Exercise effect evaluation and portraying method
Through computer vision and machine learning technology, the motion effect is automatically evaluated and the motion portrait is generated, which solves the problems of high subjectivity and cumbersome operation of the motion effect assessment in the existing technology, and realizes objective evaluation and intuitive presentation of the motion effect, providing effective analysis tools for the fields of sports training and other fields.
Patent Information
- Application Number
- CN202410241254.6
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-03-04
- Publication Date
- 2025-06-10
AI Technical Summary
The prior art is difficult to conduct objective and efficient effect evaluation during exercise, and relies on manual observation and measurement, which has high subjectivity and cumbersome operation.
Computer vision and machine learning technology are adopted to automatically evaluate and visual presentation of motion effects by collecting motion data, preprocessing, extracting motion characteristics, establishing a motion effect evaluation model and generating a motion portrait.
It realizes objective assessment and intuitive visual presentation of individual sports effects, providing effective analytical tools and feedback methods for sports training, rehabilitation and treatment.
Smart Images
Figure FT_1
Abstract
Description
Technical Field
[0001] The present invention relates to the technical fields of motion assessment and imaging technology, and particularly to a method for assessing motion effects and generating motion images. Background Art
[0002] In many fields, such as sports training, rehabilitation therapy, sports science research, etc., the assessment and analysis of individual motion effects are very important. Existing assessment methods mainly rely on manual observation and measurement, and have problems such as high subjectivity and cumbersome operation. With the development of computer vision and machine learning technologies, image or video data can be used to automatically assess motion effects and generate motion images to provide objective analysis and feedback. Summary of the Invention
[0003] To solve the problem of difficult assessment of effects during the motion process, the present invention provides a method for assessing motion effects and imaging.
[0004] To achieve the above object, the technical solution adopted by the present invention is as follows: A method for assessing motion effects and imaging, at least including: the step of collecting motion data, the step of preprocessing the data, the step of extracting motion features, the step of establishing a motion effect assessment model, and the step of generating a motion image according to the assessment result.
[0005] Through the image, users can more clearly and intuitively understand their recent motion effects and problems, compare historical motion data, and can more easily find a suitable motion mode for themselves.
[0006] The characteristics of the present invention are: By using computer vision and machine learning technologies, the motion effect can be automatically assessed and a motion image with a visual effect can be generated. Description of the Drawings
[0007] Figure 1 is a flowchart of a method for assessing motion effects and imaging provided by the patent of the present application.
[0008] Among them, 1. Motion data collection, 2. Preprocessing data, 3. Extracting motion features, 4. Motion effect assessment, 5. Drawing user motion image. Detailed Embodiment
[0009] The method for assessing motion effects and imaging of the present invention includes the following steps:
[0010] Step 1: Collect motion data. Sensors, cameras or other devices can be used to record key information during the motion process, including but not limited to postures, motion trajectories, speeds, etc.
[0011] Step 2: Preprocess the collected motion data, including steps such as data cleaning, filtering, and calibration, to obtain accurate and reliable data input.
[0012] Step 3: Use computer vision technology to analyze the preprocessed data, such as human pose estimation, joint motion recognition, etc., to extract features related to the exercise effect.
[0013] Step 4: According to the extracted features, use machine learning algorithms or deep learning models to establish an exercise effect evaluation model, which can automatically evaluate the quality of the exercise effect based on the input motion data.
[0014] Step 5: Generate an exercise portrait based on the evaluation results, which can be a static picture or a dynamic video, to visually display the exercise effect and exercise features.
[0015] Through the method of the present invention, objective evaluation and visual presentation of individual exercise effects can be achieved, providing effective analysis tools and feedback means for fields such as sports training and rehabilitation therapy.
Claims
1. A motion effect evaluation and profiling method, comprising: Steps for collecting motion data; The step of preprocessing the collected data; The steps of extracting motion features; the steps of establishing a motion effect evaluation model; and the steps of generating a motion portrait based on the evaluation results.
2. The method for collecting motion data according to claim 1, characterized in that A depth camera is used to collect the user's body movements and transmit the information to the backend server in the form of a video source.
3. The method for preprocessing data according to claim 1, characterized in that The received data is cleaned, filtered, calibrated, etc. to obtain accurate and reliable data input.
4. The method for extracting motion features according to claim 1, characterized in that Computer vision technology is used to analyze the processed data, identify human body posture and joint movement trajectory, and locate the affected muscle groups according to the movement classification.
5. The method for establishing a sports effect evaluation model according to claim 1, characterized in that The processed results are compared with the action model, and the motion effect data of the corresponding action is obtained according to the parameters designed in the action model.
6. The method for generating a motion image according to claim 1, characterized in that The muscle expression form (including but not limited to color change and shape change) is depicted according to the exercise effect data and fitted onto the skeletal frame model. At the same time, a user portrait is formed based on the user's body characteristics (such as height, weight, and body shape) and displayed to the user.