Multiple children training sensing system

Abstract

The invention discloses a multiple children training sensing system, and relates to the technical field of children education training, and the system comprises the following components: hardware architecture design, a data processing and fusion algorithm, a personalized training feedback mechanism and a cloud service and data analysis platform. The high-definition camera, the high-precision motion sensor and the high-sensitivity microphone array hardware equipment are integrated, the data processing and fusion algorithm is combined, comprehensive and accurate sensing of the training process of children is achieved, the high-definition camera can capture the facial expression and mouth shape changes of the children, and the high-precision motion sensor and the high-sensitivity microphone array hardware equipment are combined. The motion sensor monitors the motion posture, the motion track and the strength distribution of the child in real time, the high-sensitivity microphone array can capture the nuisance of the sound production of the child, and the multi-modal data acquisition mode enables the system to more accurately recognize the key behavior characteristics, the emotional state and the skill mastering condition of the child in the training process, so that the training efficiency is improved. And a solid data basis is provided for personalized training.

Description

Technical Field

[0001] The invention relates to the technical field of children's education and training, and in particular to a multiple children's training sensing system. Background Art

[0002] With the continuous advancement of science and technology, more and more parents and educators have begun to pay attention to how to use technology to improve training effects and personalized teaching. As the main body of learning, children's behavioral characteristics, emotional state and skill mastery during training are crucial to the formulation of effective training plans.

[0003] The data processing and fusion algorithms in traditional technologies have many shortcomings in children's training sensing systems. There is a lack of deep fusion and comprehensive analysis of multi-source data, and it is impossible to identify children's key behavioral characteristics, emotional states, and skill mastery during training. It is impossible to conduct comprehensive analysis of real-time collected data and cannot comprehensively evaluate children's training status. Therefore, it is particularly important to develop a multiple children's training sensing system. Summary of the invention

[0004] The purpose of the present invention is to make up for the deficiencies of the prior art and to provide a multiple children's training sensing system. The system integrates high-definition cameras, high-precision motion sensors and high-sensitivity microphone array hardware devices, combined with data processing and fusion algorithms, to achieve comprehensive and accurate perception of the children's training process.

[0005] In order to solve the above technical problems, the present invention provides the following technical solutions: a multiple children training sensing system, which includes a hardware architecture design, a data processing and fusion algorithm, a personalized training feedback mechanism, and a cloud service and data analysis platform;

[0006] The hardware architecture design: uses a high-definition camera, supports face recognition and expression analysis, can accurately capture children's facial expressions and lip shape changes, has a built-in high-precision accelerometer and gyroscope motion sensor, is designed to fit the child's body, monitors the child's movement posture, movement trajectory and force distribution in real time, and has a high-sensitivity microphone array that can capture the subtle differences in children's vocalizations;

[0007] The data processing and fusion algorithm: adopts fusion technology to synchronously collect, pre-process and extract features of camera images, motion sensor data and sound signals, designs intelligent algorithms, deeply fuses and comprehensively analyzes multi-source data, and identifies key behavioral characteristics, emotional states and skill mastery of children in training;

[0008] The personalized training feedback mechanism: based on the data analysis results, automatically generates personalized training plans and feedback suggestions, provides an interactive feedback interface, and intuitively displays training results and improvement directions in the form of animation and sound, so as to stimulate children's interest and enthusiasm in learning;

[0009] The cloud service and data analysis platform: builds a cloud server to realize remote storage, management and analysis of training data, supports parents and teachers to remotely monitor training progress, obtains professional evaluation reports, uses big data analysis technology to explore potential patterns and needs in children's training process, and continuously optimizes training plans and feedback mechanisms.

[0010] Furthermore, the hardware architecture design selects a high-definition camera with sufficient resolution and frame rate to clearly capture facial expressions and lip shape changes, ensures that the camera supports automatic white balance and exposure adjustment functions to adapt to different lighting environments, integrates a face recognition algorithm in the software embedded in the camera module, can accurately identify children's facial features, introduces an expression analysis library or algorithm, analyzes facial expressions through image processing technology, improves recognition accuracy through models, installs high-definition cameras in the training area to ensure that the range of children's activities can be clearly covered, debugs the camera, adjusts the focal length and angle parameters, and ensures that the picture quality reaches the best state;

[0011] Choose wearable device materials that are lightweight, durable, and in line with children's physical characteristics, design a wearing method that fits children's body curves, ensure that the device is not easy to fall off during exercise and does not affect children's activities, and build high-precision accelerometers and gyroscopes into wearable devices to monitor children's movements, postures, movement trajectories, and force distribution. Ensure that the sensor is closely connected to the device body to reduce errors in data transmission. Integrate a microprocessor or microcontroller to receive sensor data and perform preliminary processing. Design a data transmission protocol to ensure that sensor data can be transmitted to the main system or cloud server in real time and accurately. Let children of different ages try on the device, collect feedback, and optimize the design. The device material is harmless and has no sharp edges to ensure the safety of children during wearing.

[0012] Select a high-sensitivity, low-noise microphone unit that can clearly capture the subtle differences in children's vocalizations; use microphone array technology to allow multiple microphones to work together to improve the accuracy of sound localization and direction recognition; layout the microphone array according to the size and shape of the training area to ensure that the children's vocalization area is fully covered; fix the microphone array in the necessary position to avoid external interference; integrate sound processing algorithms into the system, including but not limited to noise suppression, echo cancellation, and sound source localization functions, to improve the purity and recognition accuracy of sound signals; combine speech recognition and pronunciation evaluation algorithms to perform real-time analysis and feedback on children's vocalizations; conduct comprehensive debugging of the entire system, including synchronization and coordination between cameras, wearable devices, and microphone arrays; optimize and adjust hardware parameters and algorithm models based on actual test results to ensure optimal system performance.

[0013] Furthermore, the data processing and fusion algorithm sets a unified timestamp generation mechanism for each data source, so that all collected data can be accurately aligned in time, and a high-precision clock source or network time protocol is used to synchronize the time of each device. The camera continuously captures the video stream and extracts image frames in real time or at a preset frequency. The motion sensor collects motion data at a high frequency, and the microphone array captures the sound signal and performs preliminary sound processing;

[0014] The images captured by the camera are de-noised, contrast enhanced, and color corrected to improve image quality. The facial area of ​​the child is located in the image, and the facial area is cropped and scaled to facilitate subsequent expression analysis and lip recognition. The motion sensor data is filtered to reduce noise and errors, and key motion features are extracted. The data is segmented or windowed to facilitate subsequent behavioral pattern recognition. The sound signal is framed, and each frame contains audio data of a certain length. The features of each frame of audio are extracted. The voice activity detection algorithm is applied to distinguish between valid speech and background noise.

[0015] The model is used to extract features from the preprocessed images to obtain feature vectors related to facial expressions and mouth shapes. The algorithm is used to extract features related to behavioral patterns from the preprocessed motion data. The speech recognition technology is used to convert the sound signal into text or phoneme sequences to extract sound features related to pronunciation quality, intonation, and speech speed.

[0016] Determine the level and specific methods of data fusion, fuse the feature vectors from different data sources to form a unified feature representation, select an appropriate model, train and test the fused features. The model should be able to identify the key behavioral characteristics, emotional state and skill mastery of children in training. Use the trained model to conduct a comprehensive analysis of the real-time collected data, output an evaluation report or real-time feedback on the children's training status, and adjust the training plan based on the analysis results;

[0017] Evaluate the overall performance of the system, including but not limited to recognition accuracy, real-time, and robustness indicators, collect user feedback, understand the advantages and disadvantages of the system in practical applications, and iteratively optimize the system based on performance evaluation and user feedback.

[0018] Furthermore, the model is used to extract features from the preprocessed image: Where m and n are the number of pixels in the width and height dimensions of the image, respectively. w(x,y) is a weight function that can assign different weights according to the position of the pixel point (x,y) in the key area of ​​the face. The key area has a larger weight and the non-key area has a smaller weight. I(x,y) is the pixel value of the image at the coordinate (x,y). (x0,y0) is the key reference point of facial expression or mouth shape. d((x,y),(x0,y0)) is the distance from the pixel point (x,y) to the key reference point (x0,y0). σ is a parameter that controls the degree of influence of distance.

[0019] Through the algorithm, features related to behavioral patterns are extracted from the preprocessed motion data: Where n is the number of motion data points, i represents the index of the i-th feature element in the feature vector, and m j is the jth data point in the motion data sequence, ω is a parameter related to the periodicity of the behavior pattern, and φ is a phase offset parameter.

[0020] Furthermore, the feature vectors from different data sources are fused: Among them, w i is the weight of the ith data source, which is determined based on the reliability and importance of the data source. A larger w can be assigned to high-quality and high-precision data sources. i The value can be in the range [0, 1], and is the jth element of the feature vector of the i-th data source, θ ij It is the angle between the feature vector of the i-th data source and a reference vector at the j-th element. By calculating the cosine of the angle and combining them, the directional relationship between the feature vectors can be considered, so as to achieve a more comprehensive fusion;

[0021] Apply the trained model to conduct comprehensive analysis of the data collected in real time: Where: y1 represents the key behavioral feature evaluation, K is the number of model submodules related to the key behavioral feature, is the feature vector of the kth submodule pair The output of , α is a tuning parameter, is the eigenvector and the kth key behavior feature reference vector , y2 represents the emotional state evaluation, L is the number of model submodules related to the emotional state, is the feature vector of the l+Kth submodule pair The output of , β is a weight parameter, is the eigenvector and the average feature vector of the lth emotional state , y3 represents the skill mastery assessment, M is the number of model submodules related to skill mastery, is the feature vector of the m+K+Lth submodule pair The output of , γ is a scaling parameter, is the ideal feature vector for the mth skill mastery.

[0023] Furthermore, the personalized training feedback mechanism determines the type of data that needs to be collected, including but not limited to the child's learning progress, answer accuracy, learning time distribution, points of interest, and difficulties, which can be collected through online learning platforms, educational apps, or smart wearable devices. The collected data is cleaned, noise and outliers are removed, and standardized for subsequent analysis. Through the model, the child's learning pattern, ability level, and potential problems can be identified;

[0024] Based on the results of data analysis, specific and quantifiable learning goals are set for each child, including short-term and long-term goals. Based on the learning goals and the learner's ability level, personalized learning paths and training tasks are automatically generated. These tasks should take into account the children's interests and weaknesses. The system should be able to dynamically adjust the training plan based on the children's learning feedback to ensure that the difficulty and rhythm of the training content are always suitable for the learner's current status.

[0025] Furthermore, the user interface is intuitive and user-friendly, ensuring that it is easy for children to understand and operate. The interface should include clear navigation and instructions, as well as visual elements that attract children. Animation and sound effects should be used to visually display training results and improvement directions. When a child completes a task, animation should be used to celebrate and give specific feedback. For errors, gentle voice and animation should be used to point out the cause of the error and how to correct it. Interactive questions and answers and mini-game elements should be added to make the learning process more interesting and challenging, and stimulate children's interest and enthusiasm in learning.

[0026] Deploy the personalized training feedback mechanism into the actual learning environment, continuously monitor children's learning progress, collect new data for further analysis and optimization, regularly evaluate the effectiveness of the personalized training feedback mechanism, including the improvement of learning outcomes, changes in learning interest and motivation, and adjust strategies based on the evaluation results to ensure continuous optimization of the system;

[0027] We collect feedback from children, teachers and parents through questionnaires and interviews, pay attention to the latest developments in educational technology, continuously introduce new technologies and methods, improve the intelligence level of the system and user experience, and continuously iterate and optimize the system based on user feedback and technological updates to ensure that the personalized training feedback mechanism always remains efficient and effective.

[0028] Furthermore, the cloud service and data analysis platform determines the specific functions that the cloud service and data analysis platform needs to implement, defines the needs and expectations of target users, understands their usage scenarios and pain points, selects a cloud service platform, creates and manages virtual machine instances on the selected cloud service platform, configures necessary security groups and network settings, installs the operating system, database, and web server necessary software, and performs basic configuration, deploys the front-end, back-end, and database services of the data analysis platform on the cloud server, and ensures effective communication between services;

[0029] Design the database architecture according to the characteristics and requirements of the training data, including but not limited to the data table structure, indexes, and relationships, import the training data into the database, and perform data cleaning and preprocessing to ensure the accuracy and consistency of the data, implement data encryption and access control security measures, and perform regular data backup to prevent data loss or leakage;

[0030] Utilize big data analysis technology to build analysis models, explore potential patterns and needs in training data, realize real-time analysis and monitoring of training data, support parents and teachers to remotely view training progress and results, and automatically generate professional evaluation reports based on analysis results, providing parents and teachers with valuable reference information;

[0031] Design and implement a user-friendly remote monitoring interface that allows parents and teachers to view training status in real time, set up real-time notifications and reminders, and promptly notify relevant users when training progress reaches certain conditions or potential problems are discovered. Based on user feedback and needs, continuously optimize training plans and feedback mechanisms to improve user experience and training results;

[0032] Monitor and maintain the system regularly to ensure the stability and security of the system. According to the actual operation of the system, optimize the system performance, improve data processing capabilities and response speed, establish a user support system, answer users' questions and concerns in a timely manner, and collect user feedback for subsequent system improvement and optimization;

[0033] Regularly collect feedback from parents, teachers and system administrators to understand their usage experience and changes in needs. Based on user feedback and market changes, iterate and upgrade the platform's functions, add new features and optimize existing functions. Pay attention to the latest technological trends and industry standards, continuously explore and apply new technologies, and enhance the platform's intelligence level and competitiveness.

[0034] Compared with the prior art, a multiple children training sensing system has the following beneficial effects:

[0035] 1. The system integrates high-definition cameras, high-precision motion sensors and high-sensitivity microphone array hardware equipment, combined with data processing and fusion algorithms, to achieve comprehensive and accurate perception of children's training process. The high-definition camera can capture children's facial expressions and lip shape changes, the motion sensor monitors children's posture, movement trajectory and force distribution in real time, and the high-sensitivity microphone array can capture the subtle differences in children's vocalization. This multimodal data collection method enables the system to more accurately identify children's key behavioral characteristics, emotional states and skill mastery during training, providing a solid data foundation for personalized training.

[0036] 2. The system automatically generates personalized training plans and feedback suggestions, and displays training results and improvement directions in an intuitive form of animation and sound through an interactive feedback interface. This personalized feedback mechanism not only stimulates children's interest and enthusiasm in learning, but also ensures that the difficulty and rhythm of the training content are always suitable for the current status of the learners. At the same time, the system supports parents and teachers to remotely monitor the progress of training, obtain professional evaluation reports, and use big data analysis technology to explore the potential rules and needs in the children's training process, so as to continuously optimize the training plan and feedback mechanism. This continuous optimization process further improves the training effect and meets the needs of children's personalized development. BRIEF DESCRIPTION OF THE DRAWINGS

[0037] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art descriptions are briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention, and for ordinary technicians in this field, other drawings can be obtained based on these drawings without creative work.

[0038] Figure 1 A flow chart of the operation of a multiple children training sensing system. DETAILED DESCRIPTION

[0039] The technical solutions in the embodiments of the present invention are described clearly and completely below. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0040] Embodiment 1

[0041] The CMOS sensor with excellent performance in low-light environment is selected to ensure that children's facial expressions and subtle changes in lip shape can be clearly captured under various lighting conditions. It supports HDR technology to reduce overexposure and loss of dark details. It integrates AI image enhancement algorithm to optimize image quality in real time, improve the accuracy and robustness of face recognition and expression analysis, and designs lens modules with adjustable focal length and viewing angle to meet the needs of children of different ages and training scenarios. The combination of high-precision accelerometer and gyroscope adopts low-power design to ensure comfort and battery life for long-term wearing. A magnetometer is added to form a complete nine-axis attitude sensor system to more accurately track the three-dimensional movement trajectory and attitude changes of children. The sensor module is wrapped with soft silicone material to ensure that it fits closely with the child's body without affecting its free movement, while reducing pressure and discomfort on the skin. Beamforming technology is used to effectively suppress environmental noise and accurately capture the direction and position of children's voice. It supports multi-channel audio recording and sound source localization, and can distinguish and analyze the individual voice of children in a group environment. It integrates automatic gain control and echo cancellation algorithms to improve recording quality and ensure the clarity and purity of sound signals.

[0042] Develop a high-precision timestamp synchronization mechanism to ensure the time consistency of data from different sources such as cameras, motion sensors, and microphones. Use hardware-level synchronization technology to reduce delays and errors in data transmission and processing. Perform denoising, enhancement, face detection, and key point location preprocessing steps on image data to extract key features of expressions and lip shapes. Where n is the number of motion data points, i represents the index of the i-th feature element in the feature vector, and m jis the jth data point in the motion data sequence, ω is a parameter related to the periodicity of the behavior pattern, φ is a phase shift parameter, the motion sensor data is filtered and detrended, acceleration, angular velocity, and attitude angular motion features are extracted, the sound signal is spectrally analyzed and feature parameters are extracted, speech features and emotional tendencies are identified, and a multimodal fusion model is constructed based on a deep learning framework. Among them, w i is the weight of the ith data source, which is determined based on the reliability and importance of the data source. A larger w can be assigned to high-quality and high-precision data sources. i The value can be in the range [0, 1], and is the jth element of the feature vector of the i-th data source, θ ij It is the angle between the feature vector of the i-th data source and a reference vector at the j-th element. By calculating the cosine of the angle and combining them, the directional relationship between the feature vectors can be considered, thereby achieving a more comprehensive fusion and designing a multi-task learning model for behavior recognition, sentiment analysis, and skill assessment to achieve a comprehensive assessment of children's training status.

[0043] Design a colorful, cartoon-style UI interface to attract children's attention, adopt gamification design principles, set up reward mechanisms and achievement systems to stimulate children's enthusiasm for participation, provide voice interaction functions, increase interactivity and fun through simulated role dialogues, dynamically adjust training content and difficulty according to children's ability level, interest preferences and training goals, introduce an AI recommendation system, and recommend the most suitable training programs and resources for children based on historical training data and behavioral analysis.

[0044] Develop a mobile APP or web platform dedicated to parents and teachers that supports real-time viewing of children's training videos, data reports and evaluation reports, provides a video playback function to facilitate reviewing the training process, discovering problems and improvements, and uses big data analysis technology to explore the potential patterns and trends in children's training data, such as skill growth curves and interest changes. Based on group data, build a child growth model to provide a scientific basis for the formulation and optimization of training programs, support AI-assisted personalized teaching program design, provide customized teaching services based on children's characteristics and needs, implement strict data encryption and transmission security measures to ensure the security of training data during transmission and storage, comply with relevant laws and regulations, clarify the scope and authority of data use, and protect the privacy of children and their parents.

[0045] Embodiment 2

[0046] It is installed at multiple key locations in the training area to ensure that it can capture children's movements, postures and facial expressions in all directions. It supports automatic white balance and exposure adjustment, adapts to different lighting environments, ensures clear and stable image quality, and integrates advanced face recognition and expression analysis algorithms to assist in identifying children's emotional state to understand their response to training. It is designed with lightweight, durable materials that meet children's physical characteristics to ensure comfortable wearing without affecting sports flexibility. It has a built-in high-precision accelerometer and gyroscope to monitor children's movements, postures, movement trajectories and force distribution in real time. It integrates a microprocessor to perform preliminary processing of sensor data and transmit it to the main system or cloud server in real time. It is designed with an ergonomic wearing method to ensure that the equipment is not easy to fall off during intense exercise.

[0047] Synchronously collect high-definition camera images, wearable device motion data and sound signals, set a unified timestamp for each data source to ensure accurate alignment of data in time, denoise, enhance contrast, and perform color correction on camera images to improve image quality, locate children's facial areas, assist in emotion analysis, filter motion sensor data, and extract key motion features. Where n is the number of motion data points, i represents the index of the i-th feature element in the feature vector, and m j is the jth data point in the motion data sequence, ω is a parameter related to the periodicity of the behavior pattern, and φ is a phase shift parameter for deep fusion. Among them, w i is the weight of the ith data source, which is determined based on the reliability and importance of the data source. A larger w can be assigned to high-quality and high-precision data sources. i The value can be in the range [0, 1], and is the jth element of the feature vector of the i-th data source, θ ij It is the angle between the feature vector of the i-th data source and a reference vector at the j-th element. By calculating the cosine of the angle and combining them, the directional relationship between the feature vectors can be considered, so as to perform a more comprehensive fusion to form a unified feature representation. Machine learning or deep learning models can be applied to train and test the fused features to identify children's key movements, skill mastery and potential problems in training.

[0048] Based on real-time and historical training data, we analyze children's motor skill levels, progress rates and potential weaknesses. Combined with children's emotional states, we evaluate their acceptance and interest in the training content. Based on the analysis results, we tailor personalized training plans and feedback suggestions for each child, design training tasks that match their skill levels and interests, ensure that the difficulty and pace of the training content are moderate, and intuitively display training results and improvement directions through animation and sound. When children complete the correct movements, we give positive feedback and rewards, and provide specific guidance and correction methods for incorrect movements.

[0049] Build a cloud server to realize remote storage, management and analysis of training data. Parents and teachers can remotely view the progress and results of training through mobile devices or computers. Use big data analysis technology to explore the potential patterns and needs in the training process, automatically generate professional evaluation reports, and provide parents and teachers with valuable reference information. Based on user feedback and data analysis results, continuously optimize training plans and feedback mechanisms, introduce new technologies and methods, and continuously improve the intelligence level of the system and user experience.

[0050] It will be apparent to those skilled in the art that the invention is not limited to the details of the exemplary embodiments described above and that the invention can be implemented in other specific forms without departing from the spirit or essential features of the invention. Therefore, the embodiments should be considered exemplary and non-limiting in all respects, and the scope of the invention is defined by the appended claims rather than the foregoing description, and it is intended that all variations falling within the meaning and scope of the equivalent elements of the claims be included in the invention. Any reference numeral in a claim should not be considered as limiting the claim to which it relates.

Claims

1. A multiple children training sensor system, characterized in that, The system includes hardware architecture design, data processing and fusion algorithms, personalized training feedback mechanism, and cloud service and data analysis platform; The hardware architecture design: uses a high-definition camera, supports face recognition and expression analysis, can accurately capture children's facial expressions and lip shape changes, has a built-in high-precision accelerometer and gyroscope motion sensor, is designed to fit the child's body, monitors the child's movement posture, movement trajectory and force distribution in real time, and has a high-sensitivity microphone array that can capture the subtle differences in children's vocalizations; The data processing and fusion algorithm: adopts fusion technology to synchronously collect, pre-process and extract features of camera images, motion sensor data and sound signals, designs intelligent algorithms, deeply fuses and comprehensively analyzes multi-source data, and identifies key behavioral characteristics, emotional states and skill mastery of children in training; The personalized training feedback mechanism: based on the data analysis results, automatically generates personalized training plans and feedback suggestions, provides an interactive feedback interface, and intuitively displays training results and improvement directions in the form of animation and sound, so as to stimulate children's interest and enthusiasm in learning; The cloud service and data analysis platform: builds a cloud server to realize remote storage, management and analysis of training data, supports parents and teachers to remotely monitor training progress, obtains professional evaluation reports, uses big data analysis technology to explore potential patterns and needs in children's training process, and continuously optimizes training plans and feedback mechanisms.

2. A multiple children training sensing system according to claim 1, characterized in that: The hardware architecture design selects a high-definition camera with sufficient resolution and frame rate to clearly capture facial expressions and changes in lip shape to adapt to different lighting environments, embeds a face recognition algorithm in the software in the camera module, and can accurately identify children's facial features, introduces an expression analysis library or algorithm, analyzes facial expressions through image processing technology, improves recognition accuracy through models, installs a high-definition camera in the training area, debugs the camera, and adjusts the focal length and angle parameters; Choose wearable device materials that are lightweight, durable, and in line with children's physical characteristics, design a wearing method that fits children's body curves, and build high-precision accelerometers and gyroscopes into wearable devices to monitor children's movements, postures, motion trajectories, and force distribution to reduce errors in data transmission. Integrate microprocessors or microcontrollers to receive sensor data and perform preliminary processing, design data transmission protocols, let children of different ages try on the devices, collect feedback, and optimize the design. The device materials are harmless and have no sharp edges to ensure the safety of children during wearing. Select high-sensitivity, low-noise microphone units that can clearly capture the subtle differences in children's vocalizations. Use microphone array technology to allow multiple microphones to work together to improve the accuracy of sound localization and direction recognition. Layout the microphone array according to the size and shape of the training area, and fix the microphone array in necessary positions to avoid external interference. Integrate sound processing algorithms into the system, including but not limited to noise suppression, echo cancellation, and sound source localization functions, to improve the purity and recognition accuracy of sound signals. Combine speech recognition and pronunciation evaluation algorithms to perform real-time analysis and feedback on children's vocalizations. Comprehensively debug the entire system, including synchronization and coordination between cameras, wearable devices, and microphone arrays. Optimize and adjust hardware parameters and algorithm models based on actual test results.

3. A multiple children training sensing system according to claim 1, characterized in that: The data processing and fusion algorithm sets a unified timestamp generation mechanism for each data source, so that all collected data can be accurately aligned in time, and a high-precision clock source or network time protocol is used to synchronize the time of each device. The camera continuously captures the video stream and extracts image frames in real time or at a preset frequency. The motion sensor collects motion data at a high frequency, and the microphone array captures the sound signal and performs preliminary sound processing. De-noise, enhance contrast, and perform color correction on the images captured by the camera to improve image quality. Locate the child's facial area from the image, crop and scale the facial area, filter the motion sensor data to reduce noise and errors, extract key motion features, segment or window the data, frame the sound signal, each frame contains audio data of a certain length, extract features from each frame of audio, and apply a voice activity detection algorithm to distinguish valid speech from background noise. The model is used to extract features from the preprocessed images to obtain feature vectors related to facial expressions and mouth shapes. The algorithm is used to extract features related to behavioral patterns from the preprocessed motion data. The speech recognition technology is used to convert the sound signal into text or phoneme sequences to extract sound features related to pronunciation quality, intonation, and speech speed. Determine the level and specific methods of data fusion, fuse the feature vectors from different data sources to form a unified feature representation, select an appropriate model, train and test the fused features. The model should be able to identify the key behavioral characteristics, emotional state and skill mastery of children in training. Use the trained model to conduct a comprehensive analysis of the real-time collected data, output an evaluation report on the children's training status, and adjust the training plan based on the analysis results. Evaluate the overall performance of the system, including but not limited to recognition accuracy, real-time, and robustness indicators, collect user feedback, understand the advantages and disadvantages of the system in practical applications, and iteratively optimize the system based on performance evaluation and user feedback.

4. A multiple children training sensing system according to claim 3, characterized in that: The model is used to extract features from the preprocessed image: Where m and n are the number of pixels in the width and height dimensions of the image, respectively. w(x,y) is a weight function that can assign different weights according to the position of the pixel point (x,y) in the key area of ​​the face. The key area has a larger weight and the non-key area has a smaller weight. I(x,y) is the pixel value of the image at the coordinate (x,y). (x0,y0) is the key reference point of facial expression or mouth shape. d((x,y),(x0,y0)) is the distance from the pixel point (x,y) to the key reference point (x0,y0). σ is a parameter that controls the degree of influence of distance. Through the algorithm, features related to behavioral patterns are extracted from the preprocessed motion data: Where n is the number of motion data points, i represents the index of the i-th feature element in the feature vector, and m j is the jth data point in the motion data sequence, ω is a parameter related to the periodicity of the behavior pattern, and φ is a phase offset parameter.

5. A multiple children training sensing system according to claim 3, characterized in that: The feature vectors from different data sources are fused: Among them, w i is the weight of the ith data source, which is determined based on the reliability and importance of the data source. A larger w can be assigned to high-quality and high-precision data sources. i The value can be in the range [0, 1], and is the jth element of the feature vector of the i-th data source, θ ij It is the angle between the feature vector of the i-th data source and a reference vector at the j-th element. By calculating the cosine of the angle and combining them, the directional relationship between the feature vectors can be considered, so as to achieve a more comprehensive fusion; Apply the trained model to conduct comprehensive analysis of the data collected in real time: Where: y1 represents the key behavioral feature evaluation, K is the number of model submodules related to the key behavioral feature, is the feature vector of the kth submodule pair The output of , α is a tuning parameter, is the eigenvector and the kth key behavior feature reference vector , y2 represents the emotional state evaluation, L is the number of model submodules related to the emotional state, is the feature vector of the l+Kth submodule pair The output of , β is a weight parameter, is the eigenvector and the average feature vector of the lth emotional state , y3 represents the skill mastery assessment, M is the number of model submodules related to skill mastery, is the feature vector of the m+K+Lth submodule pair The output of , γ is a scaling parameter, is the ideal feature vector for the mth skill mastery.

6. A multiple children training sensing system according to claim 1, characterized in that: The personalized training feedback mechanism determines the type of data that needs to be collected, including but not limited to children's learning progress, answer accuracy, learning time distribution, points of interest, and difficulties. The data can be collected through online learning platforms, educational apps, or smart wearable devices. The collected data is cleaned to remove noise and outliers, and standardized. Through the model, the learning pattern, ability level, and potential problems of children can be identified; According to the results of data analysis, specific and quantifiable learning goals are set for each child, including short-term and long-term goals. Based on the learning goals and the learner's ability level, personalized learning paths and training tasks are automatically generated. These tasks should take into account the children's interests and weaknesses. The system should be able to dynamically adjust the training plan based on the children's learning feedback, and the difficulty and rhythm of the training content are always suitable for the learner's current state.

7. A multiple children training sensing system according to claim 6, characterized in that: The said design should be an intuitive and user-friendly interface, which should include clear navigation and instructions, as well as visual elements that attract children. It should use animation and sound effects to visually demonstrate training results and improvement directions. When a child completes a task, it should celebrate with animation and give specific feedback. For errors, it should point out the cause of the error and how to correct it with gentle voice and animation. It should also add interactive questions and answers and mini-game elements to make the learning process more interesting and challenging, and stimulate children's interest and enthusiasm in learning. Deploy the personalized training feedback mechanism into the actual learning environment, continuously monitor children’s learning progress, collect new data for further analysis and optimization, regularly evaluate the effectiveness of the personalized training feedback mechanism, including the improvement of learning outcomes, changes in learning interest and motivation, and adjust strategies based on the evaluation results; We collect feedback from children, teachers and parents through questionnaires and interviews, pay attention to the latest developments in educational technology, continuously introduce new technologies and methods, improve the intelligence level of the system and user experience, and continuously iterate and optimize the system based on user feedback and technological updates.

8. A multiple children training sensing system according to claim 1, characterized in that: The cloud service and data analysis platform determines the specific functions that the cloud service and data analysis platform needs to achieve, defines the needs and expectations of target users, understands their usage scenarios and pain points, selects a cloud service platform, creates and manages virtual machine instances on the selected cloud service platform, configures necessary security groups and network settings, installs the operating system, database, and Web server necessary software, and performs basic configuration, and deploys the front-end, back-end, and database services of the data analysis platform to the cloud server; Design the database architecture according to the characteristics and requirements of the training data, including but not limited to the data table structure, indexes, and relationships, import the training data into the database, perform data cleaning and preprocessing, implement data encryption and access control security measures, and perform regular data backup; Utilize big data analysis technology to build analysis models, explore potential patterns and needs in training data, realize real-time analysis and monitoring of training data, support parents and teachers to remotely view training progress and results, and automatically generate professional evaluation reports based on analysis results, providing parents and teachers with valuable reference information; Design and implement a user-friendly remote monitoring interface that allows parents and teachers to view training status in real time, set up real-time notifications and reminders, and promptly notify relevant users when training progress reaches certain conditions or potential problems are discovered. Based on user feedback and needs, continuously optimize training plans and feedback mechanisms to improve user experience and training results; Monitor and maintain the system regularly, optimize system performance according to the actual operation of the system, improve data processing capabilities and response speed, establish a user support system, answer users' questions and concerns in a timely manner, and collect user feedback for subsequent system improvement and optimization; Regularly collect feedback from parents, teachers and system administrators to understand their usage experience and changes in needs. Based on user feedback and market changes, iterate and upgrade the platform's functions, add new features and optimize existing functions. Pay attention to the latest technological trends and industry standards, continuously explore and apply new technologies, and enhance the platform's intelligence level and competitiveness.

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