further used for:

[0021] The second computing network is used to perform hysteresis evaluation based on displacement data, and the hysteresis evaluation result is established as follows:

[0022]

[0023] Among them, T lag represents the lag time, M represents the total amount of displacement data, j represents any displacement, d j Characterizes the displacement value of the jth press, r j Characterize the rebound value of the j-th compression; and establish a second compression score according to the hysteresis evaluation result and the compression stability evaluation result.

[0024] In a feasible implementation, the analysis terminal is further used for:

[0025] The second computing network is used to perform posture stability evaluation based on angle data, and a posture stability evaluation result is established as follows:

[0026]

[0027] Among them, Δθ represents the angle deviation, K represents the total amount of angle data, q represents any angle data, θ q Represents the qth angle data, θ ref is the reference standard angle, Characterizes the rate of change of angle, Characterize the qth angle change rate; establish a second pressing score according to the pressing stability evaluation result, the hysteresis evaluation result, and the posture stability evaluation result.

[0028] In a feasible implementation, the analysis terminal is further used for:

[0029] The image analysis channel of the second computing network is called to perform data preprocessing on the visual data of the linkage signal, and then extract the joint position coordinates; the pressing posture of the trainee is fitted through the joint position coordinates to establish a posture fitting result; the pressing authentication is performed on the posture fitting result to establish a second pressing score.

[0030] In a feasible implementation, the analysis terminal is further used for:

[0031] A pressing key node is established based on the pressing signal; and a time-series weighted calculation of the posture fitting result is performed through the pressing key node to complete the pressing authentication.

[0032] The present invention discloses a high-quality cardiopulmonary resuscitation training device system, comprising: a pressing device, which is composed of a conductive film, a spring, and a silicone shell, wherein the spring is used for pressing and rebounding the pressing device, and the conductive film is used for converting a pressure signal into an electrical signal for output; a linkage acquisition module, wherein the linkage acquisition module is integrated with an acceleration sensor, a displacement sensor, an angle sensor, and a visual sensor, wherein the acceleration sensor, the displacement sensor, and the angle sensor are arranged on the pressing device, and the visual sensor is arranged on an analysis terminal, and is used for collecting the pressing posture of a trainee; an analysis terminal, wherein the analysis terminal is communicatively connected with the pressing device and the linkage acquisition module, and is used for reading the timing acquisition signals of the pressing device and the linkage acquisition module, and performing linkage analysis of the timing acquisition signals through an intelligent computing model built into the analysis terminal, and establishing a linkage analysis result; a feedback unit, wherein the feedback unit is used for receiving the linkage analysis result and generating feedback data. The high-quality cardiopulmonary resuscitation training device system disclosed by the present invention solves the technical problems of lack of trunk posture judgment and poor training consistency, and achieves the technical effects of providing trunk posture judgment and ensuring training consistency and training quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0033] Figure 1 It is a structural schematic diagram of a high-quality cardiopulmonary resuscitation training device system of the present invention;

[0034] Figure 2 The present invention is a schematic diagram of a process flow in a high-quality cardiopulmonary resuscitation training device system.

[0035] Description of the accompanying drawings: pressing device 11, linkage collection module 12, analysis terminal 13, feedback unit 14. DETAILED DESCRIPTION

[0036] The technical solution provided in the embodiments of the present invention is to solve the technical problems existing in the prior art, namely, lack of trunk posture judgment and poor training consistency.

[0037] The above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods of the specification to better understand the above technical solution. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all of the embodiments of the present invention. It should be understood that the present invention is not limited to the example embodiments used only to explain the present invention. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without making creative work are within the scope of protection of the present invention. In addition, it should be noted that, for the convenience of description, only the parts related to the present invention are shown in the drawings, rather than all of them.

[0038] Example

[0039] Figure 1 The present invention is a flow chart of a high-quality cardiopulmonary resuscitation training device system, wherein the system comprises:

[0040] The pressing device 11 is composed of a conductive film, a spring, and a silicone shell. The spring is used for the pressing and rebounding of the pressing device 11, and the conductive film is used for converting a pressure signal into an electrical signal for output.

[0041] Specifically, the pressing device 11 is composed of a conductive film, a spring and a silicone shell, wherein the conductive film is used to realize the electric signal conversion function of the pressure signal, that is, under the action of external force, the conductive film contacts with related electronic components to generate an electric signal, and transmits this signal to the subsequent processing unit, so as to accurately sense and respond to the pressing operation.

[0042] Specifically, the spring is arranged inside the pressing device and is responsible for providing a rebound force after pressing. When the external force is removed, the spring automatically returns to its initial state, so that the pressing device has good reusability while maintaining appropriate sensitivity and tactile feedback.

[0043] Optionally, the spring is designed to be replaceable, that is, when the spring performance changes or decreases, the rebound force and hand feel feedback can be restored by replacing the spring, thereby helping to ensure consistency in training.

[0044] Specifically, the silicone shell is used to wrap the conductive film and the spring to form an external protection structure. The silicone shell can not only protect the internal components from the external environment (such as dust and moisture), but also provide users with a comfortable touch and has a certain wear resistance.

[0045] Through the cooperation of the above components, the pressing device 11 realizes the efficient pressure signal capture and transmission function.

[0046] The linkage acquisition module 12 integrates an acceleration sensor, a displacement sensor, an angle sensor, and a visual sensor. The acceleration sensor, the displacement sensor, and the angle sensor are arranged on the pressing device 11, and the visual sensor is arranged on the analysis terminal 13, for collecting the pressing posture of the trainee.

[0047] Specifically, the linkage acquisition module 12 is used for data acquisition of a high-quality cardiopulmonary resuscitation training device system. The linkage acquisition module 12 performs multi-dimensional data acquisition through integrated acceleration sensors, displacement sensors, angle sensors, and visual sensors, thereby providing data support for subsequent training analysis and processing.

[0048] Specifically, the acceleration sensor is arranged at the pressing position of the pressing device 11 and around the pressing position to detect the acceleration changes generated during the pressing process, so as to capture the strength and rhythm of the pressing action in real time and provide evaluation data for the pressing quality.

[0049] Specifically, the displacement sensor is also arranged at the pressing position and the periphery of the pressing position of the pressing device 11 to accurately record the displacement of each pressing, so as to evaluate whether the pressing depth meets the standard requirements of cardiopulmonary resuscitation (such as the standard range of 5 to 6 cm).

[0050] Specifically, the angle sensor is arranged at the pressing position and around the pressing position of the pressing device 11, and is used to sense the deviation of the pressing direction and angle. Through the sensing data of the angle sensor and the sensing data of the above-mentioned acceleration sensor, it is possible to determine whether the pressing is performed along the vertical direction of the patient's sternum, thereby avoiding the weakening of the pressing effect or the increased risk of injury due to improper angles.

[0051] Specifically, the visual sensor is arranged on the analysis terminal 13, and is used to collect image information of the trainee's pressing action and posture, such as capturing the trainee's body position, hand position and overall coordination of the pressing action, to provide real-time feedback and guidance for correcting irregular operations. At the same time, by arranging the visual sensor on the analysis terminal 13, a flexible collection position is provided for the visual sensor.

[0052] Through the collaborative work of the above sensors, the linkage acquisition module 12 can realize multi-dimensional data collection of the cardiopulmonary resuscitation training process, providing trainees with evaluation data including pressure force, depth, angle and posture, etc., significantly improving the quality and accuracy of cardiopulmonary resuscitation training.

[0053] The analysis terminal 13 is communicatively connected with the pressing device 11 and the linkage acquisition module 12, and is used for reading the timing acquisition signals of the pressing device 11 and the linkage acquisition module 12, performing linkage analysis of the timing acquisition signals through the intelligent computing model built into the analysis terminal 13, and establishing linkage analysis results.

[0054] Specifically, the analysis terminal 13 is the data processing unit of the training device system of the present application, which is used to calculate the corresponding training indicators based on the timing acquisition signals collected by the pressing device 11 and the linkage acquisition module 12, and analyze the training situation of the trainees and output them as linkage analysis results.

[0055] Specifically, the analysis terminal 13 maintains real-time communication with the pressing device 11 and the linkage acquisition module 12 through wired or wireless means, and reads the pressure signal of the pressing device 11 and the multi-dimensional sensor signal of the linkage acquisition module 12 in a time sequence, namely, the acceleration, displacement, angle and pressing posture visual signal.

[0056] Optionally, the analysis terminal 13 has a built-in intelligent computing model, wherein the intelligent computing model includes multiple computing networks corresponding to multiple evaluation dimensions, which are used to determine whether the pressing actions of the trainees meet the standard requirements of cardiopulmonary resuscitation; illustratively, feature extraction and fusion analysis are performed on the multi-dimensional time series acquisition signals, including: joint analysis of pressure signals and displacement signals to determine whether the pressing force and depth meet the standards; analysis of acceleration signals and angle signals to determine the deviation of pressing rhythm and direction; and visual signal analysis to realize pressing posture evaluation.

[0057] In some embodiments, the analysis terminal 13 is used to:

[0058] The timing acquisition signal is decomposed to obtain a pressing signal collected by the pressing device 11, wherein the pressing signal includes pressing depth, pressing frequency, and pressing rebound; the first calculation network of the intelligent calculation model is called to calculate the pressing signal and establish a first pressing score, as follows:

[0059]

[0060] Among them, Q represents the first pressing score, w D 、w F 、w R are the weight factors of compression depth, compression frequency and compression rebound, D min The minimum standard for compression depth, D max The highest standard for representing compression depth, D is the compression depth data, F is min The minimum standard for compression frequency, F max The highest standard for representing compression frequency, F represents compression frequency data, R min The minimum standard for characterizing the rebound depth, R max The highest standard for representing the rebound depth, R represents the rebound depth data; the first pressing score is fed back to the intelligent computing model, and after performing linkage analysis, a linkage analysis result is established.

[0061] Specifically, first, the compression signal collected by the compression device 11 is decomposed to extract key signal components, including the compression depth of the trainee (indicating the depth applied by the trainee when pressing), the compression frequency (indicating the rhythmic frequency of the compression, usually the number of compressions per minute, i.e., the CPR frequency), and the compression rebound (i.e., the rebound depth after compression).

[0062] Specifically, the first computing network of the intelligent computing model is a mathematical evaluation model, which is used to perform compression evaluation based on the compression depth, compression frequency, and compression rebound obtained above and the standard range corresponding to each indicator, and generate a corresponding first compression score, wherein the standard range of each compression indicator includes an upper limit value (highest standard) and a lower limit value (lowest standard).

[0063] Specifically, the relative position of each indicator within the standard range is used as the corresponding evaluation sub-item, and multiple evaluation sub-items corresponding to multiple indicators are added together through a weighted method to obtain a first compression score, indicating the compression quality of the trainee.

[0064] Furthermore, the calculated first press score will be fed back to the intelligent computing model as input information for further analysis to ensure that the evaluation of the press action is further optimized and the linkage analysis result is generated through the subsequent linkage analysis (such as combining the posture data of the visual sensor, historical action data, etc.).

[0065] In some embodiments, the analysis terminal 13 is further used for:

[0066] The timing acquisition signal is decomposed to obtain a linkage signal collected by the linkage acquisition module 12, wherein the linkage signal includes acceleration data, displacement data, angle data, and visual data; the linkage signal is synchronized to the second computing network of the intelligent computing model, and a press score is calculated through the second computing network to establish a second press score; after jointly analyzing the first press score and the second press score, a linkage analysis result is established.

[0067] Specifically, the analysis terminal 13 decodes and decomposes the timing acquisition signal provided by the linkage acquisition module 12, and extracts acceleration data reflecting the acceleration change during pressing, which is used to judge the force and frequency of pressing; displacement data describing the depth and displacement of pressing; angle data reflecting the inclination or angle of the pressing device to ensure the correct direction of pressing; and visual data from the visual sensor used to analyze the trainee's gestures, position and posture, to assist in evaluating the accuracy and standardization of pressing.

[0068] Specifically, the second computing network of the intelligent computing model includes multiple groups of mathematical evaluation models corresponding to acceleration data, displacement data, angle data, and visual data. The generated second pressing score is a comprehensive evaluation indicator that reflects the performance of the trainees in multiple aspects such as overall pressing posture, acceleration performance, displacement and angle control.

[0069] Specifically, by jointly analyzing the first compression score and the second compression score, the analysis terminal 13 can comprehensively consider the impact of multiple factors such as compression depth, frequency, rebound, and posture, angle control, and motion dynamics during the compression process on the compression quality, and obtain the final linkage analysis result.

[0070] In some embodiments, the analysis terminal 13 is further used for:

[0071] The second computing network is used to perform a compression stability evaluation based on acceleration data, and a compression stability evaluation result is established as follows:

[0072]

[0073] Among them, σ a Characterizes the standard deviation of the acceleration signal, represents the mean value of acceleration, N is the total amount of acceleration data, i represents any acceleration data, a i Represents the i-th acceleration data, V a Characterizing the acceleration volatility, establishing a pressing stability evaluation result according to the standard deviation and the acceleration volatility; and establishing a second pressing score according to the pressing stability evaluation result.

[0074] Specifically, the second computing network includes a mathematical evaluation model group for evaluating the smoothness of compression, which involves the calculation of the standard deviation and acceleration volatility of the acceleration signal, wherein the standard deviation measures the absolute fluctuation of the acceleration signal, that is, the amplitude of the change of the acceleration data in the time series, and the acceleration volatility is expressed as the ratio of the standard deviation of the acceleration signal to the mean acceleration, so as to reflect the relative fluctuation degree and stability of the acceleration signal.

[0075] Specifically, based on the standard deviation and acceleration volatility, the smoothness evaluation results of compression can be established in a weighted manner, where low volatility and small standard deviation usually indicate that the compression action is more stable and meets the correct compression rhythm and strength. On the contrary, high volatility and larger standard deviation may indicate that the compression depth, frequency or rebound is unstable, which may affect the compression quality.

[0076] In some embodiments, the analysis terminal 13 is further used for:

[0077] The second computing network is used to perform hysteresis evaluation based on displacement data, and the hysteresis evaluation result is established as follows:

[0078]

[0079] Among them, T lag represents the lag time, M represents the total amount of displacement data, j represents any displacement, d jCharacterizes the displacement value of the jth press, r j Characterize the rebound value of the j-th compression; and establish a second compression score according to the hysteresis evaluation result and the compression stability evaluation result.

[0080] Specifically, a certain hysteresis phenomenon usually occurs in the pressing action, that is, the time difference between the pressing depth and the rebound during the pressing process. An appropriate pressing delay (that is, the interval or pause between two pressings) helps to ensure sufficient rebound, ensuring that the heart can be fully filled and maintain effective circulation. Therefore, by analyzing the displacement value and rebound value in the displacement data, the intensity of the above-mentioned hysteresis phenomenon can be evaluated, and then the quality of the pressing action in terms of rebound maintenance can be judged.

[0081] Specifically, the lag time indicator reflects the extent to which the time difference between compression and rebound meets the rebound requirement. If the difference between the displacement value of compression and the displacement value of rebound is too large, it means that the lag time is short and the compression action is too hasty, resulting in incomplete rebound and unable to meet the heart's congestion requirements. It can be considered that the compression action will affect blood flow and resuscitation effect.

[0082] The above-mentioned lag time can reflect the synchronization between compression and rebound. Based on the calculation and analysis of the lag time, the analysis terminal 13 can identify the inconsistency of the compression action, ensuring that the trainee can perform efficient cardiopulmonary resuscitation actions and improve the success rate and quality of resuscitation.

[0083] Optionally, the hysteresis evaluation results and the compression stability evaluation results are integrated by summing or weighted summing methods to establish a second compression score, which integrates factors such as compression depth, frequency, rebound, and compression stability, while also adding the influence of hysteresis, which helps to more comprehensively reflect the training status of the trainees.

[0084] In some embodiments, the analysis terminal 13 is further used for:

[0085] The second computing network is used to perform posture stability evaluation based on angle data, and a posture stability evaluation result is established as follows:

[0086]

[0087] Among them, Δθ represents the angle deviation, K represents the total amount of angle data, q represents any angle data, θ q Represents the qth angle data, θ ref is the reference standard angle, Characterizes the rate of change of angle, Characterize the qth angle change rate; establish a second pressing score according to the pressing stability evaluation result, the hysteresis evaluation result, and the posture stability evaluation result.

[0088] Specifically, the second computing network uses angle data to evaluate posture stability to more comprehensively assess the quality of the pressing action, wherein the posture stability evaluation results include angle change rate and angle deviation.

[0089] Specifically, the angle deviation Δθ is used to reflect the overall angle deviation level of angle data at multiple sampling points in time series, such as the average deviation between the multiple pressing angles of the current trainees and the reference standard angle. A smaller value of the angle deviation Δθ indicates that the posture is closer to the reference standard and has better stability.

[0090] Specifically, the angle change rate It is used to measure the degree of change of posture angle over time. The angle change rate at each time node is the time derivative of the angle at the corresponding time. is the average of multiple angle change rates. By calculating the average of all angle change rates, the dynamic stability of the posture can be reflected. A smaller value indicates a more stable angle change and better posture stability.

[0091] Specifically, the compression stability evaluation results, hysteresis evaluation results, and posture stability evaluation results are integrated by summing or weighted summing methods to establish a second compression score, thereby providing a more comprehensive quantitative evaluation result.

[0092] In some embodiments, Figure 2 As shown, the analysis terminal 13 is also used for:

[0093] The image analysis channel of the second computing network is called to perform data preprocessing on the visual data of the linkage signal, and then extract the joint position coordinates; the pressing posture of the trainee is fitted through the joint position coordinates to establish a posture fitting result; the pressing authentication is performed on the posture fitting result to establish a second pressing score.

[0094] Specifically, first, the image analysis channel of the second computing network is called, and visual data (such as video or image) is used as input. After processes including image enhancement (such as noise reduction, contrast adjustment, etc.), human body detection (such as yolo, R-CNN, etc.), bone node extraction (based on human posture estimation algorithm), etc., the bone nodes of the trainees' bodies are obtained in real time, and the three-dimensional coordinates of these joint nodes are output as joint position coordinates, which include, for example, the main joint nodes of the head, back, waist, hips, shoulders, upper arms, forearms, palms, thighs, calves, and feet.

[0095] Specifically, the pressing posture of the trainee is fitted by extracting the joint position coordinates. First, the extracted joint coordinates are matched with the joint coordinates of the standard posture model; then, the trainee's posture is reconstructed according to the joint coordinates; then, the deviation between the reconstructed posture and the standard posture is calculated, and the similarity score is calculated, and the output is the posture fitting result; wherein, the posture fitting is achieved by calculating the spatial geometric relationship (such as angle, distance, etc.) of the joint coordinates.

[0096] Furthermore, the posture fitting results are subjected to pressing certification to verify whether the trainee's pressing action meets the standards, including whether the posture meets the standards (such as pressing angle, arm position, etc.), whether the action is smooth (such as whether the changes in pressing frequency and depth meet the requirements), and obtain quantitative pressing certification, which is integrated into the above-mentioned second pressing score.

[0097] In some embodiments, the analysis terminal 13 is further used for:

[0098] A pressing key node is established based on the pressing signal; and a time-series weighted calculation of the posture fitting result is performed through the pressing key node to complete the pressing authentication.

[0099] Specifically, by analyzing the standard posture, multiple posture control points in the pressing action are determined and output as pressing key nodes. Exemplarily, pressing key nodes involve multiple action feature points that can mark the key moments of the pressing action (such as the start, end, maximum depth, etc. of pressing).

[0100] Specifically, the pressing action is a dynamic process, and the pressing key nodes provide time marking points for weighted calculation of the posture fitting results. Different pressing key nodes (i.e., different stages of the pressing action) correspond to different weighted weights. Pressing authentication based on timing weighted calculation helps to accurately evaluate the overall matching degree of the posture, while reducing the complex calculation process brought by the full timing calculation.

[0101] The feedback unit 14 is used to receive the linkage analysis result and generate feedback data.

[0102] Specifically, the feedback unit 14 is used to feed back the linkage analysis results as feedback data to the trainees or training managers, so as to guide the improvement of rehabilitation movements during training.

[0103] Optionally, the feedback unit 14 compares the linkage analysis result with the historical data in the database, generates a trend graph or a progress report, and adds it to the above-mentioned feedback data to show the user's training progress.

[0104] In summary, the high-quality cardiopulmonary resuscitation training device system provided by the present invention has the following technical effects:

[0105] A pressing device is composed of a conductive film, a spring, and a silicone shell, wherein the spring is used for pressing and rebounding of the pressing device, and the conductive film is used for converting the pressure signal into an electrical signal for output; a linkage acquisition module, the linkage acquisition module is integrated with an acceleration sensor, a displacement sensor, an angle sensor, and a visual sensor, the acceleration sensor, the displacement sensor, and the angle sensor are arranged on the pressing device, and the visual sensor is arranged on the analysis terminal, and is used for collecting the pressing posture of the trainee; an analysis terminal, the analysis terminal is communicatively connected with the pressing device and the linkage acquisition module, and is used for reading the timing acquisition signals of the pressing device and the linkage acquisition module, performing linkage analysis of the timing acquisition signals through an intelligent computing model built into the analysis terminal, and establishing a linkage analysis result; a feedback unit, the feedback unit is used for receiving the linkage analysis result and generating feedback data, thereby achieving the technical effect of providing trunk posture judgment and ensuring training consistency and training quality.

[0106] It should be understood that the embodiments disclosed in the present invention and the above description can enable those skilled in the art to use the present invention to implement the present invention. At the same time, the present invention is not limited to the above-mentioned embodiments. It should be understood that those skilled in the art can still modify the technical solutions recorded in the above-mentioned embodiments, or replace some of the technical features therein by equivalents; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention, and should be included in the protection scope of the present invention.

Claims

1. A high-quality cardiopulmonary resuscitation training device system, characterized in that: The system comprises: A pressing device, the pressing device is composed of a conductive film, a spring, and a silicone shell, the spring is used for pressing and rebounding the pressing device, and the conductive film is used for converting a pressure signal into an electrical signal for output; A linkage acquisition module, wherein the linkage acquisition module integrates an acceleration sensor, a displacement sensor, an angle sensor, and a visual sensor, wherein the acceleration sensor, the displacement sensor, and the angle sensor are arranged on the pressing device, and the visual sensor is arranged on the analysis terminal, and is used to collect the pressing posture of the trainee; An analysis terminal, which is in communication with the pressing device and the linkage acquisition module, and is used to read the timing acquisition signals of the pressing device and the linkage acquisition module, perform linkage analysis of the timing acquisition signals through an intelligent computing model built into the analysis terminal, and establish linkage analysis results; A feedback unit is used to receive the linkage analysis result and generate feedback data.

2. A high-quality cardiopulmonary resuscitation training device system as claimed in claim 1, characterized in that: The analysis terminal is used for: Decomposing the time series acquisition signal to obtain a pressing signal acquired by the pressing device, wherein the pressing signal includes a pressing depth, a pressing frequency, and a pressing rebound; The first calculation network of the intelligent calculation model is called to perform the pressure signal calculation and establish a first pressure score as follows: Among them, Q represents the first pressing score, w D 、w F 、w R are the weight factors of compression depth, compression frequency and compression rebound, D min The minimum standard for compression depth, D max The highest standard for representing compression depth, D is the compression depth data, F is min The minimum standard for compression frequency, F max The highest standard for representing compression frequency, F represents compression frequency data, R min The minimum standard for characterizing the rebound depth, R max The highest standard for representing the rebound depth, R represents the rebound depth data; The first pressing score is fed back to the intelligent computing model, and after the linkage analysis is performed, a linkage analysis result is established.

3. A high-quality cardiopulmonary resuscitation training device system as claimed in claim 2, characterized in that: The analysis terminal is also used for: Performing signal decomposition on the time series acquisition signal to obtain a linkage signal acquired by the linkage acquisition module, wherein the linkage signal includes acceleration data, displacement data, angle data, and visual data; Synchronizing the linkage signal to a second computing network of the intelligent computing model, performing a press score calculation through the second computing network, and establishing a second press score; After jointly analyzing the first pressing score and the second pressing score, a linkage analysis result is established.

4. A high-quality cardiopulmonary resuscitation training device system as claimed in claim 3, characterized in that: The analysis terminal is also used for: The second computing network is used to perform a compression stability evaluation based on acceleration data, and a compression stability evaluation result is established as follows: Among them, σ a Characterizes the standard deviation of the acceleration signal, represents the mean value of acceleration, N is the total amount of acceleration data, i represents any acceleration data, a i Represents the i-th acceleration data, V a Characterizing acceleration fluctuation, and establishing a pressing stability evaluation result according to the standard deviation and the acceleration fluctuation; A second compression score is established according to the compression stability evaluation result.

5. A high-quality cardiopulmonary resuscitation training device system as claimed in claim 4, characterized in that: The analysis terminal is also used for: The second computing network is used to perform hysteresis evaluation based on displacement data, and the hysteresis evaluation result is established as follows: Among them, T lag represents the lag time, M represents the total amount of displacement data, j represents any displacement, d j Characterizes the displacement value of the jth press, r j Characterizes the rebound value of the jth press; A second compression score is established according to the hysteresis evaluation result and the compression stability evaluation result.

6. A high-quality cardiopulmonary resuscitation training device system as claimed in claim 5, characterized in that: The analysis terminal is also used for: The second computing network is used to perform posture stability evaluation based on angle data, and a posture stability evaluation result is established as follows: Among them, Δθ represents the angle deviation, K represents the total amount of angle data, q represents any angle data, θ q Represents the qth angle data, θ ref is the reference standard angle, Characterizes the rate of change of angle, Characterizes the qth angle change rate; A second pressing score is established based on the pressing stability evaluation result, the hysteresis evaluation result, and the posture stability evaluation result.

7. A high-quality cardiopulmonary resuscitation training device system as claimed in claim 3, characterized in that: The analysis terminal is also used for: Calling the image analysis channel of the second computing network to perform data preprocessing on the visual data of the linkage signal and extract the joint position coordinates; Fitting the trainee's pressing posture through the joint position coordinates to establish a posture fitting result; Perform press authentication on the posture fitting result and establish a second press score.

8. A high-quality cardiopulmonary resuscitation training device system as claimed in claim 7, characterized in that: The analysis terminal is also used for: Establishing a pressing key node based on the pressing signal; The time-series weighted calculation of the posture fitting result is performed through the pressing key nodes to complete the pressing authentication.

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