WO2021184792A1 - 一种动态平衡评估方法、装置、设备及介质 - Google Patents
一种动态平衡评估方法、装置、设备及介质 Download PDFInfo
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Definitions
- the embodiment of the present invention relates to the technical field of balance assessment, and in particular to a dynamic balance assessment method, device, equipment and medium.
- Fall injuries are one of the important factors that seriously endanger the health of middle-aged and elderly people.
- the main cause of falls is generally the poor balance of the middle-aged and the elderly or the deterioration of the body's muscle strength. Therefore, monitoring the balance problems of middle-aged and elderly people, and giving balance and muscle strength training early and timely, play an important role in preventing middle-aged and elderly people from falling.
- the embodiments of the present invention provide a dynamic balance evaluation method, device, equipment, and medium, so as to improve the accuracy of dynamic balance evaluation.
- an embodiment of the present invention provides a dynamic balance evaluation method, including:
- an embodiment of the present invention also provides a dynamic balance evaluation device, including:
- the standardized data module is used to obtain the data to be evaluated, and preprocess the data to be evaluated to obtain standardized evaluation data;
- the model result acquisition module is used to input standardized evaluation data into the pre-trained balance evaluation model to obtain the output result of the dynamic balance evaluation, where the balance evaluation model is a neural network model based on time series;
- the evaluation result output module is used to determine the evaluation result according to the output result and output the evaluation result.
- an embodiment of the present invention also provides a computer device, and the device includes:
- One or more processors are One or more processors;
- Storage device for storing one or more programs
- the one or more processors implement the dynamic balance evaluation method provided by any embodiment of the present invention.
- an embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored, and when the program is executed by a processor, the dynamic balance evaluation method as provided in any embodiment of the present invention is implemented.
- standardized evaluation data is obtained by obtaining the data to be evaluated and preprocessing the data to be evaluated; inputting the standardized evaluation data into a pre-trained balance evaluation model to obtain the output result of the balance evaluation model, wherein the balance evaluation model It is a neural network model based on time series; the evaluation result is determined according to the output result, and the evaluation result is output.
- the neural network model based on the time series is used as a balance evaluation model to deeply mine the associated features in the data to be evaluated, which improves the dynamic balance evaluation Accuracy.
- FIG. 1 is a flowchart of a dynamic balance evaluation method provided by Embodiment 1 of the present invention
- Figure 2a is a flowchart of a dynamic balance assessment method provided in the second embodiment of the present invention.
- Fig. 2b is a schematic diagram of a basic neural network structure of an LSTM provided by the second embodiment of the present invention.
- Fig. 2c is a schematic diagram of a chain structure of an LSTM provided by the second embodiment of the present invention.
- 2d is a schematic diagram of a state of an LSTM cell provided by the second embodiment of the present invention.
- Fig. 2e is a schematic diagram of an LSTM forget gate provided by the second embodiment of the present invention.
- Fig. 2f is a schematic diagram of an LSTM input gate provided by the second embodiment of the present invention.
- Fig. 2g is a schematic diagram of an LSTM input gate provided by the second embodiment of the present invention.
- FIG. 3a is a schematic diagram of a construction process of a balance evaluation model provided by Embodiment 3 of the present invention.
- Fig. 3b is a schematic structural diagram of a balance evaluation model provided by the third embodiment of the present invention.
- FIG. 3c is a schematic diagram of a data structure type output by an output layer provided by Embodiment 3 of the present invention.
- FIG. 4 is a schematic structural diagram of a dynamic balance evaluation device provided by the fourth embodiment of the present invention.
- Fig. 5 is a schematic structural diagram of a computer device according to the fifth embodiment of the present invention.
- Fig. 1 is a flowchart of a dynamic balance assessment method provided by Embodiment 1 of the present invention. This embodiment can be applied to situations when dynamic balance evaluation is performed.
- the method can be executed by a dynamic balance evaluation device, which can be implemented by software and/or hardware, for example, the dynamic balance evaluation device can be configured in a computer device. As shown in Figure 1, the method includes:
- the data to be evaluated may be dynamic balance test data of the subject.
- the dynamic balance test method is not limited here.
- the dynamic balance test method can be a standing test, a balance beam test, a stepping test, and other test methods.
- the data to be evaluated may be test data obtained by testing the subject using any one of the above-mentioned test methods.
- the testee In the dynamic balance test, the testee generally needs to complete the setting action, and collect the data of the setting index during the process of the testee performing the setting action, and obtain the dynamic balance test data containing multiple index collection data.
- the data to be evaluated includes index collection data under a set action
- preprocessing the data to be evaluated to obtain standardized evaluation data includes: normalizing the index collection data to obtain a standardized evaluation data.
- there may be multiple indexes corresponding to an action and the dimensions of the test data obtained by different indexes may be different.
- the StandarScaler method in the sklearn.preprocessing library may be used to take the index collection data corresponding to each set action as a unit, and scale the index collection data corresponding to each set action to obtain standardized evaluation data.
- the data to be evaluated may be dynamic balance test data of the subject's standing test.
- the set action includes at least one of standing with eyes open on both feet, standing with eyes closed on both feet, standing on one foot with eyes open, and standing on one foot with eyes closed.
- the subject needs to complete at least one of the above actions to obtain the corresponding dynamic balance test data.
- standing with eyes open on both feet, standing with eyes closed on both feet, standing with eyes open on one foot, and standing on one foot with eyes closed as the set actions of the standing test by combining the above
- the indicators of the four movements collect data to evaluate the balance ability of the subjects.
- the index collection data under the setting action includes the peripheral area of the eye open under the setting action, the trajectory length per unit area, the total trajectory length of shaking, the trajectory length in the first direction, the trajectory length in the second direction, and the first trajectory. At least one of the average center displacement in the direction, the average center displacement in the second direction, the maximum swing diameter in the first direction, the maximum swing diameter in the second direction, the average speed in the first direction, the average speed in the second direction, and the average swing speed.
- the corresponding index collection data includes the peripheral area of the eyes, the length of the trajectory per unit area, the length of the total trajectory of shaking, and the trajectory in the first direction. Long, track length in the second direction, average center displacement in the first direction, average center displacement in the second direction, maximum swing diameter in the first direction, maximum swing diameter in the second direction, average speed in the first direction, average speed in the second direction, At least one of the average swing speeds.
- the first direction and the second direction are perpendicular, the first direction may be a horizontal direction, and the second direction may be a vertical direction.
- the peripheral area of the open eyes, the track length per unit area, the total track length of shaking, the track length in the first direction, the track length in the second direction, and the average center displacement in the first direction can be changed.
- the average center displacement in the second direction, the maximum swing diameter in the first direction, the maximum swing diameter in the second direction, the average speed in the first direction, the average speed in the second direction, and the average swing speed are used as test indicators.
- Data is collected from two indicators (each set action corresponds to 12 indicators to collect data), and the testee’s balance ability is evaluated.
- the 48 indicators corresponding to the four setting actions are shown in Table 1.
- the testee’s dynamic balance test data has a certain relevance. Taking the 48-dimensional data obtained from the standing test as an example, there is a temporal relevance between the set actions. Based on this, the 48-dimensional index collection data is also related to each other. There is a significant correlation, so there is also a certain correlation between the 48-dimensional standardized evaluation data based on the 48-dimensional index collection data. Therefore, a neural network model based on time series can be used as a balance evaluation model to mine the correlation characteristics between standardized evaluation data, and the standardized data can be input into the trained balance evaluation model to obtain the output result of the balance evaluation model.
- the output result of the balance evaluation model may be the category with the highest probability, or may be each category and the probability corresponding to each category.
- the categories in the output result of the balance assessment model may include low balance ability, medium balance ability, and high balance ability.
- the specific meaning of low balance ability, middle balance ability and high balance ability can refer to medical standards.
- S130 Determine an evaluation result according to the output result, and output the evaluation result.
- the output result When the output result is the category with the highest probability, the output result can be directly output as the evaluation result.
- the evaluation result can be determined and output according to the probability corresponding to each category.
- determining the evaluation result according to the output result includes: obtaining the probability value of each category in the output result, and selecting a category from each category as the evaluation result according to the probability value of each category. Specifically, when the output result is each category and the probability corresponding to each category, the category corresponding to the maximum probability value is used as the evaluation result. Exemplarily, if the probability of category 1 (low balance ability) is 0.6, the probability of category 2 (medium balance ability) is 0.3, and the probability of category 3 (high balance ability) is 0.1, then category 1-low balance ability is taken as The evaluation result is output.
- standardized evaluation data is obtained by obtaining the data to be evaluated and preprocessing the data to be evaluated; inputting the standardized evaluation data into a pre-trained balance evaluation model to obtain the output result of the balance evaluation model, wherein the balance evaluation model It is a neural network model based on time series; the evaluation result is determined according to the output result, and the evaluation result is output.
- the neural network model based on the time series is used as a balance evaluation model to deeply mine the associated features in the data to be evaluated, which improves the dynamic balance evaluation Accuracy.
- Fig. 2a is a flowchart of a dynamic balance assessment method provided in the second embodiment of the present invention.
- the method includes:
- the sample evaluation data may be standardized data obtained after preprocessing the sample test data, and the label corresponding to the sample evaluation data may be realized by manual labeling.
- the doctor may label the sample evaluation data to ensure the accuracy and professionalism of the label of the sample evaluation data.
- the method of obtaining sample evaluation data from sample test data can refer to the method of obtaining standardized evaluation data from the data to be evaluated in the foregoing embodiment, which will not be repeated here.
- the dynamic balance test method corresponding to the sample test data is the same as the dynamic balance test method corresponding to the original behavior data to be evaluated.
- the sample test data is the dynamic balance test data of the standing test
- the data to be evaluated should also be the dynamic balance test data of the same standing test.
- the pre-built balance evaluation model is a neural network model based on time series.
- a balance evaluation model can be constructed based on a sequence model (such as a recurrent neural network or a variant of a recurrent neural network).
- the pre-built balance evaluation model includes a feature extraction layer and a fully connected layer, where the feature extraction layer includes at least one layer of feature extraction network, and the feature extraction network is a cyclic neural network or a variant of the cyclic neural network.
- the variants of the recurrent neural network may be a long short-term memory network (Long Short-Term Memory, LSTM), a gated recurrent unit (Gated Recurrent Unit, GRU), and so on.
- the balance evaluation model includes an input layer, a feature extraction layer, a fully connected layer, and an output layer.
- the feature extraction layer includes at least one layer of feature extraction network, and the number of layers of the fully connected layer may be at least one layer. The more layers of the feature extraction network and fully connected layers, the stronger the learning ability, but too many layers are likely to cause overfitting in the training process.
- a three-layer LSTM can be selected as the feature extraction layer, the output of the first layer of the three-layer LSTM is used as the input of the second layer, and the output of the second layer is used as the input of the third layer. Through the combination of the three-layer LSTM network, It can further improve the learning ability of the model, dig deeper potential information, and avoid overfitting during model training.
- LSTM is a variant of a special recurrent neural network.
- Fig. 2b is a schematic diagram of a basic neural network structure of an LSTM provided in the second embodiment of the present invention.
- the ⁇ operation represents the sigmoid() function
- the tanh operation represents the tanh() function
- the ⁇ operation represents Pointwise multiplication
- the + operation represents addition
- X 1 represents the data input at time t 1
- H 1 represents the LSTM neuron’s output
- C 0 represents the cellular state
- an output indicates the H 0, f t, i t, ⁇ , o t represent respective output of each calculation process.
- LSTM is similar to the recurrent neural network.
- the network structure shown in Figure 2b is continuously repeated according to the time series of the input data, which is represented as a chain structure over time.
- Fig. 2c schematically shows the chain structure of the LSTM according to the time series of the input data, where X1, X2, and X3 represent the input of the input data in different time periods, H1, H2 and H3 represent the output at different moments.
- FIG. 2d is a schematic diagram of the LSTM cell state provided in the second embodiment of the present invention. It can be seen from the dotted line in Figure 2d that the cell state runs through the entire neuron of the LSTM .
- LSTM adds or deletes information to the cell state through various "gate" structures. LSTM mainly includes three gate operations: forget gate, input gate, and output gate.
- Figure 2e is a schematic diagram of an LSTM forgetting gate provided by the second embodiment of the present invention.
- the dotted line in Figure 2e represents the forgetting gate.
- the forgetting gate controls what information is thrown away in the cell state C 0.
- Figure 2f is a schematic diagram of an LSTM input gate provided by the second embodiment of the present invention.
- the dotted line in Figure 2f represents the input gate.
- the input gate determines which information is stored in the cell state.
- Fig. 2g is a schematic diagram of an LSTM input gate provided by the second embodiment of the present invention.
- the dotted line in Figure 2g represents the output gate.
- the output gate determines the output.
- S250 Determine an evaluation result according to the output result, and output the evaluation result.
- the embodiment of the present invention generates training sample data according to the sample evaluation data and the label corresponding to the sample evaluation data by acquiring the sample evaluation data and the label corresponding to the sample evaluation data; using the training sample data to train the pre-built balance evaluation model, the training is obtained.
- the balance evaluation model is obtained by using the neural network model based on time series as the balance evaluation model, so that the construction of the balance evaluation model takes into account the correlation characteristics in the dynamic balance measurement data, and improves the dynamics of the balance evaluation model. Balance the accuracy of the assessment.
- this embodiment provides a preferred embodiment.
- constructing a balance evaluation model based on LSTM is taken as an example to illustrate the construction and training of the balance evaluation model.
- Fig. 3a is a schematic diagram of the construction process of a balance evaluation model provided by the third embodiment of the present invention.
- Fig. 3a schematically shows the process of constructing a balanced evaluation model based on the long and short-term memory network.
- the construction of the balance assessment model mainly includes:
- the German Bismarck (Bismarck International Group Inc) dynamic and static balancer can be used to measure the measurement data obtained by the subject, where the measurement data mainly include: standing with eyes open with both feet and eyes closed with both feet
- the 48-dimensional indicators of standing, standing on one foot with eyes open, and standing on one foot with eyes closed are shown in Table 1. That is, the 48-dimensional measurement data of the four actions is acquired as the source data.
- the dimensions of the 48-dimensional data included in the measurement data are different, which is not conducive to the training of the deep learning model, and may cause the model to converge too slowly.
- the feature scaling (StandarScaler) method in the sklearn.preprocessing library can be used to scale the 48-dimensional data in columns (refer to the columns in Table 1), and the scaled data has a mean value of 0 and a variance of 1.
- the neural network model training based on time series can get better training results.
- a three-layer LSTM neural network model can be used to construct a balanced evaluation model.
- a three-layer LSTM can dig deeper data information, and the learning ability is also better than that of a layer of neural network.
- the network model is good, and the choice of three-layer LSTM is not just because there are more layers, but also because the more layers, the greater the risk of overfitting.
- Fig. 3b is a schematic structural diagram of a balance evaluation model provided by the third embodiment of the present invention. As shown in FIG. 3b, the balance evaluation model includes an input layer 310, a three-layer LSTM network 320, a fully connected layer 330, and an output layer 340.
- the input layer 310 combines the 48-dimensional indicators according to four actions (each action has 12-dimensional indicators), respectively corresponding to M1, M2, M3, and M4 in the figure.
- the three-layer LSTM network 320 includes a first-layer LSTM network 321, a second-layer LSTM network 322, and a third-layer LSTM network 323.
- the output of the first-layer LSTM network 321 is used as the input of the second-layer LSTM network 322.
- the output of the LSTM network 322 is used as the input of the third-layer LSTM network 323.
- the combination of the three-layer LSTM network can further improve the learning ability of the model and dig deeper potential information.
- the fully connected layer 330 includes a first fully connected layer 331 and a second fully connected layer 332.
- the two fully connected layers fix the output of the neural network to three dimensions. Each dimension represents the probability of a classification result.
- the output layer 340 is used for Output the probability of each classification result.
- FIG. 3c is a schematic diagram of a data structure type output by an output layer provided by Embodiment 3 of the present invention.
- the first dimension represents low balance ability with a probability of 0.6
- the second dimension represents medium balance ability with a probability of 0.3
- the third dimension represents high balance ability with a probability of 0.1
- the sum of the three probabilities is 1. .
- the probability of low balance ability is the largest, which means that the final result is low balance ability.
- the data format is: 48-dimensional balance index + label (balance level: low, medium, and high); the data is randomly divided into training set and test set, and the training set is used to train and predict balance Evaluate the model, use the test set to test the trained balance evaluation model.
- the test set can be used to verify the balance evaluation model.
- a variety of verification methods can be used to verify the effectiveness of the model, such as accuracy, sensitivity, ROC curve and other methods to verify the effectiveness of the model. If the detection result is that the effectiveness of the model is low, the model can be adjusted by adjusting the model structure (such as adjusting the number of layers of the basic network in the feature extraction layer), obtaining a larger number of samples for retraining, and so on.
- the method provided in the embodiments of the present invention has been verified by experiments.
- the data set is divided into training and testing sets, and cross-validation and accuracy evaluation are adopted.
- the test results show that the accuracy rate of the evaluation model on the verification set is 86.67%, and the accuracy rate on the test set is 86.32%. If the amount of data for model training is further increased, the accuracy of the model will be further improved. It can be seen that the method for constructing a balance evaluation model for dynamic balance evaluation provided by the embodiment of the present invention has a higher accuracy rate.
- a deep neural network is used to construct a balance evaluation model.
- the model has better universal ability and improves the accuracy of balance evaluation.
- a neural network model based on time series is used as The balanced evaluation model can dig out the potential relationships among the four actions, making the evaluation results more accurate.
- Fig. 4 is a schematic structural diagram of a dynamic balance evaluation device provided in the fourth embodiment of the present invention.
- the dynamic balance evaluation device can be implemented by software and/or hardware.
- the dynamic balance evaluation device can be configured in a computer device.
- the device includes a standardized data module 410, a model result acquisition module 420, and an evaluation result output module 430, where:
- the standardized data module 410 is used to obtain the data to be evaluated, preprocess the data to be evaluated, and obtain standardized evaluation data;
- the model result acquisition module 420 is configured to input standardized evaluation data into a pre-trained balance evaluation model to obtain an output result of the dynamic balance evaluation, where the balance evaluation model is a neural network model based on time series;
- the evaluation result output module 430 is configured to determine the evaluation result according to the output result, and output the evaluation result.
- the embodiment of the present invention obtains the data to be evaluated through the standardized data module, and preprocesses the data to be evaluated to obtain standardized evaluation data; the model result acquisition module inputs the standardized evaluation data into the pre-trained balance evaluation model to obtain the output of the balance evaluation model
- the balance evaluation model is a neural network model based on time series; the evaluation result output module determines the evaluation result according to the output result, and outputs the evaluation result, and uses the neural network model based on time series as the balance evaluation model to deeply explore the pending evaluation
- the associated features in the data improve the accuracy of dynamic balance assessment.
- the device further includes a model training module for:
- the pre-built balance evaluation model includes a feature extraction layer and a fully connected layer, where the feature extraction layer includes at least one layer of feature extraction network, and the feature extraction network is a cyclic neural network or a cyclic neural network Variants.
- the evaluation result output module 430 is specifically configured to:
- the data to be evaluated includes index collection data under a set action, and the standardized data module 410 is specifically used for:
- the set action includes at least one of standing with eyes open on both feet, standing with eyes closed on both feet, standing with eyes open on one foot, and standing on one foot with eyes closed.
- the index collection data under the setting action includes the peripheral area of the eye open, the track length per unit area, the total trajectory length of shaking, the trajectory length in the first direction, and the trajectory in the second direction under the setting action. Length, the average center displacement in the first direction, the average center displacement in the second direction, the maximum swing diameter in the first direction, the maximum swing diameter in the second direction, the average speed in the first direction, the average speed in the second direction, and the average swing speed at least one.
- the dynamic balance evaluation device provided by the embodiment of the present invention can execute the dynamic balance evaluation method provided by any embodiment of the present invention, and has corresponding functional modules and beneficial effects for the execution method.
- FIG. 5 is a schematic structural diagram of a computer device according to the fifth embodiment of the present invention.
- Figure 5 shows a block diagram of an exemplary computer device 512 suitable for implementing embodiments of the present invention.
- the computer device 512 shown in FIG. 5 is only an example, and should not bring any limitation to the function and application scope of the embodiment of the present invention.
- the computer device 512 is represented in the form of a general-purpose computing device.
- the components of the computer device 512 may include, but are not limited to: one or more processors 516, a system memory 528, and a bus 518 connecting different system components (including the system memory 528 and the processor 516).
- the bus 518 represents one or more of several types of bus structures, including a memory bus or a memory controller, a peripheral bus, a graphics acceleration port, a processor 516, or a local bus using any bus structure among multiple bus structures.
- these architectures include, but are not limited to, industry standard architecture (ISA) bus, microchannel architecture (MAC) bus, enhanced ISA bus, Video Electronics Standards Association (VESA) local bus, and peripheral component interconnection ( PCI) bus.
- ISA industry standard architecture
- MAC microchannel architecture
- VESA Video Electronics Standards Association
- PCI peripheral component interconnection
- Computer device 512 typically includes a variety of computer system readable media. These media may be any available media that can be accessed by the computer device 512, including volatile and nonvolatile media, removable and non-removable media.
- the system memory 528 may include computer system readable media in the form of volatile memory, such as random access memory (RAM) 530 and/or cache memory 532.
- the computer device 512 may further include other removable/non-removable, volatile/nonvolatile computer system storage media.
- the storage device 534 may be used to read and write non-removable, non-volatile magnetic media (not shown in FIG. 5, usually referred to as a "hard drive").
- a disk drive for reading and writing to removable non-volatile disks such as "floppy disks”
- a removable non-volatile disk such as CD-ROM, DVD-ROM
- other optical media read and write optical disc drives.
- each drive may be connected to the bus 518 through one or more data medium interfaces.
- the memory 528 may include at least one program product, the program product having a set (for example, at least one) of program modules, and these program modules are configured to perform the functions of the embodiments of the present invention.
- a program/utility tool 540 having a set of (at least one) program module 542 may be stored in, for example, the memory 528.
- Such program module 542 includes but is not limited to an operating system, one or more application programs, other program modules, and program data Each of these examples or some combination may include the implementation of a network environment.
- the program module 542 generally executes the functions and/or methods in the described embodiments of the present invention.
- the computer device 512 can also communicate with one or more external devices 514 (such as a keyboard, pointing device, display 524, etc.), and can also communicate with one or more devices that enable a user to interact with the computer device 512, and/or communicate with Any device (such as a network card, modem, etc.) that enables the computer device 512 to communicate with one or more other computing devices. Such communication can be performed through an input/output (I/O) interface 522.
- the computer device 512 may also communicate with one or more networks (for example, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet) through the network adapter 520.
- networks for example, a local area network (LAN), a wide area network (WAN), and/or a public network, such as the Internet
- the network adapter 520 communicates with other modules of the computer device 512 through the bus 518. It should be understood that although not shown in the figure, other hardware and/or software modules can be used in conjunction with the computer device 512, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives And data backup storage system, etc.
- the processor 516 executes various functional applications and data processing by running programs stored in the system memory 528, for example, to implement the dynamic balance evaluation method provided by the embodiment of the present invention, the method includes:
- processor can also implement the technical solution of the dynamic balance evaluation method provided by any embodiment of the present invention.
- the sixth embodiment of the present invention also provides a computer-readable storage medium on which a computer program is stored.
- the program is executed by a processor, the dynamic balance evaluation method provided in the embodiment of the present invention is implemented, and the method includes:
- the computer program stored thereon is not limited to the above method operations, and can also perform related operations of the dynamic balance evaluation method provided by any embodiment of the present invention.
- the computer storage medium of the embodiment of the present invention may adopt any combination of one or more computer-readable media.
- the computer-readable medium may be a computer-readable signal medium or a computer-readable storage medium.
- the computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, or device, or a combination of any of the above.
- computer-readable storage media include: electrical connections with one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), Erasable programmable read-only memory (EPROM or flash memory), optical fiber, portable compact disk read-only memory (CD-ROM), optical storage device, magnetic storage device, or any suitable combination of the above.
- RAM random access memory
- ROM read-only memory
- EPROM or flash memory Erasable programmable read-only memory
- CD-ROM compact disk read-only memory
- the computer-readable storage medium can be any tangible medium that contains or stores a program, and the program can be used by or in combination with an instruction execution system, apparatus, or device.
- the computer-readable signal medium may include a data signal propagated in baseband or as a part of a carrier wave, and computer-readable program code is carried therein. This propagated data signal can take many forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the foregoing.
- the computer-readable signal medium may also be any computer-readable medium other than the computer-readable storage medium.
- the computer-readable medium may send, propagate, or transmit the program for use by or in combination with the instruction execution system, apparatus, or device .
- the program code contained on the computer-readable medium can be transmitted by any suitable medium, including but not limited to wireless, wire, optical cable, RF, etc., or any suitable combination of the above.
- the computer program code used to perform the operations of the present invention can be written in one or more programming languages or a combination thereof.
- the programming languages include object-oriented programming languages—such as Java, Smalltalk, C++, and also conventional Procedural programming language-such as "C" language or similar programming language.
- the program code can be executed entirely on the user's computer, partly on the user's computer, executed as an independent software package, partly on the user's computer and partly executed on a remote computer, or entirely executed on the remote computer or server.
- the remote computer can be connected to the user's computer through any kind of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computer (for example, using an Internet service provider to pass Internet connection).
- LAN local area network
- WAN wide area network
- Internet service provider for example, using an Internet service provider to pass Internet connection.
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Abstract
Description
Claims (10)
- 一种动态平衡评估方法,其特征在于,包括:获取待评估数据,对所述待评估数据进行预处理,得到标准化评估数据;将所述标准化评估数据输入至预先训练好的平衡评估模型中,获得所述平衡评估模型的输出结果,其中,所述平衡评估模型为基于时间序列的神经网络模型;根据所述输出结果确定评估结果,并将所述评估结果进行输出。
- 根据权利要求1所述的方法,其特征在于,在将所述标准化评估数据输入至预先训练好的平衡评估模型中之前,还包括:获取样本评估数据以及所述样本评估数据对应的标签,根据所述样本评估数据以及所述样本评估数据对应的标签生成训练样本数据;使用所述训练样本数据对预先构建的平衡评估模型进行训练,得到训练好的平衡评估模型。
- 根据权利要求2所述的方法,其特征在于,所述预先构建的平衡评估模型包括特征提取层和全连接层,其中,所述特征提取层包括至少一层特征提取网络,所述特征提取网络为循环神经网络或循环神经网络的变体。
- 根据权利要求1所述的方法,其特征在于,所述根据所述输出结果确定评估结果,包括:获取所述输出结果中各类别的概率值,根据各所述类别的概率值从各所述类别中选择一类别作为所述评估结果。
- 根据权利要求1所述的方法,其特征在于,所述待评估数据包括设定动作下的指标采集数据,所述对所述待评估数据进行预处理,得到标准化评估数据,包括:将所述指标采集数据进行归一化处理,得到所述标准化评估数据。
- 根据权利要求5所述的方法,其特征在于,所述设定动作包括双足睁眼站立、双足闭眼站立、单足睁眼站立、单足闭眼站立中的至少一个。
- 根据权利要求6所述的方法,其特征在于,所述设定动作下的指标采集数据包括所述设定动作下的睁眼外周面积、单位面积轨迹长、动摇总轨迹长、第一方向轨迹长、第二方向轨迹长、第一方向平均中心变位、第二方向平均中心变位、第一方向最大动摇径、第二方向最大动摇径、第一方向平均速度、第二方向平均速度、平均摆速中的至少一个。
- 一种动态平衡评估装置,其特征在于,包括:标准化数据模块,用于获取待评估数据,对所述待评估数据进行预处理,得到标准化评估数据;模型结果获取模块,用于将所述标准化评估数据输入至预先训练好的平衡评估模型中,获得所述动态平衡评估的输出结果,其中,所述平衡评估模型为基于时间序列的神经网络模型;评估结果输出模块,用于根据所述输出结果确定评估结果,并将所述评估结果进行输出。
- 一种计算机设备,其特征在于,所述设备包括:一个或多个处理器;存储装置,用于存储一个或多个程序;当所述一个或多个程序被所述一个或多个处理器执行,使得所述一个或多个处理器实现如权利要求1-7中任一所述的动态平衡评估方法。
- 一种计算机可读存储介质,其上存储有计算机程序,其特征在于,该程序被处理器执行时实现如权利要求1-7中任一所述的动态平衡评估方法。
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