A method, device, equipment, and medium for assessing patient fall risk.

Abstract

This application provides a method, device, equipment, and medium for assessing patient fall risk. The assessment method includes: collecting gait parameters of a patient during a target gait test based on preset sensors; calculating the change parameters corresponding to the target type gait parameters based on the changes between the target type gait parameters and other types of gait parameters; preprocessing all types of gait parameters and the target type change parameters to obtain a fall risk assessment model; and inputting all types of gait parameters and the target type change parameters into the fall risk assessment model to obtain the corresponding fall risk assessment result for the patient.

Description

Technical Field

[0001] This application relates to the field of medical technology, and more specifically, to a method, apparatus, device, and medium for assessing the risk of patient falls. Background Technology

[0002] With the continuous development of the medical field, the assessment of patients' fall risk is beneficial to ensuring patients' safety and quality of life. Fall-related fractures and injuries to vital organs such as the head seriously endanger patients' health. Therefore, early assessment of patients' fall risk can help medical and care personnel identify potential fall hazards in the early stages and take appropriate preventive measures for high-risk groups to reduce health problems caused by falls and medical expenses in the subsequent rehabilitation and nursing process.

[0003] Currently, clinical assessment of patients' fall risk mainly relies on scales. These scales use tests such as standing, sitting, and turning to score patients' balance and walking ability by professional medical staff. The final score can be used to determine the patient's fall risk.

[0004] However, current assessment methods for evaluating patients' fall risk using scales often involve doctors' subjective observation and judgment. This process may be influenced by their personal experience and professional background, affecting the objectivity of the assessment results. In addition, some scales require doctors to observe and score each item, which is time-consuming and results in low diagnostic efficiency. Furthermore, existing methods only focus on a single gait parameter, and the comprehensiveness of the data involved is generally limited, leading to low accuracy of the assessment results. Summary of the Invention

[0005] In view of this, the purpose of this application is to provide a method, device, equipment, and medium for assessing patient fall risk. This method can obtain a fall risk assessment model by preprocessing all types of gait parameters and target type variation parameters, and then input the patient's gait parameters and variation parameters into the fall risk assessment model to obtain the patient's fall risk assessment results. The assessment process does not involve the doctor's subjective judgment or item-by-item observation and scoring, thus improving the objectivity of the assessment results, reducing diagnostic time, and increasing diagnostic efficiency. Simultaneously, it collects multiple types of gait parameters from the patient, including parameters related to cognitive tests, improving the comprehensiveness of the data involved in the assessment, thereby contributing to the improvement of the accuracy of the assessment results.

[0006] In a first aspect, embodiments of this application provide a method for assessing the risk of a patient falling, the assessment method comprising:

[0007] The system collects gait parameters of the patient during the target gait test based on preset sensors; wherein, the target gait test includes multiple types of gait tests, and the gait parameters include gait parameters corresponding to each type of gait test;

[0008] Based on the changes in gait parameters of the target type and those of other types, the changing parameters corresponding to the gait parameters of the target type are calculated.

[0009] Preprocess all types of gait parameters and the variation parameters of the target type to obtain a fall risk assessment model;

[0010] By inputting all types of gait parameters and target type variation parameters of the patient into the fall risk assessment model, the fall risk assessment result corresponding to the patient is obtained.

[0011] In one possible implementation, the preprocessing of all types of gait parameters and the variation parameters of the target type to obtain a fall risk assessment model includes:

[0012] Feature extraction is performed on all types of gait parameters and the variation parameters of the target type to extract the corresponding target features;

[0013] A fall risk assessment model is constructed based on the target characteristics.

[0014] In one possible implementation, training the fall risk assessment model to be trained based on the target features to obtain the trained fall risk assessment model includes:

[0015] The patient is classified based on the fall risk assessment model to obtain the classification result corresponding to the patient.

[0016] The fall risk assessment model to be trained is trained based on the classification results and the target features to obtain the trained fall risk assessment model. In one possible implementation, the step of inputting all types of gait parameters and target type variation parameters of the patient into the fall risk assessment model to obtain the fall risk assessment result corresponding to the patient includes:

[0017] The patient's gait parameters of all types and target type variation parameters are input into the fall risk assessment model to obtain the patient's fall risk parameters;

[0018] The patient's fall risk is assessed based on the fall risk parameters to obtain the corresponding fall risk assessment results.

[0019] In one possible implementation, the evaluation method further includes:

[0020] The fall risk assessment model is evaluated based on a preset assessment method to obtain the assessment index of the fall risk assessment model.

[0021] The fall risk assessment model is optimized based on the assessment indicators to obtain an optimized fall risk assessment model.

[0022] Secondly, embodiments of this application also provide a device for assessing the risk of patient falls, the device comprising:

[0023] The acquisition module is used to acquire gait parameters of the patient during the target gait test based on preset sensors; wherein, the target gait test includes multiple types of gait tests, and the gait parameters include gait parameters corresponding to each type of gait test;

[0024] The calculation module is used to calculate the change parameters corresponding to the gait parameters of the target type based on the changes between the gait parameters of the target type and the gait parameters of other types.

[0025] The first acquisition module is used to preprocess all types of gait parameters and the change parameters of the target type to obtain a fall risk assessment model;

[0026] The second acquisition module is used to input all types of gait parameters and target type change parameters of the patient into the fall risk assessment model to obtain the fall risk assessment result corresponding to the patient.

[0027] In one possible implementation, the first acquisition module is specifically used for:

[0028] Feature extraction is performed on all types of gait parameters and the variation parameters of the target type to extract the corresponding target features;

[0029] A fall risk assessment model to be trained is obtained, and the model is trained based on the target features to obtain a trained fall risk assessment model. In one possible implementation, the second acquisition module is specifically used for:

[0030] Feature extraction is performed on all types of gait parameters and the variation parameters of the target type to extract the corresponding target features;

[0031] A fall risk assessment model to be trained is obtained, and the model is trained based on the target features to obtain a trained fall risk assessment model. In one possible implementation, the second acquisition module is specifically used for:

[0032] The patient's gait parameters of all types and target type variation parameters are input into the fall risk assessment model to obtain the patient's fall risk parameters;

[0033] The patient's fall risk is assessed based on the fall risk parameters to obtain the corresponding fall risk assessment results.

[0034] In one possible implementation, the patient fall risk assessment device further includes:

[0035] An evaluation module is used to evaluate the fall risk assessment model based on a preset evaluation method to obtain the evaluation index of the fall risk assessment model.

[0036] An optimization module is used to optimize the fall risk assessment model based on the evaluation indicators to obtain an optimized fall risk assessment model.

[0037] Thirdly, embodiments of this application provide an electronic device, including: a processor, a storage medium, and a bus, wherein the storage medium stores machine-readable instructions executable by the processor, and when the electronic device is running, the processor communicates with the storage medium via the bus, and the processor executes the machine-readable instructions to perform the steps of the patient fall risk assessment method as described in any of the first aspects.

[0038] Fourthly, embodiments of this application provide a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the patient fall risk assessment method described in any one of the first aspects.

[0039] This application provides a method, apparatus, device, and medium for assessing patient fall risk. It collects gait parameters of a patient during a target gait test using pre-set sensors. Based on the changes in gait parameters of the target type and other types, it calculates the corresponding change parameters for the target type of gait parameters. It preprocesses all types of gait parameters and the change parameters of the target type to obtain a fall risk assessment model. The patient's gait parameters of all types and the change parameters of the target type are then input into the fall risk assessment model to obtain the patient's corresponding fall risk assessment result. In this application, by preprocessing all types of gait parameters and the change parameters of the target type to obtain the fall risk assessment model, and then inputting the patient's gait parameters and change parameters into the fall risk assessment model, the patient's fall risk assessment result is obtained. The assessment process does not involve the doctor's subjective judgment or item-by-item observation and scoring, improving the objectivity of the assessment results, reducing diagnostic time, and improving diagnostic efficiency. Simultaneously, by collecting multiple types of gait parameters from the patient, including parameters related to cognitive tests, it improves the comprehensiveness of the data involved in the assessment, thereby contributing to the improvement of the accuracy of the assessment results.

[0040] To make the above-mentioned objectives, features and advantages of this application more apparent and understandable, preferred embodiments are described below in detail with reference to the accompanying drawings. Attached Figure Description

[0041] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0042] Figure 1 This is a flowchart of a method for assessing the risk of patient falls according to one embodiment of this application;

[0043] Figure 2 This is a flowchart of a method for assessing patient fall risk according to another embodiment of this application;

[0044] Figure 3 This is a flowchart of a method for assessing patient fall risk according to another embodiment of this application;

[0045] Figure 4 This is a flowchart of a method for assessing patient fall risk according to another embodiment of this application;

[0046] Figure 5 This is a flowchart of a method for assessing patient fall risk according to another embodiment of this application;

[0047] Figure 6 This is a schematic diagram of the structure of a patient fall risk assessment device according to an embodiment of this application;

[0048] Figure 7 This is a schematic diagram of the structure of an electronic device according to an embodiment of this application. Detailed Implementation

[0049] To make the objectives, technical solutions, and advantages of the embodiments of this application clearer, the technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. It should be understood that the accompanying drawings in this application are for illustrative and descriptive purposes only and are not intended to limit the scope of protection of this application. Furthermore, it should be understood that the schematic drawings are not drawn to scale. The flowcharts used in this application illustrate operations implemented according to some embodiments of this application. It should be understood that the operations in the flowcharts may not be implemented in sequence, and steps without logical contextual relationships may be reversed or implemented simultaneously. In addition, those skilled in the art, guided by the content of this application, may add one or more other operations to the flowcharts, or remove one or more operations from the flowcharts.

[0050] Furthermore, the described embodiments are merely some, not all, of the embodiments of this application. The components of the embodiments of this application described and illustrated herein can typically be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of the application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0051] It should be noted that the term "comprising" will be used in the embodiments of this application to indicate the presence of the features declared thereafter, but does not exclude the addition of other features.

[0052] Given the continuous development of the medical field, assessing patient fall risk is crucial for ensuring patient safety and quality of life. Fall-related fractures and injuries to vital organs such as the head seriously endanger patients' health, affect their independence in daily life, and can even be life-threatening. Therefore, early fall risk assessment helps healthcare professionals identify potential fall hazards and implement appropriate preventative measures for high-risk individuals, reducing health problems caused by falls and subsequent medical expenses during rehabilitation. Developing effective fall risk assessment methods is therefore of great significance for both individual patients and the healthcare system as a whole, as it can identify potential risks, prevent falls, improve nursing care, and optimize the allocation of medical resources.

[0053] Currently, clinical assessment of patients' fall risk primarily relies on scales, such as the Berg Balance Scale, the Tinetti Balance and Gait Analysis, the Morse Fall Scale, and the Timed Up and Go test. These scales use standing, sitting, and turning tests to assess patients' balance and walking abilities, with the final score used to determine the patient's fall risk.

[0054] However, current assessment methods using scales to evaluate patients' fall risk typically involve physicians or trained medical professionals observing patients' behavior and movements and scoring them according to specific criteria. The scales involve the physician's subjective observation and judgment in execution, scoring, and interpretation, a process that can be influenced by their personal experience and professional background, affecting the objectivity of the assessment results. Furthermore, some scales require physicians to observe and score each item individually, which is time-consuming and leads to low diagnostic efficiency. In addition, existing methods focus only on single gait parameters, resulting in limited data comprehensiveness and consequently, lower accuracy of the assessment results.

[0055] To address this issue, this application provides a method, device, equipment, and medium for assessing patient fall risk. By preprocessing all types of gait parameters and target-type variation parameters to obtain a fall risk assessment model, and inputting the patient's gait parameters and variation parameters into the fall risk assessment model, the patient's fall risk assessment results are obtained. The assessment process does not involve the doctor's subjective judgment or item-by-item observation and scoring, improving the objectivity of the assessment results, reducing diagnostic time, and increasing diagnostic efficiency. Simultaneously, by collecting multiple types of gait parameters from the patient, including parameters related to cognitive tests, the comprehensiveness of the data involved in the assessment is improved, thereby contributing to the accuracy of the assessment results.

[0056] Figure 1 This is a flowchart of a method for assessing patient fall risk according to one embodiment of this application, as shown below. Figure 1 As shown, the method for assessing patient fall risk in this application embodiment may specifically include:

[0057] S101 collects gait parameters of the patient during the target gait test based on preset sensors.

[0058] In this embodiment, gait parameters are quantitative parameters of relevant motor function collected when the patient performs a gait test. The preset sensor is a sensor that is pre-set to collect the patient's gait parameters, such as a depth vision sensor or a wireless electromyography sensor. The target gait test includes multiple types of gait tests, and the gait parameters include the gait parameters corresponding to each type of gait test. The patient's gait parameters during the target gait test are collected based on the preset sensor for subsequent processing.

[0059] It should be noted that the target gait test in this application can include three types of gait tests: gait test, fast gait test, and dual-task gait test. Gait parameters can include walking speed, stride length, stride height, stride width, stance phase, swing phase, double support phase, step speed, swing speed, completion time, and turn time. This application uses this as an example for description, but it does not constitute a limitation on the target gait test and gait parameters. Specific settings can be made according to actual circumstances. Among them, the fast gait test requires the patient to walk at the fastest possible speed, while the dual-task gait test conducts cognitive tests (such as calculation, memory, and cognitive tasks) simultaneously with the gait test.

[0060] The specific explanations of the above gait parameters are as follows: stride speed is the distance / duration between the start and end points (m / s), stride length is the distance between two landings of a single foot (m), stride height is the highest distance off the ground during the swing of a single foot (m), stride width is the width of the left and right feet in each frame (m), the standing phase is the percentage of standing time within each left (or right) stride cycle (%), the swing phase is the percentage of swing time within each left (or right) stride cycle (%), the double support phase is the percentage of standing time with both feet within each stride cycle (%), stride speed is the stride distance / stride time of the left (right) foot (m / s), swing speed is the swing distance / swing time of a single foot (m / s), completion time is the total time taken to complete the entire gait test, and turn time is the time taken from the last foot lift when there is a turning tendency (turn start) to the first foot lift when straightening begins (turn end).

[0061] S102, based on the changes between the gait parameters of the target type and the gait parameters of other types, calculate the changing parameters corresponding to the gait parameters of the target type.

[0062] In this embodiment of the application, other types refer to types other than the target type, and the change refers to the change of the gait parameters of the target type relative to the gait parameters of other types. Based on the change between the gait parameters of the target type and the gait parameters of other types in the gait parameters of the patient during the target gait test collected in step S101, the change parameters of the gait parameters of the target type relative to the gait parameters of other types can be calculated, such as the improvement rate.

[0063] It should be noted that this application uses the target type of fast gait test and other types of gait test as examples for description. The changing parameters corresponding to the gait parameters of the target type are the changing parameters of fast gait test relative to gait test. There are no restrictions on this, and it can be set according to the actual situation.

[0064] Optionally, the improvement rates of gait speed and stride length in the fast gait test can be used as variation parameters. For example, specifically, the improvement rate of each parameter in the fast gait test relative to the gait parameters in the regular gait test can be obtained using the following formula:

[0065] Improvement rate = (Gait parameters from rapid gait test - Gait parameters from gait test) / Gait parameters from gait test × 100%

[0066] Therefore, the change parameters of each parameter in the gait parameters of the fast gait test can be obtained, namely the improvement rate of speed, stride length, stride height, stride width, stance phase, swing phase, double support phase, step speed, swing speed, completion time and turning time, etc. Based on this, the improvement rates of stride length and stride height can be taken as the change parameters of the fast gait test.

[0067] S103 preprocesses all types of gait parameters and target type variation parameters to obtain a fall risk assessment model.

[0068] In this embodiment of the application, the fall risk assessment model is a model for assessing the patient's fall risk. The fall risk assessment model is obtained by preprocessing the gait parameters corresponding to all types of gait tests of the patient collected in step S101 and the change parameters of the target type of gait parameters relative to other types of gait parameters calculated in step S102, so as to carry out subsequent processing.

[0069] It should be noted that the preprocessing of all types of gait parameters and target type variation parameters can include outlier removal, standardization, etc. It can be understood that the preprocessing of the above parameters can ensure the data quality and consistency of gait parameters.

[0070] S104: Input all types of gait parameters and target type variation parameters of the patient into the fall risk assessment model to obtain the corresponding fall risk assessment results for the patient.

[0071] In this embodiment of the application, after obtaining the fall risk assessment model in step S103, all types of gait parameters and target type change parameters of the patient can be collected, and these parameters can be input into the fall risk assessment model. Thus, the output result of the fall risk assessment model is the fall risk assessment result corresponding to the patient.

[0072] The patient fall risk assessment method provided in this application collects gait parameters of the patient during a target gait test using preset sensors. Based on the changes between the target type gait parameters and other types of gait parameters, the method calculates the change parameters corresponding to the target type gait parameters. All types of gait parameters and the change parameters of the target type are preprocessed to obtain a fall risk assessment model. The patient's gait parameters and the change parameters of the target type are then input into the fall risk assessment model to obtain the corresponding fall risk assessment result. This method, by preprocessing all types of gait parameters and the change parameters of the target type to obtain a fall risk assessment model, and then inputting the patient's gait parameters and change parameters into the model, yields the patient's fall risk assessment result. The assessment process does not involve the doctor's subjective judgment or item-by-item observation and scoring, improving the objectivity of the assessment results, reducing diagnostic time, and increasing diagnostic efficiency. Furthermore, by collecting multiple types of gait parameters from the patient, including parameters related to cognitive tests, the comprehensiveness of the data involved in the assessment is improved, thereby contributing to the accuracy of the assessment results.

[0073] Furthermore, such as Figure 2 As shown, step S103 in the above embodiment, "preprocessing all types of gait parameters and target type variation parameters to obtain a fall risk assessment model," may specifically include the following steps:

[0074] S201, extract features from all types of gait parameters and target type variation parameters to extract the corresponding target features.

[0075] In this embodiment, the target features refer to the gait parameters of the target type gait test, the variation parameters compared to other types of gait tests, and the features corresponding to the gait parameters of another type besides the target type and other types. After analyzing and extracting features from all types of gait parameters and the variation parameters of the target type obtained in step S102, the corresponding target features are extracted for subsequent processing. Specifically, feature extraction is performed on the gait parameters of the gait test, the fast gait test, and the dual-task gait test, as well as the variation parameters of the fast gait test, to extract the features of the fast gait test parameters, the variation parameters of the fast gait test parameters compared to the gait test parameters, and the dual-task gait test parameters as target features.

[0076] S202, Obtain the fall risk assessment model to be trained, and train the fall risk assessment model to be trained based on the target features to obtain the trained fall risk assessment model.

[0077] In this embodiment of the application, a fall risk assessment model to be trained is obtained, and the fall risk assessment model to be trained is trained based on the target features corresponding to all types of gait parameters and target type change parameters extracted in step S201, so as to obtain a trained fall risk assessment model for subsequent processing.

[0078] Furthermore, such as Figure 3 As shown, step S202 in the above embodiment, "training the fall risk assessment model to be trained based on the target features to obtain the trained fall risk assessment model," may specifically include the following steps:

[0079] S301, classify patients based on a fall risk assessment model to obtain the corresponding classification results for each patient.

[0080] In this embodiment, the classification result is the result of classifying the patient, for example, having a history of falls within the past six months or not having a history of falls within the past six months. This can determine whether the patient has a history of falls within the past six months, distinguish between patients with a history of falls within the past six months and those without, and classify the patient. Based on the fall risk assessment model obtained in the above embodiment, the patient can be classified, and the corresponding classification result can be obtained for subsequent processing.

[0081] S302, based on the classification results and target features, train the fall risk assessment model to be trained to obtain the trained fall risk assessment model.

[0082] In this embodiment, based on the patient classification results obtained in step S301, the target features extracted from all types of gait parameters and target type change parameters obtained in the above embodiments, the fall risk assessment model to be trained is trained, and the trained fall risk assessment model can be obtained for subsequent processing.

[0083] Furthermore, such as Figure 4 As shown, step S104 in the above embodiment, "inputting all types of gait parameters and target type change parameters of the patient into the fall risk assessment model to obtain the fall risk assessment result corresponding to the patient," may specifically include the following steps:

[0084] S401 inputs all types of gait parameters and target type variation parameters of the patient into the fall risk assessment model to obtain the patient's fall risk parameters.

[0085] In this embodiment, by inputting all types of gait parameters of the patient and the change parameters of the target type into the fall risk assessment model, the patient's fall risk parameters can be obtained for subsequent processing. Optionally, the fall risk parameter can be a fall risk probability. For example, by inputting the gait parameters corresponding to the three types of newly collected gait tests, fast gait tests, and dual-task gait tests, and the change parameters of the fast gait test, into the fall risk assessment model, the patient's corresponding fall risk probability, such as 30%, can be obtained.

[0086] S402 assesses a patient’s fall risk based on fall risk parameters to obtain the corresponding fall risk assessment results.

[0087] In this embodiment of the application, the patient's fall risk is further assessed based on the fall risk parameters obtained in step S401, thereby obtaining the corresponding fall risk assessment result.

[0088] For example, when the probability of falling is 30%, the patient's fall risk assessment result is determined based on this probability of falling.

[0089] Optionally, the correspondence between fall risk assessment results and fall risk parameters can be set. For example, a fall risk parameter below 10% (fall risk probability) corresponds to a fall risk assessment result of "low fall risk". A fall risk parameter between 10% and 40% (fall risk probability) corresponds to a fall risk assessment result of "medium fall risk". A fall risk parameter above 40% (fall risk probability) corresponds to a fall risk assessment result of "high fall risk". Different treatments can be applied based on these different fall risk assessment results. For example, patients with "high fall risk" can receive special attention.

[0090] Furthermore, such as Figure 5 As shown, the patient fall risk assessment method in this application embodiment may further include the following steps:

[0091] S501, evaluate the fall risk assessment model based on the preset assessment method to obtain the assessment index of the fall risk assessment model.

[0092] In this embodiment, the evaluation method is a pre-set scheme for evaluating the fall risk assessment model, such as cross-validation. The evaluation index is a measurement index for evaluating the fall risk assessment model, such as accuracy and sensitivity. The fall risk assessment model obtained in the above embodiment is evaluated based on the preset evaluation method to obtain the evaluation index of the fall risk assessment model.

[0093] S502, optimize the fall risk assessment model based on the assessment indicators to obtain the optimized fall risk assessment model.

[0094] In this embodiment, the fall risk assessment model is optimized based on the evaluation indicators obtained in step S501 to obtain an optimized fall risk assessment model. It should be noted that by using evaluation methods such as cross-validation to evaluate the fall risk assessment model and optimizing it based on evaluation indicators such as accuracy and sensitivity, the accuracy and generalization ability of the fall risk assessment model are ensured.

[0095] Figure 6 This is a flowchart of a patient fall risk assessment device according to one embodiment of this application, as shown below. Figure 6 As shown, the patient fall risk assessment device 600 of this application embodiment may specifically include:

[0096] The acquisition module 601 is used to acquire gait parameters of the patient during the target gait test based on preset sensors. The target gait test includes multiple types of gait tests, and the gait parameters include the gait parameters corresponding to each type of gait test.

[0097] The calculation module 602 is used to calculate the change parameters corresponding to the gait parameters of the target type based on the changes between the gait parameters of the target type and the gait parameters of other types.

[0098] The first acquisition module 603 is used to preprocess all types of gait parameters and target type variation parameters to obtain a fall risk assessment model.

[0099] The second acquisition module 604 is used to input all types of gait parameters and target type change parameters of the patient into the fall risk assessment model to obtain the fall risk assessment results corresponding to the patient.

[0100] In one possible implementation, the first acquisition module 603 is specifically used for:

[0101] Feature extraction is performed on all types of gait parameters and target type variation parameters to extract the corresponding target features;

[0102] A fall risk assessment model to be trained is obtained, and the model is trained based on target features to obtain a trained fall risk assessment model. In one possible implementation, the second acquisition module 604 is specifically used for:

[0103] Feature extraction is performed on all types of gait parameters and target type variation parameters to extract the corresponding target features;

[0104] A fall risk assessment model to be trained is obtained, and the model is trained based on target features to obtain a trained fall risk assessment model. In one possible implementation, the second acquisition module 604 is specifically used for:

[0105] By inputting all types of gait parameters and target type variation parameters of the patient into the fall risk assessment model, the patient's fall risk parameters are obtained;

[0106] The patient's fall risk is assessed based on fall risk parameters to obtain the corresponding fall risk assessment results.

[0107] In one possible implementation, the patient fall risk assessment device 600 further includes:

[0108] The assessment module is used to evaluate the fall risk assessment model based on a preset assessment method to obtain the assessment indicators of the fall risk assessment model.

[0109] The optimization module is used to optimize the fall risk assessment model based on the evaluation indicators to obtain an optimized fall risk assessment model.

[0110] The patient fall risk assessment device provided in this application collects gait parameters of the patient during a target gait test based on preset sensors. Based on the changes between the target type gait parameters and other types of gait parameters, it calculates the change parameters corresponding to the target type gait parameters. It preprocesses all types of gait parameters and the target type change parameters to obtain a fall risk assessment model. The patient's gait parameters and the target type change parameters are then input into the fall risk assessment model to obtain the corresponding fall risk assessment result. This patient fall risk assessment device, by preprocessing all types of gait parameters and the target type change parameters to obtain a fall risk assessment model, and then inputting the patient's gait parameters and change parameters into the fall risk assessment model, obtains the patient's fall risk assessment result. The assessment process does not involve the doctor's subjective judgment or item-by-item observation and scoring, improving the objectivity of the assessment results, reducing diagnostic time, and improving diagnostic efficiency. Simultaneously, it collects multiple types of gait parameters from the patient, including parameters related to cognitive tests, improving the comprehensiveness of the data involved in the assessment, thereby contributing to the improvement of the accuracy of the assessment results.

[0111] like Figure 7As shown in the embodiment of this application, an electronic device 700 includes a processor 701, a memory 702, and a bus. The memory 702 stores machine-readable instructions executable by the processor 701. When the electronic device is running, the processor 701 communicates with the memory 702 via the bus, and the processor 701 executes the machine-readable instructions to perform the steps of the patient fall risk assessment method described above.

[0112] Specifically, the memory 702 and processor 701 mentioned above can be general-purpose memory and processor, without any specific limitations. When the processor 701 runs the computer program stored in the memory 702, it can execute the above-mentioned method for assessing the patient's risk of falling.

[0113] Corresponding to the above-described method for assessing the risk of patient falls, this application also provides a computer-readable storage medium storing a computer program, which, when executed by a processor, performs the steps of the above-described method for assessing the risk of patient falls.

[0114] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems and devices described above can be referred to the corresponding processes in the method embodiments, and will not be repeated here. In the several embodiments provided in this application, it should be understood that the disclosed systems, devices, and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of modules is only a logical functional division, and in actual implementation, there may be other division methods. Furthermore, multiple modules or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed mutual coupling or direct coupling or communication connection can be through some communication interfaces; the indirect coupling or communication connection of devices or modules can be electrical, mechanical, or other forms.

[0115] 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 units can be selected to achieve the purpose of this embodiment according to actual needs.

[0116] In addition, the functional units in the various embodiments of this application 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.

[0117] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a processor-executable, non-volatile, computer-readable storage medium. Based on this understanding, the technical solution of this application, in essence, or the part that contributes to the prior art, or a portion of the technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the steps of the deployment methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0118] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A method for assessing the risk of falls in patients, characterized in that, The evaluation method includes: The system collects gait parameters of the patient during the target gait test based on preset sensors; wherein, the target gait test includes multiple types of gait tests, and the gait parameters include gait parameters corresponding to each type of gait test; Based on the changes in gait parameters for the target type and those for other types, the change parameters corresponding to the gait parameters for the target type are calculated. Here, "other types" refers to types other than the target type, and the change parameter is the improvement rate. When the target type is a fast gait test and the other types are gait tests, the improvement rate can be obtained using the following formula: Improvement rate = (Gait parameters for fast gait test - Gait parameters for gait test) / Gait parameters for gait test × 100%. Preprocessing is performed on all types of gait parameters and the variable parameters of the target type to obtain a fall risk assessment model; wherein, the preprocessing of all types of gait parameters and the variable parameters of the target type to obtain a fall risk assessment model includes: extracting features from all types of gait parameters and the variable parameters of the target type to extract corresponding target features; obtaining a fall risk assessment model to be trained, and training the fall risk assessment model to be trained based on the target features to obtain a trained fall risk assessment model; The patient's gait parameters of all types and target type variation parameters are input into the fall risk assessment model to obtain the patient's corresponding fall risk assessment result. This process includes: inputting the patient's gait parameters of all types and target type variation parameters into the fall risk assessment model to obtain the patient's fall risk parameters; and assessing the patient's fall risk based on these fall risk parameters to obtain the corresponding fall risk assessment result.

2. The evaluation method according to claim 1, characterized in that, The step of training the fall risk assessment model to be trained based on the target features to obtain the trained fall risk assessment model includes: The patient is classified based on the fall risk assessment model to obtain the classification result corresponding to the patient. The fall risk assessment model to be trained is trained based on the classification results and the target features to obtain the trained fall risk assessment model.

3. The evaluation method according to claim 1, characterized in that, The evaluation method also includes: The fall risk assessment model is evaluated based on a preset assessment method to obtain the assessment index of the fall risk assessment model. The fall risk assessment model is optimized based on the assessment indicators to obtain an optimized fall risk assessment model.

4. A device for assessing patient fall risk, characterized in that, The assessment device, applied to the patient fall risk assessment method as described in any one of claims 1-3, comprises: The acquisition module is used to acquire gait parameters of the patient during the target gait test based on preset sensors; wherein, the target gait test includes multiple types of gait tests, and the gait parameters include gait parameters corresponding to each type of gait test; The calculation module is used to calculate the change parameters corresponding to the gait parameters of the target type based on the changes between the gait parameters of the target type and the gait parameters of other types; wherein, the other types are types other than the target type, and the change parameter is the improvement rate. When the target type is a fast gait test and the other types are gait tests, the improvement rate can be obtained using the following formula: Improvement rate = (gait parameters of fast gait test - gait parameters of gait test) / gait parameters of gait test × 100%; The first acquisition module is used to preprocess all types of gait parameters and the change parameters of the target type to obtain a fall risk assessment model; wherein, the first acquisition module is also used to extract features from all types of gait parameters and the change parameters of the target type to extract corresponding target features; to obtain a fall risk assessment model to be trained, and to train the fall risk assessment model to be trained based on the target features to obtain a trained fall risk assessment model; The second acquisition module is used to input all types of gait parameters and target type change parameters of the patient into the fall risk assessment model to obtain the fall risk assessment result corresponding to the patient; wherein, the second acquisition module is specifically used to input all types of gait parameters and target type change parameters of the patient into the fall risk assessment model to obtain the patient's fall risk parameters; and to assess the patient's fall risk based on the fall risk parameters to obtain the corresponding fall risk assessment result.

5. The evaluation apparatus according to claim 4, characterized in that, The first acquisition module is further configured to: The patient is classified based on the fall risk assessment model to obtain the classification result corresponding to the patient. The fall risk assessment model to be trained is trained based on the classification results and the target features to obtain the trained fall risk assessment model.

6. An electronic device, characterized in that, include: The device includes a processor, a memory, and a bus, wherein the memory stores machine-readable instructions executable by the processor, and when the electronic device is in operation, the processor communicates with the memory via the bus, and the machine-readable instructions, when executed by the processor, perform the steps of the patient fall risk assessment method as described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, performs the steps of the patient fall risk assessment method as described in any one of claims 1 to 4.

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