Gesture correction method and device based on AI

Abstract

The invention provides an AI-based gesture correction method and device, and the method comprises the following steps: S1, data collection: capturing the hand motion and posture of a user through a high-precision sensor or camera; s2, gesture recognition: analyzing the collected hand actions and gestures through an AI algorithm, and recognizing the types of gestures; s3, action analysis: comparing the recognized gesture with a preset standard gesture by using a vision algorithm and a posture recognition algorithm, and evaluating the fluency and accuracy of the gesture; s4, real-time feedback: giving real-time feedback to the user through a visual interface or audio prompt according to the result of the action analysis; and S5, correcting suggestions: automatically generating the correcting suggestions when the gesture is found to be inaccurate through action analysis. The AI-based gesture correction method and device provided by the invention aim at improving the efficiency of embroidery teaching and creation through an intelligent technology.

Description

Technical Field

[0001] The present invention relates to the field of automated correction equipment, and in particular to an AI-based gesture correction method and device. Background Art

[0002] Embroidery is a craft that uses colored silk, velvet, and cotton threads to embroider various exquisite patterns on base fabrics such as satin and cloth with the help of needle movement and piercing. Embroidery is a traditional folk craft that is both ornamental and practical. It is a precious cultural heritage of my country and even the world. It occupies an important position in the history of Chinese arts and crafts. Among them, Suzhou embroidery, Sichuan embroidery, Guangdong embroidery, and Hunan embroidery are known as the four famous embroidery styles. With the development and changes of economy and culture, the application of embroidery is no longer limited to traditional clothing and craft exhibits, but has expanded to many new areas such as embroidered scarves, bags, shoes, etc.

[0003] With the continuous improvement of modern living standards, patterns with embroidery are deeply loved by consumers. The traditional embroidery process relies on manual operation, with low production efficiency and difficulty in large-scale replication; at the same time, the learning curve of embroidery skills is long and not friendly to beginners. Summary of the invention

[0004] In order to make up for the deficiencies in the prior art, the present invention provides an AI-based gesture correction method, aiming to improve the efficiency of embroidery teaching and creation through intelligent technology.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] The gesture correction method based on AI includes the following steps:

[0007] S1. Data collection: Use high-precision sensors or cameras to capture the user's hand movements and postures;

[0008] S2. Gesture recognition: Analyze the collected hand movements and postures through AI algorithms to identify the types of gestures;

[0009] S3. Motion analysis: Use visual algorithms and gesture recognition algorithms to compare the recognized gestures with preset standard gestures to evaluate the fluency and accuracy of the gestures;

[0010] S4. Real-time feedback: Based on the results of motion analysis, users are given real-time feedback through a visual interface or audio prompts;

[0011] S5. Correction suggestions: When motion analysis finds that the gesture is inaccurate, correction suggestions are automatically generated.

[0012] Furthermore, the following steps are also included: S6. Learning records: record the user's learning process and progress to provide data support for subsequent analysis and personalized guidance.

[0013] Furthermore, the following steps are also included: S7. Personalized training: providing personalized training plans and correction schemes based on the user's learning records.

[0014] Furthermore, the gesture recognition adopts the YOLO model.

[0015] The present invention also provides an AI-based gesture correction device, comprising the following modules:

[0016] A data collection module, used to collect the user's hand movements and postures;

[0017] A gesture recognition module, used to recognize user gestures;

[0018] Motion analysis module, used to compare user gestures with preset standard gestures;

[0019] Real-time feedback module, used to feedback the results of the motion analysis module;

[0020] The correction suggestion module is used to generate correction suggestions and guide users to adjust gesture deviations.

[0021] Furthermore, it also includes a learning record module for recording the user's learning process and progress.

[0022] Furthermore, it also includes a personalized training module for generating a personalized training plan and correction scheme based on the data of the learning record module.

[0023] Compared with the prior art, the present invention has the following beneficial technical effects:

[0024] 1. The present invention collects data through high-precision sensors or cameras, and uses AI algorithms for gesture recognition and motion analysis. It can analyze and correct gestures during embroidery in real time, which greatly improves the efficiency and accuracy of embroidery teaching and creation.

[0025] 2. Through the application of the present invention, users can quickly master the correct embroidery gestures and improve learning efficiency. At the same time, the personalized training program enables users of different levels to get guidance suitable for themselves, thereby enhancing the adaptability of the system.

[0026] 3. The present invention also has the positive effect of promoting the inheritance of embroidery skills and improving the quality of works. BRIEF DESCRIPTION OF THE DRAWINGS

[0027] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0028] Many specific details are described in the following description to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from the description, and those skilled in the art can make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0029] Example 1

[0030] like Figure 1 As shown, the gesture correction method based on AI includes the following steps:

[0031] S1. Data collection: Use high-precision sensors or cameras to capture the user's hand movements and postures.

[0032] S2. Gesture recognition: The collected hand movements and postures are analyzed through AI algorithms to identify the types of gestures. Deep learning-based methods, such as the YOLOv8 / v7 / v6 / v5 model, can be used to analyze images frame by frame and identify the gestures in each frame of the video.

[0033] S3. Motion analysis: Use visual algorithms and posture recognition algorithms to compare the recognized gestures with preset standard gestures to evaluate the fluency and accuracy of the gestures.

[0034] S4. Real-time feedback: Based on the results of motion analysis, real-time feedback is given to users through a visual interface or audio prompts; enabling users to promptly understand whether their movements are accurate and make corresponding adjustments.

[0035] S5. Correction suggestions: When motion analysis finds that the gesture is inaccurate, correction suggestions are automatically generated to guide the user to adjust the gesture and improve learning efficiency and accuracy.

[0036] S6. Learning records: record the user's learning process and progress, and provide data support for subsequent analysis and personalized guidance.

[0037] S7. Personalized training: Provide personalized training plans and correction schemes based on the user's learning records to meet the specific needs of different users and improve learning efficiency.

[0038] The present invention also provides an AI-based gesture correction device, comprising the following modules:

[0039] A data collection module, used to collect the user's hand movements and postures;

[0040] A gesture recognition module, used to recognize user gestures;

[0041] Motion analysis module, used to compare user gestures with preset standard gestures;

[0042] Real-time feedback module, used to feedback the results of the motion analysis module;

[0043] The correction suggestion module is used to generate correction suggestions and guide users to adjust gesture deviations.

[0044] The learning record module is used to record the user's learning process and progress.

[0045] The personalized training module is used to generate personalized training plans and correction schemes based on the data of the learning record module.

[0046] The present invention can be applied to the following scenarios:

[0047] 1. Personalized learning assistance: AI technology can provide personalized learning suggestions and guidance based on students' learning habits and abilities. For example, Tsinghua University uses an AI teaching assistant system to provide 24-hour personalized learning support, intelligent assessment and feedback to assist students in thinking deeply and inspire learning inspiration.

[0048] 2. Interactive teaching: Through AI gesture recognition technology, teachers can conduct more vivid interactive teaching, such as using gestures to control the playback of courseware, enhancing the teacher's intention expression and students' participation.

[0049] 3. Virtual labs and simulations: In complex disciplines such as science, programming, and language learning, AI technology can break down concepts into easy-to-understand modules, provide virtual labs and simulation experiences, and enhance learning outcomes.

[0050] 4. Distance education: AI technology can assist distance education, provide real-time feedback and personalized guidance, and enable teachers to understand the learning situation in a timely manner and implement personalized teaching.

[0051] 5. Intelligent assessment and feedback: AI systems can automatically score and provide feedback to help students understand their learning progress and areas for improvement.

[0052] 6. Enhanced classroom interaction: Using AI technologies, such as expression recognition and gesture recognition, can enhance classroom interaction and allow teachers to understand students' learning status and needs in a timely manner.

[0053] 7. Innovation in teaching content: AI can help teachers innovate teaching content and methods. For example, through AI immersive courseware, the teacher’s avatar can be integrated with the courseware content to achieve a more intuitive interactive effect.

[0054] 8. Assisting special educational needs: AI technology can provide assistance to students with special educational needs, such as through speech-to-text or text-to-speech technology, to help students with hearing or vision impairments better participate in learning.

[0055] 9. Automation of teaching management: AI can handle some tasks in teaching management, such as attendance, homework collection and distribution, reducing the administrative burden on teachers.

[0056] 10. Intelligent tool applications: AI visual technologies such as ultra-clear picture taking, intelligent fill light, and face attendance can improve the educational experience and teaching efficiency.

[0057] The above are only 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 embodiments, it is still possible for those skilled in the art to modify the technical solutions described in the aforementioned embodiments or to replace some of the technical features therein with equivalents. However, any modifications, equivalent replacements, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. The AI-based gesture correction method is characterized by: The following steps are involved: S1. Data collection: Use high-precision sensors or cameras to capture the user's hand movements and postures; S2. Gesture recognition: Analyze the collected hand movements and postures through AI algorithms to identify the types of gestures; S3. Motion analysis: Use visual algorithms and gesture recognition algorithms to compare the recognized gestures with preset standard gestures to evaluate the fluency and accuracy of the gestures; S4. Real-time feedback: Based on the results of motion analysis, users are given real-time feedback through a visual interface or audio prompts; S5. Correction suggestions: When motion analysis finds that the gesture is inaccurate, correction suggestions are automatically generated.

2. The AI-based gesture correction method according to claim 1, characterized in that: The following steps are also included: S6. Learning records: record the user's learning process and progress, and provide data support for subsequent analysis and personalized guidance.

3. The AI-based gesture correction method according to claim 2, characterized in that: The following steps are also included: S7. Personalized training: Provide personalized training plans and correction schemes based on the user's learning records.

4. The AI-based gesture correction method according to any one of claims 1 to 3, characterized in that: The gesture recognition adopts the YOLO model.

5. AI-based gesture correction device, characterized by: A data collection module, used to collect the user's hand movements and postures; A gesture recognition module, used to recognize user gestures; Motion analysis module, used to compare user gestures with preset standard gestures; Real-time feedback module, used to feedback the results of the motion analysis module; The correction suggestion module is used to generate correction suggestions and guide users to adjust gesture deviations.

6. The AI-based gesture correction device according to claim 5, characterized in that: The learning record module is used to record the user's learning process and progress.

7. The AI-based gesture correction device according to claim 6, characterized in that: The personalized training module is used to generate personalized training plans and correction schemes based on the data of the learning record module.

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