Wearable sitting posture monitoring and correcting system and implementation method thereof

Abstract

The invention belongs to the field of wearable intelligent equipment, and particularly relates to a wearable sitting posture monitoring and correcting system and an implementation method thereof.The wearable sitting posture monitoring and correcting system comprises a sitting posture capturing module used for transmitting collected sitting posture information to a sitting posture analysis module for matching analysis through a Bluetooth module after signal processing; the sitting posture analysis module is used for identifying the sitting posture type according to the sitting posture information sent by the sitting posture capturing module and evaluating the torso bending degree; the sitting posture type and the trunk bending angle are sent to a sitting posture feedback module; the sitting posture feedback module is used for displaying the sitting posture type and the trunk bending angle in real time, and judging whether to trigger an alarm according to respective threshold values of the sitting posture type, the bending angle and the duration; alarm times and use time are automatically recorded, and the ratio is calculated to evaluate the qualified state of the sitting posture. According to the sitting posture monitoring and correcting system, image recognition is not needed, the privacy of a user is protected, meanwhile, environmental factors such as light and shielding are completely avoided, and therefore measurement errors are reduced.

Description

TECHNICAL FIELD

[0001] The present application belongs to the field of wearable smart devices, in particular to a wearable sitting posture monitoring and correcting system and an implementation method thereof. BACKGROUND

[0002] With the change of human lifestyle and the increase of work and study pressure, the phenomenon of long-term sitting is becoming more and more serious. The uneven distribution of local body force caused by long-term bad sitting posture increases the risk of diseases such as cervical spondylosis, scoliosis, lumbar disc herniation and cardiovascular diseases. Sitting posture monitoring as an effective intervention method can help users adjust bad sitting posture in time, thereby preventing the occurrence of related diseases.

[0003] At present, the common sitting posture monitoring method is mainly based on visual image processing technology and mechanical sensing technology relying on accelerometers and gyroscopes. The image processing technology is greatly affected by the environment, the recognition accuracy is easily disturbed, and it may involve user privacy problems; and the mechanical sensing device relying on accelerometers, gyroscopes and the like often needs to be installed on a high-rigidity substrate to ensure its measurement accuracy and stability, which not only affects the user's wearing comfort, but also has the problems of freedom limitation and performance decline under dynamic working conditions. Therefore, it is urgent to develop a wearable sitting posture monitoring and correcting system with high signal recognition accuracy and good user experience. SUMMARY

[0004] The purpose of the present application is to provide a wearable sitting posture monitoring and correcting system and an implementation method thereof, which has the characteristics of high recognition accuracy, high wearing comfort and can provide sitting posture feedback information to overcome the problems existing in the background technology.

[0005] The technical solution adopted by the present application to achieve the above purpose is: a wearable sitting posture monitoring and correcting system, comprising:

[0006] A sitting posture capturing module for transmitting the collected sitting posture information to a sitting posture analysis module for matching analysis after signal processing through a Bluetooth module;

[0007] A sitting posture analysis module for identifying the sitting posture type and evaluating the trunk bending degree of the sitting posture information sent by the sitting posture capturing module; and sending the sitting posture type and trunk bending angle to a sitting posture feedback module;

[0008] A sitting posture feedback module for real-time display of the sitting posture type and trunk bending angle, and judgment of whether to trigger an alarm according to the respective threshold values of the sitting posture type, bending angle and duration; automatic recording of the alarm number and use time, and calculation of the ratio to evaluate the sitting posture qualified state.

[0009] The sitting posture capturing module comprises a controller, a power supply, a Bluetooth module, a wearable one-piece garment and a plurality of flexible mechanical sensors.

[0010] The flexible mechanical sensor is adhered to the monitoring part of the human spine, cervical vertebrae and shoulder of the one-piece garment through an elastic tape, and is used for collecting the sitting posture information of the corresponding position respectively; and is connected with the input pin of the controller through a wire, and sends the collected sitting posture information to the controller;

[0011] The controller is embedded in the wearable one-piece garment, and is connected with the flexible mechanical sensor through a wire, and is used for transmitting the received sitting posture information to the sitting posture analysis module through a Bluetooth module after signal processing.

[0012] The power supply is connected with the controller through a power adapter, a battery or a USB to supply power for the controller.

[0013] The flexible mechanical sensor is one or any of a flexible strain sensor, a flexible pressure sensor, a flexible acceleration sensor and a flexible bending sensor with an elastic modulus less than 10 MPa.

[0014] The sitting posture analysis module is arranged in the background of the mobile terminal, and includes a sitting posture type information library and a sitting posture angle information library.

[0015] The sitting posture type information library is used for establishing a signal sample of different sitting posture types of a user through a supervised learning classification algorithm to identify the sitting posture type.

[0016] The sitting posture angle information library is used for evaluating the bending degree of the trunk by establishing a mapping relationship database.

[0017] The mapping relationship database is a mapping relationship data set of the bending angle of the key bone joint part of the spine, cervical vertebrae and shoulder and the sensor signal.

[0018] The sitting posture feedback module includes a user interface of the mobile terminal, a timing device and an alarm device.

[0019] The user interface is used for receiving and displaying the sitting posture type and the bending angle of the trunk in real time.

[0020] The alarm device is used for triggering an alarm based on the sitting posture type, the bending angle and the continuous time length threshold value defined by the user.

[0021] The alarm mode includes mobile terminal alarm and external device alarm.

[0022] The mobile terminal alarm is displayed on the user interface in real time through a text, an image or an audio file.

[0023] The external device alarm includes vibrator alarm, buzzer alarm or indicator light alarm.

[0024] The timing device and alarm device are used for automatically recording the alarm times and use time, calculating the ratio to evaluate the sitting posture qualified state.

[0025] An implementation method of a wearable sitting posture monitoring and correction system, comprising the following steps:

[0026] S1: Collect human sitting posture information through a plurality of flexible mechanical sensors arranged on a wearable one-piece garment; after signal processing by a controller, the collected sitting posture data is wirelessly transmitted to an analysis module;

[0027] S2: Identify the sitting posture type through a pre-trained sitting posture classification model; evaluate the trunk bending angle based on the mapping relationship between the sensor signal and the bending angle;

[0028] S3: The sitting posture feedback module displays the sitting posture type and bending angle in real time; and monitors the sitting posture duration and judges whether to trigger an alarm;

[0029] S4: The sitting posture feedback module evaluates the sitting posture qualified state according to the ratio of the alarm times to the total use time.

[0030] In step S2, the construction of the sitting posture classification model comprises the following steps:

[0031] S2-1: Collect N times of error sitting posture signal samples corresponding to the monitoring part of the user in forward leaning, left leaning, right leaning, left forward leaning and right forward leaning;

[0032] S2-2: Model training: import 70% of the sample data into a MATLAB classification learner based on a supervised learning classification algorithm, and use a logistic regression algorithm for supervised learning training; extract the time domain features and frequency domain features of each group of samples;

[0033] S2-3: Model verification: input the remaining 30% of the sample data into the trained model for verification, and calculate the recognition accuracy through a confusion matrix;

[0034] S2-4: Extract the current sensor signal through the same time domain features and frequency domain features, and input it into the trained logistic regression model for classification; the model outputs the posture category label with the highest probability;

[0035] In step S2, the mapping relationship between the sensor signal and the bending angle is used to evaluate the trunk bending angle, specifically:

[0036] 1) Use a digital inclinometer to measure the trunk bending angle α at the first lumbar vertebra of the user; set 4 calibration points: α = 10°, 25°, 40°, 50°;

[0037] 2) At each calibration angle, record the output resistance value of the sacrum top sensor synchronously to obtain the angle-resistance value correspondence;

[0038] 3) Establish a discrete mapping library of angle-signal value, obtain the current sensor resistance value R a ; if the current sensor resistance value R a , the angle is calculated through a linear interpolation formula, and the real-time bending angle value a is output.

[0039] The alarm triggering condition of step S3 includes at least one of the following:

[0040] (a) being identified as an incorrect sitting posture and lasting for a duration greater than a first threshold value m;

[0041] (b) the trunk bending angle being greater than an angle threshold value and lasting for a duration greater than a second threshold value k;

[0042] The alarm execution mode includes at least one of the following:

[0043] The mobile terminal interface pops up a warning image, a peripheral vibrator or a buzzer is started, and the alarm frequency is accumulated and the triggering time point is recorded.

[0044] The step S4 is specifically:

[0045] The alarm frequency in a unit of time is calculated, that is:

[0046]

[0047] Wherein, N is the alarm frequency, and T is the total use time;

[0048] When R≤0.025, it is evaluated as: the sitting posture is qualified; when R>0.025, it is evaluated as: the sitting posture needs to be corrected;

[0049] The user interface generates a historical sitting posture health report containing the R value, including:

[0050] The following indicators are counted according to the time dimension:

[0051] (1) The cumulative duration proportion of various incorrect sitting postures;

[0052] (2) The cumulative duration of the trunk bending angle greater than 40°;

[0053] (3) The change trend of the alarm frequency R value;

[0054] Based on the historical data, the sitting posture improvement suggestions are generated:

[0055] (a) When the left-leaning / right-leaning sitting posture proportion is greater than 30%, the user interface prompts to pay attention to the sitting posture balance;

[0056] (b) When the R value is greater than 0.025 for three consecutive days, the user interface prompts to strengthen the sitting posture correction training.

[0057] The present application has the following advantages and benefits:

[0058] 1.The sitting posture monitoring and correcting system of the present application does not require image recognition, completely avoids environmental factors such as light and shielding while protecting the privacy of the user, thereby reducing measurement errors.

[0059] 2.The present application selects a flexible mechanical sensor with an elastic modulus close to human skin (about 2 MPa), combines wearable design to improve wearing comfort, and improves monitoring accuracy by realizing the deformation ability of various postures of the sensor and the human body.

[0060] 3.The present application can provide real-time sitting posture type and angle information, combine it with time factors, provide multi-dimensional sitting posture information feedback, and users can customize alarm reminders according to personal preferences. BRIEF DESCRIPTION OF DRAWINGS

[0061] Figure 1 The wearable sitting posture monitoring and correcting system framework composition schematic diagram of the present application;

[0062] Among them, 1 is a sitting posture capturing module, 2 is a sitting posture analysis module, and 3 is a sitting posture feedback module.

[0063] Figure 2 The test confusion matrix of the sitting posture type information library of the present application;

[0064] Figure 3 The test schematic diagram and test results of the trunk bending angle of the present application. DETAILED DESCRIPTION

[0065] The present application will be further described in detail below in combination with the drawings and examples.

[0066] As shown in the wearable sitting posture monitoring and correcting system framework composition schematic diagram of the present application, the present application is a wearable sitting posture monitoring and correcting system, which comprises: Figure 1 (I) Sitting posture capturing module

[0067] The sitting posture capturing module 1 comprises a controller, a power supply, a Bluetooth module, a wearable one-piece clothing, and a plurality of flexible mechanical sensors; and is used for transmitting the collected sitting posture information to the sitting posture analysis module for matching analysis after signal processing through the Bluetooth module.

[0068] The flexible mechanical sensor is adhered to the monitoring parts of the human spine, cervical vertebrae, and shoulder of the one-piece clothing through an elastic tape, and is used for collecting the sitting posture information of the corresponding positions respectively; and is connected to the input pin of the controller through a wire, and sends the collected sitting posture information to the controller.

[0069]

[0070] ​The flexible mechanical sensor is one or any of a flexible strain sensor, a flexible pressure sensor, a flexible acceleration sensor, and a flexible bending sensor, and has an elastic modulus less than 10 MPa.

[0071] The controller is embedded in the wearable one-piece garment and connected with the flexible mechanical sensor through a wire, and is configured to transmit, through a Bluetooth module, the received sitting posture information to the sitting posture analysis module after signal processing.

[0072] (II) Sitting posture analysis module

[0073] The sitting posture analysis module 2 is arranged in the background of the mobile terminal and includes a sitting posture type information library and a sitting posture angle information library.

[0074] The sitting posture type information library is configured to identify the sitting posture type by training signal samples of different sitting posture types of the user through a supervised learning classification algorithm, so as to identify the sitting posture type.

[0075] The sitting posture angle information library is configured to evaluate the trunk bending degree by establishing a mapping relationship database.

[0076] The mapping relationship database is a mapping relationship data set of the bending angles of key bone joint parts of the spine, the cervical vertebrae and the shoulder and the sensor signals.

[0077] (III) Sitting posture feedback module

[0078] The sitting posture feedback module 3 includes a user interface of the mobile terminal, a timing device and an alarm device.

[0079] The user interface is configured to receive and display in real time the sitting posture type and the trunk bending angle.

[0080] The alarm device is configured to trigger an alarm based on the user-defined thresholds of the sitting posture type, the bending angle and the duration.

[0081] The alarm mode includes mobile terminal alarm and external device alarm.

[0082] The mobile terminal alarm is displayed in real time on the user interface through text, images or audio files.

[0083] The external device alarm includes vibrator alarm, buzzer alarm or indicator light alarm.

[0084] Timing device and alarm device for automatically recording the number of alarms and the time of use, calculating the ratio to assess the qualified state of sitting posture.

[0085] As Figure 1 shown, in combination with the system framework diagram of the present application, the implementation method of a wearable sitting posture monitoring and correction system of the present application comprises the following steps:

[0086] S1: Collect human sitting posture information through a plurality of flexible mechanical sensors arranged on the wearable one-piece clothing; after signal processing by the controller, the collected sitting posture data is wirelessly transmitted to the analysis module;

[0087] S2: Identify the sitting posture category through a pre-trained sitting posture classification model, specifically as Figure 2 shown, comprising the following steps:

[0088] S2-1: Collect N times of error sitting posture signal samples of the user corresponding to the monitoring part in forward leaning, left leaning, right leaning, left forward leaning and right forward leaning;

[0089] S2-2: Model training: import 70% of the sample data into the MATLAB classification learner based on the supervised learning classification algorithm, and use the logistic regression algorithm for supervised learning training; extract the time domain features and frequency domain features of each group of samples;

[0090] S2-3: Model verification: input the remaining 30% of the sample data into the trained model for verification, and calculate the recognition accuracy through the confusion matrix;

[0091] S2-4: Extract the same time domain features and frequency domain features from the current sensor signal, input them into the trained logistic regression model for classification, and output the sitting posture category label with the highest probability;

[0092] Based on the mapping relationship between the sensor signal and the bending angle, the trunk bending angle is evaluated;

[0093] 1) Use a digital inclinometer to measure the trunk bending angle α at the first lumbar vertebra of the user; set 4 calibration points: α = 10°, 25°, 40°, 50°;

[0094] 2) At each calibration angle, record the output resistance value of the sacral top sensor synchronously to obtain the angle-resistance value correspondence;

[0095] 3) Establish a discrete mapping library of angle-signal value to obtain the current sensor resistance value R a ; if the current sensor resistance value R a , calculate the angle through the linear interpolation formula and output the real-time bending angle value α.

[0096] S3: The sitting posture feedback module displays the sitting posture category and bending angle in real time; monitors the sitting posture duration and determines whether to trigger an alarm;

[0097] The alarm triggering conditions include at least one of the following:

[0098] (a) The identified incorrect sitting posture lasts for a duration greater than a first threshold m;

[0099] (b) The trunk bending angle is greater than an angle threshold, and the duration is greater than a second threshold k;

[0100] The alarm execution mode includes at least one of the following:

[0101] The mobile terminal interface pops up a warning image, starts an external vibrator or buzzer, and accumulates the number of alarms and records the triggering time point.

[0102] S4: The sitting posture feedback module evaluates the sitting posture qualified state according to the ratio of the number of alarms to the total usage duration.

[0103] Calculate the alarm frequency per unit time, that is:

[0104]

[0105] Where N is the number of alarms, and T is the total usage time in minutes;

[0106] When R≤0.025, it is evaluated as: sitting posture qualified; when R>0.025, it is evaluated as: sitting posture needs to be corrected;

[0107] The user interface generates a historical sitting posture health report containing the R value, including:

[0108] The following indicators are counted according to the time dimension:

[0109] (1) The cumulative duration proportion of each type of incorrect sitting posture;

[0110] (2) The cumulative duration of the trunk bending angle >40°;

[0111] (3) The change trend of the alarm frequency R value;

[0112] Based on the historical data, generate a sitting posture improvement suggestion:

[0113] (a) When the left-leaning / right-leaning sitting posture proportion is >30%, the user interface prompts to pay attention to sitting posture balance;

[0114] (b) When the R value is >0.025 for 3 consecutive days, the user interface prompts to strengthen the sitting posture correction training.

[0115] Embodiment 1:

[0116] As Figure 1As shown, this embodiment provides a wearable sitting posture monitoring and correction system, including: a sitting posture capture module, a sitting posture analysis module, and a sitting posture feedback module.

[0117] The sitting posture capture module consists of a wearable bodysuit and four flexible strain sensors with an elastic modulus of 6.7 MPa. The four sensors are attached to the bodysuit using medical elastic tape, located 70 mm to the left and right of the T10 thoracic vertebra, the top of the sacrum, and the thoracolumbar junction. A USB power supply is connected to an Arduino nano microcontroller for power. Wires connect the four flexible strain sensors to input pins of the Arduino nano microcontroller, which in turn is connected to the communication pins of an HC06 Bluetooth module. The sitting posture information collected by the sensors is processed by the controller and then transmitted via the Bluetooth module to a mobile device for analysis.

[0118] The sitting posture analysis module includes a sitting posture type information library and a sitting posture angle information library placed in the background of the mobile terminal. The above-mentioned sitting posture capture module collects 49 signals of five common incorrect sitting postures of users, namely, leaning forward, leaning left, leaning right, leaning left forward, and leaning right forward, for a total of 245 data samples. 70% of the data samples are imported into the MATLAB classification learner, and the classification training is performed using an efficient logistic regression algorithm. The characteristic values ​​that can distinguish and identify the five sitting postures are extracted to establish a sitting posture type information library for accurately identifying the user's sitting posture type. The remaining 30% of the data samples are used to test the recognition accuracy of the sitting posture type of the above sitting posture type information library, and the test accuracy can reach 98.6% (such as Figure 2 The trunk bending angle at the first lumbar vertebra is defined as α by a digital inclinometer, as shown in Figure 3 As shown in the figure, a sitting posture angle information library is established, mapping the spinal curvature angle to the sensor signal using the output resistance values ​​corresponding to torso curvature angles α of 10°, 25°, 40°, and 50°, for assessing the degree of torso curvature. The sitting posture type information library and the sitting posture angle information library work together to comprehensively identify the user's sitting posture type and spinal curvature degree.

[0119] The sitting posture feedback module comprises a user interface of the mobile terminal and a timing device and an alarm device connected to the mobile terminal, the user interface is used for displaying the recognition result of the sitting posture recognition module, and real-time sitting posture type and sitting posture angle information is provided. The timing device and the alarm device are used for multi-dimensional sitting posture information and setting a custom alarm reminder. The user interface is created by a GUI (graphical user interface) application of MATLAB software, including a real-time sitting posture type display area and a real-time sitting posture angle display area, and the data models of a sitting posture type information library and a sitting posture angle information library are respectively called through a callback function, so that the current sitting posture type and trunk bending angle of the user are matched and displayed. The timer of the computer is used as the timing device, and the use time of the system is recorded after the system starts to run through a script. The alarm is a red alarm bell picture file stored in the local disk of the computer. The user presets the alarm threshold as follows: the user does not change the posture for 10 minutes in a certain wrong sitting posture, and / or the trunk bending angle reaches 40° or above for more than 5 minutes; and the user-defined alarm reminder is that an alarm image is popped up on the user interface and lasts for 3 seconds, and the alarm number is accumulated once.

[0120] On December 2, 2024, the user wore the flexible sensor completely, and started the sitting posture monitoring and correction system at 8:00 am. At 8:50 am, the user interface alarm image popped up and lasted for 3 seconds, and the alarm number was accumulated once. At this time, the feedback information of the user interface was “left forward leaning posture lasting for 10 minutes”, reminding the user to adjust the posture immediately. At 10:23 am, the user interface alarm image popped up again, and the alarm number was accumulated twice. At this time, the feedback information of the user interface was “trunk forward leaning angle 40° lasting for 5 minutes”, reminding to adjust the posture. The ratio of the alarm number 2 to the use time 2 hours and 23 minutes is 0.014, which is less than the maximum value 0.025 of “sitting posture qualified”, and the evaluation of the sitting posture habit is “sitting posture qualified”.

[0121] In combination with Embodiment 1, the three core advantages of the present application are fully verified:

[0122] Privacy protection: the flexible sensor replaces the camera to avoid image privacy risks;

[0123] Measurement accuracy: 98.6% classification accuracy of the logistic regression model + angle mapping ± 1.5° error;

[0124] Health management value: R value quantitative evaluation and trend warning function provides scientific management basis for sedentary population.

[0125] In summary, the present application fundamentally solves the problems of environmental interference, privacy leakage and poor wearing comfort in the background art, and provides a safe and reliable industrial-level solution for spine health management through the innovative integration of wearable flexible sensing technology and multi-dimensional health evaluation algorithm.

[0126] Those skilled in the art can understand that the above description is only preferred embodiments of the present application, and the features described in various embodiments of the present disclosure and / or claims can be combined or combined, even if such combination or combination is not explicitly described in the present disclosure. It is not intended to limit the present application, although the present application has been described in detail with reference to the foregoing embodiments, those skilled in the art can modify the technical solutions described in the foregoing embodiments or make equivalent replacement for part of the technical features, and any modification, equivalent replacement, improvement, etc. within the spirit and principles of the present application shall be included in the protection scope of the present application.

[0127] Although preferred embodiments of the present application have been described, those skilled in the art can make further changes and modifications to these embodiments once they know the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application. Obviously, those skilled in the art can make various modifications and variations to the present application without departing from the spirit and scope of the present application. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalent technologies, the present application also intends to include these modifications and variations.

Claims

1. A wearable sitting posture monitoring and correction system, characterized in that: include: The sitting posture capture module is used to transmit the collected sitting posture information to the sitting posture analysis module through the Bluetooth module for matching analysis after signal processing; The sitting posture analysis module is used to identify the sitting posture type based on the sitting posture information sent by the sitting posture capture module and evaluate the degree of trunk bending; the sitting posture type and trunk bending angle are sent to the sitting posture feedback module; The sitting posture feedback module is used to display the sitting posture type and torso bending angle in real time, and determine whether to trigger an alarm based on the respective thresholds of sitting posture type, bending angle and duration; it automatically records the number of alarms and usage time, and calculates the ratio to evaluate the sitting posture qualification status.

2. A wearable sitting posture monitoring and correction system according to claim 1, characterized in that: The sitting posture capture module includes a controller, a power supply, a Bluetooth module, a wearable bodysuit, and multiple flexible mechanical sensors; The flexible mechanical sensors are attached to the monitoring parts of the bodysuit corresponding to the spine, cervical vertebrae, and shoulders through elastic tape, and are used to collect sitting posture information at the corresponding positions; The sensor is connected to the input pin of the controller via a wire to send the collected sitting posture information to the controller; The controller is embedded in the wearable bodysuit and connected to the flexible mechanical sensor via a wire, and is used to process the received sitting posture information and transmit it to the sitting posture analysis module via a Bluetooth module; The power supply is connected to the controller via a power adapter, a battery or a USB to supply power to the controller.

3. A wearable sitting posture monitoring and correction system according to claim 2, characterized in that: The flexible mechanical sensor is one or any one of a flexible strain sensor, a flexible pressure sensor, a flexible acceleration sensor, and a flexible bending sensor with an elastic modulus less than 10 MPa.

4. A wearable sitting posture monitoring and correction system according to claim 1, characterized in that: The sitting posture analysis module is set in the background of the mobile terminal, including: a sitting posture type information library and a sitting posture angle information library; The sitting posture type information database is used to train the user's signal samples of different sitting posture types through a supervised learning classification algorithm to identify the sitting posture type; The sitting posture angle information database is used to evaluate the degree of trunk bending by establishing a mapping relationship database; The established mapping relationship database is a data set of mapping relationships between the bending angles of key bone joints in the spine, cervical spine, and shoulders and sensor signals.

5. The wearable sitting posture monitoring and correction system according to claim 1, characterized in that: The sitting posture feedback module includes a user interface of the mobile terminal, a timing device and an alarm device; The user interface is used to receive and display in real time the type of sitting posture and the torso bending angle; The alarm device is used to trigger an alarm based on user-defined sitting posture type, bending angle and duration threshold; Among them, the alarm mode includes: mobile terminal alarm and external device alarm; The mobile terminal alarm is displayed in real time on the user interface through text, image or audio file; The external device alarm includes: a vibrator alarm, a buzzer alarm or an indicator light alarm; The timing device and the alarm device are used to automatically record the number of alarms and the usage time, and calculate the ratio to evaluate the qualified sitting posture.

6. The method for implementing a wearable sitting posture monitoring and correction system according to claim 1, wherein: The following steps are involved: S1: The human body sitting posture information is collected by multiple flexible mechanical sensors installed on the wearable bodysuit; the collected sitting posture data is processed by the controller and then wirelessly transmitted to the analysis module; S2: Identify sitting posture types using a pre-trained sitting posture classification model; evaluate trunk bending angle based on the mapping relationship between sensor signals and bending angles; S3: The sitting posture feedback module displays the sitting posture type and bending angle in real time; it also monitors the duration of the sitting posture and determines whether to trigger an alarm; S4: The sitting posture feedback module evaluates the sitting posture qualification status based on the ratio of the number of alarms to the total usage time.

7. The method for implementing a wearable sitting posture monitoring and correction system according to claim 6, characterized in that: In step S2, the construction of the sitting posture classification model includes the following steps: S2-1: Collect N samples of the user's five incorrect sitting posture signals, namely, leaning forward, leaning left, leaning right, leaning left forward, and leaning right forward, at the corresponding monitoring part. S2-2: Model training: 70% of the sample data is imported into the MATLAB classification learner based on the supervised learning classification algorithm, and supervised learning training is performed using the logistic regression algorithm; the time domain features and frequency domain features of each group of samples are extracted; S2-3: Model validation: The remaining 30% of sample data is input into the trained model for validation, and the recognition accuracy is calculated using the confusion matrix; S2-4: The current sensor signal is extracted using the same time domain features and frequency domain features, and input into the trained logistic regression model for classification; the model outputs the sitting posture category label with the highest probability.

8. The method for implementing a wearable sitting posture monitoring and correction system according to claim 6, wherein: In step S2, the trunk bending angle is evaluated based on the mapping relationship between the sensor signal and the bending angle, specifically: 1) Use a digital inclinometer to measure the user's trunk flexion angle α at the first lumbar vertebra; set four calibration points: α = 10°, 25°, 40°, and 50°; 2) At each calibration angle, synchronously record the output resistance value of the sensor at the top of the sacrum to obtain the corresponding relationship between angle and resistance value; 3) Establish a discrete mapping library of angle-signal value to obtain the current sensor resistance value R a ; If the current sensor resistance value R a , the angle is calculated by the linear interpolation formula and the real-time bending angle value α is output.

9. The method for implementing a wearable sitting posture monitoring and correction system according to claim 6, characterized in that: The alarm triggering condition of step S3 includes at least one of the following: (a) Identified as an incorrect sitting posture and the duration is ≥ the first threshold m; (b) the trunk bending angle is ≥ the angle threshold, and the duration is ≥ the second threshold k; The alarm execution mode includes at least one of the following: A warning image pops up on the mobile terminal interface, an external vibrator or buzzer is activated, the number of alarms is accumulated, and the triggering time is recorded.

10. The method for implementing a wearable sitting posture monitoring and correction system according to claim 6, wherein: The step S4 is specifically as follows: Calculate the alarm frequency per unit time, that is: Where N is the number of alarms and T is the total number of minutes used; When R≤0.025, the assessment is: sitting posture is acceptable; when R>0.025, the assessment is: sitting posture needs to be corrected; the user interface generates a historical sitting posture health report containing the R value, including: The following indicators are counted by time dimension: (1) The cumulative duration of each incorrect sitting posture; (2) cumulative duration of trunk flexion angle >40°; (3) The changing trend of the alarm frequency R value; Generate posture improvement suggestions based on historical data: (a) When the proportion of left-leaning / right-leaning sitting postures is greater than 30%, the user interface prompts attention to sitting balance; (b) When the R value is > 0.025 for three consecutive days, the user interface prompts to strengthen sitting posture correction training.

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