A method and apparatus for assessing ankle dorsiflexion function

Abstract

The present application provides a kind of ankle dorsiflexion function evaluation method and device, applied to rehabilitation evaluation technical field, comprising: using infrared sensor, according to preset rule, ankle dorsiflexion function detection is carried out, and ankle dorsiflexion function detection data is obtained;Based on the ankle dorsiflexion function detection data, dorsiflexion function evaluation is carried out.The present application can provide ankle dorsiflexion function evaluation label to evaluate the data basis of the biological characteristics such as muscle strength of ankle and its muscle group, with the characteristics of strong intuitiveness, high accuracy.

Description

Technical Field

[0001] This invention relates to the field of rehabilitation evaluation technology, and in particular to a method and device for assessing ankle dorsiflexion function. Background Technology

[0002] Ankle dorsiflexion function refers to the ability of the ankle joint to bend upwards. In rehabilitation, assessing ankle dorsiflexion function is an important means of evaluating rehabilitation effectiveness. Ankle dorsiflexion function is generally measured through quantitative tests. The most commonly used test method is to measure the patient's ability to dorsiflex their foot upwards while seated.

[0003] Traditional sports rehabilitation primarily relies on physician assessment and manual training. From initial treatment to later rehabilitation and recovery, traditional rehabilitation training and evaluation are conducted one-on-one between the patient and physician. Physicians typically assess ankle dorsiflexion ability based on experience, a process potentially prone to subjectivity, inaccuracy, and inconsistency. This is because physicians may evaluate ankle dorsiflexion ability based on personal experience or subjective judgment, leading to differing assessments of the same patient by different physicians, and even different assessments by the same physician at different times. This traditional method of relying on physicians' subjective evaluation of ankle dorsiflexion ability has limitations and lacks data-driven support. Therefore, the efficiency and effectiveness of treatment are influenced by many subjective factors.

[0004] Meanwhile, existing laboratory-grade optical motion capture systems, such as Vicon 3D, while highly accurate, are expensive and limited to laboratory environments. Wearable devices based on inertial sensors suffer from discomfort and sensor drift, resulting in low ecological validity. These methods and instruments also cannot quantify the stability of movement rhythms or gender-differentiated fatigue responses, leading to a lack of personalized adjustment criteria for rehabilitation programs. Therefore, there is an urgent need to develop a low-cost, non-contact, high-precision ankle dorsiflexion function assessment system. Summary of the Invention

[0005] To address the issues of subjectivity, inaccuracy, and inconsistency inherent in traditional diagnostic methods that rely on physicians' subjective evaluations of ankle dorsiflexion ability, this invention provides a method and apparatus for assessing ankle dorsiflexion function. The technical solution is as follows:

[0006] On the one hand, a method for assessing ankle dorsiflexion function is provided, which is implemented by an ankle dorsiflexion function assessment device, and the method includes:

[0007] S1: Using an infrared sensor, ankle dorsiflexion function is detected according to preset rules to obtain ankle dorsiflexion function detection data;

[0008] S2: Based on the ankle dorsiflexion function test data, the dorsiflexion duration data is obtained through calculation;

[0009] S3: Based on the ankle dorsiflexion function test data, the number of dorsiflexion cycles is calculated.

[0010] S4: Based on the ankle dorsiflexion function detection data, the initial value of the infrared light signal intensity and the dorsiflexion amplitude data are obtained through calculation. The dorsiflexion amplitude data is then corrected and calculated to obtain the corrected dorsiflexion amplitude data. The initial value of the intensity is the intensity of the infrared light signal before the start of each dorsiflexion event. The correction process is to remove the initial value of the infrared light signal intensity from the intensity of the infrared light signal in the dorsiflexion event and obtain the remaining intensity of the infrared light signal. The corrected dorsiflexion amplitude data includes the mean and maximum value of the remaining intensity of the infrared light signal in each dorsiflexion event.

[0011] S5: Based on the backflexion duration data and the corrected backflexion amplitude data, the backflexion function assessment index is obtained through the backflexion function analysis formula;

[0012] S6: The dorsiflexion function is evaluated by comparing the ankle dorsiflexion function test data, the number of dorsiflexion cycles, the dorsiflexion function assessment index, and the preset benchmark data.

[0013] Preferably, before step S1, which uses an infrared sensor to perform ankle dorsiflexion function detection according to preset rules and obtains ankle dorsiflexion function detection data, the method further includes:

[0014] S01: Determine the preset installation location of the infrared sensor;

[0015] The step S01 of determining the preset location for installing the infrared sensor includes:

[0016] S011: Determine the location point O where the heel intersects the ground;

[0017] S012: Determine the location point S of a point taken at any position on the instep of the subject;

[0018] S013: Obtain the position point S0 of the projection point of S, wherein the line connecting the position point S to the position point S0 is perpendicular to the ground.

[0019] S014: Connect point O and point S with a straight line to obtain line segment OS;

[0020] S015: Connect point O and point S0 with a straight line to obtain line segment OS0;

[0021] S016: Determine the installation height according to formula (1):

[0022] H=L×sinα (1)

[0023] Where H is the installation height of the infrared sensor, L represents the length of line segment OS, α is the sum of α0 and α1, α0 represents the angle between the foot surface and the horizontal plane; α1 represents the angle between OS and the foot surface;

[0024] S017: Determine the longitudinal position of the installation according to formula (2):

[0025] W=L×cosα (2)

[0026] Where W represents the length of line segment OS0;

[0027] S018: Calculate the toe movement range based on the lower limb DH model, ensuring that the infrared sensor installation parameters cover the ankle dorsiflexion trajectory:

[0028] y=49.997sin(0.0441x-0.4547)-41.3483sin(0.0493x-10.0000)(3)

[0029] Formula (3) is the ankle dorsiflexion trajectory.

[0030] Preferably, step S1 utilizes an infrared sensor to perform ankle dorsiflexion function detection according to preset rules, obtaining ankle dorsiflexion function detection data, including:

[0031] S11: The infrared sensor installed at a preset position continuously emits infrared light to detect the feet;

[0032] S12: After detecting the foot, perform an ankle dorsiflexion test within a selected time period. The ankle dorsiflexion test involves continuously emitting infrared light and receiving infrared light signals reflected back from the foot. The infrared light signals reflected back from the foot include a series of infrared light intensity for the selected time interval reflected back from the foot and a corresponding timestamp.

[0033] S13: According to the preset rules, based on the infrared light signal reflected back from the foot, mark each dorsiflexion event to obtain dorsiflexion event data. The dorsiflexion event is the data of the entire process after the foot reaches the dorsiflexion posture requirement and maintains the dorsiflexion posture.

[0034] S14: Extract the start time and end time of each dorsiflexion event from the dorsiflexion event data;

[0035] S15: Extract the initial value, mean value, and maximum value of the infrared light signal intensity for each dorsiflexion event from the dorsiflexion event data;

[0036] S16: The start time and end time of each dorsiflexion event, as well as the initial, average, and maximum values ​​of the infrared light signal intensity of each dorsiflexion event, are aggregated into ankle dorsiflexion function detection data.

[0037] Preferably, the dorsiflexion duration data obtained by S2 based on the ankle dorsiflexion function detection data includes:

[0038] S21: Based on ankle dorsiflexion function test data, extract the start time and end time of each dorsiflexion event, and subtract the start time from the end time of each dorsiflexion event to obtain the duration of each dorsiflexion event;

[0039] S22: Sort the duration of each dorsiflexion event according to the start time of each dorsiflexion event to obtain the time series data of the duration of all dorsiflexion events;

[0040] S23: Based on the time series data of the duration of all backbend events, a fitting algorithm is used to calculate the change index of the time series data. The fitting algorithm includes at least one of the following methods: least squares, moving average, exponential smoothing, trend fitting, seasonal fitting, and autoregressive moving average. The change index of the time series data is the rate of change of the infrared light signal per unit time.

[0041] Preferably, in step S5, based on the backflexion duration data and the corrected backflexion amplitude data, a backflexion function assessment index is obtained through a backflexion function analysis formula, including:

[0042] S51: Based on the dorsiflexion duration data, calculate the dorsiflexion change index using formula (4):

[0043]

[0044] Among them, C i x is the dorsiflexion variation index. i β1 represents the duration of a single flexion event, β2 represents the weight of the physical phase, and β1 represents the weight of the endurance phase.

[0045] S52: Based on the backflexion duration data, backflexion change index, and backflexion amplitude data after correction, the backflexion function assessment index is obtained through formula (5):

[0046] Ii = Mean(x) i )+α1C i +α2Adj i (5)

[0047] Where Ii is the backflexion function assessment index, α1 is the weight of the backflexion change index, α2 is the weight of the corrected backflexion amplitude data, and Adj i Data on the flexion amplitude after correction of a flexion event;

[0048] S53: Based on the backflexion duration data, the periodicity of consecutive backflexion events is calculated using formulas (6) and (7):

[0049]

[0050] in, The mean period of fatigue-induced dorsiflexion. The mean cycle of dorsiflexion after fatigue is T, the total duration of dorsiflexion is N, and the total number of dorsiflexions is N.

[0051] When ΔT ≥ 10%, the fatigue correction model is activated using formula (8):

[0052] Ii fatigue =Ii×(1+γ·ΔT) (8)

[0053] Where γ is the weighting coefficient for the subject's gender and age:

[0054] γ = 0.35 + 0.15 × S gender +0.02×A age (9)

[0055] Among them, S gender For gender parameters, 0 for female and 1 for male, A age For age;

[0056] S54: Based on the time series data of dorsiflexion duration, the rhythm variation coefficient is calculated using formula (10):

[0057]

[0058] Where σT is the standard deviation of the dorsiflexion periodic potential field and T is the average period; when CV≥15%, it is judged as rhythm disorder, triggering a warning of abnormal neuromuscular control.

[0059] Preferably, before performing the dorsiflexion function assessment in step S6 by comparing the ankle dorsiflexion function test data, the number of dorsiflexion cycles, the dorsiflexion function assessment index, and preset benchmark data, the following steps are included:

[0060] S02: Obtain preset baseline data;

[0061] The acquisition of preset reference data in S02 includes:

[0062] S021: Using an infrared sensor, perform a preset number of ankle dorsiflexion function tests on different individuals according to preset rules to obtain an ankle dorsiflexion function test dataset;

[0063] S022: Based on the ankle dorsiflexion function test dataset, cluster analysis was performed to obtain multiple ankle dorsiflexion function groups;

[0064] S023: Calculate the dorsiflexion function assessment index for each person in each ankle dorsiflexion function test in each ankle dorsiflexion function group, and obtain the dorsiflexion function assessment index set.

[0065] S024: Using visualization methods, display the data of each ankle dorsiflexion function group. Based on the visualization analysis results and combined with the social data of different people, assess the dorsiflexion function of each ankle dorsiflexion function group, and use the assessment results as labels to obtain a label set.

[0066] S025: Combine the ankle dorsiflexion function test dataset, dorsiflexion function assessment index set, and labels to obtain the preset benchmark data.

[0067] Preferably, step S6 involves comparing the ankle dorsiflexion function test data, the number of dorsiflexion cycles, the dorsiflexion function assessment index, and preset benchmark data to perform a dorsiflexion function assessment, including:

[0068] S61: Based on preset benchmark data, extract the ankle dorsiflexion function test dataset, dorsiflexion function assessment index set, and label set;

[0069] S62: Based on the ankle dorsiflexion function test data and the ankle dorsiflexion function test dataset, perform multi-time series analysis to obtain ankle dorsiflexion function groups that meet preset thresholds. The multi-time series analysis includes: Euclidean distance, dynamic time bending distance, singular value decomposition method and point distribution feature-based method.

[0070] S63: Extract the dorsiflexion function assessment index of the ankle dorsiflexion function group that meets the preset threshold, and use it as the benchmark assessment index;

[0071] S64: Calculate the difference between the dorsiflexion function assessment index and the benchmark assessment index. If the difference is within a preset range, extract the labels of the ankle dorsiflexion function groups that meet the preset threshold as the dorsiflexion function assessment results.

[0072] On the other hand, an ankle dorsiflexion function assessment device is provided, which is applied to an ankle dorsiflexion function assessment method, and the device includes:

[0073] Functional detection module: Used to detect ankle dorsiflexion function using an infrared sensor according to preset rules, and obtain ankle dorsiflexion function detection data;

[0074] Dorsiflexion duration module: used to calculate dorsiflexion duration data based on the ankle joint dorsiflexion function detection data;

[0075] Dorsiflexion count module: used to calculate the dorsiflexion count data based on the ankle joint dorsiflexion function test data;

[0076] The dorsiflexion amplitude module is used to calculate the initial value of the infrared light signal intensity and dorsiflexion amplitude data based on the ankle dorsiflexion function detection data. It then performs correction processing and calculation on the dorsiflexion amplitude data to obtain corrected dorsiflexion amplitude data. The initial value of the intensity is the intensity of the infrared light signal before the start of each dorsiflexion event. The correction processing is to remove the initial value of the infrared light signal intensity from the intensity of the infrared light signal in the dorsiflexion event and obtain the remaining intensity of the infrared light signal. The corrected dorsiflexion amplitude data includes the mean and maximum value of the remaining intensity of the infrared light signal in each dorsiflexion event.

[0077] Assessment Index Module: Based on the backflexion duration data and the corrected backflexion amplitude data, the backflexion function assessment index is obtained through the backflexion function analysis formula;

[0078] Functional assessment module: used to assess dorsiflexion function by comparing the ankle dorsiflexion function test data, the number of dorsiflexion cycles, the dorsiflexion function assessment index, and preset benchmark data.

[0079] On the other hand, an ankle dorsiflexion function assessment device is provided, the ankle dorsiflexion function assessment device comprising: a processor; a memory, the memory storing computer-readable instructions, which, when executed by the processor, implement any of the methods described above for ankle dorsiflexion function assessment.

[0080] On the other hand, a computer-readable storage medium is provided, wherein at least one instruction is stored therein, the at least one instruction being loaded and executed by a processor to implement any of the above-described methods for assessing ankle dorsiflexion function.

[0081] The beneficial effects of the technical solutions provided in the embodiments of the present invention include at least the following:

[0082] Ankle dorsiflexion function assessment methods can detect patients' ankle dorsiflexion function based on data, improve the accuracy of sports rehabilitation diagnosis and treatment, enhance the reproducibility of the assessment process, and also improve the efficiency and accuracy of the diagnosis and treatment process.

[0083] At the same time, patient data can be compared with baseline data, which helps doctors to develop and adjust exercise rehabilitation treatment plans in a timely manner. This enables the visualization and digitization of test data, facilitating more scientific and effective subsequent exercise rehabilitation treatment for patients.

[0084] A fatigue correction model was established to quantify fatigue dynamics. By defining weighting coefficients and the rate of change of the mean dorsiflexion cycle, physiological fatigue was transformed into quantifiable parameters. Neuromuscular control function was quantified using the coefficient of variation of rhythm, addressing the problem of traditional assessments relying on subjective experience.

[0085] Finally, an ankle dorsiflexion function assessment label is provided to assess the biological characteristics of the ankle joint and its muscle groups, such as muscle strength, which is highly intuitive and accurate. Attached Figure Description

[0086] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0087] Figure 1 This is a flowchart of an ankle dorsiflexion function assessment method provided by an embodiment of the present invention;

[0088] Figure 2 This is a schematic diagram of a method for determining the installation height and position of an infrared sensor according to an embodiment of the present invention;

[0089] Figure 3 a- Figure 3 b is a simulation diagram of the standing and sitting postures of the lower limb model provided in the embodiment of the present invention;

[0090] Figure 4 This is a comparison diagram of the theoretical displacement trajectory and the simulated displacement trajectory provided in the embodiments of the present invention;

[0091] Figure 5 This is a block diagram of an ankle dorsiflexion function assessment device provided in an embodiment of the present invention;

[0092] Figure 6 This is a schematic diagram of the structure of an ankle dorsiflexion function assessment device provided in an embodiment of the present invention. Detailed Implementation

[0093] The technical solution of the present invention will now be described with reference to the accompanying drawings.

[0094] In embodiments of the present invention, words such as "exemplarily," "for example," etc., are used to indicate that something is an example, illustration, or description. Any embodiment or design described as "exemplary" in the present invention should not be construed as being more preferred or advantageous than other embodiments or designs. Specifically, the use of the word "exemplary" is intended to present the concept in a concrete manner. Furthermore, in embodiments of the present invention, the meaning expressed by "and / or" can be both, or either one.

[0095] In the embodiments of this invention, the terms "image" and "picture" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent. Similarly, the terms "of," "corresponding (relevant)," and "corresponding" may sometimes be used interchangeably. It should be noted that, without emphasizing the distinction between them, their intended meanings are consistent.

[0096] In this embodiment of the invention, sometimes a subscript such as W1 may be mistakenly written as a non-subscript form such as W1. When the difference is not emphasized, the meaning they express is the same.

[0097] To make the technical problems, technical solutions and advantages of the present invention clearer, a detailed description will be given below in conjunction with the accompanying drawings and specific embodiments.

[0098] This invention provides a method for assessing ankle dorsiflexion function, which can be implemented using an ankle dorsiflexion function assessment device, which can be a terminal or a server. Figure 1 The flowchart shown illustrates the ankle dorsiflexion function assessment method. The process of this method may include the following steps:

[0099] S1: Using an infrared sensor, ankle dorsiflexion function is detected according to preset rules to obtain ankle dorsiflexion function detection data;

[0100] Preferably, before S1, it further includes:

[0101] S01: Determine the preset installation location of the infrared sensor;

[0102] The step S01 of determining the preset location for installing the infrared sensor includes:

[0103] S011: As Figure 2 As shown, determine the position point O where the heel intersects the ground;

[0104] S012: Determine the location point S of a point taken at any position on the instep of the subject;

[0105] S013: Obtain the position point S0 of the projection point of S, wherein the line connecting the position point S to the position point S0 is perpendicular to the ground.

[0106] S014: Connect point O and point S with a straight line to obtain line segment OS;

[0107] S015: Connect point O and point S0 with a straight line to obtain line segment OS0;

[0108] S016: Determine the installation height according to formula (1):

[0109] H=L×sinα (1)

[0110] Where H is the installation height of the infrared sensor, L represents the length of line segment OS, α is the sum of α0 and α1, α0 represents the angle between the foot surface and the horizontal plane; α1 represents the angle between OS and the foot surface;

[0111] S017: Determine the longitudinal position of the installation according to formula (2):

[0112] W=L×cosα (2)

[0113] Where W represents the length of line segment OS0;

[0114] S018: Calculate the toe movement range based on the lower limb DH model, ensuring that the infrared sensor installation parameters cover the ankle dorsiflexion trajectory:

[0115] y=49.997sin(0.0441x-0.4547)-41.3483sin(0.0493x-10.0000)(3)

[0116] Formula (3) is the ankle dorsiflexion trajectory.

[0117] Specifically, the lower limb kinematic modeling process is as follows:

[0118] To optimize the installation position of the infrared sensor and verify the detection logic, this invention establishes a simplified kinematic model of the lower limb. The hip joint (θ0), knee joint (θ1), and ankle joint (θ2) are simplified as single-axis rotational joints. The origin O0 of the coordinate system is set at the rotational axis of the hip joint, with the x0 axis along the thigh direction; coordinate system O1 is set at the rotational axis of the knee joint, with the x1 axis along the lower leg direction; coordinate system O2 is set at the rotational axis of the ankle joint, with the x2 axis along the foot direction; all z-axis are along the extension of the axis, and the y-axis is determined according to the right-hand rule. The DH parameter table is established as shown in Table 1 below:

[0119] Table 1

[0120]

[0121]

[0122] The change matrix A between adjacent links i :

[0123] A i =Rot(Z,θ) i )×Trans(a i ,0,0)×Rot(X,α i )

[0124] Substituting the parameters from Table 1 into the above equation, we obtain the total transformation matrix T of the toe relative to the origin:

[0125]

[0126] Where x, y, and z represent the coordinates of the foot in the origin coordinate system O0, the simplified kinematic formula for the lower limb model is expressed as:

[0127]

[0128] A lower limb model was constructed using Matlab Robotics, with the thigh, calf, and foot ratio set to 1:1:0.6. Human clinical gait data were input into the lower limb model and kinematic formulas, and the simulated trajectory was compared with the theoretical trajectory. The error was ≤2%, verifying the reliability of the model.

[0129] Mathematical models of ankle dorsiflexion movement extracted from clinical gait data:

[0130] y=49.997sin(0.0441x-0.4547)-41.3483sin(0.0493x-10.0000)

[0131] Based on the above kinematic model, the foot dorsiflexion trajectory moves in the vertical plane. The installation height H needs to cover the toe displacement range during ankle dorsiflexion to ensure that the infrared light path is orthogonal to the foot movement. Relevant experimental data can be found here. Figure 3 a- Figure 3 b, Figure 4 As shown.

[0132] Preferably, S1 includes:

[0133] S11: The infrared sensor installed at a preset position continuously emits infrared light to detect the feet;

[0134] S12: After detecting the foot, perform an ankle dorsiflexion test within a selected time period. The ankle dorsiflexion test involves continuously emitting infrared light and receiving infrared light signals reflected back from the foot. The infrared light signals reflected back from the foot include a series of infrared light intensity for the selected time interval reflected back from the foot and a corresponding timestamp.

[0135] S13: According to the preset rules, based on the infrared light signal reflected back from the foot, mark each dorsiflexion event to obtain dorsiflexion event data. The dorsiflexion event is the data of the entire process after the foot reaches the dorsiflexion posture requirement and maintains the dorsiflexion posture.

[0136] S14: Extract the start time and end time of each dorsiflexion event from the dorsiflexion event data;

[0137] S15: Extract the initial value, mean value, and maximum value of the infrared light signal intensity for each dorsiflexion event from the dorsiflexion event data;

[0138] S16: The start time and end time of each dorsiflexion event, as well as the initial, average, and maximum values ​​of the infrared light signal intensity of each dorsiflexion event, are aggregated into ankle dorsiflexion function detection data.

[0139] It's important to note that the ankle joint is located at the distal end of the three major joints of the lower limb. It's an approximately uniaxial flexion-extension joint, and its axis of rotation changes with ankle movement, making it a crucial joint in lower limb movement. The main movements of human joints include translation, flexion and extension, adduction and abduction, rotation, and circumduction. Flexion refers to a decrease in the angle between the two bones connecting the joint during movement, while extension refers to an increase in the angle. The flexion and extension movements of the foot reflect the rotation of the hindlimb bud in early embryonic development; therefore, the definition of ankle flexion and extension differs from that of other joints. Raising the toes and bringing the dorsum of the foot towards the front of the lower leg constitutes ankle extension, also known as dorsiflexion, while pointing the toes downward constitutes ankle flexion, also known as plantar flexion. Studies have shown that sex and age significantly affect the passive torque, maximum voluntary dorsiflexion strength, and range of motion during ankle dorsiflexion. However, healthy adults have a 10-degree range of ankle dorsiflexion, independent of sex and age.

[0140] In some embodiments, when the ankle dorsiflexes, the foot lifts off the ground and the toes are pointed to a certain height. If the infrared proximity sensor is not installed at the correct height, the foot will block the infrared light emitted by the sensor. When the infrared proximity sensor detects an obstacle in front, it outputs a low-level digital signal, thereby realizing the function of detecting ankle dorsiflexion.

[0141] It should be further explained that the detection process requires the use of preset baseline intensity ranges and preset dorsiflexion intensity ranges for the infrared light signal. These two ranges were obtained in advance through multiple experiments. The experiments require a certain number of participants of different ages, genders, and heights. Infrared light signals and their intensities are collected for each participant with and without the instep, and with and without the instep.

[0142] S2: Based on the ankle dorsiflexion function test data, the dorsiflexion duration data is obtained through calculation;

[0143] Preferably, S2 includes:

[0144] S21: Based on ankle dorsiflexion function test data, extract the start time and end time of each dorsiflexion event, and subtract the start time from the end time of each dorsiflexion event to obtain the duration of each dorsiflexion event;

[0145] S22: Sort the duration of each dorsiflexion event according to the start time of each dorsiflexion event to obtain the time series data of the duration of all dorsiflexion events;

[0146] S23: Based on the time series data of the duration of all backbend events, a fitting algorithm is used to calculate the change index of the time series data. The fitting algorithm includes at least one of the following methods: least squares, moving average, exponential smoothing, trend fitting, seasonal fitting, and autoregressive moving average. The change index of the time series data is the rate of change of the infrared light signal per unit time.

[0147] It should be noted that the ankle dorsiflexion test utilizes an infrared proximity sensor to detect approaching obstacles. The infrared proximity sensor is positioned at a certain height on the side of the ankle dorsiflexion test counter, with the transmitter and receiver at a 90° angle to the ground. When the foot is flat on the ground, the infrared proximity sensor detects no obstacle and outputs a high-level digital signal. However, when the ankle dorsiflexes, and the foot lifts off the ground and the toes are pointed to a certain height, the foot blocks the infrared light emitted by the proximity sensor. At this point, the infrared proximity sensor detects an obstacle and outputs a low-level digital signal, thus detecting ankle dorsiflexion.

[0148] In some embodiments, the infrared proximity sensor that can be selected can detect obstacles at distances ranging from a maximum of 20 mm to 300 mm. The detection distance range varies slightly for obstacles of different colors, but this does not affect the use.

[0149] S3: Based on the ankle dorsiflexion function test data, the number of dorsiflexion cycles is calculated.

[0150] In some embodiments, displaying and recording the number of ankle dorsiflexion repetitions on a mobile terminal, such as a mobile phone, using digital methods, helps doctors to develop and adjust exercise rehabilitation treatment plans in a timely manner. This visualization and digitization of test data facilitates more scientific and effective subsequent exercise rehabilitation treatment for patients.

[0151] S4: Based on the ankle dorsiflexion function detection data, the initial value of the infrared light signal intensity and the dorsiflexion amplitude data are obtained through calculation. The dorsiflexion amplitude data is then corrected and calculated to obtain the corrected dorsiflexion amplitude data. The initial value of the intensity is the intensity of the infrared light signal before the start of each dorsiflexion event. The correction process is to remove the initial value of the infrared light signal intensity from the intensity of the infrared light signal in the dorsiflexion event and obtain the remaining intensity of the infrared light signal. The corrected dorsiflexion amplitude data includes the mean and maximum value of the remaining intensity of the infrared light signal in each dorsiflexion event.

[0152] In some embodiments, adjusting the intensity of an infrared light signal first requires determining a reference value for the infrared light signal intensity. This can be obtained by measuring with a standard instrument or a light source of known intensity. The intensity of the target infrared light signal is measured using an infrared light detection instrument or sensor and recorded. The measured intensity of the target signal is compared with the reference value to determine the degree of deviation. Based on the degree of deviation, the position of the light source, its output power, or the sensor settings can be adjusted to bring the intensity of the target signal closer to the reference value. After adjustment, the intensity of the target signal is measured again using an infrared light detection instrument or sensor to ensure that the adjusted signal intensity meets the expected target.

[0153] S5: Based on the backflexion duration data and the corrected backflexion amplitude data, the backflexion function assessment index is obtained through the backflexion function analysis formula;

[0154] Preferably, S5 includes:

[0155] S51: Based on the dorsiflexion duration data, calculate the dorsiflexion change index using formula (4):

[0156]

[0157] Among them, C i x is the dorsiflexion variation index. i β1 represents the duration of a single flexion event, β2 represents the weight of the physical phase, and β1 represents the weight of the endurance phase.

[0158] S52: Based on the backflexion duration data, backflexion change index, and backflexion amplitude data after correction, the backflexion function assessment index is obtained through formula (5):

[0159] Ii = Mean(x) i )+α1C i +α2Adj i (5)

[0160] Where Ii is the backflexion function assessment index, α1 is the weight of the backflexion change index, α2 is the weight of the corrected backflexion amplitude data, and Adj i Data on the flexion amplitude after correction of a flexion event;

[0161] S53: Based on the backflexion duration data, the periodicity of consecutive backflexion events is calculated using formulas (6) and (7):

[0162]

[0163] in, The mean period of fatigue-induced dorsiflexion. The mean cycle of dorsiflexion after fatigue is T, the total duration of dorsiflexion is N, and the total number of dorsiflexions is N.

[0164] When ΔT ≥ 10%, the fatigue correction model is activated using formula (8):

[0165] Ii fatigue =Ii×(1+γ·ΔT) (8)

[0166] Where γ is the weighting coefficient for the subject's gender and age:

[0167] γ = 0.35 + 0.15 × S gender +0.02×A age (9)

[0168] Among them, S gender For gender parameters, 0 for female and 1 for male, A age For age;

[0169] S54: Based on the time series data of dorsiflexion duration, the rhythm variation coefficient is calculated using formula (10):

[0170]

[0171] Where σT is the standard deviation of the dorsiflexion periodic potential field and T is the average period; when CV≥15%, it is judged as rhythm disorder, triggering a warning of abnormal neuromuscular control.

[0172] In some embodiments, the first three backflexion events are used as the results of the substantive phase test, and subsequent events are used as endurance tests.

[0173] It should be noted that the backbend amplitude data after correction of a backbend event can be either the mean or the maximum value.

[0174] It should be further noted that the weight values ​​need to take into account the differences between age groups. The initial conditions for the number and amplitude of dorsiflexion differ in different age groups.

[0175] S6: The dorsiflexion function is evaluated by comparing the ankle dorsiflexion function test data, the number of dorsiflexion cycles, the dorsiflexion function assessment index, and the preset benchmark data.

[0176] Preferably, before S6, it includes:

[0177] S02: Obtain preset baseline data;

[0178] The acquisition of preset reference data in S02 includes:

[0179] S021: Using an infrared sensor, perform a preset number of ankle dorsiflexion function tests on different individuals according to preset rules to obtain an ankle dorsiflexion function test dataset;

[0180] S022: Based on the ankle dorsiflexion function test dataset, cluster analysis was performed to obtain multiple ankle dorsiflexion function groups;

[0181] S023: Calculate the dorsiflexion function assessment index for each person in each ankle dorsiflexion function test in each ankle dorsiflexion function group, and obtain the dorsiflexion function assessment index set.

[0182] S024: Using visualization methods, display the data of each ankle dorsiflexion function group. Based on the visualization analysis results and combined with the social data of different people, assess the dorsiflexion function of each ankle dorsiflexion function group, and use the assessment results as labels to obtain a label set.

[0183] S025: Combine the ankle dorsiflexion function test dataset, dorsiflexion function assessment index set, and labels to obtain the preset benchmark data.

[0184] Preferably, S6 includes:

[0185] S61: Based on preset benchmark data, extract the ankle dorsiflexion function test dataset, dorsiflexion function assessment index set, and label set;

[0186] S62: Based on the ankle dorsiflexion function test data and the ankle dorsiflexion function test dataset, perform multi-time series analysis to obtain ankle dorsiflexion function groups that meet preset thresholds. The multi-time series analysis includes: Euclidean distance, dynamic time bending distance, singular value decomposition method and point distribution feature-based method.

[0187] S63: Extract the dorsiflexion function assessment index of the ankle dorsiflexion function group that meets the preset threshold, and use it as the benchmark assessment index;

[0188] S64: Calculate the difference between the dorsiflexion function assessment index and the benchmark assessment index. If the difference is within a preset range, extract the labels of the ankle dorsiflexion function groups that meet the preset threshold as the dorsiflexion function assessment results.

[0189] In some embodiments, it is necessary to obtain ankle dorsiflexion function assessments from a certain number of individuals as a control group. This control group-based labeling can be based on expert opinion or experience-based assessment.

[0190] It should be noted that once a multi-time series stream analysis method is selected, the threshold needs to be estimated in advance, as different methods correspond to different thresholds.

[0191] The above is an introduction to the method embodiments. The following describes the solution described in this application through device embodiments.

[0192] Figure 5 This is a block diagram illustrating an ankle dorsiflexion function assessment device according to an exemplary embodiment, the device being used in an ankle dorsiflexion function assessment method. (Refer to...) Figure 5 The device includes a function detection module, a dorsiflexion duration module, a dorsiflexion count module, a dorsiflexion amplitude module, an evaluation index module, and a function evaluation module.

[0193] Functional detection module: Used to detect ankle dorsiflexion function using an infrared sensor according to preset rules, and obtain ankle dorsiflexion function detection data;

[0194] Dorsiflexion duration module: used to calculate dorsiflexion duration data based on the ankle joint dorsiflexion function detection data;

[0195] Dorsiflexion count module: used to calculate the dorsiflexion count data based on the ankle joint dorsiflexion function test data;

[0196] The dorsiflexion amplitude module is used to calculate the initial value of the infrared light signal intensity and dorsiflexion amplitude data based on the ankle dorsiflexion function detection data. It then performs correction processing and calculation on the dorsiflexion amplitude data to obtain corrected dorsiflexion amplitude data. The initial value of the intensity is the intensity of the infrared light signal before the start of each dorsiflexion event. The correction processing is to remove the initial value of the infrared light signal intensity from the intensity of the infrared light signal in the dorsiflexion event and obtain the remaining intensity of the infrared light signal. The corrected dorsiflexion amplitude data includes the mean and maximum value of the remaining intensity of the infrared light signal in each dorsiflexion event.

[0197] Assessment Index Module: Based on the backflexion duration data and the corrected backflexion amplitude data, the backflexion function assessment index is obtained through the backflexion function analysis formula;

[0198] Functional assessment module: used to assess dorsiflexion function by comparing the ankle dorsiflexion function test data, the number of dorsiflexion cycles, the dorsiflexion function assessment index, and preset benchmark data.

[0199] This invention also provides an ankle dorsiflexion function assessment device, which includes: a processor; a memory, wherein the memory stores computer-readable instructions, and when the processor executes the computer-readable instructions, it implements any of the methods described above for ankle dorsiflexion function assessment.

[0200] This invention also provides a computer-readable storage medium storing at least one instruction, which is loaded and executed by a processor to implement any of the above-described methods for assessing ankle dorsiflexion function.

[0201] In some embodiments, patient data can be visualized along with baseline data, which helps doctors to develop and adjust exercise rehabilitation treatment plans in a timely manner. This visualization and digitization of test data facilitates more scientific and effective subsequent exercise rehabilitation treatment for patients.

[0202] Figure 6 This is a schematic diagram of the structure of an ankle dorsiflexion function assessment device provided in an embodiment of the present invention, as shown below. Figure 6 As shown, the ankle dorsiflexion function assessment device may include the above-mentioned Figure 5 The ankle dorsiflexion function assessment device shown. Optionally, the ankle dorsiflexion function assessment device 410 may include a processor 2001.

[0203] Optionally, the ankle dorsiflexion function assessment device 410 may also include a memory 2002 and a transceiver 2003.

[0204] The processor 2001, memory 2002, and transceiver 2003 can be connected via a communication bus.

[0205] The following is combined with Figure 6 A detailed description of each component of the ankle dorsiflexion function assessment device 410 is provided below:

[0206] The processor 2001 is the control center of the ankle dorsiflexion function assessment device 410. It can be a single processor or a collective term for multiple processing elements. For example, the processor 2001 can be one or more central processing units (CPUs), application-specific integrated circuits (ASICs), or one or more integrated circuits configured to implement embodiments of the present invention, such as one or more digital signal processors (DSPs), or one or more field-programmable gate arrays (FPGAs).

[0207] Optionally, the processor 2001 can perform various functions of the ankle dorsiflexion function assessment device 410 by running or executing software programs stored in the memory 2002 and calling data stored in the memory 2002.

[0208] In a specific implementation, as one example, the processor 2001 may include one or more CPUs, for example... Figure 6 CPU0 and CPU1 are shown in the diagram.

[0209] In a specific implementation, as one example, the ankle dorsiflexion function assessment device 410 may also include multiple processors, such as... Figure 6 The processors 2001 and 2004 are shown. Each of these processors can be a single-core processor or a multi-core processor. Here, "processor" can refer to one or more devices, circuits, and / or processing cores used to process data (e.g., computer program instructions).

[0210] The memory 2002 is used to store the software program that executes the present invention, and is controlled by the processor 2001 to execute it. The specific implementation method can be referred to the above method embodiment, and will not be repeated here.

[0211] Optionally, the memory 2002 may be a read-only memory (ROM) or other type of static storage device capable of storing static information and instructions, random access memory (RAM) or other type of dynamic storage device capable of storing information and instructions, or electrically erasable programmable read-only memory (EEPROM), compact disc read-only memory (CD-ROM) or other optical disc storage, optical disc storage (including compressed optical discs, laser discs, optical discs, digital universal optical discs, Blu-ray discs, etc.), magnetic disk storage media or other magnetic storage devices, or any other medium capable of carrying or storing desired program code in the form of instructions or data structures and accessible by a computer, but not limited thereto. The memory 2002 may be integrated with the processor 2001 or exist independently, and may be connected via the interface circuit of the ankle dorsiflexion function assessment device 410. Figure 6 (Not shown in the figure) is coupled to processor 2001, and the embodiments of the present invention do not specifically limit this.

[0212] The transceiver 2003 is used to communicate with network devices or with terminal devices.

[0213] Alternatively, transceiver 2003 may include a receiver and a transmitter. Figure 6 (Not shown separately). The receiver is used to implement the receiving function, and the transmitter is used to implement the transmitting function.

[0214] Alternatively, the transceiver 2003 can be integrated with the processor 2001 or exist independently, and can be connected to the interface circuit of the ankle dorsiflexion function assessment device 410. Figure 6 (Not shown in the figure) is coupled to processor 2001, and the embodiments of the present invention do not specifically limit this.

[0215] It should be noted that, Figure 6 The structure of the ankle dorsiflexion function assessment device 410 shown does not constitute a limitation on the router. Actual knowledge structure identification devices may include more or fewer components than shown, or combine certain components, or have different component arrangements.

[0216] Furthermore, the technical effect of the ankle dorsiflexion function assessment device 410 can be referred to the technical effect of the ankle dorsiflexion function assessment method described in the above method embodiments, and will not be repeated here.

[0217] It should be understood that the processor 2001 in this embodiment of the invention can be a central processing unit (CPU), or it can be other general-purpose processors, digital signal processors (DSPs), application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor can be a microprocessor or any conventional processor.

[0218] It should also be understood that the memory in the embodiments of the present invention can be volatile memory or non-volatile memory, or may include both volatile and non-volatile memory. The non-volatile memory can be read-only memory (ROM), programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), or flash memory. The volatile memory can be random access memory (RAM), which is used as an external cache. By way of example, but not limitation, many forms of random access memory (RAM) are available, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate synchronous DRAM (DDR SDRAM), enhanced synchronous DRAM (ESDRAM), synchronous linked DRAM (SLDRAM), and direct rambus RAM (DR RAM).

[0219] The above embodiments can be implemented, in whole or in part, by software, hardware (such as circuits), firmware, or any other combination thereof. When implemented using software, the above embodiments can be implemented, in whole or in part, as a computer program product. The computer program product includes one or more computer instructions or computer programs. When the computer instructions or computer programs are loaded or executed on a computer, all or part of the processes or functions described in the embodiments of the present invention are generated. The computer can be a general-purpose computer, a special-purpose computer, a computer network, or other programmable device. The computer instructions can be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another. For example, the computer instructions can be transmitted from one website, computer, server, or data center to another website, computer, server, or data center via wired (e.g., infrared, wireless, microwave, etc.) means. The computer-readable storage medium can be any available medium that a computer can access or a data storage device such as a server or data center that includes one or more sets of available media. The available medium can be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. A semiconductor medium can be a solid-state drive.

[0220] It should be understood that the term "and / or" in this article is merely a description of the relationship between related objects, indicating that three relationships can exist. For example, A and / or B can represent: A existing alone, A and B existing simultaneously, or B existing alone. A and B can be singular or plural. Additionally, the character " / " in this article generally indicates an "or" relationship between the preceding and following related objects, but it can also represent an "and / or" relationship. Please refer to the context for a more accurate understanding.

[0221] In this invention, "at least one" means one or more, and "more than one" means two or more. "At least one of the following" or similar expressions refer to any combination of these items, including any combination of a single item or a plurality of items. For example, at least one of a, b, or c can represent: a, b, c, ab, ac, bc, or abc, where a, b, and c can be a single item or multiple items.

[0222] It should be understood that, in various embodiments of the present invention, the order of the above-mentioned process numbers does not imply the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0223] Those skilled in the art will recognize that the units and algorithm steps of the various examples described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are implemented in hardware or software depends on the specific application and design constraints of the technical solution. Those skilled in the art can use different methods to implement the described functions for each specific application, but such implementations should not be considered beyond the scope of this invention.

[0224] Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the devices, apparatuses, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here.

[0225] In the several embodiments provided by this invention, it should be understood that the disclosed devices, apparatuses, and methods can be implemented in other ways. For example, the apparatus embodiments described above are merely illustrative; for instance, the division of units is only a logical functional division, and in actual implementation, there may be other division methods. For example, multiple units or components may be combined or integrated into another device, or some features may be ignored or not executed. Furthermore, the coupling or direct coupling or communication connection shown or discussed may be through some interfaces; the indirect coupling or communication connection between devices or units may be electrical, mechanical, or other forms.

[0226] The units described as separate components may or may not be physically separate. The components shown as units 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.

[0227] In addition, the functional units in the various embodiments of the present invention can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit.

[0228] If the aforementioned functions are implemented as software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, or the part that contributes to the prior art, or a part 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 methods described in the various embodiments of the present invention. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, read-only memory (ROM), random access memory (RAM), magnetic disks, or optical disks.

[0229] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the technical scope disclosed in the present invention should be included within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A method for assessing ankle dorsiflexion function, characterized in that, include: S1: Using an infrared sensor, ankle dorsiflexion function is detected according to preset rules to obtain ankle dorsiflexion function detection data. The method further includes: S01: Determine the preset installation location of the infrared sensor; The step S01 of determining the preset location for installing the infrared sensor includes: S011: Determine the location point O where the heel intersects the ground; S012: Determine the location point S of a point taken at any position on the instep of the subject; S013: Obtain the position point S0 of the projection point of S, wherein the line connecting the position point S to the position point S0 is perpendicular to the ground. S014: Connect point O and point S with a straight line to obtain line segment OS; S015: Connect point O and point S0 with a straight line to obtain line segment OS0; S016: Determine the installation height according to formula (1): （1） in, For the installation height of the infrared sensor, Indicates the length of line segment OS. yes 0 and The sum of 1, 0 represents the angle between the sole of the foot and the horizontal plane; 1 represents the angle between OS and the sole of the foot; S017: Determine the longitudinal position of the installation according to formula (2): （2） in, Indicates the length of line segment OS0; S018: Calculate the toe movement range based on the lower limb DH model, ensuring that the infrared sensor installation parameters cover the ankle dorsiflexion trajectory: (3) Formula (3) is the ankle dorsiflexion trajectory; S2: Based on the ankle dorsiflexion function test data, the dorsiflexion duration data is obtained through calculation; S3: Based on the ankle dorsiflexion function test data, the number of dorsiflexion cycles is calculated. S4: Based on the ankle dorsiflexion function detection data, the initial value of the infrared light signal intensity and the dorsiflexion amplitude data are obtained through calculation. The dorsiflexion amplitude data is then corrected and calculated to obtain the corrected dorsiflexion amplitude data. The initial value of the intensity is the intensity of the infrared light signal before the start of each dorsiflexion event. The correction process is to remove the initial value of the infrared light signal intensity from the intensity of the infrared light signal in the dorsiflexion event and obtain the remaining intensity of the infrared light signal. The corrected dorsiflexion amplitude data includes the mean and maximum value of the remaining intensity of the infrared light signal in each dorsiflexion event. S5: Based on the backflexion duration data and the corrected backflexion amplitude data, the backflexion function assessment index is obtained through the backflexion function analysis formula, including fatigue state detection, specifically including: S51: Based on the dorsiflexion duration data, the dorsiflexion change index is calculated using formula (4): （4） in, The dorsiflexion variation index, The duration of a single backbend event. For the actual stage weight, Weighting for endurance stage; S52: Based on the backflexion duration data, backflexion change index, and backflexion amplitude data after correction, the backflexion function assessment index is obtained through formula (5): （5） in, This is an index for assessing dorsiflexion function. The weighting of the dorsiflexion change index, Weights for the corrected dorsiflexion amplitude data. Data on the flexion amplitude after correction of a flexion event; S53: Based on the backflexion duration data, the periodicity of consecutive backflexion events is calculated using formulas (6) and (7): in, The mean period of fatigue-induced dorsiflexion. The mean period of dorsiflexion after fatigue. T For the average week of autumn, N This represents the total number of dorsiflexes. when When the fatigue rate is ≥10%, the fatigue correction model is activated using formula (8): in, Weighting coefficients for the gender and age of the subjects: in, This is a gender parameter; 0 for female and 1 for male. For age; S54: Based on the time series data of dorsiflexion duration, the rhythm variation coefficient is calculated using formula (10): in, The standard deviation of the dorsiflexion period. T For the average period of the northern region; when CV When the percentage is ≥15%, it is judged as rhythm disorder, triggering a warning of abnormal neuromuscular control; S6: Based on the ankle dorsiflexion function test data, the dorsiflexion count data, the dorsiflexion function assessment index, and the preset benchmark data, a dorsiflexion function assessment is performed. The preset benchmark data includes: S021: Using an infrared sensor, perform a preset number of ankle dorsiflexion function tests on different individuals according to preset rules to obtain an ankle dorsiflexion function test dataset; S022: Based on the ankle dorsiflexion function test dataset, cluster analysis was performed to obtain multiple ankle dorsiflexion function groups; S023: Calculate the dorsiflexion function assessment index for each person in each ankle dorsiflexion function test in each ankle dorsiflexion function group, and obtain the dorsiflexion function assessment index set. S024: Using visualization methods, display the data of each ankle dorsiflexion function group. Based on the visualization analysis results and combined with the social data of different people, assess the dorsiflexion function of each ankle dorsiflexion function group, and use the assessment results as labels to obtain a label set. S025: Combine the ankle dorsiflexion function test dataset, dorsiflexion function assessment index set, and labels to obtain the preset benchmark data.

2. The method for assessing ankle dorsiflexion function according to claim 1, characterized in that, S1 utilizes an infrared sensor to perform ankle dorsiflexion function detection according to preset rules, obtaining ankle dorsiflexion function detection data, including: S11: The infrared sensor installed at a preset position continuously emits infrared light to detect the feet; S12: After detecting the foot, perform an ankle dorsiflexion test within a selected time period. The ankle dorsiflexion test involves continuously emitting infrared light and receiving infrared light signals reflected back from the foot. The infrared light signals reflected back from the foot include a series of infrared light intensity for the selected time interval reflected back from the foot and a corresponding timestamp. S13: According to the preset rules, based on the infrared light signal reflected back from the foot, mark each dorsiflexion event to obtain dorsiflexion event data. The dorsiflexion event is the data of the entire process after the foot reaches the dorsiflexion posture requirement and maintains the dorsiflexion posture. S14: Extract the start time and end time of each dorsiflexion event from the dorsiflexion event data; S15: Extract the initial value, mean value, and maximum value of the infrared light signal intensity for each dorsiflexion event from the dorsiflexion event data; S16: The start and end times of each dorsiflexion event, as well as the initial, average, and maximum values ​​of the infrared light signal intensity of each dorsiflexion event, are aggregated into ankle dorsiflexion function detection data.

3. The method for assessing ankle dorsiflexion function according to claim 1, characterized in that, The S2, based on the ankle dorsiflexion function detection data, calculates the dorsiflexion duration data, including: S21: Based on ankle dorsiflexion function test data, extract the start time and end time of each dorsiflexion event, and subtract the start time from the end time of each dorsiflexion event to obtain the duration of each dorsiflexion event; S22: Sort the duration of each dorsiflexion event according to the start time of each dorsiflexion event to obtain the time series data of the duration of all dorsiflexion events; S23: Based on the time series data of the duration of all backflexion events, a fitting algorithm is used to calculate the change index of the time series data. The fitting algorithm includes at least one of the following methods: least squares, moving average, exponential smoothing, trend fitting, seasonal fitting, and autoregressive moving average. The change index of the time series data is the rate of change of backflexion duration per unit time.

4. The method for assessing ankle dorsiflexion function according to claim 1, characterized in that, The S6 step compares the ankle dorsiflexion function test data, the dorsiflexion count data, the dorsiflexion function assessment index, and preset benchmark data to perform a dorsiflexion function assessment, including: S61: Based on preset benchmark data, extract the ankle dorsiflexion function test dataset, dorsiflexion function assessment index set, and label set; S62: Based on the ankle dorsiflexion function test data and the ankle dorsiflexion function test dataset, perform multi-time series analysis to obtain ankle dorsiflexion function groups that meet preset thresholds. The multi-time series analysis includes: Euclidean distance, dynamic time bending distance, singular value decomposition method and point distribution feature-based method. S63: Extract the dorsiflexion function assessment index of the ankle dorsiflexion function group that meets the preset threshold, and use it as the benchmark assessment index; S64: Calculate the difference between the dorsiflexion function assessment index and the benchmark assessment index. If the difference is within a preset range, extract the labels of the ankle dorsiflexion function groups that meet the preset threshold as the dorsiflexion function assessment results.

5. An ankle dorsiflexion function assessment device, characterized in that, The apparatus is suitable for the method according to any one of claims 1-4, and the apparatus comprises: Functional detection module: used to perform ankle dorsiflexion function detection using an infrared sensor according to preset rules, and obtain ankle dorsiflexion function detection data. The method further includes: S01: Determine the preset installation location of the infrared sensor; The step S01 of determining the preset location for installing the infrared sensor includes: S011: Determine the location point O where the heel intersects the ground; S012: Determine the location point S of a point taken at any position on the instep of the subject; S013: Obtain the position point S0 of the projection point of S, wherein the line connecting the position point S to the position point S0 is perpendicular to the ground. S014: Connect point O and point S with a straight line to obtain line segment OS; S015: Connect point O and point S0 with a straight line to obtain line segment OS0; S016: Determine the installation height according to formula (1): （1） in, For the installation height of the infrared sensor, Indicates the length of line segment OS. yes 0 and The sum of 1, 0 represents the angle between the sole of the foot and the horizontal plane; 1 represents the angle between OS and the sole of the foot; S017: Determine the longitudinal position of the installation according to formula (2): （2） in, Indicates the length of line segment OS0; S018: Calculate the toe movement range based on the lower limb DH model, ensuring that the infrared sensor installation parameters cover the ankle dorsiflexion trajectory: (3) Formula (3) is the ankle dorsiflexion trajectory; Dorsiflexion duration module: used to calculate dorsiflexion duration data based on the ankle joint dorsiflexion function detection data; Dorsiflexion count module: used to calculate the dorsiflexion count data based on the ankle joint dorsiflexion function test data; The dorsiflexion amplitude module is used to calculate the initial value of the infrared light signal intensity and dorsiflexion amplitude data based on the ankle dorsiflexion function detection data. It then performs correction processing and calculation on the dorsiflexion amplitude data to obtain corrected dorsiflexion amplitude data. The initial value of the intensity is the intensity of the infrared light signal before the start of each dorsiflexion event. The correction processing is to remove the initial value of the infrared light signal intensity from the intensity of the infrared light signal in the dorsiflexion event and obtain the remaining intensity of the infrared light signal. The corrected dorsiflexion amplitude data includes the mean and maximum value of the remaining intensity of the infrared light signal in each dorsiflexion event. The assessment index module is used to obtain the backflexion function assessment index based on the backflexion duration data and the corrected backflexion amplitude data, using the backflexion function analysis formula. Specifically, it includes: S51: Based on the dorsiflexion duration data, the dorsiflexion change index is calculated using formula (4): （4） in, The dorsiflexion variation index, The duration of a single backbend event. For the actual stage weight, Weighting for endurance stage; S52: Based on the backflexion duration data, backflexion change index, and backflexion amplitude data after correction, the backflexion function assessment index is obtained through formula (5): （5） in, This is an index for assessing dorsiflexion function. The weighting of the dorsiflexion change index, Weights for the corrected dorsiflexion amplitude data. Data on the flexion amplitude after correction of a flexion event; S53: Based on the backflexion duration data, the periodicity of consecutive backflexion events is calculated using formulas (6) and (7): in, The mean period of fatigue dorsiflexion. The mean period of dorsiflexion after fatigue. T For the average week of autumn, N This represents the total number of dorsiflexes. when When the fatigue rate is ≥10%, the fatigue correction model is activated using formula (8): in, Weighting coefficients for the gender and age of the subjects: in, This is a gender parameter; 0 for female and 1 for male. For age; S54: Based on the time series data of dorsiflexion duration, the rhythm variation coefficient is calculated using formula (10): in, The standard deviation of the dorsiflexion period. T For the average period of the northern region; when CV When the percentage is ≥15%, it is judged as rhythm disorder, triggering a warning of abnormal neuromuscular control; Functional assessment module: used to assess dorsiflexion function by comparing the ankle dorsiflexion function test data, the number of dorsiflexion cycles, the dorsiflexion function assessment index, and preset benchmark data. The preset benchmark data includes: S021: Using an infrared sensor, perform a preset number of ankle dorsiflexion function tests on different individuals according to preset rules to obtain an ankle dorsiflexion function test dataset; S022: Based on the ankle dorsiflexion function test dataset, cluster analysis was performed to obtain multiple ankle dorsiflexion function groups; S023: Calculate the dorsiflexion function assessment index for each person in each ankle dorsiflexion function test in each ankle dorsiflexion function group, and obtain the dorsiflexion function assessment index set. S024: Using visualization methods, display the data of each ankle dorsiflexion function group. Based on the visualization analysis results and combined with the social data of different people, assess the dorsiflexion function of each ankle dorsiflexion function group, and use the assessment results as labels to obtain a label set. S025: Combine the ankle dorsiflexion function test dataset, dorsiflexion function assessment index set, and labels to obtain the preset benchmark data.

6. An electronic device, characterized in that, The electronic device includes: a housing, a processor, a memory, a circuit board, and a power supply circuit, wherein the circuit board is disposed inside the space enclosed by the housing, and the processor and the memory are disposed on the circuit board; the power supply circuit is used to supply power to various circuits or devices of the electronic device; the memory is used to store executable program code; the processor runs a program corresponding to the executable program code by reading the executable program code stored in the memory, for executing the method described in any one of claims 1 to 4.

7. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores one or more programs, which can be executed by one or more processors to implement the method described in any one of claims 1 to 4.

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