Personalized child and teenager poor posture correction scheme design method and system

Abstract

The invention relates to the technical field of sitting posture correction, in particular to a personalized child and teenager poor posture correction scheme design method and system, and the system comprises a data acquisition module, a data preprocessing module, a data analysis module, a data storage module, a man-machine interaction module, a dynamic alarm module and a control management module. Child information is obtained from multiple aspects, data are analyzed and processed through a multi-modal hybrid neural network, a sitting posture correction scheme is made, and the correction efficiency and correction effect of the sitting posture of a child are improved; the sitting posture of the child is monitored in real time, and when the sitting posture is abnormal, a voice guiding mode is adopted to enable the child to recover the normal sitting posture, so that the child can understand conveniently; through a mode of monitoring the muscle fatigue degree of the child, the precision of adjusting the sitting posture of the child is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of sitting posture correction, and in particular to a method and system for designing a personalized correction program for poor posture of children and adolescents. Background Art

[0002] Children and adolescents are in the period of physical development and formative development, and correct sitting posture has a great impact on their body shape and eyes. Therefore, in order to ensure the good development of the body shape and eyes of children and adolescents, parents generally correct and control their children's sitting posture through a sitting posture correction system based on big data analysis disclosed in the invention patent with announcement number CN108451179B and a method and system for intelligently controlling children's use of screen terminals disclosed in the invention patent with announcement number CN113807252B.

[0003] However, during use, it was found that most of the existing sitting posture correction and control systems are mandatory, making it difficult to make personalized adjustments for different children. In addition, the monitoring data is relatively simple and the problem analysis is relatively simple. Some children's incorrect sitting posture may be caused by many factors such as insufficient support of the back muscles and abdominal muscles, incomplete bone development, and lack of concentration. It is difficult to achieve the goal of improving children's sitting posture through mandatory control alone, and it may even make children more tired and difficult to focus on learning, resulting in poor practicality. Therefore, there is an urgent need for a personalized design method and system for poor posture correction programs for children and adolescents. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method and system for designing a personalized correction plan for poor posture of children and adolescents, which can obtain information about the child from multiple aspects, analyze and process the data through a multimodal hybrid neural network, and formulate a sitting posture correction plan at the same time, thereby improving the efficiency and correction effect of the child's sitting posture; the child's sitting posture is monitored in real time, and when the sitting posture is abnormal, voice guidance is used to restore the child to a normal sitting posture, which is convenient for the child to understand; and the accuracy of adjusting the child's sitting posture is improved by monitoring the child's muscle fatigue.

[0005] The present invention provides a personalized system for designing a correction plan for poor posture in children and adolescents, comprising: Data acquisition module: collects information about the child and the environment around the child; Data preprocessing module: time-aligns the collected data, performs filtering and noise reduction, and grayscales the image; Data analysis module: Analyzes the processed data to determine whether the child's sitting posture is correct, and generates a sitting posture correction plan based on the analysis results; Data storage module: records the collected information, analysis results, generated solutions and system operation status; Human-computer interaction module: provides an interactive platform between the system, children and parents, facilitating parental control of the system or providing corresponding training videos based on the sitting posture correction plan; Dynamic alarm module: When a child sits in an incorrect posture for a period of time exceeding the preset time, the system will sound an alarm and use voice to guide the child to adjust the sitting posture until the child sits in the correct posture; Control and management module: Identify the child's identity and centrally manage the data preprocessing module, data analysis module, data storage module and dynamic alarm module.

[0006] Preferably, the data acquisition module includes: Information input unit: Parents manually input their child's age, height, weight, whether the family has genetic diseases, the height of the chair and table where the child is sitting, and the surrounding brightness into the information input unit; Data collection unit: monitors the child's sitting posture, muscle fatigue and spinal curvature.

[0007] Preferably, the data acquisition unit includes: 3D camera: real-time monitoring of the child’s sitting posture; Myoelectric collector: Attach the myoelectric collector to the child's back and abdominal muscles to monitor muscle fatigue; Infrared temperature sensor: attach the infrared temperature sensor to different parts of the child's body to monitor the temperature changes at different parts of the child's body; High-precision position sensor: Attach high-precision position sensors to the child's cervical, thoracic and lumbar vertebrae to monitor the curvature of the child's spine.

[0008] Preferably, the data preprocessing module includes: Dynamic Time Warping Unit: aligns the time of data acquired by different sensors; Filtering and noise reduction unit: performs filtering and noise reduction on the data; Grayscale processing unit: grayscale processing of image data to reduce the difficulty of image data processing by the data analysis module; Logical verification unit: Verify the data uploaded by parents to determine whether the data is correct; Missing value filling unit: automatically fill in non-essential data that has not been uploaded; Data transmission unit: transmits the processed data to the data analysis module.

[0009] Preferably, the data analysis module includes: Spinal curvature calculation unit: uses a Transformer neural network to analyze and process data obtained from high-precision position sensors to determine the curvature direction and degree of the child's spine; Abnormal posture recognition unit: This unit uses a spatiotemporal attention graph convolutional network to determine whether the child's sitting posture is correct based on data obtained by the 3D camera and the infrared temperature sensor. Correction plan generation unit: Based on the analysis results of the spinal curvature calculation unit and the abnormal posture recognition unit, as well as the data obtained by the electromyography collector and the data uploaded by the child, analysis and processing are performed to generate a sitting posture correction plan.

[0010] Preferably, the correction scheme generating unit includes: LSTM network: Analyzes the child's spinal curvature data and muscle fatigue data; Fully connected layer: Analyzes the environmental data input by the child; Attention mechanism layer: Dynamically weighted fusion of temporal features and static features. When monitoring the child's muscle fatigue but no abnormal sitting posture, the weight of the environment adjustment suggestion and the child's muscle training suggestion are adjusted. When monitoring the child's sitting posture is abnormal, the weight of the posture adjustment reminder suggestion is adjusted.

[0011] Preferably, the data storage module includes: Data classification unit: classifies monitoring data, data uploaded by parents, analysis results, correction plans and system operation data; Data management unit: limits the storage period of data, automatically deletes data after the storage period expires, and ensures that the data storage unit has sufficient storage space Data storage unit: stores the classified data.

[0012] Preferably, the data storage unit includes: On-body storage: Local storage of monitored data and data uploaded by parents; Cloud storage: store training videos and other training and posture correction data.

[0013] Preferably, the control management module includes: Identity verification unit: Verifies the child's identity, facilitating the system's design of sitting posture correction plans for multiple children; Centralized control unit: centrally manages the data pre-processing module, data analysis module, data storage module and dynamic alarm module; Operation recording unit: records the operation process of the system.

[0014] The present invention provides a personalized system for designing a correction plan for poor posture in children and adolescents, comprising the following steps: S1. Parents manually input their child's age, height, weight, whether the family has genetic diseases, the height of the chair and table where the child is sitting, and the surrounding brightness into the information input unit; S2. Use a 3D camera to monitor the child's sitting posture in real time. Attach electromyographic collectors to the child's back and abdominal muscles to monitor muscle fatigue. Attach infrared temperature sensors to different parts of the child's body to monitor temperature changes. Attach high-precision position sensors to the child's cervical, thoracic, and lumbar vertebrae to monitor the curvature of the child's spine. S3. Align the time of data acquired by different sensors through the dynamic time warping unit, perform filtering and noise reduction processing on the data through the filtering and noise reduction unit, perform grayscale processing on the image data through the grayscale processing unit to reduce the difficulty of image data processing by the data analysis module, verify the data uploaded by the parent through the logic verification unit to determine whether the data is correct, automatically fill in the non-essential data that has not been uploaded through the missing value filling unit, and transmit the processed data to the data analysis module through the data transmission unit; S4. Use the Transformer neural network to analyze and process the data obtained by the high-precision position sensor to determine the curvature direction and degree of the child's spine. Use the spatiotemporal attention graph convolutional network to determine whether the child's sitting posture is correct based on the data obtained by the 3D camera and the infrared temperature sensor. S6. Analyze and process the analysis results of the spinal curvature calculation unit and the abnormal posture recognition unit, as well as the data obtained by the electromyography collector and the data uploaded by the child, to generate a sitting posture correction plan; S5. Demonstrate the training movements and sitting posture requirements in the sitting posture correction program in the form of a video through the human-machine reinforcement module; S7. The data collection unit monitors the child's future sitting posture in real time. If the child's sitting posture is incorrect and exceeds the preset time, the system will issue an alarm and use voice guidance to guide the child to adjust the sitting posture until the child sits in the correct posture; S8. After a period of training, repeat the above steps again to generate a new sitting posture correction plan for the child; S9. Repeat the above steps until the child's sitting posture is completely corrected.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. Obtain child information from multiple aspects, analyze and process the data through a multimodal hybrid neural network, and formulate a posture correction plan to improve the efficiency and effect of posture correction for children; 2. Monitor the child's sitting posture in real time. When the sitting posture is abnormal, use voice guidance to restore the child to a normal sitting posture, which is easy for children to understand; 3. Improve the accuracy of adjusting the child's sitting posture by monitoring the child's muscle fatigue. BRIEF DESCRIPTION OF THE DRAWINGS

[0016] Figure 1 2. It is a schematic structural diagram of a personalized children and adolescents' poor posture correction program design system of the present invention; Figure 2 It is a structural diagram of the data acquisition module of the present invention; Figure 3 It is a structural diagram of the data acquisition unit of the present invention; Figure 4 It is a structural diagram of the data preprocessing module of the present invention; Figure 5 It is a structural diagram of the data analysis module of the present invention; Figure 6 It is a structural diagram of the correction scheme generating unit of the present invention; Figure 7 It is a structural diagram of the data storage module of the present invention; Figure 8 It is a structural diagram of the control management module of the present invention. DETAILED DESCRIPTION

[0017] To facilitate understanding of the present invention, the present invention will be described more fully below with reference to the accompanying drawings. The present invention can be implemented in many different forms and is not limited to the embodiments described herein. On the contrary, the purpose of providing these embodiments is to make the disclosure of the present invention more thorough and comprehensive.

[0018] like Figures 1 to 8 As shown, a personalized system for designing a correction plan for poor posture in children and adolescents includes: Data acquisition module: collects information about the child and the environment around the child; Data preprocessing module: time-aligns the collected data, performs filtering and noise reduction, and grayscales the image; Data analysis module: Analyzes the processed data to determine whether the child's sitting posture is correct, and generates a sitting posture correction plan based on the analysis results; Data storage module: records the collected information, analysis results, generated solutions and system operation status; Human-computer interaction module: provides an interactive platform between the system, children and parents, facilitating parental control of the system or providing corresponding training videos based on the sitting posture correction plan; Dynamic alarm module: When a child sits in an incorrect posture for a period of time exceeding the preset time, the system will sound an alarm and use voice to guide the child to adjust the sitting posture until the child sits in the correct posture; Control and management module: identifies the child's identity and centrally manages the data pre-processing module, data analysis module, data storage module and dynamic alarm module; The data acquisition module includes: Information input unit: Parents manually input their child's age, height, weight, whether the family has genetic diseases, the height of the chair and table where the child is sitting, and the surrounding brightness into the information input unit; Data collection unit: monitors the child's sitting posture, muscle fatigue and spinal curvature; The data acquisition unit includes: 3D camera: real-time monitoring of the child’s sitting posture; Myoelectric collector: Attach the myoelectric collector to the child's back and abdominal muscles to monitor muscle fatigue; Infrared temperature sensor: attach the infrared temperature sensor to different parts of the child's body to monitor the temperature changes at different parts of the child's body; High-precision position sensor: Attach high-precision position sensors to the child's cervical, thoracic, and lumbar vertebrae to monitor the curvature of the child's spine; The data preprocessing module includes: Dynamic Time Warping Unit: aligns the time of data acquired by different sensors; Filtering and noise reduction unit: performs filtering and noise reduction on the data; Grayscale processing unit: grayscale processing of image data to reduce the difficulty of image data processing by the data analysis module; Logical verification unit: Verify the data uploaded by parents to determine whether the data is correct; Missing value filling unit: automatically fill in non-essential data that has not been uploaded; Data transmission unit: transmits the processed data to the data analysis module; The data analysis module includes: Spinal curvature calculation unit: uses a Transformer neural network to analyze and process data obtained from high-precision position sensors to determine the curvature direction and degree of the child's spine; Abnormal posture recognition unit: This unit uses a spatiotemporal attention graph convolutional network to determine whether the child's sitting posture is correct based on data obtained by the 3D camera and the infrared temperature sensor. Correction plan generation unit: This unit generates a sitting posture correction plan based on the analysis results of the spinal curvature calculation unit and the abnormal posture recognition unit, as well as the data obtained by the electromyography collector and the data uploaded by the child. The correction scheme generating unit includes: LSTM network: Analyzes the child's spinal curvature data and muscle fatigue data; Fully connected layer: Analyzes the environmental data input by the child; Attention mechanism layer: Dynamically weighted fusion of temporal features and static features. When monitoring a child's muscle fatigue but no abnormal sitting posture, the weight of the environment adjustment suggestion and the child's muscle training suggestion is determined. When monitoring a child's sitting posture is abnormal, the weight of the posture adjustment reminder suggestion is determined. The data storage module includes: Data classification unit: classifies monitoring data, data uploaded by parents, analysis results, correction plans and system operation data; Data management unit: limits the storage period of data, automatically deletes data after the storage period expires, and ensures that the data storage unit has sufficient storage space Data storage unit: stores classified data; The data storage unit includes: On-body storage: Local storage of monitored data and data uploaded by parents; Cloud storage: store training videos and other training and posture correction data; The control management module includes: Identity verification unit: Verifies the child's identity, facilitating the system's design of sitting posture correction plans for multiple children; Centralized control unit: centrally manages the data pre-processing module, data analysis module, data storage module and dynamic alarm module; Operation recording unit: records the operation process of the system; A method for designing a personalized correction program for poor posture in children and adolescents, comprising the following steps: S1. Parents manually input their child's age, height, weight, whether the family has genetic diseases, the height of the chair and table where the child is sitting, and the surrounding brightness into the information input unit; S2. Use a 3D camera to monitor the child's sitting posture in real time. Attach electromyographic collectors to the child's back and abdominal muscles to monitor muscle fatigue. Attach infrared temperature sensors to different parts of the child's body to monitor temperature changes. Attach high-precision position sensors to the child's cervical, thoracic, and lumbar vertebrae to monitor the curvature of the child's spine. S3. Align the time of data acquired by different sensors through the dynamic time warping unit, perform filtering and noise reduction processing on the data through the filtering and noise reduction unit, perform grayscale processing on the image data through the grayscale processing unit to reduce the difficulty of image data processing by the data analysis module, verify the data uploaded by the parent through the logic verification unit to determine whether the data is correct, automatically fill in the non-essential data that has not been uploaded through the missing value filling unit, and transmit the processed data to the data analysis module through the data transmission unit; S4. Use the Transformer neural network to analyze and process the data obtained by the high-precision position sensor to determine the curvature direction and degree of the child's spine. Use the spatiotemporal attention graph convolutional network to determine whether the child's sitting posture is correct based on the data obtained by the 3D camera and the infrared temperature sensor. S6. Analyze and process the analysis results of the spinal curvature calculation unit and the abnormal posture recognition unit, as well as the data obtained by the electromyography collector and the data uploaded by the child, to generate a sitting posture correction plan; S5. Demonstrate the training movements and sitting posture requirements in the sitting posture correction program in the form of a video through the human-machine reinforcement module; S7. The data collection unit monitors the child's future sitting posture in real time. If the child's sitting posture is incorrect and exceeds the preset time, the system will issue an alarm and use voice guidance to guide the child to adjust the sitting posture until the child sits in the correct posture; S8. After a period of training, repeat the above steps again to generate a new sitting posture correction plan for the child; S9. Repeat the above steps until the child's sitting posture is completely corrected.

[0019] A method and system for designing a personalized correction plan for poor posture in children and adolescents. S1. Parents manually input the child's age, height, weight, whether the family has genetic diseases, the height of the chair and table where the child sits, and the surrounding brightness into an information input unit; S2. Use a 3D camera to monitor the child's sitting posture in real time. Attach electromyographic collectors to the child's back and abdominal muscles to monitor muscle fatigue. Attach infrared temperature sensors to different parts of the child's body to monitor temperature changes. Attach high-precision position sensors to the child's cervical, thoracic, and lumbar vertebrae to monitor the curvature of the child's spine. S3. Align the time of data acquired by different sensors through the dynamic time warping unit, perform filtering and noise reduction processing on the data through the filtering and noise reduction unit, perform grayscale processing on the image data through the grayscale processing unit to reduce the difficulty of image data processing by the data analysis module, verify the data uploaded by the parent through the logic verification unit to determine whether the data is correct, automatically fill in the non-essential data that has not been uploaded through the missing value filling unit, and transmit the processed data to the data analysis module through the data transmission unit; S4. Use the Transformer neural network to analyze and process the data obtained by the high-precision position sensor to determine the curvature direction and degree of the child's spine. Use the spatiotemporal attention graph convolutional network to determine whether the child's sitting posture is correct based on the data obtained by the 3D camera and the infrared temperature sensor. S6. Analyze and process the analysis results of the spinal curvature calculation unit and the abnormal posture recognition unit, as well as the data obtained by the electromyography collector and the data uploaded by the child, to generate a sitting posture correction plan; S5. Demonstrate the training movements and sitting posture requirements in the sitting posture correction program in the form of a video through the human-machine reinforcement module; S7. The data collection unit monitors the child's future sitting posture in real time. If the child's sitting posture is incorrect and exceeds the preset time, the system will issue an alarm and use voice guidance to guide the child to adjust the sitting posture until the child sits in the correct posture; S8. After a period of training, repeat the above steps again to generate a new sitting posture correction plan for the child; S9. Repeat the above steps until the child's sitting posture is completely corrected.

[0020] The sensor attached to the child and the part in contact with the child are made of medical silica gel and medical double-sided tape; the double-sided tape is replaceable.

[0021] The above is only a preferred embodiment 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 technical principles of the present invention. These improvements and modifications should also be regarded as the scope of protection of the present invention.

Claims

1. A personalized system for designing correction plans for poor posture in children and adolescents, characterized by: include: Data acquisition module: collects information about the child and the environment around the child; Data preprocessing module: time-aligns the collected data, performs filtering and noise reduction, and grayscales the image; Data analysis module: Analyzes the processed data to determine whether the child's sitting posture is correct, and generates a sitting posture correction plan based on the analysis results; Data storage module: records the collected information, analysis results, generated solutions and system operation status; Human-computer interaction module: provides an interactive platform between the system, children and parents, facilitating parental control of the system or providing corresponding training videos based on the sitting posture correction plan; Dynamic alarm module: When a child sits in an incorrect posture for a period of time exceeding the preset time, the system will sound an alarm and use voice to guide the child to adjust the sitting posture until the child sits in the correct posture; Control and management module: Identify the child's identity and centrally manage the data preprocessing module, data analysis module, data storage module and dynamic alarm module.

2. A personalized children and adolescents' poor posture correction program design system as claimed in claim 1, characterized in that: The data acquisition module includes: Information input unit: Parents manually input their child's age, height, weight, whether the family has genetic diseases, the height of the chair and table where the child is sitting, and the surrounding brightness into the information input unit; Data collection unit: monitors the child's sitting posture, muscle fatigue and spinal curvature.

3. A personalized children and adolescents' poor posture correction program design system as claimed in claim 1, characterized in that: The data acquisition unit includes: 3D camera: real-time monitoring of the child’s sitting posture; Myoelectric collector: Attach the myoelectric collector to the child's back and abdominal muscles to monitor muscle fatigue; Infrared temperature sensor: attach the infrared temperature sensor to different parts of the child's body to monitor the temperature changes at different parts of the child's body; High-precision position sensor: Attach high-precision position sensors to the child's cervical, thoracic and lumbar vertebrae to monitor the curvature of the child's spine.

4. A personalized children and adolescents' poor posture correction program design system as claimed in claim 3, characterized in that: The data preprocessing module includes: Dynamic Time Warping Unit: aligns the time of data acquired by different sensors; Filtering and noise reduction unit: performs filtering and noise reduction on the data; Grayscale processing unit: grayscale processing of image data to reduce the difficulty of image data processing by the data analysis module; Logical verification unit: Verify the data uploaded by parents to determine whether the data is correct; Missing value filling unit: automatically fill in non-essential data that has not been uploaded; Data transmission unit: transmits the processed data to the data analysis module.

5. A personalized children and adolescents' poor posture correction program design system as claimed in claim 4, characterized in that: The data analysis module includes: Spinal curvature calculation unit: uses a Transformer neural network to analyze and process data obtained from high-precision position sensors to determine the curvature direction and degree of the child's spine; Abnormal posture recognition unit: This unit uses a spatiotemporal attention graph convolutional network to determine whether the child's sitting posture is correct based on data obtained by the 3D camera and the infrared temperature sensor. Correction plan generation unit: Based on the analysis results of the spinal curvature calculation unit and the abnormal posture recognition unit, as well as the data obtained by the electromyography collector and the data uploaded by the child, analysis and processing are performed to generate a sitting posture correction plan.

6. A personalized children and adolescents' poor posture correction program design system as claimed in claim 5, characterized in that: The correction scheme generating unit includes: LSTM network: Analyzes the child's spinal curvature data and muscle fatigue data; Fully connected layer: Analyzes the environmental data input by the child; Attention mechanism layer: Dynamically weighted fusion of temporal features and static features. When monitoring the child's muscle fatigue but no abnormal sitting posture, the weight of the environment adjustment suggestion and the child's muscle training suggestion are adjusted. When monitoring the child's sitting posture is abnormal, the weight of the posture adjustment reminder suggestion is adjusted.

7. The personalized system for designing correction plans for poor posture of children and adolescents according to claim 1, characterized in that: The data storage module includes: Data classification unit: classifies monitoring data, data uploaded by parents, analysis results, correction plans and system operation data; Data management unit: limits the storage period of data, automatically deletes data after the storage period expires, and ensures that the data storage unit has sufficient storage space Data storage unit: stores the classified data.

8. A personalized system for designing correction plans for poor posture in children and adolescents as claimed in claim 7, characterized in that: The data storage unit includes: On-body storage: Local storage of monitored data and data uploaded by parents; Cloud storage: store training videos and other training and posture correction data.

9. A personalized system for designing correction plans for poor posture in children and adolescents according to claim 11, characterized in that: The control management module includes: Identity verification unit: Verifies the child's identity, facilitating the system's design of sitting posture correction plans for multiple children; Centralized control unit: centrally manages the data pre-processing module, data analysis module, data storage module and dynamic alarm module; Operation recording unit: records the operation process of the system.

10. A method for designing a personalized correction program for poor posture in children and adolescents, characterized by: The following steps are involved: S1. Parents manually input their child's age, height, weight, whether the family has genetic diseases, the height of the chair and table where the child is sitting, and the surrounding brightness into the information input unit; S2. Use a 3D camera to monitor the child's sitting posture in real time. Attach electromyographic collectors to the child's back and abdominal muscles to monitor muscle fatigue. Attach infrared temperature sensors to different parts of the child's body to monitor temperature changes. Attach high-precision position sensors to the child's cervical, thoracic, and lumbar vertebrae to monitor the curvature of the child's spine. S3. Align the time of data acquired by different sensors through the dynamic time warping unit, perform filtering and noise reduction processing on the data through the filtering and noise reduction unit, perform grayscale processing on the image data through the grayscale processing unit to reduce the difficulty of image data processing by the data analysis module, verify the data uploaded by the parent through the logic verification unit to determine whether the data is correct, automatically fill in the non-essential data that has not been uploaded through the missing value filling unit, and transmit the processed data to the data analysis module through the data transmission unit; S4. Use the Transformer neural network to analyze and process the data obtained by the high-precision position sensor to determine the curvature direction and degree of the child's spine. Use the spatiotemporal attention graph convolutional network to determine whether the child's sitting posture is correct based on the data obtained by the 3D camera and the infrared temperature sensor. S6. Analyze and process the analysis results of the spinal curvature calculation unit and the abnormal posture recognition unit, as well as the data obtained by the electromyography collector and the data uploaded by the child, to generate a sitting posture correction plan; S5. Demonstrate the training movements and sitting posture requirements in the sitting posture correction program in the form of a video through the human-machine reinforcement module; S7. The data collection unit monitors the child's future sitting posture in real time. If the child's sitting posture is incorrect and exceeds the preset time, the system will issue an alarm and use voice guidance to guide the child to adjust the sitting posture until the child sits in the correct posture; S8. After a period of training, repeat the above steps again to generate a new sitting posture correction plan for the child; S9. Repeat the above steps until the child's sitting posture is completely corrected.

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