Motion posture correction method and system, wearable intelligent device and storage medium

Abstract

The invention relates to the technical field of communication, and further relates to a motion posture correction method and system, wearable intelligent equipment and a storage medium. The method comprises the steps that actual human body data and a target image of a user are collected, and the target image comprises a static reference object; constructing a coordinate axis based on the target image and the actual size of the static reference object; performing frame-by-frame analysis on the motion posture of the user in the motion process based on the coordinate axis and the high-speed camera, and determining actual motion data of the limb part of the user; synthesizing a motion posture video of the user based on the actual human body data, the actual motion data and an artificial intelligence algorithm, and determining a first motion posture of the user; and comparing the first motion posture with a standard motion posture, and generating a corresponding motion report, so that the user corrects the motion posture based on the motion report. According to the method, the exercise posture correction accuracy is improved.

Description

Technical Field

[0001] The present application relates to the field of communication technology, and further to a method, system, wearable smart device and storage medium for correcting a sports posture. Background Art

[0002] The traditional method of correcting sports posture is to have professional coaches or trainers directly observe the user's movements and use their experience and expertise to provide immediate feedback and guidance. The advantage of this method is that it can provide personalized guidance to help users adjust their movements instantly and optimize their sports performance. However, this method has some limitations. For example, manual guidance may be subjective and inaccurate, which reduces the accuracy of sports posture correction. Summary of the invention

[0003] In order to solve the above technical problems, the present application provides a method, system, wearable smart device and storage medium for correcting sports posture, which improves the accuracy of sports posture correction.

[0004] In a first aspect, the present application provides a method for correcting a motion posture, comprising: collecting actual human body data and a target image of a user, wherein the target image includes a static reference object; constructing a coordinate axis based on the actual sizes of the target image and the static reference object; performing frame-by-frame analysis of the motion posture of the user during the motion process based on the coordinate axis and a high-speed camera, and determining the actual motion data of the user's limbs, wherein the actual motion data includes at least one of the distance moved by the limb, the angle moved by the limb, the speed moved by the limb, the amplitude moved by the limb, and the stability of the limb; based on the actual human body data, the actual motion data and an artificial intelligence algorithm, synthesizing a video recording of the user's motion posture, and determining a first motion posture of the user; comparing the first motion posture with a standard motion posture, and generating a corresponding motion report, so that the user can correct the motion posture based on the motion report.

[0005] The above motion posture correction method collects the user's actual body data and a target image containing a static reference object, uses the actual size of the target image and the static reference object to construct a coordinate axis, and combines a high-speed camera to analyze the user's posture during the movement frame by frame to determine the actual motion data of the user's limbs, including the moving distance, angle, amplitude and stability. Based on these data and artificial intelligence algorithms, the user's motion posture video is synthesized and compared with the standard motion posture to generate a detailed motion report. This method can provide accurate motion posture analysis and real-time feedback, helping users identify and correct posture deviations, thereby improving the accuracy of motion posture correction.

[0006] In one implementation, the target image also includes the user, and the frame-by-frame analysis of the user's movement posture during the movement based on the coordinate axis and the high-speed camera to determine the actual movement data of the user's body parts specifically includes: determining the proportion of the user's body part in the target image based on the size of the user in the target image and the target image; determining a first ratio based on the actual size of the static reference object and the size of the static reference object in the target image; determining the actual movement data of the user's body parts based on the actual body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis.

[0007] The above method for correcting motion posture determines the first ratio by analyzing the size ratio of the user in the target image and the actual size of the static reference object and its size in the target image, and determines the actual motion data of the user's limbs by combining the actual human body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis. This method not only improves the accuracy and reliability of motion analysis, but also makes the evaluation and correction of motion posture more objective through quantitative data support, helps users to optimize their motion posture more effectively, and also reduces the cost of motion posture correction and improves users' sports enthusiasm.

[0008] In one implementation, it also includes: comparing the actual motion data with the standard motion data; comparing the user's motion posture during exercise with the standard motion posture through the high-speed camera; when the actual motion data is inconsistent with the standard motion data or the motion posture is inconsistent with the standard motion posture, reminding the user to stop exercising or correct the motion posture.

[0009] The above method for correcting exercise posture compares the actual exercise data with the standard exercise data, and uses a high-speed camera to monitor the consistency of the user's posture during exercise with the standard exercise posture in real time. When it is found that the actual exercise data is inconsistent with the standard exercise data or the exercise posture is inconsistent with the standard exercise posture, the user is promptly reminded to stop exercising or correct the exercise posture. This method monitors and feedbacks the user's exercise performance in real time to ensure the correctness and safety of the exercise. The instant feedback mechanism helps users avoid the risk of injury caused by improper posture, while improving the effect and quality of exercise training, and promoting users to form correct exercise habits, thereby achieving better health and fitness effects.

[0010] In one implementation, it also includes: collecting heart rate change data and amplitude change data of the user during exercise through sensors, wherein the amplitude change data is the amplitude change data of the limb part corresponding to the sensor; based on the heart rate change data, the amplitude change data, the actual human body data, the actual motion data and the artificial intelligence algorithm, synthesizing the user's exercise posture video.

[0011] The above method for correcting the sports posture collects the user's heart rate change data and amplitude change data during the exercise through sensors, wherein the amplitude change data refers to the amplitude change data of the limb part corresponding to the sensor; these data are used as supplementary information, combined with actual human body data, actual motion data and artificial intelligence algorithms to synthesize the user's motion posture video, and then obtain a more comprehensive first motion posture. Subsequently, the first motion posture is compared with the standard motion posture to generate a corresponding motion report. This method improves the comprehensiveness of the user's motion posture correction, making the generated motion report more instructive, thereby helping users to improve their sports skills in a targeted manner, improve sports efficiency, reduce the risk of injury, and optimize overall sports performance.

[0012] In a second aspect, the present application also provides a system for correcting motion posture, comprising: an acquisition module, configured to acquire actual human body data and a target image of a user, wherein the target image includes a static reference object; a construction module, configured to construct a coordinate axis based on the actual size of the target image and the static reference object; a processing module, configured to perform frame-by-frame analysis of the motion posture of the user during the motion process based on the coordinate axis and a high-speed camera, and determine the actual motion data of the user's limbs, wherein the actual motion data includes at least one of the distance moved by the limb, the angle moved by the limb, the speed moved by the limb, the amplitude moved by the limb, and the stability of the limb; a synthesis module, configured to synthesize the user's motion posture video based on the actual motion data and an artificial intelligence algorithm, and determine the user's first motion posture; a generation module, configured to compare the motion posture video with the standard motion posture video, and generate a corresponding motion report, so that the user can correct the motion posture based on the motion report.

[0013] In one implementation, the target image also includes the user; the processing module is configured to determine the proportion of the user's body part in the target image based on the size of the user in the target image and the target image; the processing module is configured to determine the first ratio based on the actual size of the static reference object and the size of the static reference object in the target image; the processing module is configured to determine the actual movement data of the user's limbs based on the actual human body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis.

[0014] In one implementation, the generation module is configured to compare the actual motion data with the standard motion data; the generation module is configured to compare the motion posture of the user during exercise with the standard motion posture through the high-speed camera; the processing module is configured to remind the user to stop exercising or correct the motion posture when the actual motion data is inconsistent with the standard motion data or the motion posture is inconsistent with the standard motion posture.

[0015] In one implementation, it also includes: a sensor configured to collect heart rate change data and amplitude change data of the user during exercise, wherein the amplitude change data is the amplitude change data of the limb part corresponding to the sensor; the synthesis module is configured to synthesize the user's exercise posture video based on the heart rate change data, the amplitude change data, the actual human body data, the actual motion data and the artificial intelligence algorithm.

[0016] In a third aspect, the present application also provides a wearable smart device, comprising any of the above-mentioned systems for correcting movement postures.

[0017] In a fourth aspect, the present application also provides a computer storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above methods for correcting the movement posture.

[0018] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0019] 1. By collecting the user's actual body data and the target image containing static reference objects, the actual size of the target image and the static reference object is used to construct the coordinate axis, and the user's posture during the movement is analyzed frame by frame with a high-speed camera to determine the actual movement data of the user's limbs, including the moving distance, angle, amplitude and stability. Based on these data and artificial intelligence algorithms, the user's movement posture video is synthesized and compared with the standard movement posture to generate a detailed movement report. This method can provide accurate movement posture analysis and real-time feedback, helping users identify and correct posture deviations, thereby improving the accuracy of movement posture correction.

[0020] 2. Determine the first ratio by analyzing the user's size ratio in the target image and the actual size of the static reference object and its size in the target image, and determine the actual motion data of the user's limbs by combining the actual human body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis. This method not only improves the accuracy and reliability of motion analysis, but also makes the evaluation and correction of motion posture more objective through quantitative data support, helps users optimize their motion posture more effectively, and also reduces the cost of correcting motion posture and improves users' sports enthusiasm.

[0021] 3. By comparing the actual motion data with the standard motion data, and using a high-speed camera to monitor the consistency of the user's posture during exercise with the standard motion posture in real time, when it is found that the actual motion data is inconsistent with the standard motion data or the motion posture is inconsistent with the standard motion posture, the user is promptly reminded to stop exercising or correct the motion posture. This method monitors and feedbacks the user's exercise performance in real time to ensure the correctness and safety of the exercise. The instant feedback mechanism helps users avoid the risk of injury caused by improper posture, while improving the effect and quality of sports training, and promoting users to form correct exercise habits, thereby achieving better health and fitness results.

[0022] 4. Collect the user's heart rate change data and amplitude change data during exercise through sensors, where the amplitude change data refers to the amplitude change data of the limb part corresponding to the sensor; use these data as supplementary information, combine actual human body data, actual motion data and artificial intelligence algorithms to synthesize the user's motion posture video, and then obtain a more comprehensive first motion posture. Subsequently, compare the first motion posture with the standard motion posture to generate a corresponding motion report. This method improves the comprehensiveness of the user's motion posture correction and makes the generated motion report more instructive, thereby helping users to improve their sports skills in a targeted manner, improve sports efficiency, reduce the risk of injury, and optimize overall sports performance. BRIEF DESCRIPTION OF THE DRAWINGS

[0023] The preferred implementation modes will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0024] Figure 1 A flow chart of a method for correcting a sports posture provided by an embodiment of the present application is shown;

[0025] Figure 2 A flowchart for determining actual motion data provided by an embodiment of the present application is shown. DETAILED DESCRIPTION

[0026] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0027] In order to simplify the drawings, only the parts related to the invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0028] It should be further understood that the term “and / or” used in the specification and appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0029] In this document, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense, for example, it can be a fixed connection, a detachable connection, or an integral connection; it can be a mechanical connection or an electrical connection; it can be a direct connection, or it can be indirectly connected through an intermediate medium, or it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0030] In addition, in the description of the present application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0031] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

[0032] Sports posture analysis is a technology that evaluates the position and movement of various parts of the body when an individual performs sports activities or exercises, with the aim of optimizing movement efficiency, improving performance and preventing injuries. By using tools such as video recording, 3D motion capture systems, wearable sensors or professional software, analysts are able to measure and evaluate key parameters such as the distance, angle, speed and coordination of limb movement. This analysis helps users understand their movement patterns, identify posture deviations, and provide targeted training suggestions to improve technology, enhance muscle balance and flexibility, while reducing the risk of sports injuries caused by improper posture.

[0033] Wearable smart devices refer to electronic devices that can be worn on the body, usually integrated with sensors, software and connectivity functions, and are designed to monitor the user's health, sports performance and daily activities. Wearable smart devices include but are not limited to smart watches, fitness trackers, smart glasses, smart clothing, etc., which can collect and analyze data in real time, such as heart rate, number of steps, sleep quality and calorie consumption. The embodiment of the present application integrates sensors, high-speed cameras and artificial intelligence algorithms (AI) on wearable smart devices, analyzes the user's motion posture during exercise frame by frame, determines relevant motion data, and generates a motion report based on the motion data to instruct the user to correct the motion posture, which can achieve at least one of the following beneficial effects: avoiding the subjectivity and error problems of manual guidance, and improving the accuracy of motion posture correction; or reducing the cost of motion posture correction and improving the user's sports enthusiasm.

[0034] The following is explained with reference to the accompanying drawings:

[0035] Reference Figure 1 , which shows a flow chart of a method for correcting a sports posture provided by an embodiment of the present application. Figure 1 As shown, including:

[0036] S100, collecting actual human body data of the user and a target image, where the target image includes a static reference object.

[0037] S110, constructing a coordinate axis based on the actual size of the target image and the static reference object.

[0038] S120, analyzing the user's movement posture during exercise frame by frame based on the coordinate axis and the high-speed camera to determine the actual movement data of the user's limbs, the actual movement data including at least one of the distance moved by the limbs, the angle moved by the limbs, the speed moved by the limbs, the amplitude moved by the limbs, and the stability of the limbs.

[0039] S130, synthesizing the user's motion posture video based on actual human body data, actual motion data and artificial intelligence algorithm, and determining the user's first motion posture.

[0040] S140, comparing the first exercise posture with the standard exercise posture, and generating a corresponding exercise report, so that the user can correct the exercise posture based on the exercise report.

[0041] The user's actual body data is collected, including but not limited to height, weight, upper arm length, forearm length, waist circumference, shoulder circumference, chest circumference, leg length, and the user's body shape data evaluated by artificial intelligence algorithms.

[0042] The target image including the user and the static reference object is captured by the high-speed camera on the wearable smart device. The coordinate axis is constructed based on the actual size of the target image and the static reference object. After the user starts exercising, the high-speed camera on the wearable smart device continuously records and analyzes the changes in the user's movement posture, and determines the actual movement data of the user's limbs based on the coordinate axis. Among them, the actual movement data corresponding to the different movements performed by the user are also different. For example, when the user exercises the biceps by dumbbell curls, the actual movement data is the distance moved by the limbs (the distance moved by the limbs can be the distance between the starting position and the end position of the user's arm or the distance moved by the user's shoulder), the amplitude of the movement of the limbs (the amplitude of the movement of the limbs can be the amplitude of the arm bending) and the speed of the movement of the limbs (the speed of the movement of the limbs can be the curling speed). For another example, when the user is swimming, the actual movement data is the amplitude of the movement of the limbs (the amplitude of the movement of the limbs can be the amplitude of the arm swing or the amplitude of the leg kicking) and the angle of the movement of the limbs (the angle of the movement of the limbs can be the angle of the user's body tilt). For example, when a user is running, the actual motion data include the angle of movement of the limbs (the angle of movement of the limbs can be the forward leaning angle of the user's body), the stability of the limbs (the stability of the limbs can be the torso stability of the user), the amplitude of movement of the limbs (the amplitude of movement of the limbs can be the amplitude of swinging of the arms and legs), etc.

[0043] The actual human body data and actual motion data of the user are sent to the artificial intelligence module in the wearable intelligent device, and the actual human body data and actual motion data of the user are synthesized into the user's motion posture video during the motion process through the artificial intelligence algorithm, and the user's first motion posture (the first motion posture is essentially the user's motion posture during the motion process) is determined through the motion posture video. The first motion posture is compared with the standard motion posture in the big data to generate a motion report. When there is a difference between the user's first motion posture and the standard motion posture, the user can correct his first motion posture based on the motion report, and the user can also determine whether his athletic ability is up to standard based on the motion report. Among them, the body shape corresponding to the standard motion posture is consistent with the user's body shape. The source of the standard motion posture can be the national standard, the literature data provided by the international authoritative sports organization, the motion posture research data and papers provided by the authoritative university, the national standards for athletes of all levels, the standards of the national authority for youth health standards and other authoritative organizations, etc. If the user is an abnormal body user, the requirements of the standard motion posture can be reduced based on the user's body shape.

[0044] The embodiment of the present application collects the actual human body data of the user and the target image containing the static reference object, uses the actual size of the target image and the static reference object to construct the coordinate axis, and combines the high-speed camera to analyze the user's posture during the movement frame by frame to determine the actual movement data of the user's limbs, including the movement distance, angle, amplitude and stability. Based on these data and artificial intelligence algorithms, the user's movement posture video is synthesized and compared with the standard movement posture to generate a detailed movement report. This method can provide accurate movement posture analysis and real-time feedback, help users identify and correct posture deviations, thereby improving the accuracy of movement posture correction.

[0045] Reference Figure 2 , which shows a flow chart of determining actual motion data provided by an embodiment of the present application. Figure 2 As shown, including:

[0046] S200, determining a proportion of a user's body part in the target image based on the size of the user in the target image and the target image.

[0047] S210: Determine a first ratio based on an actual size of the static reference object and a size of the static reference object in the target image.

[0048] S220, determining actual movement data of the user's body parts based on actual body data, the proportion of the user's body part in the target image, the first ratio, and the coordinate axis.

[0049] Before the user starts exercising, a target image including the user and a static reference object is captured by a high-speed camera on a wearable smart device. The size of the user in the target image is measured, and based on the size of the user in the target image and the size of the target image, the proportion of the user's body part in the target image is determined. The actual size of the static reference object and the size of the static reference object in the target image are measured, and based on the measured size, the proportional relationship between the actual size of the static reference object and the size of the static reference object in the target image is determined, that is, the first ratio is determined. Then, based on the actual human body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis, the actual motion data of the user's limbs during the exercise is calculated. Based on the actual human body data, the actual motion data and the artificial intelligence algorithm, the user's motion posture video is synthesized, and compared with the standard motion posture, and a detailed motion report is generated, so that the user can correct his or her own motion posture based on the motion report.

[0050] The embodiment of the present application determines the first ratio by analyzing the size ratio of the user in the target image and the actual size of the static reference object and its size in the target image, and determines the actual motion data of the user's limbs by combining the actual human body data, the ratio of the user's body part in the target image, the first ratio and the coordinate axis. This method not only improves the accuracy and reliability of motion analysis, but also makes the evaluation and correction of motion posture more objective through quantitative data support, helps users to optimize motion posture more effectively, and also reduces the cost of motion posture correction and improves the user's sports enthusiasm.

[0051] In one embodiment of the present application, it also includes: comparing actual motion data with standard motion data; comparing the user's motion posture during exercise with the standard motion posture through a high-speed camera; when the actual motion data is inconsistent with the standard motion data or the motion posture is inconsistent with the standard motion posture, reminding the user to stop exercising or correct the motion posture.

[0052] When the wearable smart device analyzes the user's motion posture during exercise frame by frame through a high-speed camera and a coordinate axis, some motion postures can be directly compared with the standard motion postures. When the comparison results are inconsistent, an alarm is issued to the user to remind the user to stop exercising or correct the motion posture. For example, when the user is exercising the biceps with dumbbell curls, the user's wrist posture can be directly compared with the standard wrist posture of dumbbell curls. When the comparison results are inconsistent, the user is reminded in real time to correct the motion posture, or based on the comparison results, it is determined that the user may currently be exhausted or at risk of exercise (the risk of exercise can be that the user may be injured by pressure when performing dumbbell curls), and then the user is reminded to stop exercising. For another example, when the user is swimming, the user's head breathing posture, the time the user's arms are underwater, the time the user's arms are above the water, the number of times the user's legs kick or kick the water, etc., can also be directly compared with the standard posture corresponding to swimming. When the comparison results are inconsistent, the user is reminded in real time to correct the exercise posture, or based on the comparison results, it is determined that the user may be currently exhausted or at risk of exercise (exercise risks may be incorrect head breathing posture during swimming, causing the user to choke on water, etc.).

[0053] Similarly, the actual motion data of the user during exercise can also be compared with the standard motion data, and when the comparison results are inconsistent, an alarm is issued to the user to remind the user to stop exercising or correct the motion posture. For example, when the user is running, the amplitude of the movement of the user's limbs, the angle of movement of the limbs, the stability of the limbs, etc. can be directly compared with the standard motion data corresponding to running. When the comparison results are inconsistent, the user is reminded in real time to correct the motion posture, or based on the comparison results, it is determined that the user may currently be exhausted (for example, when the user's arm swing amplitude and leg swing amplitude during running are inconsistent with the standard motion data, the user may be exhausted) or sports risks (for example, when the leg swing amplitude is inconsistent with the standard motion data, the user's ankle may be injured, or it is determined that the user's heel or forefoot landing has sports risks).

[0054] The embodiment of the present application compares the actual motion data with the standard motion data, and uses a high-speed camera to monitor the consistency of the user's posture during exercise with the standard motion posture in real time. When it is found that the actual motion data is inconsistent with the standard motion data or the motion posture is inconsistent with the standard motion posture, the user is promptly reminded to stop exercising or correct the motion posture. This method monitors and feedbacks the user's exercise performance in real time to ensure the correctness and safety of the exercise. The instant feedback mechanism helps users avoid the risk of injury caused by improper posture, while improving the effect and quality of sports training, and promoting users to form correct exercise habits, thereby achieving better health and fitness effects.

[0055] In one embodiment of the present application, it also includes: collecting the user's heart rate change data and amplitude change data during exercise through sensors, wherein the amplitude change data is the amplitude change data of the limb part corresponding to the sensor; based on the heart rate change data, amplitude change data, actual human body data, actual motion data and artificial intelligence algorithm, synthesizing the user's exercise posture video.

[0056] Wearable smart devices can have multiple sensors built in, including but not limited to gravity sensors, heart rate sensors, acceleration sensors, and gyroscopes. The sensors can collect the user's heart rate changes during exercise and the amplitude changes of the limb parts corresponding to the sensors in real time. Among them, the amplitude changes of the limb parts corresponding to the sensors are different depending on the limb parts on which the user wears the wearable smart device. For example, in dumbbell curls, the sensor collects the amplitude changes of the wrist between lifting and lowering in one action; in running, the sensor collects the amplitude changes of the arm swing, etc. In addition, the heart rate change data and amplitude change data collected by the sensor can be the values ​​of a certain movement or the values ​​of the entire movement process. The heart rate change data and amplitude change data are used as supplements, and the user's movement posture video is synthesized based on actual human body data, actual movement data, and artificial intelligence algorithms to obtain a more comprehensive first movement posture. Then the first movement posture is compared with the standard movement posture to generate a corresponding movement report.

[0057] Similarly, the data collected by the sensor can be compared with standard exercise data to determine whether the user's body is exhausted or at risk of exercise. For example, the user's heart health risk can be determined through heart rate change data.

[0058] The embodiment of the present application uses sensors to collect the heart rate change data and amplitude change data of the user during exercise, wherein the amplitude change data refers to the amplitude change data of the limb part corresponding to the sensor; these data are used as supplementary information, combined with actual human body data, actual motion data and artificial intelligence algorithms to synthesize the user's motion posture video, thereby obtaining a more comprehensive first motion posture. Subsequently, the first motion posture is compared with the standard motion posture to generate a corresponding motion report. This method improves the comprehensiveness of the user's motion posture correction, making the generated motion report more instructive, thereby helping users to improve their sports skills in a targeted manner, improve sports efficiency, reduce the risk of injury, and optimize overall sports performance.

[0059] The embodiment of the present application also provides a system for correcting motion posture, which is used to execute the method or means described in any of the above embodiments, including: an acquisition module, configured to acquire actual human body data and a target image of a user, wherein the target image includes a static reference object; a construction module, configured to construct a coordinate axis based on the actual size of the target image and the static reference object; a processing module, configured to analyze the motion posture of the user during the motion process frame by frame based on the coordinate axis and a high-speed camera, and determine the actual motion data of the user's limbs, wherein the actual motion data includes at least one of the distance moved by the limbs, the angle moved by the limbs, the speed moved by the limbs, the amplitude moved by the limbs, and the stability of the limbs; a synthesis module, configured to synthesize the user's motion posture video based on the actual motion data and an artificial intelligence algorithm, and determine the user's first motion posture; a generation module, configured to compare the motion posture video with the standard motion posture video, and generate a corresponding motion report, so that the user can correct the motion posture based on the motion report.

[0060] In one embodiment of the present application, the target image also includes a user; the processing module is configured to determine the proportion of the user's body part in the target image based on the size of the user in the target image and the target image; the processing module is configured to determine the first ratio based on the actual size of the static reference object and the size of the static reference object in the target image; the processing module is configured to determine the actual movement data of the user's limbs based on the actual human body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis.

[0061] In one embodiment of the present application, the generation module is configured to compare the actual motion data with the standard motion data; the generation module is configured to compare the user's motion posture during exercise with the standard motion posture through a high-speed camera; the processing module is configured to remind the user to stop exercising or correct the motion posture when the actual motion data is inconsistent with the standard motion data or the motion posture is inconsistent with the standard motion posture.

[0062] In one embodiment of the present application, it also includes: a sensor, configured to collect heart rate change data and amplitude change data of the user during exercise, wherein the amplitude change data is the amplitude change data of the limb part corresponding to the sensor; a synthesis module, configured to synthesize the user's exercise posture video based on the heart rate change data, the amplitude change data, the actual human body data, the actual motion data and the artificial intelligence algorithm.

[0063] An embodiment of the present application also provides a wearable smart device, comprising a system for correcting exercise posture according to any of the above embodiments.

[0064] An embodiment of the present application also provides a computer storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of the method for correcting the movement posture described in any of the above embodiments are implemented.

[0065] It should be noted that the above embodiments can be freely combined as needed. The above are only preferred embodiments of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be regarded as the protection scope of the present invention.

Claims

1. A method for correcting a sports posture, characterized in that: include: Collecting actual human body data of the user and a target image, wherein the target image includes a static reference object; constructing a coordinate axis based on the actual sizes of the target image and the static reference object; Analyzing the motion posture of the user during the motion process frame by frame based on the coordinate axis and the high-speed camera to determine the actual motion data of the user's limbs, wherein the actual motion data includes at least one of the distance moved by the limbs, the angle moved by the limbs, the speed moved by the limbs, the amplitude moved by the limbs, and the stability of the limbs; synthesizing the user's motion posture video based on the actual human body data, the actual motion data and the artificial intelligence algorithm, and determining the user's first motion posture; The first movement posture is compared with a standard movement posture, and a corresponding movement report is generated, so that the user can correct the movement posture based on the movement report.

2. The method for correcting exercise posture according to claim 1, characterized in that: The target image also includes the user, and the frame-by-frame analysis of the user's motion posture during the motion process based on the coordinate axis and the high-speed camera to determine the actual motion data of the user's limbs specifically includes: Determine a proportion of a user's body part in the target image based on the size of the user in the target image and the target image; Determining a first ratio based on an actual size of the static reference object and a size of the static reference object in the target image; Based on the actual human body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis, the actual movement data of the user's body part is determined.

3. The method for correcting exercise posture according to claim 1, characterized in that: Also includes: Comparing the actual motion data with the standard motion data; Comparing the user's motion posture during exercise with a standard motion posture by using the high-speed camera; When the actual motion data is inconsistent with the standard motion data or the motion posture is inconsistent with the standard motion posture, the user is reminded to stop exercising or correct the motion posture.

4. The method for correcting exercise posture according to claim 3, characterized in that: Also includes: Collecting the heart rate change data and amplitude change data of the user during exercise through sensors, wherein the amplitude change data is the amplitude change data of the limb part corresponding to the sensor; Based on the heart rate change data, the amplitude change data, the actual human body data, the actual motion data and the artificial intelligence algorithm, a video recording of the user's motion posture is synthesized.

5. A sports posture correction system, characterized in that: include: A collection module, configured to collect actual human body data of the user and a target image, wherein the target image includes a static reference object; A construction module, configured to construct a coordinate axis based on the actual size of the target image and the static reference object; a processing module configured to analyze the motion posture of the user during the motion process frame by frame based on the coordinate axis and the high-speed camera to determine the actual motion data of the user's limbs, wherein the actual motion data includes at least one of the distance moved by the limbs, the angle moved by the limbs, the speed moved by the limbs, the amplitude moved by the limbs, and the stability of the limbs; a synthesis module configured to synthesize the user's motion posture video based on the actual motion data and the artificial intelligence algorithm, and determine the user's first motion posture; The generation module is configured to compare the motion posture video with the standard motion posture video and generate a corresponding motion report so that the user can correct the motion posture based on the motion report.

6. The sports posture correction system according to claim 5, characterized in that: The target image also includes the user; The processing module is configured to determine a proportion of a user's body part in the target image based on a size of the user in the target image and the target image; The processing module is configured to determine a first ratio based on an actual size of the static reference object and a size of the static reference object in the target image; The processing module is configured to determine actual movement data of the user's limbs based on the actual human body data, the proportion of the user's body part in the target image, the first ratio and the coordinate axis.

7. The sports posture correction system according to claim 5, characterized in that: The generating module is configured to compare the actual motion data with the standard motion data; The generating module is configured to compare the user's motion posture during the motion process with a standard motion posture through the high-speed camera; The processing module is configured to remind the user to stop exercising or correct the exercise posture when the actual exercise data is inconsistent with the standard exercise data or the exercise posture is inconsistent with the standard exercise posture.

8. The sports posture correction system according to claim 7, characterized in that: Also includes: A sensor is configured to collect heart rate change data and amplitude change data of the user during exercise, wherein the amplitude change data is the amplitude change data of the limb part corresponding to the sensor; The synthesis module is configured to synthesize the user's movement posture video based on the heart rate change data, the amplitude change data, the actual human body data, the actual motion data and the artificial intelligence algorithm.

9. A wearable smart device, characterized in that: A system for correcting sports posture comprising the sports posture correction system described in any one of claims 5-8.

10. A computer storage medium, characterized in that: A computer program is stored thereon, and when the computer program is executed by a processor, the steps of the method for correcting the movement posture described in any one of claims 1-4 are implemented.

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