CN111436940A - Gait health assessment method and device - Google Patents

Abstract

Embodiments of the present invention provide a gait health assessment method and device. The method includes: acquiring a gait pressure signal, and identifying a gait phase based on the gait pressure signal using a fuzzy logic inference rule; recording the phase sequence of the gait phase; Based on the standard gait sequence of the Perry model, the abnormal gait cycles deviating from the standard gait are counted according to the medical standard phase duration; Evaluation basis. In the embodiment of the present invention, a fuzzy logic inference system is applied in the process of gait phase detection to realize stable and continuous gait phase detection. Trapezoidal and triangular membership functions are used in the output membership function, and the individual gait phase sequence is not preset. Internal variability of individual gait, suitable for the needs of gait assessment of various populations.

Description

Gait health assessment method and device

technical field

The invention relates to the technical field of biometric identification, and in particular, to a gait health assessment method and device.

Background technique

Walking is one of the most common daily activities. The health of the gait and the degree of health can reflect the health of the human body to a certain extent. Gait analysis is the recording and analysis of human kinematic and kinetic information to quantify the factors that control lower extremity function during walking. Accurate gait phase identification is the basis for the analysis of individual gait, determining the gait cycle, the phase in the cycle and its duration, which can effectively reflect the health of the gait.

The existing gait analysis methods include the following:

(1) Qualitative analysis: mainly used in rehabilitation patients, relying on the qualitative analysis of experienced clinicians, and the evaluation of patient rehabilitation is easily affected by subjective;

(2) Quantitative analysis of large-scale equipment: large-scale gait analysis systems such as infrared light spot catchers, force measuring platforms, and surface electromyography instruments have high precision and complete functions, but they are expensive and limited by location and space. Medical resources are scarce. It is difficult to popularize under the current situation;

(3) Low-cost, miniaturized, portable gait analysis system: The existing low-cost, miniaturized, and portable gait analysis systems mostly use the threshold method, which can only achieve basic gait cycle segmentation, but a reasonable threshold size It is difficult to determine; in addition, existing gait analysis methods are only suitable for specific populations, and the phase sequence of human gait is preset based on experience, failing to fully consider the internal differences of individual gaits.

SUMMARY OF THE INVENTION

The embodiments of the present invention provide a gait health assessment method and device to solve the defects of the prior art gait assessment method which is susceptible to subjective influence, has low accuracy, relies too much on complex instruments, and does not consider individual differences.

In a first aspect, an embodiment of the present invention provides a gait health assessment method, including:

Acquiring a gait pressure signal, and using fuzzy logic inference rules to identify the gait phase based on the gait pressure signal;

recording the phase sequence of the gait phases;

calculating a gait phase duration, and an overall gait cycle, based on the phase sequence interval;

Based on the standard gait sequence of the Perry model, the abnormal gait cycle deviating from the standard gait is counted according to the medical standard phase duration;

Based on the abnormal gait cycle and the overall gait cycle, the abnormal cycle ratio is obtained, and the abnormal cycle ratio is used as the basis for gait health evaluation.

Preferably, the gait phase identification using fuzzy logic inference rules based on the gait pressure signal specifically includes:

selecting the input and output fuzzy sets of the gait pressure signal;

Define the input and output membership functions of the gait pressure signal;

Design the fuzzy logic inference rule based on the input-output fuzzy set and the input-output membership function, and establish a fuzzy rule table according to the fuzzy logic inference rule;

Preset operations are performed on the input and output variables of the gait pressure signal based on the fuzzy logic inference rules to obtain an aggregation of fuzzy inference results;

The gait phase is obtained by aggregating the fuzzy inference results for defuzzification processing.

Preferably, the selection of the input and output fuzzy sets of the gait pressure signal specifically includes:

The input includes the analog voltage value corresponding to the gait pressure signal collected by the ADC, and the output includes the gait phase value;

Wherein, the gait phase value corresponds to a gait cycle, the gait cycle includes a stance phase and a swing phase, and the stance phase includes a first touchdown period, a weight-bearing reaction period, a mid-stance phase, an end-stance phase, and an early swing phase , the swing phase includes the early swing phase, the middle swing phase and the end swing phase.

Preferably, the input and output membership function for defining the gait pressure signal specifically includes:

Using a sigmoid membership function to fuzzify the analog voltage value, dividing the analog voltage value into a first input fuzzy set and a second input fuzzy set, and the analog voltage value is within a preset voltage interval;

The trapezoidal membership function is used to fuzzify the first touchdown period and the swing phase, and the triangular membership function is used to fuzzify the load-bearing reaction period, the mid-support phase, the end of the support phase, and the early swing phase to obtain output variables. , the output variable is within the range of the preset output variable interval, and corresponds to the preset interval.

Preferably, the fuzzy logic inference rule is designed based on the input-output fuzzy set and the input-output membership function, and a fuzzy rule table is established according to the fuzzy logic inference rule rule, which specifically includes:

Design the fuzzy logic inference rule according to the first input fuzzy set, the second input fuzzy set and the output variable, and construct the fuzzy rule table based on the fuzzy logic inference rule;

Based on the fuzzy logic inference rules, a fuzzy inference rule matrix is defined to represent the fuzzy logic inference rules.

Preferably, the preset operation is performed on the input and output variables of the gait pressure signal based on the fuzzy logic inference rules to obtain an aggregation of fuzzy inference results, which specifically includes:

Substitute the input and output variables into the input and output membership function to obtain the membership;

According to the membership degree, combined with the fuzzy rule table, the triggered rule is obtained;

Obtaining the rule premise credibility through the preset operation;

Based on the premise credibility of the rule, perform the preset operation on the input and output variables to obtain the credibility of the rule;

A union of several rule inference results is extracted based on the rule credibility, and the union is aggregated as the fuzzy inference result.

Preferably, the de-fuzzification process is performed by aggregating the fuzzy inference results to obtain the gait phase, which specifically includes:

The fuzzy inference results are aggregated and defuzzified by the average maximum method to obtain the gait phase.

In a second aspect, an embodiment of the present invention provides a gait health assessment device, including:

an acquisition and identification module for acquiring a gait pressure signal, and using fuzzy logic inference rules to identify the gait phase based on the gait pressure signal;

a recording module for recording the phase sequence of the gait phase;

a calculation module for calculating the gait phase duration, and the overall gait cycle, according to the phase sequence interval;

The statistical module is used to count the abnormal gait cycles that deviate from the standard gait according to the medical standard phase duration based on the standard gait sequence of the Perry model;

A processing module, configured to obtain the abnormal cycle ratio based on the abnormal gait cycle and the overall gait cycle, and use the abnormal cycle ratio as a gait health evaluation basis.

In a third aspect, an embodiment of the present invention provides an electronic device, including:

A memory, a processor, and a computer program stored on the memory and executable on the processor, the processor implementing any of the steps of the gait health assessment method when the processor executes the program.

In a fourth aspect, an embodiment of the present invention provides a non-transitory computer-readable storage medium on which a computer program is stored, and when the computer program is executed by a processor, implements any one of the steps of the gait health assessment method.

The gait health assessment method and device provided by the embodiments of the present invention realize stable and continuous gait phase detection by applying a fuzzy logic inference system in the process of gait phase detection. The individual gait phase sequence is formed by default, and the internal differences of individual gaits are fully considered, which is suitable for the gait evaluation needs of various groups of people.

Description of drawings

In order to illustrate the embodiments of the present invention or the technical solutions in the prior art more clearly, the following briefly introduces the accompanying drawings that need to be used in the description of the embodiments or the prior art. Obviously, the drawings in the following description are For some embodiments of the present invention, for those of ordinary skill in the art, other drawings can also be obtained according to these drawings without creative efforts.

1 is a structural diagram of a wearable gait analysis system provided by an embodiment of the present invention;

2 is a distribution diagram of a pressure sensor provided by an embodiment of the present invention;

3 is a flowchart of a gait health assessment method provided by an embodiment of the present invention;

4 is a schematic diagram of a gait phase model provided by an embodiment of the present invention:

5 is a structural diagram of a gait health assessment device according to an embodiment of the present invention;

FIG. 6 is a structural block diagram of an electronic device provided by an embodiment of the present invention.

Detailed ways

In order to make the purposes, technical solutions and advantages of the embodiments of the present invention clearer, the technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments These are some embodiments of the present invention, but not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the protection scope of the present invention.

Aiming at the problem of human gait analysis and evaluation, the embodiment of the present invention designs a gait health evaluation method and device. Gait analysis requires accurate segmentation of the gait cycle. In the embodiment of the present invention, multiple pressure signals are collected through multiple piezoelectric ceramic sensors, and each gait phase is clearly identified by using a gait segmentation algorithm, and the duration of each gait phase is recorded at the same time. , and then effectively evaluate the gait health status through the gait analysis algorithm.

FIG. 1 is a structural diagram of a wearable gait analysis system provided by an embodiment of the present invention. As shown in FIG. 1 , the wearable gait analysis system consists of 8 pressure sensors, a digital-to-analog/analog-to-digital conversion unit (ADC), and a microcontroller. (MCU) and wireless communication module. The system consists of three subsystems: signal acquisition subsystem, signal analysis subsystem, and wireless communication subsystem.

The signal acquisition subsystem consists of a pressure sensor and an ADC, which are mainly responsible for collecting the pressure sensor signal. The distribution of the pressure sensor is shown in Figure 2. The pressure sensors GRF1-GRF3 are located at the heel, the pressure sensors GRF4-GRF7 are located at the phalanges, and the pressure sensor GRF8 at the thumb.

将数字压力值转换成模拟电压值，转换后的电压取值范围是[0,3.3V]，此处的3.3V只是工作电压的一个示例，对于其他电压值，本发明实施例不作限制。无线通信子系统将信号分析子系统计算得出的结果传输到智能手机，或通过无线基站发送到后台。无线通信的方式包括但不限于：蓝牙、WiFi、4G、5G。The digital value range of the pressure signal collected by the pressure sensor is [0,4096], using the formula:

Convert the digital pressure value into an analog voltage value, and the converted voltage value range is [0, 3.3V], where 3.3V is only an example of the working voltage, and other voltage values are not limited in the embodiment of the present invention. The wireless communication subsystem transmits the result calculated by the signal analysis subsystem to the smartphone, or sends it to the background through the wireless base station. The methods of wireless communication include but are not limited to: Bluetooth, WiFi, 4G, 5G.

The gait phase identification and gait evaluation of the signal analysis subsystem are completed in the microcontroller (MCU), including fuzzy logic reasoning, gait phase recognition and gait evaluation.

FIG. 3 is a flowchart of a gait health assessment method provided by an embodiment of the present invention, as shown in FIG. 3 , including:

S1, acquiring a gait pressure signal, and using fuzzy logic inference rules to identify the gait phase based on the gait pressure signal;

S2, record the phase sequence of the gait phase;

S3, calculating the gait phase duration and the overall gait cycle according to the phase sequence interval;

S4, based on the standard gait sequence of the Perry model, according to the medical standard phase duration to count the abnormal gait cycles that deviate from the standard gait;

S5 , obtaining an abnormal cycle ratio based on the abnormal gait cycle and the overall gait cycle, and using the abnormal cycle ratio as a gait health evaluation basis.

Specifically, gait health assessment aims to build a model that can identify the gait health of different individuals, without presetting the phase sequence to form a specific individual gait, fully considering the structural differences of individual gaits, and gait assessment by measuring gait This is achieved by the "symmetry" of the gait and the "homogeneity" of the gait, where "symmetry" measures the similarity between the pressure signals generated by the left and right feet during each step; of repeating on time. Therefore, an assessment of an individual's gait can be performed from the sequence of phases in the gait cycle and the duration of each phase.

The steps to assess whether gait is normal are as follows:

1) Mark transitional gait events from one gait phase to another, i.e. gait phase identification;

2) Record the sequence of gait phases. The system does not preset the phase sequence of individual gait, and only judges the current gait phase according to the pressure signal obtained by the detection, which can clearly reflect the gait phase at each moment;

3) Calculate the gait phase duration. The gait phase duration is recorded with the interval between two phases of the same side foot (such as the right foot) as a gait cycle, and compared with medical standards: IC accounts for about 2% of the gait cycle, and LR accounts for about 2% of the gait cycle. About 10%, MS about 19% of gait cycle, TS about 19% of gait cycle, PS about 12% of gait cycle, SW about 38% of gait cycle;

以异常周期占比作为步态健康评估的依据。4) Taking the gait sequence (IC—>LR—>MS—>TS—>PS—>SW) proposed by the Perry model as the standard gait, the divided gait phases differ from the Perry model and the gait phase duration obviously deviates from the steps. The gait cycle of the medical standard shown in (3) is collectively referred to as the abnormal gait cycle, and the abnormal cycle accounts for the formula:

The percentage of abnormal cycles was used as the basis for gait health assessment.

The embodiment of the present invention realizes the stable and continuous gait phase detection by applying the fuzzy logic inference system in the gait phase detection process, does not preset the formation of the individual gait phase sequence, fully considers the internal difference of the individual gait, and is suitable for various gait phases. Gait assessment needs of the population.

Based on the above embodiment, the gait phase identification using fuzzy logic inference rules based on the gait pressure signal specifically includes:

selecting the input and output fuzzy sets of the gait pressure signal;

Define the input and output membership functions of the gait pressure signal;

Design the fuzzy logic inference rule based on the input-output fuzzy set and the input-output membership function, and establish a fuzzy rule table according to the fuzzy logic inference rule;

Preset operations are performed on the input and output variables of the gait pressure signal based on the fuzzy logic inference rules to obtain an aggregation of fuzzy inference results;

The gait phase is obtained by aggregating the fuzzy inference results for defuzzification processing.

Specifically, gait phase recognition is accomplished through fuzzy logic reasoning, which includes the following steps: (1) selecting input and output fuzzy sets; (2) defining input and output membership functions; (3) establishing a fuzzy control table; (4) establishing Fuzzy control rules; (5) Fuzzy reasoning; (6) Defuzzification.

Based on any of the above embodiments, the selecting the input and output fuzzy sets of the gait pressure signal specifically includes:

The input includes the analog voltage value corresponding to the gait pressure signal collected by the ADC, and the output includes the gait phase value;

Wherein, the gait phase value corresponds to a gait cycle, the gait cycle includes a stance phase and a swing phase, and the stance phase includes a first touchdown period, a weight-bearing reaction period, a mid-stance phase, an end-stance phase, and an early swing phase , the swing phase includes the early swing phase, the middle swing phase and the end swing phase.

Specifically, the input is the corresponding analog voltage value collected by the ADC; the output is the gait phase value.

According to medical standards, for a specific lower limb (such as the right limb), as shown in Figure 4, the gait cycle consists of a stance phase (Stance) and a swing phase (Swing, SW). 60%, the swing phase accounts for about 40% of the entire gait cycle. According to the Perry gait model, each gait cycle is divided into 8 stages, as shown in Figure 3, of which 5 stages belong to the standing phase, namely: Initial Contact (IC), weight-bearing reaction period ( Loading Response (LR), Mid Stance (MS), Terminal Stance (TS) and Pre-Swing (PS); 3 stages belong to the swing phase, namely: Early Swing (Initial Stance) Swing, Mid Swing and Terminal Swing, these three phases are collectively referred to as SW.

The detailed description of each gait phase is as follows:

IC: GRF1-GRF3 in the heel begins to measure force.

LR: The outside of the forefoot begins to touch the ground and GRF4-GRF5 begins to measure the force.

MS: The inner side of the forefoot starts to touch the ground, GRF6-GRF7 starts to measure force, GRF8 may or may not measure force depending on gait.

TS: The human center of gravity moves forward and the heel no longer touches the ground, i.e., GRF1-GRF3 no longer measure forces.

PS: Only the thumb and toe touch the ground, i.e. only the GRF8 measures the force.

SW: The feet do not touch the ground, and the GRF1-GRF8 signals remain at 0.

Using the pressure signal, the detection of 6 gait phases is realized, namely: IC, LR, MS, TS, PS and swing phase SW.

Based on any of the above-mentioned embodiments, the input and output membership functions of defining the gait pressure signal specifically include:

Using a sigmoid membership function to fuzzify the analog voltage value, dividing the analog voltage value into a first input fuzzy set and a second input fuzzy set, and the analog voltage value is within a preset voltage interval;

The trapezoidal membership function is used to fuzzify the first touchdown period and the swing phase, and the triangular membership function is used to fuzzify the load-bearing reaction period, the mid-support phase, the end of the support phase, and the early swing phase to obtain output variables. , the output variable is within the range of the preset output variable interval, and corresponds to the preset interval.

Specifically, a sigmoid-type membership function (Member Function, MF) is used to fuzzify each input variable (voltage value corresponding to the pressure value), and the pressure is divided into two fuzzy sets: L (corresponding to membership function 1, MF1), S ( Corresponding to membership function 2, MF2). L represents a large pressure value, and S represents a small pressure value. The value range of the input variable: [0, 3.3], that is, a preset voltage interval, which is not limited in the embodiment of the present invention.

The trapezoidal membership function and the triangular membership function are used to fuzzify each output variable (gait phase), and the gait phase is divided into 6 fuzzy sets: IC (corresponding to membership function 1, MF1), LR (corresponding to membership function 2, MF2), MS (corresponding to membership function 3, MF3), TS (corresponding to membership function 4, MF4), PS (corresponding to membership function 5, MF5) and SW (corresponding to membership function 6, MF6). Among them, the triangular membership function is a special form of the trapezoidal membership function. IC and SW use trapezoidal membership functions, and the rest use triangular membership functions. The value range of the preset output variable interval: [0, 12], the output value is 0-2 corresponding to the gait phase IC, that is, 2 is the preset interval, and so on.

Based on any of the above embodiments, the design of the fuzzy logic inference rule based on the input-output fuzzy set and the input-output membership function, and the establishment of a fuzzy rule table according to the fuzzy logic inference rule, specifically includes:

Design the fuzzy logic inference rule according to the first input fuzzy set, the second input fuzzy set and the output variable, and construct the fuzzy rule table based on the fuzzy logic inference rule;

Based on the fuzzy logic inference rules, a fuzzy inference rule matrix is defined to represent the fuzzy logic inference rules.

Specifically, the rules are designed according to the pressure value of the pressure sensor corresponding to each gait phase. The larger the pressure value (L), the greater the possibility of this position touching the ground; the smaller the pressure value (S), the more likely this position is touching the ground. less likely. The embodiment of the present invention has a total of 8 input variables, and each variable has 2 possible language values (L is large, S is small), which may constitute 2 ⁸ =256 fuzzy inference rules.

Fuzzy rules take the form of if...then, for example, the form of rule 10 is: if(GRF1 is S)AND(GRF2 is S)AND(GRF3 is S)AND(GRF4 is S)AND(GRF5 is S)AND(GRF6 is S) AND (GRF7 is S) AND (GRF8 is S) then gait-phase is SW. The specific expression form of fuzzy rules is cumbersome, and is replaced by fuzzy inference rule matrix in programming. For example, rule 10, the corresponding rule matrix is [2 2 2 2 2 2 2 2 6 11].

Here, the fuzzy inference rule list matrix should follow the following principles: if the system has m inputs and n outputs, the first m vector elements in the rule structure correspond to the 1st to mth inputs, and the next n columns correspond to n output. The first column element is the membership function pointer related to input1 (L corresponds to 1, S corresponds to 2, / corresponds to 0), and the m+1 column element is the membership function pointer related to output1 (1 corresponds to IC, 2 corresponds to LR, 3 Corresponds to MS, 4 corresponds to TS, 5 corresponds to PS, 6 corresponds to SW), and so on. The m+n+1th column is the weight value related to the rule (usually the value is 1), and the m+n+2th column specifies the rule connection method (1 represents an AND relationship, 2 represents an OR relationship). The number of lines is equal to the number of rules that need to be added. The fuzzy rule table is shown in Table 1:

Table 1

Based on any of the above-mentioned embodiments, the preset operation is performed on the input and output variables of the gait pressure signal based on the fuzzy logic inference rules to obtain an aggregation of fuzzy inference results, which specifically includes:

Substitute the input and output variables into the input and output membership function to obtain the membership;

According to the membership degree, combined with the fuzzy rule table, the triggered rule is obtained;

Obtaining the rule premise credibility through the preset operation;

Based on the premise credibility of the rule, perform the preset operation on the input and output variables to obtain the credibility of the rule;

A union of several rule inference results is extracted based on the rule credibility, and the union is aggregated as the fuzzy inference result.

Wherein, the de-fuzzification process is performed by aggregating the fuzzy inference results to obtain the gait phase, which specifically includes:

The fuzzy inference results are aggregated and defuzzified by the average maximum method to obtain the gait phase.

Specifically, the fuzzy reasoning process adopted in the embodiment of the present invention is as follows:

1) Rule matching: Substitute the plantar pressure value measured by the current pressure sensor into the membership function to obtain the corresponding membership degree.

2) Rule triggering: According to the degree of membership, combined with the fuzzy inference rule table, the triggered rules are obtained.

3) Rule premise reasoning: within the same rule, the premise credibility of the rule is obtained through the relationship of "and".

4) Reasoning of each rule: "AND" the input and output to obtain the credibility of the rule.

5) Fuzzy system output aggregation: take the union of the inference results of each rule to obtain the aggregation of fuzzy inference results.

6) Defuzzification: The fuzzy system needs to defuzzify the output to obtain accurate inference results. Here, the average maximum method (mom) is used for defuzzification.

FIG. 5 is a structural diagram of a gait health assessment device provided by an embodiment of the present invention. As shown in FIG. 5 , it includes: an acquisition and identification module 51, a recording module 52, a calculation module 53, a statistics module 54, and a processing module 55; wherein:

The acquisition and recognition module 51 is used to acquire the gait pressure signal, and based on the gait pressure signal, fuzzy logic inference rules are used to identify the gait phase; the recording module 52 is used to record the phase sequence of the gait phase; the calculation module 53 is used to The phase sequence interval calculates the gait phase duration, and the overall gait cycle; the statistical module 54 is used for the standard gait sequence based on the Perry model, and according to the medical standard phase duration, the abnormal gait cycle that deviates from the standard gait is counted; the processing module 55 is used to obtain the abnormal cycle ratio based on the abnormal gait cycle and the overall gait cycle, and use the abnormal cycle ratio as a gait health evaluation basis.

The apparatuses provided in the embodiments of the present invention are generally used to execute the above corresponding methods, and the specific implementations thereof are the same as those of the methods, and the involved algorithmic processes are the same as those of the corresponding methods, which will not be repeated here.

The embodiment of the present invention realizes the stable and continuous gait phase detection by applying the fuzzy logic inference system in the gait phase detection process, does not preset the formation of the individual gait phase sequence, fully considers the internal difference of the individual gait, and is suitable for various gait phases. Gait assessment needs of the population.

FIG. 6 illustrates a schematic diagram of the physical structure of an electronic device. As shown in FIG. 6 , the electronic device may include: a processor (processor) 610, a communication interface (Communications Interface) 620, a memory (memory) 630 and a communication bus 640, The processor 610 , the communication interface 620 , and the memory 630 communicate with each other through the communication bus 640 . The processor 610 can invoke the logic instructions in the memory 630 to perform the following methods: acquiring a gait pressure signal, identifying a gait phase based on the gait pressure signal using a fuzzy logic inference rule; recording the phase sequence of the gait phase; Calculate the gait phase duration, and the overall gait cycle based on the phase sequence interval; based on the Perry model standard gait sequence, and count the abnormal gait cycles deviating from the standard gait according to the medical standard phase duration; based on the abnormal gait The abnormal cycle ratio is obtained from the cycle and the overall gait cycle, and the abnormal cycle ratio is used as a gait health evaluation basis.

In addition, the above-mentioned logic instructions in the memory 630 can be implemented in the form of software functional units and can be stored in a computer-readable storage medium when sold or used as an independent product. Based on this understanding, the technical solution of the present invention can be embodied in the form of a software product in essence, or the part that contributes to the prior art or the part of the technical solution. The computer software product is stored in a storage medium, including Several instructions are used to cause a computer device (which may be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in the various embodiments of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, Read-Only Memory (ROM, Read-Only Memory), Random Access Memory (RAM, Random Access Memory), magnetic disk or optical disk and other media that can store program codes .

On the other hand, an embodiment of the present invention further provides a non-transitory computer-readable storage medium on which a computer program is stored, and the computer program is implemented by a processor to execute the transmission method provided by the above embodiments, for example, including : obtain a gait pressure signal, and use fuzzy logic inference rules to identify the gait phase based on the gait pressure signal; record the phase sequence of the gait phase; calculate the gait phase duration according to the phase sequence interval, and the overall pace gait cycle; based on the standard gait sequence of the Perry model, the abnormal gait cycle deviating from the standard gait is counted according to the medical standard phase duration; the abnormal cycle proportion is obtained based on the abnormal gait cycle and the overall gait cycle, and The percentage of abnormal cycles mentioned above was used as the basis for gait health evaluation.

The device embodiments described above are only illustrative, wherein the units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in One place, or it can be distributed over multiple network elements. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution in this embodiment. Those of ordinary skill in the art can understand and implement it without creative effort.

From the description of the above embodiments, those skilled in the art can clearly understand that each embodiment can be implemented by means of software plus a necessary general hardware platform, and certainly can also be implemented by hardware. Based on this understanding, the above-mentioned technical solutions can be embodied in the form of software products in essence or the parts that make contributions to the prior art, and the computer software products can be stored in computer-readable storage media, such as ROM/RAM, magnetic A disc, an optical disc, etc., includes several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform the methods described in various embodiments or some parts of the embodiments.

Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, but not to limit them; although the present invention has been described in detail with reference to the foregoing embodiments, those of ordinary skill in the art should understand that it can still be The technical solutions described in the foregoing embodiments are modified, or some technical features thereof are equivalently replaced; and these modifications or replacements do not make the essence of the corresponding technical solutions deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims (10)

1. A gait health assessment method, comprising:

acquiring a gait pressure signal, and identifying a gait phase by adopting a fuzzy logic inference rule based on the gait pressure signal;

recording the phase sequence of the gait phase;

calculating gait phase duration and overall gait cycle according to the phase sequence interval;

counting abnormal gait cycles deviating from standard gait according to medical standard phase duration based on a standard gait sequence of a Perry model;

and obtaining an abnormal cycle proportion based on the abnormal gait cycle and the overall gait cycle, and taking the abnormal cycle proportion as a gait health evaluation basis.

2. The gait health assessment method according to claim 1, wherein said identifying gait phase based on said gait pressure signal using fuzzy logic inference rules comprises:

selecting an input-output fuzzy set of the gait pressure signal;

defining an input and output membership function of the gait pressure signal;

designing the fuzzy logic inference rule based on the input and output fuzzy set and the input and output membership function, and establishing a fuzzy rule table according to the fuzzy logic inference rule;

performing preset operation on input and output variables of the gait pressure signals based on the fuzzy logic reasoning rule to obtain fuzzy reasoning result aggregation;

and aggregating the fuzzy reasoning results to perform defuzzification processing to obtain the gait phase.

3. The gait health assessment method according to claim 2, wherein the selecting the fuzzy input/output set of gait pressure signals comprises:

inputting an analog voltage value corresponding to the gait pressure signal acquired by an ADC (analog-to-digital converter), and outputting a gait phase value;

the gait phase value corresponds to a gait cycle, the gait cycle comprises a standing phase and a swinging phase, the standing phase comprises a first touchdown phase, a bearing reaction phase, a support phase middle phase, a support phase end phase and a swinging early phase, and the swinging phase comprises a swinging phase early phase, a swinging phase middle phase and a swinging phase end phase.

4. The gait health assessment method according to claim 3, wherein the defining the input and output membership functions of the gait pressure signal comprises:

fuzzifying the analog voltage value by adopting a sigmoid membership function, and dividing the analog voltage value into a first input fuzzy set and a second input fuzzy set, wherein the analog voltage value is in a preset voltage interval range;

fuzzifying the first touchdown period and the swing phase by adopting a trapezoidal membership function, and fuzzifying the bearing reaction period, the support phase middle period, the support phase final period and the swing early period by adopting a triangular membership function to obtain an output variable, wherein the output variable is in a preset output variable interval range and corresponds to the output variable at preset intervals.

5. The gait health assessment method according to claim 4, wherein the designing the fuzzy logic inference rule based on the input-output fuzzy set and the input-output membership function, and establishing a fuzzy rule table according to the fuzzy logic inference rule, specifically comprises:

designing the fuzzy logic inference rule according to the first input fuzzy set, the second input fuzzy set and the output variable, and constructing the fuzzy rule table based on the fuzzy logic inference rule;

and defining a fuzzy inference rule matrix to represent the fuzzy inference rule based on the fuzzy inference rule.

6. The gait health assessment method according to claim 2, wherein the preset operation is performed on the input and output variables of the gait pressure signal based on the fuzzy logic inference rule to obtain a fuzzy inference result aggregate, specifically comprising:

substituting the input and output variables into the input and output membership function to obtain membership;

obtaining a triggered rule by combining the fuzzy rule table according to the membership degree;

obtaining the reliability of the rule precondition through the preset operation;

performing the preset operation on the input and output variables based on the rule precondition credibility to obtain rule credibility;

and extracting a union of a plurality of rule reasoning results based on the rule credibility, wherein the union is used as the fuzzy reasoning result aggregation.

7. The gait health assessment method according to claim 2, wherein the step of aggregating the fuzzy inference results to perform defuzzification processing to obtain the gait phase comprises:

and aggregating the fuzzy inference results by adopting an average maximum value method to perform defuzzification processing to obtain the gait phase.

8. A gait health assessment apparatus, characterized by comprising:

the acquisition and identification module is used for acquiring gait pressure signals and identifying gait phases by adopting a fuzzy logic reasoning rule based on the gait pressure signals;

the recording module is used for recording the phase sequence of the gait phase;

the calculating module is used for calculating the gait phase duration and the overall gait cycle according to the phase sequence interval;

the statistical module is used for counting abnormal gait cycles deviating from standard gait according to medical standard phase duration based on a standard gait sequence of a Perry model;

and the processing module is used for obtaining an abnormal cycle proportion based on the abnormal gait cycle and the overall gait cycle, and taking the abnormal cycle proportion as a gait health evaluation basis.

9. An electronic device comprising a memory, a processor and a computer program stored on the memory and executable on the processor, wherein the processor when executing the program implements the steps of the gait health assessment method of any of claims 1 to 7.

10. A non-transitory computer readable storage medium having a computer program stored thereon, wherein the computer program when executed by a processor implements the steps of the gait health assessment method according to any of claims 1 to 7.

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