A method for adjusting the angle of an operating table based on patient pose recognition

Abstract

This invention relates to the field of medical device technology and discloses a method for adjusting the angle of an operating table based on patient pose recognition. The method includes: constructing a pose adjustment database based on an angle adjustment system; extracting a set of reference target poses from the pose adjustment database; obtaining a target selection pose and an initial pose; extracting a set of reference initial poses from the pose adjustment database; using the initial poses, identifying a target retrieval pose from the set of reference initial poses; retrieving a target pose adjustment scheme from the pose adjustment database based on the target selection pose and the target retrieval pose; driving the target operating table using the target pose adjustment scheme; acquiring and detecting vital signs based on vital sign monitoring signals; confirming that the detected vital signs are preset normal vital signs; and generating a visualized adjustment result based on the driven target operating table and a result presentation unit. The main objective of this invention is to improve the accuracy and intelligence of operating table angle adjustment.

Description

Technical Field

[0001] This invention relates to a method for adjusting the angle of an operating table based on patient posture recognition, belonging to the field of medical equipment technology. Background Technology

[0002] During surgery, adjusting the angle of the operating table is crucial for the smooth progress of the procedure and ensuring patient comfort. Correspondingly, achieving accuracy and intelligent automation in operating table angle adjustment has become an urgent problem to be solved.

[0003] Currently, the adjustment of the operating table angle mostly relies on manual adjustment.

[0004] While the above methods can adjust the angle of the operating table, they rely too heavily on personal experience, which may lead to inaccurate adjustments. Furthermore, the lack of a visualization unit for the adjusted operating table may prevent the system from assessing patient comfort, resulting in inaccuracies and a lack of intelligence in the current methods for adjusting the operating table angle. Summary of the Invention

[0005] This invention provides a method, device, and computer-readable storage medium for adjusting the angle of an operating table based on patient posture recognition. Its main purpose is to improve the accuracy and intelligence of adjusting the angle of the operating table.

[0006] To achieve the above objectives, the present invention provides a method for adjusting the angle of an operating table based on patient pose recognition, comprising:

[0007] The system receives an angle adjustment command and confirms the angle adjustment environment based on the command. The angle adjustment environment includes an angle adjustment system and a target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit.

[0008] A pose adjustment database is constructed based on the angle adjustment system. The pose adjustment database stores multiple pose adjustment scheme nodes, and the pose adjustment scheme nodes include a reference initial pose, a reference target pose, and a pose adjustment scheme.

[0009] A reference target pose set is extracted from the pose adjustment database. The pose selection unit receives the target selection pose selected by the user based on the reference target pose set. The pose recognition unit confirms the reception of the pose recognition instruction from the pose recognition unit. Based on the pose recognition instruction, the patient's initial pose is confirmed. A reference initial pose set is extracted from the pose adjustment database. Using the initial pose, the target retrieval pose is identified from the reference initial pose set.

[0010] Based on the target selection pose and the target retrieval pose, the target pose adjustment scheme is retrieved from the pose adjustment database, and the target operating table is driven by the target pose adjustment scheme.

[0011] The system confirms receipt of a vital sign monitoring command from the vital sign monitoring unit, parses the vital sign monitoring command to obtain a vital sign monitoring signal, acquires the detected vital signs based on the vital sign monitoring signal, and after confirming that the detected vital signs are preset normal vital signs, it generates a visual adjustment result based on the driven target operating table and the result presentation unit to realize the adjustment of the angle of the target operating table.

[0012] Optionally, the step of constructing a pose adjustment database based on the angle adjustment system includes:

[0013] A set of simulation models for simulating patients and a set of fitted poses for characterizing patient poses are obtained. The set of simulation models includes multiple simulation models and the set of fitted poses includes multiple fitted poses. Multiple fitting adjustment nodes are obtained by combining each of the multiple simulation models and each of the multiple fitted poses. Each fitting adjustment node includes a simulation model and a fitted pose.

[0014] For each of the multiple fit adjustment nodes, perform the following operation:

[0015] Based on the pre-confirmed target adjustment pose and fitted adjustment nodes, multiple fitted adjustment schemes are obtained. For each of the multiple fitted adjustment schemes, the following operations are performed:

[0016] The target operating table is driven based on the fitting adjustment scheme, and the pressure monitoring unit corresponding to each of the multiple first adjustment units corresponding to the target operating table and the target operating table in the drive are used to obtain the monitoring pressure timing.

[0017] The monitored pressure time series are summarized to obtain a monitored pressure time series set, and a pose adjustment database is obtained based on the monitored pressure time series set.

[0018] Optionally, the step of obtaining the pose adjustment database based on the monitored pressure time series includes:

[0019] A set of evaluation indicators is obtained to evaluate the fitting adjustment scheme. Initial evaluation values ​​are obtained based on the set of evaluation indicators, the monitoring pressure time series set, and the pre-constructed analytic hierarchy process. The initial evaluation values ​​correspond one-to-one with the fitting adjustment scheme.

[0020] The initial evaluation values ​​are summarized to obtain an initial evaluation value set. Using a preset screening evaluation threshold, a target evaluation value set is extracted from the initial evaluation value set. The target evaluation value set includes multiple target evaluation values, and all target evaluation values ​​are greater than or equal to the screening evaluation threshold.

[0021] For each target evaluation value in the target evaluation value set, perform the following operation:

[0022] The comprehensive evaluation value is calculated based on the target evaluation value, and the calculation formula is as follows:

[0023]

[0024] Where Z represents the comprehensive evaluation value, α, β, and γ are preset coefficients, p represents the target evaluation value, t represents the time required to fit the adjustment scheme corresponding to the target evaluation value, h represents the height that the first adjustment unit needs to adjust, j represents the angle that the first adjustment unit needs to adjust, n represents the monitoring pressure of the first adjustment unit during adjustment, m represents the number of first adjustment units required to adjust the pose of the simulation model in the fitting adjustment scheme corresponding to the target evaluation value, and f(h,j,n) i Let i represent the energy consumption function of the i-th first adjustment unit among m first adjustment units when performing pose adjustment;

[0025] The comprehensive evaluation values ​​are summarized to obtain a comprehensive evaluation value set. The target adjustment scheme is determined using the comprehensive evaluation value set. The target adjustment scheme is the fitting adjustment scheme corresponding to the smallest comprehensive evaluation value in the comprehensive evaluation value set.

[0026] Based on the target adjustment scheme, a pose adjustment database was identified.

[0027] Optionally, the step of identifying the pose adjustment database based on the target adjustment scheme includes:

[0028] Based on the fitted pose corresponding to the target adjustment scheme, obtain the pose image and fitted pose vector, and identify the pose features in the pose image, including pose length and pose width.

[0029] The initial monitoring pressure set is obtained by using the monitoring pressure time series set corresponding to the target adjustment scheme. The initial monitoring pressure set is the set of the first monitoring pressure in each monitoring pressure time series in the monitoring pressure time series set.

[0030] By associating the fitted pose, the target adjusted pose, the fitted pose vector, the pose features, the initial monitored pressure set, and the target adjustment scheme, a pose adjustment node is obtained.

[0031] By summarizing the pose adjustment nodes, a pose adjustment database is obtained.

[0032] Optionally, the step of identifying the target retrieval pose from a reference initial pose set using the initial pose includes:

[0033] Based on the initial pose, an initial pose vector is obtained, and the patient's initial features and reference monitoring pressure set are obtained. The initial features include feature length and feature width, and the reference monitoring pressure set includes multiple reference monitoring pressures. The reference monitoring pressures in the reference monitoring pressure set are sorted in descending order of reference monitoring pressure to obtain a reference monitoring pressure sequence.

[0034] By associating the initial pose vector, initial features, and reference monitoring pressure sequence, the retrieved pose node is obtained;

[0035] For each reference initial pose in the reference initial pose set, perform the following operation:

[0036] The fitted pose vector, pose features, and initial monitoring pressure set corresponding to the reference initial pose are identified in the pose adjustment database to obtain the identified pose node. The initial monitoring pressure sequence is obtained based on the initial monitoring pressure set. The identified pose node is updated using the initial monitoring pressure sequence to obtain the matching pose node. The node similarity is calculated using the pre-constructed pose similarity formula, the matching pose node, and the retrieved pose node.

[0037] The node similarities are summarized to obtain a node similarity set. The target retrieval pose is determined based on the node similarity set, wherein the target retrieval pose is the reference initial pose corresponding to the node with the largest similarity in the node similarity set.

[0038] Optionally, the pose similarity formula is as follows:

[0039]

[0040] Where X represents node similarity, ω1, ω2, ... ω3 are all preset coefficients. CS() represents the calculation of cosine similarity, DS() represents the calculation of Euclidean distance, W0 and W1 represent the initial pose vector corresponding to the retrieved pose node and the fitted pose vector corresponding to the matched pose node, respectively. p0 and p1 represent the reference monitoring pressure sequence corresponding to the retrieved pose node and the initial monitoring pressure sequence corresponding to the matched pose node, respectively. z0 and z1 represent the initial feature corresponding to the retrieved pose node and the pose feature corresponding to the matched pose node, respectively.

[0041] Optionally, the step of acquiring and detecting vital signs based on the vital sign monitoring signals includes:

[0042] Based on the vital sign monitoring signal, a vital sign echo signal is obtained. An intermediate frequency signal is then obtained and analyzed using the vital sign monitoring signal and the vital sign echo signal to acquire a frequency identification model set for frequency identification. This set includes multiple frequency identification models. The following operations are performed on each frequency identification model in the set:

[0043] The initial vital signs are obtained by using the frequency identification model and analyzing the intermediate frequency signal. The initial vital signs include the initial respiratory rate and the initial heart rate. The initial respiratory rate and the initial heart rate in the initial vital signs are summarized to obtain the initial respiratory rate set and the initial heart rate set. The initial respiratory rate set includes multiple initial respiratory rates, and the initial respiratory rates correspond one-to-one with the frequency identification model.

[0044] The respiratory frequency variance is obtained based on the initial respiratory frequency set, wherein the respiratory frequency variance is the variance of multiple initial respiratory frequencies in the initial respiratory frequency set, and the respiratory frequency variance is compared with a preset frequency variance threshold.

[0045] If the respiratory rate variance is greater than or equal to the frequency variance threshold, then the respiratory rate mean is obtained based on the initial respiratory rate set, wherein the respiratory rate mean is the average of multiple initial respiratory rates in the initial respiratory rate set, and the following operation is performed on each initial respiratory rate in the initial respiratory rate set:

[0046] Calculate the absolute difference between the initial respiratory rate and the mean respiratory rate to obtain the screening difference. Summarize the screening differences to obtain a screening difference set. Sort the screening differences in the screening difference set in descending order to obtain a screening difference sequence. Use the screening difference sequence to identify the target respiratory rate, where the target respiratory rate is the initial respiratory rate corresponding to the first screening difference in the screening difference sequence. Remove the target respiratory rate from the initial respiratory rate set to obtain an updated respiratory rate set. Use the updated respiratory rate set as the initial respiratory rate set and return to the step of obtaining the respiratory rate variance based on the initial respiratory rate set until the respiratory rate variance is less than the frequency variance threshold.

[0047] If the respiratory rate variance is less than the frequency variance threshold, the detected respiratory rate is obtained based on the initial respiratory rate set, wherein the detected respiratory rate is the average of multiple initial respiratory rate sets in the initial respiratory rate set.

[0048] Based on the initial heart rate set, the detected heart rate is obtained, and the detected respiratory rate and the detected heart rate are correlated to obtain the detected vital signs.

[0049] Optionally, obtaining the initial pose vector based on the initial pose includes:

[0050] A set of joint nodes is identified in the initial pose, wherein the set of joint nodes includes multiple joint nodes, and the following operation is performed on each joint node in the set of joint nodes:

[0051] Based on the joint nodes, obtain the joint three-dimensional coordinates and identification ordinal numbers, and use the joint three-dimensional coordinates and identification ordinal numbers to identify the joint nodes to obtain the identified joint nodes;

[0052] The identified joint nodes are summarized to obtain an identified joint node set. The identified joint nodes in the identified joint node set are sorted in ascending order of their identifier ordinal numbers to obtain an identified joint node sequence.

[0053] Extract the initial joint nodes sequentially from the identified joint node sequence, and perform the following operations on the extracted initial joint nodes:

[0054] Using the initial joint node, a target joint node is identified in the identified joint node sequence, wherein the target joint node is adjacent to and lags behind the initial joint node;

[0055] Calculate the Euclidean distance between the three-dimensional coordinates of the initial joint node and the three-dimensional coordinates of the target joint node to obtain the joint spacing. Summarize the joint spacings to obtain a set of joint spacings. Sort the joint spacings in the set according to the time corresponding to the acquisition of the joint spacings from earliest to latest to obtain a joint spacing sequence. Fit the joint spacing sequence to the initial pose vector, wherein the initial pose vector is as follows:

[0056] W0 = {j1,j2,j3,j...} a ,…j b}

[0057] Among them, j1, j2, j3, j a ...

[0058] Optionally, confirming that the detected vital signs are preset normal vital signs includes:

[0059] Obtain the reference heart rate range and the reference respiratory rate range. When the detected respiratory rate corresponding to the detected vital sign does not belong to the reference respiratory rate range and the detected heart rate corresponding to the detected vital sign does not belong to the reference heart rate range, the pre-constructed vital sign warning signal is sent to the initiator of the angle adjustment command.

[0060] Otherwise, confirm that the detected vital signs are normal.

[0061] Optionally, the visualization adjustment results generated based on the driven target operating table and the result presentation unit include:

[0062] The visualization parameters of each of the multiple first adjustment units corresponding to the target operating table after driving are obtained. The visualization parameters include adjustment height, initial size, initial position, adjustment angle and monitoring pressure. The visualization parameters are summarized to obtain a visualization parameter set.

[0063] The system confirms receipt of a visualization instruction from the result presentation unit, obtains a patient representation model based on the visualization instruction, and generates a visualization model set using the visualization parameter set. The visualization model set includes multiple visualization models marked with monitoring pressure, and each visualization model marked with monitoring pressure corresponds one-to-one with the first adjustment unit.

[0064] Based on the target selection pose, the constraint set is identified, and the visualization adjustment result is constructed using the constraint set, the visualization model set, and the patient representation model.

[0065] To address the above problems, the present invention also provides an electronic device, the electronic device comprising:

[0066] At least one processor; and,

[0067] A memory communicatively connected to the at least one processor; wherein,

[0068] The memory stores instructions that can be executed by the at least one processor to implement the above-described method for adjusting the operating table angle based on patient pose recognition.

[0069] To address the aforementioned problems, the present invention also provides a computer-readable storage medium storing at least one instruction, which is executed by a processor in an electronic device to implement the above-described operating table angle adjustment method based on patient pose recognition.

[0070] Compared to the problems described in the background art, the present invention first receives an angle adjustment command, and confirms the angle adjustment environment based on the angle adjustment command. The angle adjustment environment includes an angle adjustment system and a target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit. Therefore, the embodiments of the present invention set the target operating table as multiple first adjustment units capable of independent height and angle adjustment, thus laying the foundation for improving the accuracy of angle adjustment of the operating table. Each first adjustment unit is equipped with a pressure monitoring unit, which can monitor the pressure borne by the first adjustment unit in real time, thereby providing timely feedback on the force on different parts of the patient. This improves the intelligence level of the target operating table in the embodiments of the present invention during angle adjustment. The present invention constructs a pose adjustment database based on the angle adjustment system. The pose adjustment database stores multiple pose adjustment scheme nodes, and the pose adjustment scheme nodes include a reference initial pose, a reference target pose, and a pose adjustment scheme. Therefore, the present invention improves the intelligence level of the target operating table during angle adjustment. Before angle adjustment, multiple posture adjustment schemes were formulated, and each scheme considered the patient's relevant characteristics. This ensured that the formulated schemes, while meeting the requirements for accurate angle adjustment, also considered patient comfort, the time and energy required for angle adjustment, thereby improving the intelligence level of angle adjustment for the target operating table. This invention confirms receipt of vital sign monitoring commands from the vital sign monitoring unit, parses the commands to obtain vital sign monitoring signals, acquires and detects vital signs based on these signals, and confirms that the detected vital signs are those of a preset normal living being. After the procedure, a visual adjustment result is generated based on the driven target operating table and the result presentation unit, realizing the adjustment of the target operating table angle. It is evident that this embodiment of the invention considers the patient's vital signs when adjusting the target operating table angle. When the patient's vital signs are in danger, it can provide timely warnings, thereby improving the intelligence level of the target operating table angle adjustment. Furthermore, it can combine the patient's actual situation and provide intuitive feedback on the angle adjustment result in a visual form, which is beneficial for reading the force on various parts of the patient, thus improving the intelligence level of the target operating table angle adjustment. Therefore, the main purpose of the operating table angle adjustment method, device, electronic device, and computer-readable storage medium based on patient pose recognition proposed in this invention is to improve the accuracy and intelligence level of operating table angle adjustment. Attached Figure Description

[0071] Figure 1 This is a flowchart illustrating an embodiment of the operating table angle adjustment method based on patient pose recognition provided by the present invention.

[0072] Figure 2 This is a schematic diagram of the structure of an electronic device that implements the operating table angle adjustment method based on patient pose recognition, according to an embodiment of the present invention.

[0073] The objectives, features, and advantages of this invention will be further explained in conjunction with the embodiments and with reference to the accompanying drawings. Detailed Implementation

[0074] It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.

[0075] This application provides a method for adjusting the angle of an operating table based on patient pose recognition. The executing entity of this method includes, but is not limited to, at least one of the following electronic devices that can be configured to execute the method provided in this application: a server, a terminal, etc. In other words, the method for adjusting the angle of an operating table based on patient pose recognition can be executed by software or hardware installed on a terminal device or a server device. The server includes, but is not limited to, a single server, a server cluster, a cloud server, or a cloud server cluster.

[0076] Example 1:

[0077] Reference Figure 1 The diagram shown is a flowchart illustrating an operating table angle adjustment method based on patient pose recognition according to an embodiment of the present invention. In this embodiment, the operating table angle adjustment method based on patient pose recognition includes:

[0078] S1. Receive an angle adjustment command and confirm the angle adjustment environment based on the angle adjustment command. The angle adjustment environment includes an angle adjustment system and a target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit.

[0079] It should be explained that the angle adjustment command is used to adjust the angle of the target operating table. The angle adjustment environment refers to the necessary environment for adjusting the angle of the target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit. For the specific application of the units, please refer to the following embodiments. Generally, before confirming the size and position of each first adjustment unit in the target operating table, multiple medical personnel can participate in setting the adjustable height, adjustable angle, and size of the first adjustment unit based on their actual clinical experience. Optionally, a lifting bracket with a self-locking function can be used as the height adjustment unit, and a screw and nut mechanism can be used as the angle adjustment unit. Other technologies can achieve the same effect, and will not be elaborated here. The height adjustment unit and the angle adjustment unit can respectively adjust the height and tilt angle of the first adjustment unit. The pressure monitoring unit is used to monitor the pressure on the first adjustment unit. Optionally, a pressure sensor can be used as the pressure monitoring unit. Other technologies can achieve the same effect.

[0080] It is understood that the main purpose of this invention is to adjust the patient's position based on the patient's actual situation, and to improve the accuracy and intelligence of the angle adjustment of the operating table.

[0081] For example, before performing surgery on a patient, the patient's attending physician, Xiao Zhang, issues the angle adjustment command and confirms the angle adjustment environment. Xiao Zhang then selects the appropriate posture in the angle adjustment system based on the required posture for the surgery and uses the target operating table to adjust the patient's posture, thereby improving the intelligence of the surgery. Furthermore, considering that the patient's posture may need to be adjusted during the surgery, Xiao Zhang can dynamically adjust the patient's posture through the target operating table without affecting the normal progress of the surgery.

[0082] S2. Construct a pose adjustment database based on the angle adjustment system. The pose adjustment database stores multiple pose adjustment scheme nodes, and the pose adjustment scheme nodes include a reference initial pose, a reference target pose, and a pose adjustment scheme.

[0083] It should be explained that the construction of the pose adjustment database based on the angle adjustment system includes:

[0084] A set of simulation models for simulating patients and a set of fitted poses for characterizing patient poses are obtained. The set of simulation models includes multiple simulation models and the set of fitted poses includes multiple fitted poses. Multiple fitting adjustment nodes are obtained by combining each of the multiple simulation models and each of the multiple fitted poses. Each fitting adjustment node includes a simulation model and a fitted pose.

[0085] For each of the multiple fit adjustment nodes, perform the following operation:

[0086] Based on the pre-confirmed target adjustment pose and fitted adjustment nodes, multiple fitted adjustment schemes are obtained. For each of the multiple fitted adjustment schemes, the following operations are performed:

[0087] The target operating table is driven based on the fitting adjustment scheme, and the pressure monitoring unit corresponding to each of the multiple first adjustment units corresponding to the target operating table and the target operating table in the drive are used to obtain the monitoring pressure timing.

[0088] The monitored pressure time series are summarized to obtain a monitored pressure time series set, and a pose adjustment database is obtained based on the monitored pressure time series set.

[0089] It should be understood that the simulation model refers to a model used to simulate the patient's weight and height. Optionally, a biomechanical solid model can be used as the simulation model. Other techniques can achieve the same effect, which will not be elaborated here. The fitted pose is used to characterize the patient's pose in the initial state. Here, the initial state refers to the patient's position on the target operating table, and the fitted pose can also characterize the patient's local features. Generally, in practical applications, it cannot be guaranteed that every patient will lie in the same position on the operating table. Therefore, the fitted pose can also characterize the position of the patient or simulation model on the operating table. For example, models with the same height and weight may express different fitted poses when representing the same body position due to different local features in the model construction. Here, local features are features used to characterize different disease states of the patient. The fitting adjustment scheme refers to the scheme composed of the relevant parameters of the first adjustment unit that needs to be adjusted when adjusting the fitted pose corresponding to the simulation model to the target adjustment pose. Here, the parameters that can be adjusted when adjusting the first adjustment unit include: height and angle. For example, if the patient is in a supine position, but this position is represented by individuals of different heights or weights, different postures can be obtained using this position. The purpose of considering posture in this invention is to improve the accuracy of angle adjustment for the target operating table.

[0090] It is understood that the multiple fitting adjustment schemes can be obtained using a pre-trained neural network model, the target adjustment pose, and the fitting adjustment nodes. Other techniques can achieve the same effect, and will not be elaborated further here. For example, the neural network model can be trained with the goal of whether the patient's adjusted pose matches the target pose. The trained neural network model can then obtain the order in which different first adjustment units are adjusted, including the height and angle required for each first adjustment unit. Generally, when using different fitting adjustment schemes, the adjusted fitted pose may differ significantly from the target adjustment pose due to the different schemes. Therefore, it is necessary to select an accurate and comfortable fitting adjustment scheme from among the multiple schemes. The specific selection process is detailed in subsequent embodiments.

[0091] Furthermore, the pressure monitoring time sequence refers to the pressure sequence obtained after monitoring the pressure on the target operating table during operation using a pressure monitoring unit. Here, only one pressure monitoring unit is used as an example; the other pressure monitoring units can achieve the same effect and will not be elaborated further. For example, a set of monitoring pressures is obtained using a preset monitoring frequency and pressure monitoring units. This set includes multiple monitoring pressures, which are then sorted according to the order in which they were acquired, from earliest to latest, to obtain the pressure monitoring time sequence.

[0092] Furthermore, the step of acquiring the pose adjustment database based on the monitored pressure time series includes:

[0093] A set of evaluation indicators is obtained to evaluate the fitting adjustment scheme. Initial evaluation values ​​are obtained based on the set of evaluation indicators, the monitoring pressure time series set, and the pre-constructed analytic hierarchy process. The initial evaluation values ​​correspond one-to-one with the fitting adjustment scheme.

[0094] The initial evaluation values ​​are summarized to obtain an initial evaluation value set. Using a preset screening evaluation threshold, a target evaluation value set is extracted from the initial evaluation value set. The target evaluation value set includes multiple target evaluation values, and all target evaluation values ​​are greater than or equal to the screening evaluation threshold.

[0095] For each target evaluation value in the target evaluation value set, perform the following operation:

[0096] The comprehensive evaluation value is calculated based on the target evaluation value, and the calculation formula is as follows:

[0097]

[0098] Where Z represents the comprehensive evaluation value, α, β, and γ are preset coefficients, p represents the target evaluation value, t represents the time required to fit the adjustment scheme corresponding to the target evaluation value, h represents the height that the first adjustment unit needs to adjust, j represents the angle that the first adjustment unit needs to adjust, n represents the monitoring pressure of the first adjustment unit during adjustment, m represents the number of first adjustment units required to adjust the pose of the simulation model in the fitting adjustment scheme corresponding to the target evaluation value, and f(h,j,n) i Let i represent the energy consumption function of the i-th first adjustment unit among m first adjustment units when performing pose adjustment;

[0099] The comprehensive evaluation values ​​are summarized to obtain a comprehensive evaluation value set. The target adjustment scheme is determined using the comprehensive evaluation value set. The target adjustment scheme is the fitting adjustment scheme corresponding to the smallest comprehensive evaluation value in the comprehensive evaluation value set.

[0100] Based on the target adjustment scheme, a pose adjustment database was identified.

[0101] It should be explained that the evaluation index is a set of indicators used to evaluate the fitting adjustment scheme in conjunction with the monitoring pressure time series. Optionally, the evaluation index set includes: safety indicators and reliability indicators, and the safety indicators include: posture naturalness and joint pressure distribution, while the reliability indicators include pose accuracy and joint angles. Joint pressure distribution and joint angles are both related to the monitoring pressure time series. Posture naturalness refers to whether the adjusted fitted pose conforms to the natural posture of the human body. For example, whether there are joints with excessive twisting or bending. Joint pressure distribution is obtained by analyzing the monitoring pressure in the monitoring pressure time series. For example, when the change trend of the monitoring pressure time series is slow and the pressure distribution is uniform, the joint pressure distribution is considered good, and therefore, a higher score is given to the joint pressure distribution in this case. Pose accuracy is used to characterize the precision between the adjusted fitted pose and the target adjusted pose. Optionally, this can be achieved by converting both the adjusted fitted pose and the target adjusted pose into vectors and then solving for the Euclidean distance between the two vectors. Other techniques can achieve the same effect, and will not be elaborated further here. Joint angles refer to the angles of the adjusted simulation model at the target joint. They are used to evaluate the stability of the adjusted simulation model in maintaining that pose. Here, the target joint refers to the joint whose pose needs to be kept unchanged, and the target joint may consist of more than one joint. For example, if the pose corresponding to the target joint is to ensure that the simulation model is in a supine position, then the joint angle can be represented by the angle between the back of the simulation model and the operating table. When the angle is 0 degrees, it indicates that the simulation model is in a supine position, while when the angle is 90 degrees, it indicates that the simulation model is in a lateral position.

[0102] It is understandable that the techniques for obtaining initial evaluation values ​​using the analytic hierarchy process (AHP), monitoring pressure time series sets, and evaluation index sets are existing technologies and will not be elaborated upon here. Using the aforementioned screening evaluation threshold, identifying the target evaluation value set within the initial evaluation value set involves eliminating fitting adjustment schemes that do not meet the expected results when adjusting the pose of the simulation model, thereby retaining fitting adjustment schemes that meet the expected results. Generally, when adjusting the fitted pose to the target adjustment pose, there may be more than one fitting adjustment scheme. Therefore, it is necessary to identify the best fitting adjustment scheme from among the multiple fitting adjustment schemes that meet the requirements. Here, the best adjustment scheme refers to the target adjustment scheme. In this embodiment of the invention, the time and energy consumption required for pose adjustment are also considered when calculating the comprehensive evaluation value, thereby improving the timeliness and intelligence of the target adjustment scheme in response. Optionally, a support vector regression algorithm is used to construct the energy consumption function for monitoring pressure, height, and angle. Other techniques can achieve the same effect and will not be elaborated upon here.

[0103] It should be explained that the step of confirming the pose adjustment database based on the target adjustment scheme includes:

[0104] Based on the fitted pose corresponding to the target adjustment scheme, obtain the pose image and fitted pose vector, and identify the pose features in the pose image, including pose length and pose width.

[0105] The initial monitoring pressure set is obtained by using the monitoring pressure time series set corresponding to the target adjustment scheme. The initial monitoring pressure set is the set of the first monitoring pressure in each monitoring pressure time series in the monitoring pressure time series set.

[0106] By associating the fitted pose, the target adjusted pose, the fitted pose vector, the pose features, the initial monitored pressure set, and the target adjustment scheme, a pose adjustment node is obtained.

[0107] By summarizing the pose adjustment nodes, a pose adjustment database is obtained.

[0108] Understandably, a pose image refers to an image fitted to the pose. A fitted pose vector refers to the vector corresponding to the fitted pose. The pose length and pose width express the body shape characteristics of the simulated model. Optionally, a target recognition algorithm and the pose image are used to obtain a target bounding box, with the length of the target bounding box being the pose length and the width of the target bounding box being the pose width. The target bounding box refers to the smallest rectangular box containing the simulated model in the pose image, and the technique of obtaining the target bounding box using a target recognition algorithm is existing technology and will not be elaborated here. The method for obtaining the fitted pose vector is the same as the method for obtaining the initial pose vector and will not be elaborated here.

[0109] For example, if there are 5 monitoring pressure time series in the monitoring pressure time series set, and the first monitoring pressure in each of the 5 monitoring pressure time series is 230N, 240N, 250N, 245N and 300N respectively, then the initial monitoring pressure set includes 230N, 240N, 250N, 245N and 300N.

[0110] It should be explained that the initial reference pose and the fitted pose are defined the same way, and will not be repeated here. The target reference pose and the target adjustment pose are defined the same way, and will not be repeated here. The pose adjustment scheme is defined the same way as the target adjustment scheme, and will not be repeated here.

[0111] S3. Extract a reference target pose set from the pose adjustment database, receive the target selection pose selected by the user based on the reference target pose set using the pose selection unit, confirm receipt of the pose recognition instruction from the pose recognition unit, confirm the patient's initial pose based on the pose recognition instruction, extract a reference initial pose set from the pose adjustment database, and identify the target retrieval pose from the reference initial pose set using the initial pose.

[0112] It should be explained that the step of identifying the target retrieval pose from the reference initial pose set using the initial pose includes:

[0113] Based on the initial pose, an initial pose vector is obtained, and the patient's initial features and reference monitoring pressure set are obtained. The initial features include feature length and feature width, and the reference monitoring pressure set includes multiple reference monitoring pressures. The reference monitoring pressures in the reference monitoring pressure set are sorted in descending order of reference monitoring pressure to obtain a reference monitoring pressure sequence.

[0114] By associating the initial pose vector, initial features, and reference monitoring pressure sequence, the retrieved pose node is obtained;

[0115] For each reference initial pose in the reference initial pose set, perform the following operation:

[0116] The fitted pose vector, pose features, and initial monitoring pressure set corresponding to the reference initial pose are identified in the pose adjustment database to obtain the identified pose node. The initial monitoring pressure sequence is obtained based on the initial monitoring pressure set. The identified pose node is updated using the initial monitoring pressure sequence to obtain the matching pose node. The node similarity is calculated using the pre-constructed pose similarity formula, the matching pose node, and the retrieved pose node.

[0117] The node similarities are summarized to obtain a node similarity set. The target retrieval pose is determined based on the node similarity set, wherein the target retrieval pose is the reference initial pose corresponding to the node with the largest similarity in the node similarity set.

[0118] Furthermore, the method for obtaining the reference monitoring pressure set is the same as the method for obtaining the initial monitoring pressure set, and will not be repeated here. The method for obtaining the initial monitoring pressure sequence using the initial monitoring pressure set is the same as the method for obtaining the reference monitoring pressure sequence using the reference monitoring pressure set, and will not be repeated here. Updating the identification pose node using the initial monitoring pressure sequence means updating the initial monitoring pressure set in the identification pose node to the initial monitoring pressure sequence.

[0119] Understandably, the pose similarity formula is as follows:

[0120]

[0121] Where X represents node similarity, ω1, ω2, ... ω3 are all preset coefficients. CS() represents the calculation of cosine similarity, DS() represents the calculation of Euclidean distance, W0 and W1 represent the initial pose vector corresponding to the retrieved pose node and the fitted pose vector corresponding to the matched pose node, respectively. P0 and P1 represent the reference monitoring pressure sequence corresponding to the retrieved pose node and the initial monitoring pressure sequence corresponding to the matched pose node, respectively. z0 and z1 represent the initial feature corresponding to the retrieved pose node and the pose feature corresponding to the matched pose node, respectively.

[0122] Furthermore, the reference monitoring pressure sequence or the initial monitoring pressure sequence is not only related to the patient's pose, but may also be related to factors such as the patient's habits and physical characteristics. Therefore, the number of reference monitoring pressures in the reference monitoring pressure sequence may not be the same as the number of initial monitoring pressures in the initial monitoring pressure sequence. Here, different coefficients are used to improve the accuracy of node similarity calculation. Generally, calculations cannot be performed when two vectors have different dimensions. Therefore, before calculating two vectors with different dimensions, it is necessary to unify the dimensions of the two vectors. In this embodiment of the invention, zero-padding is used to unify the dimensions of the two vectors. Other techniques can achieve the same effect, and will not be elaborated further here. The definition of the initial feature is consistent with that of the pose feature, and will not be elaborated further here. The definition of the initial pose is the same as that of the reference initial pose, and will not be elaborated further here.

[0123] S4. Based on the target selection pose and the target retrieval pose, retrieve the target pose adjustment scheme from the pose adjustment database, and use the target pose adjustment scheme to drive the target operating table.

[0124] Understandably, the target selection pose and the target retrieval pose are the patient's initial pose and the pose that the patient needs to maintain, respectively. Generally, the target retrieval pose also considers factors such as the patient's body shape. Therefore, by using the target retrieval pose and the target selection pose, a target pose adjustment scheme that better meets the adjustment needs can be retrieved from the pose adjustment database. For example, when a patient has a disability, their initial pose may not represent their body shape characteristics. Therefore, by combining the initial monitoring pressure sequence and initial characteristics used to represent the patient's body shape distribution, a target pose adjustment scheme that better matches the patient's body shape can be accurately retrieved from the pose adjustment database, thereby improving the accuracy and intelligence of the angle adjustment of the operating table. Driving the target operating table using the target pose adjustment scheme means adjusting the first adjustment unit in the target operating table to the required height and angle according to the height and angle required for each first adjustment unit in the target pose adjustment scheme.

[0125] S5. Confirm receipt of vital sign monitoring instructions from the vital sign monitoring unit, parse the vital sign monitoring instructions to obtain vital sign monitoring signals, acquire detected vital signs based on the vital sign monitoring signals, and after confirming that the detected vital signs are preset normal vital signs, generate visual adjustment results based on the driven target operating table and result presentation unit to realize the adjustment of the angle of the target operating table.

[0126] Furthermore, the acquisition and detection of vital signs based on the vital sign monitoring signals includes:

[0127] Based on the vital sign monitoring signal, a vital sign echo signal is obtained. An intermediate frequency signal is then obtained and analyzed using the vital sign monitoring signal and the vital sign echo signal to acquire a frequency identification model set for frequency identification. This set includes multiple frequency identification models. The following operations are performed on each frequency identification model in the set:

[0128] The initial vital signs are obtained by using the frequency identification model and analyzing the intermediate frequency signal. The initial vital signs include the initial respiratory rate and the initial heart rate. The initial respiratory rate and the initial heart rate in the initial vital signs are summarized to obtain the initial respiratory rate set and the initial heart rate set. The initial respiratory rate set includes multiple initial respiratory rates, and the initial respiratory rates correspond one-to-one with the frequency identification model.

[0129] The respiratory frequency variance is obtained based on the initial respiratory frequency set, wherein the respiratory frequency variance is the variance of multiple initial respiratory frequencies in the initial respiratory frequency set, and the respiratory frequency variance is compared with a preset frequency variance threshold.

[0130] If the respiratory rate variance is greater than or equal to the frequency variance threshold, then the respiratory rate mean is obtained based on the initial respiratory rate set, wherein the respiratory rate mean is the average of multiple initial respiratory rates in the initial respiratory rate set, and the following operation is performed on each initial respiratory rate in the initial respiratory rate set:

[0131] Calculate the absolute difference between the initial respiratory rate and the mean respiratory rate to obtain the screening difference. Summarize the screening differences to obtain a screening difference set. Sort the screening differences in the screening difference set in descending order to obtain a screening difference sequence. Use the screening difference sequence to identify the target respiratory rate, where the target respiratory rate is the initial respiratory rate corresponding to the first screening difference in the screening difference sequence. Remove the target respiratory rate from the initial respiratory rate set to obtain an updated respiratory rate set. Use the updated respiratory rate set as the initial respiratory rate set and return to the step of obtaining the respiratory rate variance based on the initial respiratory rate set until the respiratory rate variance is less than the frequency variance threshold.

[0132] If the respiratory rate variance is less than the frequency variance threshold, the detected respiratory rate is obtained based on the initial respiratory rate set, wherein the detected respiratory rate is the average of multiple initial respiratory rate sets in the initial respiratory rate set.

[0133] Based on the initial heart rate set, the detected heart rate is obtained, and the detected respiratory rate and the detected heart rate are correlated to obtain the detected vital signs.

[0134] It should be explained that the vital sign monitoring signal refers to the signal used to monitor the patient's vital signs. Optionally, the vital sign monitoring signal is the transmitted signal of a millimeter radar. The vital sign echo signal refers to the echo signal corresponding to the vital sign monitoring signal. The analyzed intermediate frequency signal refers to the intermediate frequency signal of the vital sign monitoring signal and the vital sign echo signal. The methods of obtaining the vital sign echo signal using the vital sign monitoring signal and obtaining the analyzed intermediate frequency signal using the vital sign monitoring signal and the vital sign echo signal are existing technologies and will not be elaborated here. The frequency recognition model refers to the model or algorithm used to identify the patient's heart rate and respiratory rate. Optionally, the variational mode decomposition algorithm is used as the frequency recognition model. Other technologies can achieve the same effect and will not be elaborated here. The driven target operating table refers to the target operating table after angle adjustment.

[0135] Generally speaking, since the probability of a frequency identification model correctly identifying heart rate and respiratory rate is not 100%, analyzing the results of multiple frequency identification models can improve the accuracy of the obtained patient heart rate and respiratory rate. When the respiratory rate variance is greater than or equal to the frequency variance threshold, it indicates that there is a large error in a portion of the initial respiratory rates obtained using the frequency identification model set. This error may be caused by misidentification by the frequency identification model.

[0136] Furthermore, a larger screening difference indicates a greater difference between the initial respiratory frequency corresponding to that screening difference and the remaining initial respiratory frequencies in the initial respiratory frequency set. Therefore, the initial respiratory frequency corresponding to that screening difference is considered an incorrectly identified initial respiratory frequency. When the respiratory frequency variance is less than the frequency variance threshold, it indicates that the initial respiratory frequencies in the initial respiratory frequency set are relatively consistent. Therefore, obtaining the detected respiratory frequency at this time can improve the accuracy of the obtained detected respiratory frequency. The method for obtaining the detected heart rate is the same as the method for obtaining the detected respiratory rate, and will not be described again here.

[0137] It is understood that obtaining the initial pose vector based on the initial pose includes:

[0138] A set of joint nodes is identified in the initial pose, wherein the set of joint nodes includes multiple joint nodes, and the following operation is performed on each joint node in the set of joint nodes:

[0139] Based on the joint nodes, obtain the joint three-dimensional coordinates and identification ordinal numbers, and use the joint three-dimensional coordinates and identification ordinal numbers to identify the joint nodes to obtain the identified joint nodes;

[0140] The identified joint nodes are summarized to obtain an identified joint node set. The identified joint nodes in the identified joint node set are sorted in ascending order of their identifier ordinal numbers to obtain an identified joint node sequence.

[0141] Extract the initial joint nodes sequentially from the identified joint node sequence, and perform the following operations on the extracted initial joint nodes:

[0142] Using the initial joint node, a target joint node is identified in the identified joint node sequence, wherein the target joint node is adjacent to and lags behind the initial joint node;

[0143] Calculate the Euclidean distance between the three-dimensional coordinates of the initial joint node and the three-dimensional coordinates of the target joint node to obtain the joint spacing. Summarize the joint spacings to obtain a set of joint spacings. Sort the joint spacings in the set according to the time corresponding to the acquisition of the joint spacings from earliest to latest to obtain a joint spacing sequence. Fit the joint spacing sequence to the initial pose vector, wherein the initial pose vector is as follows:

[0144] W0 = {j1,j2,j3,j...} a ,…j b}

[0145] Among them, j1, j2, j3, j a ...

[0146] It should be understood that a joint node refers to a node used to characterize the position of a patient's joints. For example, the patient's knees, head, etc., are all joint nodes. Optionally, a set of joint nodes can be identified in the initial pose using a pre-trained neural network model. Joint 3D coordinates refer to the three-dimensional coordinates used to characterize the joint nodes. The method for obtaining joint 3D coordinates is the same as the method for obtaining joint nodes, and will not be repeated here. The identifier ordinal number refers to a numerical value used to characterize the order of each joint node in the joint node set. For example, if three different joint nodes are identified in the initial pose, the identifier ordinal number can be obtained according to the order from top to bottom and from left to right. The identifier ordinal number corresponding to the joint node located in the upper left corner of the three different joint nodes is 1, and the identifier ordinal number corresponding to the joint node located in the lower right corner of the three different joint nodes is 3.

[0147] For example, if the identifier joint node sequence includes 4 identifier joint nodes, then the first initial joint node extracted is the first identifier joint node in the identifier joint node sequence, and the target joint node identified using the initial joint node is the second identifier joint node in the identifier joint node sequence. The first joint spacing in the joint spacing sequence is the Euclidean distance between the three-dimensional coordinates of the joint corresponding to the first identifier joint node and the three-dimensional coordinates of the joint corresponding to the second identifier joint node.

[0148] Furthermore, confirming that the detected vital signs are preset normal vital signs includes:

[0149] Obtain the reference heart rate range and the reference respiratory rate range. When the detected respiratory rate corresponding to the detected vital sign does not belong to the reference respiratory rate range and the detected heart rate corresponding to the detected vital sign does not belong to the reference heart rate range, the pre-constructed vital sign warning signal is sent to the initiator of the angle adjustment command.

[0150] Otherwise, confirm that the detected vital signs are normal.

[0151] It should be explained that the reference heart rate range refers to the range of normal heart rate, and the reference respiratory rate range refers to the range of normal respiratory rate. Vital signs warning signals are signals used to indicate abnormal vital signs in the patient on the target operating table. Normal vital signs confirm that the patient's vital signs are normal.

[0152] Understandably, the visualization adjustment results generated based on the driven target operating table and the result presentation unit include:

[0153] The visualization parameters of each of the multiple first adjustment units corresponding to the target operating table after driving are obtained. The visualization parameters include adjustment height, initial size, initial position, adjustment angle and monitoring pressure. The visualization parameters are summarized to obtain a visualization parameter set.

[0154] The system confirms receipt of a visualization instruction from the result presentation unit, obtains a patient representation model based on the visualization instruction, and generates a visualization model set using the visualization parameter set. The visualization model set includes multiple visualization models marked with monitoring pressure, and each visualization model marked with monitoring pressure corresponds one-to-one with the first adjustment unit.

[0155] Based on the target selection pose, the constraint set is identified, and the visualization adjustment result is constructed using the constraint set, the visualization model set, and the patient representation model.

[0156] It should be explained that "adjustment height" and "adjustment angle" refer to the height and angle adjusted when performing height and angle adjustments on the first adjustment unit, respectively. "Initial size" refers to the dimensions of the first adjustment unit, including but not limited to its length, width, and height. "Initial position" refers to the position of the first adjustment unit on the target operating table. This initial position can be represented using three-dimensional coordinates; other techniques can achieve the same effect, which will not be elaborated further here. "Monitoring pressure" refers to the pressure monitored by the pressure monitoring unit in the first adjustment unit. "Patient representation model" refers to a model used to represent the patient's posture. Optionally, the patient representation model can be obtained through three-dimensional reconstruction technology. The "visualization model" refers to a model generated using the adjustment height, adjustment angle, initial position, and initial size, and this model is used to represent the first adjustment unit. The purpose of labeling the monitoring pressure on the visualization model is to present the force distribution on different parts of the patient, and then, based on the patient's actual sensations, adjust the angle of the target operating table. For example, if the monitoring pressure corresponding to the visualization model is 200N, then the visualization model will be labeled as "200-visualization model." Other techniques can achieve the same effect, which will not be elaborated further here. A constraint set refers to a collection of constraints used to constrain the visualization model and the patient representation model. This constraint set includes, but is not limited to, fixed and parallel conditions. Generally, each first adjustment unit corresponds to a visualization model, and different first adjustment units have different positions when in contact with the patient. For example, different first adjustment units may contact the patient's back or buttocks, respectively. Constructing the visualization adjustment result using the constraint set, the visualization model set, and the patient representation model means assembling the visualization model and the patient representation model from the constraint set to generate a three-dimensional model that represents the patient's pose on the target operating table, the contact between the first adjustment unit and the patient, and the force exerted on the first adjustment unit. This three-dimensional model is the visualization adjustment result. Here, the three-dimensional model of the contact between the first adjustment unit and the patient and the force exerted on the first adjustment unit is represented by the height, angle, and monitored pressure of the first adjustment unit.

[0157] Compared to the problems described in the background art, the present invention first receives an angle adjustment command, and confirms the angle adjustment environment based on the angle adjustment command. The angle adjustment environment includes an angle adjustment system and a target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit. Therefore, the embodiments of the present invention set the target operating table as multiple first adjustment units capable of independent height and angle adjustment, thus laying the foundation for improving the accuracy of angle adjustment of the operating table. Each first adjustment unit is equipped with a pressure monitoring unit, which can monitor the pressure borne by the first adjustment unit in real time, thereby providing timely feedback on the force on different parts of the patient. This improves the intelligence level of the target operating table in the embodiments of the present invention during angle adjustment. The present invention constructs a pose adjustment database based on the angle adjustment system. The pose adjustment database stores multiple pose adjustment scheme nodes, and the pose adjustment scheme nodes include a reference initial pose, a reference target pose, and a pose adjustment scheme. Therefore, the present invention improves the intelligence level of the target operating table during angle adjustment. Before angle adjustment, multiple posture adjustment schemes were formulated, and each scheme considered the patient's relevant characteristics. This ensured that the formulated schemes, while meeting the requirements for accurate angle adjustment, also considered patient comfort, the time and energy required for angle adjustment, thereby improving the intelligence level of angle adjustment for the target operating table. This invention confirms receipt of vital sign monitoring commands from the vital sign monitoring unit, parses the commands to obtain vital sign monitoring signals, acquires and detects vital signs based on these signals, and confirms that the detected vital signs are those of a preset normal living being. After the procedure, a visual adjustment result is generated based on the driven target operating table and the result presentation unit, realizing the adjustment of the target operating table angle. It is evident that this embodiment of the invention considers the patient's vital signs when adjusting the target operating table angle. When the patient's vital signs are in danger, it can provide timely warnings, thereby improving the intelligence level of the target operating table angle adjustment. Furthermore, it can combine the patient's actual situation and provide intuitive feedback on the angle adjustment result in a visual form, which is beneficial for reading the force on various parts of the patient, thus improving the intelligence level of the target operating table angle adjustment. Therefore, the main purpose of the operating table angle adjustment method, device, electronic device, and computer-readable storage medium based on patient pose recognition proposed in this invention is to improve the accuracy and intelligence level of operating table angle adjustment.

[0158] Example 2:

[0159] like Figure 2The diagram shown is a schematic representation of an electronic device for implementing a method for adjusting the angle of an operating table based on patient pose recognition, according to an embodiment of the present invention.

[0160] The electronic device 1 may include a processor 10, a memory 11, a bus 12 and a communication interface 13, and may also include a computer program stored in the memory 11 and executable on the processor 10, such as an operating table angle adjustment program based on patient pose recognition.

[0161] The memory 11 includes at least one type of readable storage medium, such as flash memory, portable hard drive, multimedia card, card-type memory (e.g., SD or DX memory), magnetic memory, magnetic disk, optical disk, etc. In some embodiments, the memory 11 can be an internal storage unit of the electronic device 1, such as a portable hard drive. In other embodiments, the memory 11 can be an external storage device of the electronic device 1, such as a plug-in portable hard drive, SmartMediaCard (SMC), SecureDigital (SD) card, or FlashCard. Furthermore, the memory 11 can include both internal and external storage units of the electronic device 1. The memory 11 can be used not only to store application software and various types of data installed on the electronic device 1, such as the code for an operating table angle adjustment program based on patient posture recognition, but also to temporarily store data that has been output or will be output.

[0162] In some embodiments, the processor 10 may be composed of integrated circuits, such as a single packaged integrated circuit or multiple integrated circuits with the same or different functions, including combinations of one or more central processing units (CPUs), microprocessors, digital processing chips, graphics processors, and various control chips. The processor 10 is the control unit of the electronic device, connecting various components of the entire electronic device through various interfaces and lines. It executes programs or modules stored in the memory 11 (e.g., an operating table angle adjustment program based on patient posture recognition) and calls data stored in the memory 11 to perform various functions of the electronic device 1 and process data.

[0163] The bus can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This bus can be divided into an address bus, a data bus, a control bus, etc. The bus is configured to enable communication between the memory 11 and at least one processor 10, etc.

[0164] Figure 2 Only electronic devices with components are shown; it will be understood by those skilled in the art that... Figure 2 The structure shown does not constitute a limitation on the electronic device 1, and may include fewer or more components than shown, or combine certain components, or have different component arrangements.

[0165] For example, although not shown, the electronic device 1 may also include a power supply (such as a battery) to power various components. Preferably, the power supply can be logically connected to the at least one processor 10 through a power management device, thereby enabling functions such as charging management, discharging management, and power consumption management. The power supply may also include one or more DC or AC power supplies, recharging devices, power fault detection circuits, power converters or inverters, power status indicators, and other arbitrary components. The electronic device 1 may also include various sensors, Bluetooth modules, Wi-Fi modules, etc., which will not be described in detail here.

[0166] Furthermore, the electronic device 1 may also include a network interface. Optionally, the network interface may include a wired interface and / or a wireless interface (such as a Wi-Fi interface, a Bluetooth interface, etc.), which is typically used to establish communication connections between the electronic device 1 and other electronic devices.

[0167] Optionally, the electronic device 1 may further include a user interface, which may be a display, an input unit (such as a keyboard), or a standard wired or wireless interface. Optionally, in some embodiments, the display may be an LED display, a liquid crystal display, a touch-sensitive liquid crystal display, or an OLED (Organic Light-Emitting Diode) touchscreen. The display may also be appropriately referred to as a screen or display unit, used to display information processed in the electronic device 1 and to display a visual user interface.

[0168] It should be understood that the embodiments described are for illustrative purposes only and are not limited to this structure in the scope of the patent application.

[0169] The operating table angle adjustment program based on patient pose recognition, stored in the memory 11 of the electronic device 1, is a combination of multiple instructions. When run in the processor 10, it can achieve the following:

[0170] The system receives an angle adjustment command and confirms the angle adjustment environment based on the command. The angle adjustment environment includes an angle adjustment system and a target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit.

[0171] A pose adjustment database is constructed based on the angle adjustment system. The pose adjustment database stores multiple pose adjustment scheme nodes, and the pose adjustment scheme nodes include a reference initial pose, a reference target pose, and a pose adjustment scheme.

[0172] A reference target pose set is extracted from the pose adjustment database. The pose selection unit receives the target selection pose selected by the user based on the reference target pose set. The pose recognition unit confirms the reception of the pose recognition instruction from the pose recognition unit. Based on the pose recognition instruction, the patient's initial pose is confirmed. A reference initial pose set is extracted from the pose adjustment database. Using the initial pose, the target retrieval pose is identified from the reference initial pose set.

[0173] Based on the target selection pose and the target retrieval pose, the target pose adjustment scheme is retrieved from the pose adjustment database, and the target operating table is driven by the target pose adjustment scheme.

[0174] The system confirms receipt of a vital sign monitoring command from the vital sign monitoring unit, parses the vital sign monitoring command to obtain a vital sign monitoring signal, acquires the detected vital signs based on the vital sign monitoring signal, and after confirming that the detected vital signs are preset normal vital signs, it generates a visual adjustment result based on the driven target operating table and the result presentation unit to realize the adjustment of the angle of the target operating table.

[0175] Specifically, the processor 10's implementation method for the above instructions can be found in [reference needed]. Figures 1 to 2 The descriptions of the relevant steps in the corresponding embodiments are not repeated here.

[0176] Furthermore, if the modules / units integrated in the electronic device 1 are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. The computer-readable storage medium can be volatile or non-volatile. For example, the computer-readable medium may include: any entity or device capable of carrying the computer program code, a recording medium, a USB flash drive, a portable hard drive, a magnetic disk, an optical disk, a computer memory, or a read-only memory (ROM).

[0177] The present invention also provides a computer-readable storage medium storing a computer program, which, when executed by a processor of an electronic device, can perform the following:

[0178] The system receives an angle adjustment command and confirms the angle adjustment environment based on the command. The angle adjustment environment includes an angle adjustment system and a target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit.

[0179] A pose adjustment database is constructed based on the angle adjustment system. The pose adjustment database stores multiple pose adjustment scheme nodes, and the pose adjustment scheme nodes include a reference initial pose, a reference target pose, and a pose adjustment scheme.

[0180] A reference target pose set is extracted from the pose adjustment database. The pose selection unit receives the target selection pose selected by the user based on the reference target pose set. The pose recognition unit confirms the reception of the pose recognition instruction from the pose recognition unit. Based on the pose recognition instruction, the patient's initial pose is confirmed. A reference initial pose set is extracted from the pose adjustment database. Using the initial pose, the target retrieval pose is identified from the reference initial pose set.

[0181] Based on the target selection pose and the target retrieval pose, the target pose adjustment scheme is retrieved from the pose adjustment database, and the target operating table is driven by the target pose adjustment scheme.

[0182] The system confirms receipt of a vital sign monitoring command from the vital sign monitoring unit, parses the vital sign monitoring command to obtain a vital sign monitoring signal, acquires the detected vital signs based on the vital sign monitoring signal, and after confirming that the detected vital signs are preset normal vital signs, it generates a visual adjustment result based on the driven target operating table and the result presentation unit to realize the adjustment of the angle of the target operating table.

[0183] The modules described as separate components may or may not be physically separate. The components shown as modules may or may not be physical units; that is, they may be located in one place or distributed across multiple network units. Some or all of the modules can be selected to achieve the purpose of this embodiment according to actual needs.

[0184] Furthermore, the functional modules in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or in the form of hardware plus software functional modules.

[0185] It will be apparent to those skilled in the art that the present invention is not limited to the details of the exemplary embodiments described above, and that the present invention can be implemented in other specific forms without departing from the spirit or essential characteristics of the present invention.

[0186] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail with reference to preferred embodiments, those skilled in the art should understand that modifications or equivalent substitutions can be made to the technical solutions of the present invention without departing from the spirit and scope of the technical solutions of the present invention.

Claims

1. A method for adjusting the angle of an operating table based on patient pose recognition, characterized in that, The method includes: The system receives an angle adjustment command and confirms the angle adjustment environment based on the command. The angle adjustment environment includes an angle adjustment system and a target operating table. The angle adjustment system includes a pose selection unit, a result presentation unit, a pose recognition unit, and a vital sign monitoring unit. The target operating table includes multiple first adjustment units, and each first adjustment unit includes an angle adjustment unit, a height adjustment unit, and a pressure monitoring unit. A pose adjustment database is constructed based on the angle adjustment system. The pose adjustment database stores multiple pose adjustment scheme nodes, and the pose adjustment scheme nodes include a reference initial pose, a reference target pose, and a pose adjustment scheme. A reference target pose set is extracted from the pose adjustment database. The pose selection unit receives the target selection pose selected by the user based on the reference target pose set. The pose recognition unit confirms the reception of the pose recognition instruction from the pose recognition unit. Based on the pose recognition instruction, the patient's initial pose is confirmed. A reference initial pose set is extracted from the pose adjustment database. Using the initial pose, the target retrieval pose is identified from the reference initial pose set. The step of identifying the target retrieval pose from a reference initial pose set using the initial pose includes: Based on the initial pose, an initial pose vector is obtained, and the patient's initial features and reference monitoring pressure set are obtained. The initial features include feature length and feature width, and the reference monitoring pressure set includes multiple reference monitoring pressures. The reference monitoring pressures in the reference monitoring pressure set are sorted in descending order of reference monitoring pressure to obtain a reference monitoring pressure sequence. By associating the initial pose vector, initial features, and reference monitoring pressure sequence, the retrieved pose node is obtained; For each reference initial pose in the reference initial pose set, perform the following operation: The fitted pose vector, pose features, and initial monitoring pressure set corresponding to the reference initial pose are identified in the pose adjustment database to obtain the identified pose node. The initial monitoring pressure sequence is obtained based on the initial monitoring pressure set. The identified pose node is updated using the initial monitoring pressure sequence to obtain the matching pose node. The node similarity is calculated using the pre-constructed pose similarity formula, the matching pose node, and the retrieved pose node. The node similarities are summarized to obtain a node similarity set. The target retrieval pose is determined based on the node similarity set, wherein the target retrieval pose is the reference initial pose corresponding to the node with the largest similarity in the node similarity set. Based on the target selection pose and the target retrieval pose, the target pose adjustment scheme is retrieved from the pose adjustment database, and the target operating table is driven by the target pose adjustment scheme. The system confirms receipt of a vital sign monitoring command from the vital sign monitoring unit, parses the vital sign monitoring command to obtain a vital sign monitoring signal, acquires the detected vital signs based on the vital sign monitoring signal, and after confirming that the detected vital signs are preset normal vital signs, it generates a visual adjustment result based on the driven target operating table and the result presentation unit to realize the adjustment of the angle of the target operating table.

2. The operating table angle adjustment method based on patient pose recognition as described in claim 1, characterized in that, The construction of the pose adjustment database based on the angle adjustment system includes: A set of simulation models for simulating patients and a set of fitted poses for characterizing patient poses are obtained. The set of simulation models includes multiple simulation models and the set of fitted poses includes multiple fitted poses. Multiple fitting adjustment nodes are obtained by combining each of the multiple simulation models and each of the multiple fitted poses. Each fitting adjustment node includes a simulation model and a fitted pose. For each of the multiple fit adjustment nodes, perform the following operation: Based on the pre-confirmed target adjustment pose and fitted adjustment nodes, multiple fitted adjustment schemes are obtained. For each of the multiple fitted adjustment schemes, the following operations are performed: The target operating table is driven based on the fitting adjustment scheme, and the pressure monitoring unit corresponding to each of the multiple first adjustment units corresponding to the target operating table and the target operating table in the drive are used to obtain the monitoring pressure timing. The monitored pressure time series are summarized to obtain a monitored pressure time series set, and a pose adjustment database is obtained based on the monitored pressure time series set.

3. The operating table angle adjustment method based on patient pose recognition as described in claim 2, characterized in that, The step of obtaining the pose adjustment database based on the monitored pressure time series includes: A set of evaluation indicators is obtained to evaluate the fitting adjustment scheme. Initial evaluation values ​​are obtained based on the set of evaluation indicators, the monitoring pressure time series set, and the pre-constructed analytic hierarchy process. The initial evaluation values ​​correspond one-to-one with the fitting adjustment scheme. The initial evaluation values ​​are summarized to obtain an initial evaluation value set. Using a preset screening evaluation threshold, a target evaluation value set is extracted from the initial evaluation value set. The target evaluation value set includes multiple target evaluation values, and all target evaluation values ​​are greater than or equal to the screening evaluation threshold. For each target evaluation value in the target evaluation value set, perform the following operation: The comprehensive evaluation value is calculated based on the target evaluation value, and the calculation formula is as follows: Where Z represents the comprehensive evaluation value, α, β, and γ are preset coefficients, p represents the target evaluation value, t represents the time required to fit the adjustment scheme corresponding to the target evaluation value, h represents the height that the first adjustment unit needs to adjust, j represents the angle that the first adjustment unit needs to adjust, n represents the monitoring pressure of the first adjustment unit during adjustment, m represents the number of first adjustment units required to adjust the pose of the simulation model in the fitting adjustment scheme corresponding to the target evaluation value, and f(h,j,n) i Let i represent the energy consumption function of the i-th first adjustment unit among m first adjustment units when performing pose adjustment; The comprehensive evaluation values ​​are summarized to obtain a comprehensive evaluation value set. The target adjustment scheme is determined using the comprehensive evaluation value set. The target adjustment scheme is the fitting adjustment scheme corresponding to the smallest comprehensive evaluation value in the comprehensive evaluation value set. Based on the target adjustment scheme, a pose adjustment database was identified.

4. The operating table angle adjustment method based on patient pose recognition as described in claim 3, characterized in that, The process of identifying the pose adjustment database based on the target adjustment scheme includes: Based on the fitted pose corresponding to the target adjustment scheme, obtain the pose image and fitted pose vector, and identify the pose features in the pose image, including pose length and pose width. The initial monitoring pressure set is obtained by using the monitoring pressure time series set corresponding to the target adjustment scheme. The initial monitoring pressure set is the set of the first monitoring pressure in each monitoring pressure time series in the monitoring pressure time series set. By associating the fitted pose, the target adjusted pose, the fitted pose vector, the pose features, the initial monitored pressure set, and the target adjustment scheme, a pose adjustment node is obtained. By summarizing the pose adjustment nodes, a pose adjustment database is obtained.

5. The operating table angle adjustment method based on patient pose recognition as described in claim 4, characterized in that, The pose similarity formula is as follows: Where X represents node similarity, ω1, ω2, ... ω3 are all preset coefficients. CS() represents the calculation of cosine similarity, DS() represents the calculation of Euclidean distance, W0 and W1 represent the initial pose vector corresponding to the retrieved pose node and the fitted pose vector corresponding to the matched pose node, respectively. P0 and P1 represent the reference monitoring pressure sequence corresponding to the retrieved pose node and the initial monitoring pressure sequence corresponding to the matched pose node, respectively. z0 and z1 represent the initial feature corresponding to the retrieved pose node and the pose feature corresponding to the matched pose node, respectively.

6. The operating table angle adjustment method based on patient pose recognition as described in claim 5, characterized in that, The acquisition of detected vital signs based on the vital sign monitoring signals includes: Based on the vital sign monitoring signal, a vital sign echo signal is obtained. An intermediate frequency signal is then obtained and analyzed using the vital sign monitoring signal and the vital sign echo signal to acquire a frequency identification model set for frequency identification. This set includes multiple frequency identification models. The following operations are performed on each frequency identification model in the set: The initial vital signs are obtained by using the frequency identification model and analyzing the intermediate frequency signal. The initial vital signs include the initial respiratory rate and the initial heart rate. The initial respiratory rate and the initial heart rate in the initial vital signs are summarized to obtain the initial respiratory rate set and the initial heart rate set. The initial respiratory rate set includes multiple initial respiratory rates, and the initial respiratory rates correspond one-to-one with the frequency identification model. The respiratory frequency variance is obtained based on the initial respiratory frequency set, wherein the respiratory frequency variance is the variance of multiple initial respiratory frequencies in the initial respiratory frequency set, and the respiratory frequency variance is compared with a preset frequency variance threshold. If the respiratory rate variance is greater than or equal to the frequency variance threshold, then the respiratory rate mean is obtained based on the initial respiratory rate set, wherein the respiratory rate mean is the average of multiple initial respiratory rates in the initial respiratory rate set, and the following operation is performed on each initial respiratory rate in the initial respiratory rate set: Calculate the absolute difference between the initial respiratory rate and the mean respiratory rate to obtain the screening difference. Summarize the screening differences to obtain a screening difference set. Sort the screening differences in the screening difference set in descending order to obtain a screening difference sequence. Use the screening difference sequence to identify the target respiratory rate, where the target respiratory rate is the initial respiratory rate corresponding to the first screening difference in the screening difference sequence. Remove the target respiratory rate from the initial respiratory rate set to obtain an updated respiratory rate set. Use the updated respiratory rate set as the initial respiratory rate set and return to the step of obtaining the respiratory rate variance based on the initial respiratory rate set until the respiratory rate variance is less than the frequency variance threshold. If the respiratory rate variance is less than the frequency variance threshold, the detected respiratory rate is obtained based on the initial respiratory rate set, wherein the detected respiratory rate is the average of multiple initial respiratory rate sets in the initial respiratory rate set. Based on the initial heart rate set, the detected heart rate is obtained, and the detected respiratory rate and the detected heart rate are correlated to obtain the detected vital signs.

7. The operating table angle adjustment method based on patient pose recognition as described in claim 6, characterized in that, The step of obtaining the initial pose vector based on the initial pose includes: A set of joint nodes is identified in the initial pose, wherein the set of joint nodes includes multiple joint nodes, and the following operation is performed on each joint node in the set of joint nodes: Based on the joint nodes, obtain the joint three-dimensional coordinates and identification ordinal numbers, and use the joint three-dimensional coordinates and identification ordinal numbers to identify the joint nodes to obtain the identified joint nodes; The identified joint nodes are summarized to obtain an identified joint node set. The identified joint nodes in the identified joint node set are sorted in ascending order of their identifier ordinal numbers to obtain an identified joint node sequence. Extract the initial joint nodes sequentially from the identified joint node sequence, and perform the following operations on the extracted initial joint nodes: Using the initial joint node, a target joint node is identified in the identified joint node sequence, wherein the target joint node is adjacent to and lags behind the initial joint node; Calculate the Euclidean distance between the three-dimensional coordinates of the initial joint node and the three-dimensional coordinates of the target joint node to obtain the joint spacing. Summarize the joint spacings to obtain a set of joint spacings. Sort the joint spacings in the set according to the time corresponding to the acquisition of the joint spacings from earliest to latest to obtain a joint spacing sequence. Fit the joint spacing sequence to the initial pose vector, wherein the initial pose vector is as follows: W0={j1,j2,j3…,j a ,…j b } Among them, j1, j2, j3, j a ...

8. The operating table angle adjustment method based on patient pose recognition as described in claim 7, characterized in that, The confirmation that the detected vital signs are preset normal vital signs includes: Obtain the reference heart rate range and the reference respiratory rate range. When the detected respiratory rate corresponding to the detected vital sign does not belong to the reference respiratory rate range and the detected heart rate corresponding to the detected vital sign does not belong to the reference heart rate range, the pre-constructed vital sign warning signal is sent to the initiator of the angle adjustment command. Otherwise, confirm that the detected vital signs are normal.

9. The operating table angle adjustment method based on patient pose recognition as described in claim 8, characterized in that, The target operating table and result presentation unit based on the driven system generate visual adjustment results, including: The visualization parameters of each of the multiple first adjustment units corresponding to the target operating table after driving are obtained. The visualization parameters include adjustment height, initial size, initial position, adjustment angle and monitoring pressure. The visualization parameters are summarized to obtain a visualization parameter set. The system confirms receipt of a visualization instruction from the result presentation unit, obtains a patient representation model based on the visualization instruction, and generates a visualization model set using the visualization parameter set. The visualization model set includes multiple visualization models marked with monitoring pressure, and each visualization model marked with monitoring pressure corresponds one-to-one with the first adjustment unit. Based on the target selection pose, the constraint set is identified, and the visualization adjustment result is constructed using the constraint set, the visualization model set, and the patient representation model.

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