Human body gait analysis method and system based on big data

Abstract

The invention discloses a human body gait analysis method and system based on big data, and belongs to the technical field of gait analysis. The system comprises a user information association module, a data acquisition module, a data analysis module, an analysis database, an intelligent evaluation module and a digital display module. The user information association module is used for associating a patient with sensor equipment for monitoring human body gait data and determining basic information parameters associated with the patient; the data acquisition module is used for acquiring human body gait data when a patient walks; the data analysis module is used for determining the total gravity center change of the body when the patient walks; the analysis database is used for integrating the human body gait data when each patient walks; determining a risk value of falling of the body under the change of each total gravity center of the body when the patient walks; the intelligent evaluation module is used for evaluating the athletic ability of the patient; and the digital display module is used for digitally displaying the exercise ability evaluation result of the patient.

Description

Technical Field

[0001] The present invention relates to the technical field of gait analysis, and specifically to a method and system for human gait analysis based on big data. Background Art

[0002] Gait analysis is a process of systematically observing, measuring, and analyzing the motion characteristics of a human body during walking. It collects and analyzes gait-related data through various sensors, camera devices, and calculation methods to reveal the normality and abnormality of gait, and is often used to study and evaluate the biomechanical characteristics, motion patterns, and physiological states of humans during walking; by extracting and selecting the characteristics of human motion during walking, the abnormal conditions of gait can be accurately reflected. However, in the face of complex gait abnormalities, the existing technology has a low recognition accuracy and it is difficult to comprehensively evaluate the motor ability of patients based on their gait data; in addition, most systems only focus on the changes of a single joint or part during feature extraction, and do not fully consider the integrity and dynamics of gait, resulting in insufficient robustness and generalization ability of the system. Summary of the Invention

[0003] The purpose of the present invention is to provide a method and system for human gait analysis based on big data to solve the problems raised in the above background art.

[0004] To solve the above technical problems, the present invention provides the following technical solution: A method for human gait analysis based on big data, the method includes the following steps:

[0005] Step S1: A patient associates with a sensor device for monitoring human gait data, and collects human gait data of the patient during walking; the human gait data includes the centroid position information of each body segment of the patient during walking;

[0006] Step S2: Analyze the collected human gait data, and determine the total center of gravity change of the patient's body during walking according to the centroid position information of each body segment of the patient during walking and the weight of each body segment;

[0007] Step S3: Establish an analysis database, and integrate the human gait data of each patient during walking in the analysis database; according to the human gait data of the patient during a fall and the total center of gravity change of the patient's body during walking in the analysis database, obtain the fall risk value of each total center of gravity change of the patient's body during walking;

[0008] Step S4: Evaluate the motor ability of the patient according to the fall risk value of each total center of gravity change of the patient's body during walking; establish an activity log of the evaluation results of the patient's motor ability at different times, and digitally display the evaluation results of the patient's motor ability.

[0009] A human gait analysis system based on big data, the system includes a user information association module, a data collection module, a data analysis module, an analysis database, an intelligent evaluation module, and a digital display module;

[0010] The user information association module is used to associate a patient with a sensor device for monitoring human gait data, and determine the basic information parameters of the associated patient. The basic information parameters include the patient's height, weight, age, gender, and the lengths of each body segment of the patient;

[0011] The data collection module is used to collect human gait data when the patient walks; the human gait data includes the centroid position information of each body segment when the patient walks; and send the collected human gait data to the data analysis module and the analysis database;

[0012] The data analysis module is used to analyze the human gait data collected by the data collection module, and determine the total center of gravity change of the patient's body when walking according to the centroid position information of each body segment and the weight of each body segment when the patient walks; send the determined total center of gravity change of the patient's body when walking to the intelligent evaluation module;

[0013] The analysis database is used to integrate the human gait data of each patient when walking; obtain the fall risk value of each total center of gravity change of the patient's body when walking according to the human gait data when the patient falls in the analysis database; send the fall risk value of each total center of gravity change of the patient's body when walking to the intelligent evaluation module;

[0014] The intelligent evaluation module is used to evaluate the patient's motor ability according to the total center of gravity change of the patient's body when walking sent by the data analysis module and the fall risk value of each total center of gravity change of the patient's body when walking sent by the analysis database; send the evaluation result of the patient's motor ability to the digital display module;

[0015] The digital display module is used to establish an activity log of the evaluation results of the patient's motor ability at different times, and digitally display the evaluation results of the patient's motor ability.

[0016] An electronic device, including: a processor and a memory, wherein, a computer program that can be called by the processor is stored in the memory;

[0017] The processor executes the above-mentioned human gait analysis method based on big data by calling the computer program stored in the memory.

[0018] A computer-readable storage medium stores instructions, and when the instructions run on a computer, the computer is made to execute the above-mentioned human gait analysis method based on big data.

[0019] Compared with the prior art, the beneficial effects achieved by the present invention are as follows: By determining the centroid coordinates and weights of each body segment of the patient and calculating the total center of gravity change of the body during the patient's walking, the gait data of the human body is analyzed from the integrity and dynamics of the patient's human body, improving the robustness and generalization ability of the analysis system; predicting the risk of the patient falling according to the total center of gravity change, improving the accuracy of the human gait analysis of the patient; and facilitating medical staff to more intuitively understand the patient's health status by calculating the exercise ability evaluation value of the patient. BRIEF DESCRIPTION OF THE DRAWINGS

[0020] Figure 1 is a schematic diagram of the steps of the method for analyzing human gait based on big data according to the present invention;

[0021] Figure 2 is a schematic diagram of the structure of the system for analyzing human gait based on big data according to the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0022] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0023] The present invention collects the centroid position information of each body segment during the patient's walking, determines the total center of gravity change of the body during the patient's walking according to the collected centroid position information; evaluates the exercise ability of the patient according to the risk value of the patient falling under different total center of gravity changes, improves the accuracy of the analysis of the human gait data of the patient, helps medical staff to more intuitively understand the patient's health status, and formulates a more personalized rehabilitation plan.

[0024] Please refer to Figure 1 - Figure 2 , the present invention provides the following technical solutions:

[0025] Please refer to Figure 1 , in the first embodiment: A method for analyzing human gait based on big data is provided, and the method includes the following steps:

[0026] Step S1: The patient associates a sensor device for monitoring human gait data and collects the human gait data during the patient's walking; the human gait data includes the centroid position information of each body segment during the patient's walking.

[0027] Further, determine the basic information parameters of the associated patient. The basic information parameters include the patient's height, weight, age, gender, and the lengths of each body segment of the patient. Among them, human gait data is collected through sensor devices, and the sensor devices include high-speed cameras for capturing the movement trajectories of each body segment of the patient and mechanical measurement devices for capturing the movement acceleration and direction of each body segment of the patient.

[0028] Further, the method for determining the centroid position information of each body segment when the patient is walking is as follows: Based on the lengths of each body segment of the patient, determine the joint connection points of each body segment of the patient. Establish a three-dimensional space model with respect to the X-axis, Y-axis, and Z-axis. According to the movement trajectories, movement acceleration, and direction of each body segment of the patient, determine the movement trajectories of each joint connection point in the three-dimensional space model. According to the movement trajectories of each joint connection point, respectively determine the coordinate changes of the proximal segment and distal segment of each body segment of the patient at different time stamps t. According to the coordinate changes of the proximal segment and distal segment of each body segment, determine the centroid position information of each body segment when the patient is walking, according to the calculation formula:

[0029]

[0030] Among them, and respectively represent the coordinates of the i-th body segment of the patient on the X-axis, Y-axis, and Z-axis at different time stamps t. and respectively represent the coordinates of the proximal segment of the i-th body segment of the patient on the X-axis, Y-axis, and Z-axis at different time stamps t. and respectively represent the coordinates of the distal segment of the i-th body segment of the patient on the X-axis, Y-axis, and Z-axis at different time stamps t. i ∈ {1, 2,..., n}; n represents the number of body segments of the patient; B i represents the percentage of the length from the proximal segment to the centroid position of the i-th body segment of the patient in the length from the proximal segment to the distal segment.

[0031] In this implementation, the patient's body segments are divided into head, neck, upper torso, lower torso, upper arm, forearm, thigh, calf, hand, and foot. When collecting the human gait data of the patient walking, take the center point of one of the high-speed cameras as the origin. By establishing a three-dimensional space model, according to the video data collected by the high-speed camera, and combining the mechanical measurement device in the sensor device to capture the patient's movement acceleration and direction, introduce the occlusion perception mechanism algorithm, extract the 3D pose information of the human body from the video data, so as to capture the movement trajectories of each body segment of the patient and determine the movement changes of each body segment of the patient. The mechanical measurement device includes an accelerometer and a gyroscope.

[0032] It should be noted that the proximal end of the segment represents the end of the patient's body segment closer to the body center; the distal end of the segment represents the end of the patient's body segment away from the body center; according to the movement trajectories of the patient's body segments, the position coordinates of the proximal and distal ends of the segments in each of the patient's body segments are determined, and combined with the lengths of the body segments, the centroid position coordinates of each of the patient's body segments at the corresponding time stamp are obtained, so as to calculate and that is, the centroid position information of each body segment when the patient walks, and it is convenient to subsequently determine the total center of gravity change of the patient's body when walking; in this embodiment, B i is obtained from anthropometric literature or experimental data of anthropometry.

[0033] Step S2: Analyze the collected human gait data, and determine the total center of gravity change of the patient's body according to the centroid position information of each body segment and the weight of each body segment when the patient walks.

[0034] Specifically, the method steps for determining the total center of gravity change of the patient's body when walking are as follows: according to the basic information parameters of the patient, determine the weight of each body segment of the patient, and according to the centroid position information of each body segment when the patient walks, calculate the coordinates of the total center of gravity of the patient's body on the X-axis, Y-axis, and Z-axis at different time stamps t

[0035]

[0036] wherein G represents the patient's body weight; g i represents the weight of the patient's i-th body segment.

[0037] In this embodiment, in the process of determining the weight of each body segment of the patient, it is determined by using the known relative mass distribution table of each human body segment through the basic information parameters of the patient. For example: the upper trunk of a male accounts for 27.23% of the relative mass of the body weight, and the lower trunk accounts for 14.19% of the relative mass of the body weight; the trunk of a female accounts for 27.48% of the relative mass of the body weight, and the lower trunk accounts for 14.10% of the relative mass of the body weight; among them, the weight of each body segment of the patient can be adjusted according to the specific situation of the patient; since the centroid of an object or system is the weighted average position of all mass elements, therefore, according to the principle of moment balance, by determining the centroid coordinates and weights of each body segment of the patient, calculate the total center of gravity coordinates of the patient's body when walking, and obtain the total center of gravity change of the patient's body when walking; analyzing the human gait data from the integrity and dynamics of the patient's body improves the robustness and generalization ability of the analysis system, and predicts the risk of the patient falling according to the total center of gravity change, improving the accuracy of the analysis of the patient's human gait.

[0038] Step S3: Establish an analysis database and integrate the human gait data of each patient during walking; based on the human gait data of the patient during a fall and the change in the total center of gravity of the patient's body during walking, obtain the fall risk value for each change in the total center of gravity of the patient's body during walking.

[0039] Specifically, the method steps are as follows:

[0040] Step S31: Establish an analysis database in the cloud, send the collected human gait data of each patient during walking to the analysis database in the cloud for storage and continuous update.

[0041] Step S32: Analyze the human gait data stored in the analysis database. Take the centroid position information of the patient's foot as the analysis origin, determine the coordinate changes of the analysis origin on the X-axis, Y-axis, and Z-axis, and determine the coordinate changes of the total center of gravity of the patient's body on the X-axis, Y-axis, and Z-axis during walking; connect the analysis origin coordinates and the total center of gravity coordinates to obtain the angle value between the connecting line and the horizontal plane, and use the obtained angle value as the total center of gravity angle.

[0042] Step S33: Analyze the human gait data of the patient during a fall in the analysis database. According to the total center of gravity angle corresponding to the patient's fall, determine the frequency of occurrence of each total center of gravity angle during the patient's fall in the analysis database, and use it as the fall risk value for each change in the total center of gravity.

[0043] It should be noted that the frequency of occurrence of each total center of gravity angle during the patient's fall in the analysis database, that is, the proportion of the number of times the patient falls at each total center of gravity angle in the amount of human gait data corresponding to the total center of gravity angle in the analysis database. The higher the frequency of occurrence, the higher the fall risk value of the patient; in this implementation, according to the accuracy of data collection by the high-speed camera and sensor devices, when analyzing the coordinates of the total center of gravity of the patient's body during walking, determine the coordinate point analysis range ε of the total center of gravity coordinate value. For example, take and the total center of gravity coordinate points within as the human gait data under the same total center of gravity angle for analysis. The higher the accuracy of data collection, the smaller the value of ε, thereby improving the accuracy of human gait analysis; by determining the fall risk value for each change in the total center of gravity of the patient's body during walking, it is convenient to evaluate the patient's motor ability; based on the analysis of the human gait data of each patient during walking collected in the analysis database and updating the analysis database, realize the autonomous analysis of the fall risk value calculation for each change in the total center of gravity of the patient.

[0044] Step S4: Evaluate the patient's motor ability based on the risk value of falling due to the change of the total center of gravity of the patient's body during walking; establish an activity log of the evaluation results of the patient's motor ability at different times, and digitally display the evaluation results of the patient's motor ability.

[0045] Specifically, the method steps are as follows:

[0046] Step S41: Determine the coordinates of the total center of gravity of the patient's body on the X-axis, Y-axis, and Z-axis during walking at different timestamps t Determine the angle of the total center of gravity at different timestamps t, and obtain the risk value F of the patient falling at different timestamps t according to Step S33 t ;

[0047] Step S42: Evaluate the patient's motor ability according to F t , determine the evaluation value H of the patient's motor ability, according to the calculation formula:

[0048]

[0049] where t 1 represents the start time when the patient is walking; t 2 represents the end time when the patient is walking;

[0050] Step S43: Determine the evaluation value of the patient's motor ability at different times, establish an activity log about the patient, and digitally display the evaluation value of the patient's motor ability at different times.

[0051] It should be noted that by means of intelligent calculation, the human gait data of the patient is analyzed, and according to the risk value of the patient falling, the evaluation value of the patient's motor ability is calculated, so as to help medical staff more intuitively understand the patient's health status and assist medical staff in decision-making evaluation; in this implementation, medical staff can view the evaluation value of the patient's motor ability at different times through the patient's activity log, so as to formulate a more personalized rehabilitation plan; at the same time, according to different evaluation values of motor ability, corresponding rehabilitation plans are set, and the system can push the corresponding rehabilitation plan according to the currently calculated evaluation value of the patient's motor ability, so as to achieve the intelligent level of the system.

[0052] Please refer to Figure 2 , in the second embodiment: A human gait analysis system based on big data is provided, which includes a user information association module, a data acquisition module, a data analysis module, an analysis database, an intelligent evaluation module, and a digital display module;

[0053] The user information association module is used to associate a patient with a sensor device for monitoring human gait data and determine the basic information parameters of the associated patient. The basic information parameters include the patient's height, weight, age, gender, and the lengths of each body segment of the patient.

[0054] The data acquisition module is used to acquire human gait data of the patient during walking. The human gait data includes the centroid position information of each body segment of the patient during walking. The acquired human gait data is sent to the data analysis module and the analysis database.

[0055] The data analysis module is used to analyze the human gait data acquired by the data acquisition module, and determine the total center of gravity change of the patient's body during walking according to the centroid position information of each body segment and the weight of each body segment of the patient during walking. The determined total center of gravity change of the patient's body during walking is sent to the intelligent evaluation module.

[0056] The analysis database is used to integrate the human gait data of each patient during walking. According to the human gait data of the patient during a fall in the analysis database, the risk value of falling for each total center of gravity change of the patient's body during walking is obtained. The risk value of falling for each total center of gravity change of the patient's body during walking is sent to the intelligent evaluation module.

[0057] The intelligent evaluation module is used to evaluate the patient's motor ability according to the total center of gravity change of the patient's body during walking sent by the data analysis module and the risk value of falling for each total center of gravity change of the patient's body during walking sent by the analysis database. The evaluation result of the patient's motor ability is sent to the digital display module.

[0058] The digital display module is used to establish an activity log of the evaluation results of the patient's motor ability at different times and digitally display the evaluation results of the patient's motor ability.

[0059] Furthermore, the digital display module includes a human-computer interaction platform, which is used to display the basic information parameters associated with the patient, the centroid position information of each body segment of the patient during walking, the weight of each body segment of the patient, the total center of gravity change of the patient's body during walking, and the risk value of falling for each total center of gravity change of the patient's body during walking. Among them, the user can modify the weight of each body segment of the patient through the human-computer interaction platform according to the specific situation of the patient.

[0060] In this embodiment:

[0061] This system is a human gait analysis system for predicting the degree of a patient's health recovery. The patient associates a sensor device for monitoring human gait data through the user information association module and determines the basic information parameters.

[0062] The data acquisition module collects the human gait data of the patient during walking; and sends the collected human gait data to the data analysis module and the analysis database;

[0063] The data analysis module analyzes the human gait data collected by the data acquisition module to determine the total center of gravity change of the patient's body during walking; and sends the determined total center of gravity change of the patient's body during walking to the intelligent evaluation module;

[0064] The analysis database integrates the human gait data of each patient during walking, obtains the risk value of falling due to the total center of gravity change of each part of the patient's body during walking and sends it to the intelligent evaluation module;

[0065] The intelligent evaluation module evaluates the patient's motor ability and sends the evaluation result of the patient's motor ability to the digital display module;

[0066] The digital display module establishes an activity log of the evaluation results of the patient's motor ability at different times and digitally displays the evaluation results of the patient's motor ability; wherein, a human-computer interaction platform is provided, and when the user modifies the weight of each body part of the patient through the human-computer interaction platform, the modified weight of each body part of the patient is sent to the data analysis module.

[0067] In the third embodiment: An electronic device is provided, including a processor and a memory, wherein, a computer program callable by the processor is stored in the memory;

[0068] The processor executes the steps of implementing the above-mentioned human gait analysis method based on big data by calling the computer program stored in the memory.

[0069] In the fourth embodiment: A computer-readable storage medium is provided, storing instructions, when the instructions run on a computer, enabling the computer to execute the steps of the above-mentioned human gait analysis method based on big data to achieve the following functions: associating a sensor device for monitoring human gait data; collecting human gait data of the patient during walking; determining the total center of gravity change of the patient's body during walking; determining the risk value of falling due to the total center of gravity change of each part of the patient's body during walking; evaluating the patient's motor ability.

[0070] The computer-readable storage medium includes: various media for storing program codes such as USB flash drives, mobile hard disks, read-only memories, random access memories, magnetic disks or optical discs.

[0071] Finally, it should be noted that the above are only the preferred embodiments of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or perform equivalent replacements for some of the technical features. Any modifications, equivalent replacements, improvements, etc. made within the spirit and principle of the present invention shall be included within the protection scope of the present invention.

Claims

1. A human gait analysis method based on big data, characterized by: The method comprises the following steps: Step S1: The patient is associated with a sensor device for monitoring human gait data to collect the human gait data of the patient when walking; the human gait data includes the center of mass position information of each body segment of the patient when walking; Step S2, analyzing the collected human gait data, and determining the change of the total center of gravity of the patient's body when walking according to the center of mass position information of each body segment and the weight of each body segment when the patient walks; Step S3, establishing an analysis database, integrating the human gait data of each patient when walking in the analysis database; obtaining the risk value of falling due to each total center of gravity change of the patient's body when walking according to the human gait data of the patient when falling in the analysis database and the total center of gravity change of the patient's body when walking; Step S4, assessing the patient's motor ability according to the risk value of falling due to the changes in the total center of gravity of the patient's body when walking; establishing an activity log of the patient's motor ability assessment results at different periods, and digitally displaying the patient's motor ability assessment results.

2. The human gait analysis method based on big data according to claim 1, characterized in that: Determine basic information parameters of the associated patient, wherein the basic information parameters include the patient's height, weight, age, gender and the length of each body segment of the patient; wherein, human gait data is collected by a sensor device, wherein the sensor device includes a high-speed camera for capturing the motion trajectory of each body segment of the patient and a mechanical measurement device for capturing the acceleration and direction of motion of each body segment of the patient.

3. The human gait analysis method based on big data according to claim 2, characterized in that: The method for determining the center of mass position information of each body segment of the patient when walking is as follows: determine the joint connection points of each body segment of the patient according to the length of each body segment of the patient; establish a three-dimensional space model about the X-axis, Y-axis and Z-axis, and determine the motion trajectory of each joint connection point of the patient in the three-dimensional space model according to the motion trajectory, motion acceleration and direction of the patient; determine the coordinate changes of the proximal end of each body segment of the patient at different timestamps t according to the motion trajectory of each joint connection point, and determine the coordinate changes of the distal end of each body segment of the patient at different timestamps t; determine the center of mass position information of each body segment when the patient walks according to the coordinate changes of the proximal end and distal end of each body segment, according to the calculation formula: in, and Respectively represent the coordinates of the patient's ith body segment on the X-axis, Y-axis, and Z-axis at different timestamps t; and Respectively represent the coordinates of the proximal end of the segment of the i-th body segment of the patient on the X-axis, Y-axis, and Z-axis at different time stamps t; and Respectively represent the coordinates of the distal end of the segment of the i-th body segment of the patient on the X-axis, Y-axis and Z-axis at different timestamps t; i∈{1,2,...,n}; n represents the number of body segments of the patient; B i It represents the percentage of the length from the proximal end of the segment to the center of mass of the patient's i-th body segment to the length from the proximal end to the distal end of the segment.

4. The human gait analysis method based on big data according to claim 3, characterized in that: The method steps for determining the change of the total center of gravity of the patient's body when walking are as follows: according to the basic information parameters of the patient, the weight of each body segment of the patient is determined, and according to the center of mass position information of each body segment when the patient walks, the coordinates of the total center of gravity of the patient's body on the X-axis, Y-axis and Z-axis when the patient walks at different time stamps t are calculated. in, G represents the patient's weight; g i represents the weight of the patient's i-th body segment.

5. The human gait analysis method based on big data according to claim 4 is characterized in that: The method steps of step S3 are: Step S31, establishing an analysis database in the cloud, and sending the collected human gait data of each patient when walking to the analysis database in the cloud for storage and continuous updating; Step S32, analyzing the human gait data stored in the analysis database, taking the center of mass position information of the patient's foot as the analysis origin, determining the coordinate changes of the analysis origin on the X-axis, Y-axis and Z-axis, and determining the coordinate changes of the total center of gravity of the patient's body on the X-axis, Y-axis and Z-axis when walking; connecting the coordinates of the analysis origin and the coordinates of the total center of gravity to obtain the angle value between the connecting line and the horizontal plane, and using the obtained angle value as the total center of gravity angle; Step S33, analyzing the human gait data of the patient when falling in the analysis database, and determining the frequency of each total center of gravity angle when the patient falls in the analysis database according to the total center of gravity angle corresponding to the patient's fall, and using it as the risk value of falling due to each total center of gravity change.

6. The human gait analysis method based on big data according to claim 5, characterized in that: The method steps of step S4 are: Step S41: Determine the coordinates of the total center of gravity of the patient's body on the X-axis, Y-axis and Z-axis when walking at different time stamps t Determine the total center of gravity angle at different timestamps t, and obtain the risk value F of the patient falling at different timestamps t according to step S33 t ; Step S42: According to F t , evaluate the patient's motor ability and determine the patient's motor ability evaluation value H, according to the calculation formula: Wherein, t1 represents the start time when the patient walks; t2 represents the end time when the patient walks; Step S43: determine the patient's exercise capacity assessment value at different stages, and create an activity log for the patient to digitally display the patient's exercise capacity assessment value at different stages.

7. Human gait analysis system based on big data, characterized by: The system includes a user information association module, a data acquisition module, a data analysis module, an analysis database, an intelligent evaluation module and a digital display module; The user information association module is used to associate the patient with the sensor device for monitoring human gait data, and determine the basic information parameters of the associated patient, wherein the basic information parameters include the patient's height, weight, age, gender, and the length of each body segment of the patient; The data acquisition module is used to collect human gait data of the patient when walking; the human gait data includes the center of mass position information of each body segment of the patient when walking; and the collected human gait data is sent to the data analysis module and the analysis database; The data analysis module is used to analyze the human gait data collected by the data collection module, determine the change of the total center of gravity of the patient's body when walking according to the center of mass position information of each body segment and the weight of each body segment when the patient walks; and send the determined change of the total center of gravity of the patient's body when walking to the intelligent evaluation module; The analysis database is used to integrate the human gait data of each patient when walking; according to the human gait data of the patient when falling in the analysis database, the risk value of falling due to each change of the total center of gravity of the patient's body when walking is obtained; and the risk value of falling due to each change of the total center of gravity of the patient's body when walking is sent to the intelligent evaluation module; The intelligent evaluation module is used to evaluate the patient's athletic ability according to the change of the total center of gravity of the patient's body when walking sent by the data analysis module and the risk value of falling according to each change of the total center of gravity of the patient's body when walking sent by the analysis database; and send the patient's athletic ability evaluation result to the digital display module; The digital display module is used to establish an activity log of the patient's motor ability evaluation results at different periods and to digitally display the patient's motor ability evaluation results.

8. The human gait analysis system based on big data according to claim 7, characterized in that: The digital display module includes a human-computer interaction platform, which is used to display basic information parameters associated with the patient, the center of mass position information of each body segment of the patient when walking, the weight of each body segment of the patient, the change of the total center of gravity of the patient's body when walking, and the risk value of falling due to the change of the total center of gravity of the patient's body when walking; wherein, the user can modify the weight of each body segment of the patient through the human-computer interaction platform according to the specific situation of the patient.

9. An electronic device, characterized in that: include: A processor and a memory, wherein the memory stores a computer program that can be called by the processor; The processor executes the human gait analysis method based on big data described in any one of claims 1 to 6 by calling the computer program stored in the memory.

10. A computer-readable storage medium, characterized in that: Instructions are stored, and when the instructions are executed on a computer, the computer executes the human gait analysis method based on big data as described in any one of claims 1 to 6.

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