Attitude sensing and fatigue early warning method and system based on intelligent sportswear

Abstract

The invention discloses a posture sensing and fatigue early warning method and system based on an intelligent sportswear, and the method comprises the steps: collecting posture data and electromyographic signals in real time when a user moves through a flexible stretching sensor and an electromyographic sensor on the intelligent sportswear; after processing the collected data, analyzing attitude normalization based on a biomechanical algorithm, and evaluating the muscle fatigue degree in combination with the electromyographic signals; when the abnormal posture exists or the fatigue degree exceeds a preset threshold value, an early warning action is triggered, specifically, tactile feedback is output through the flexible vibration motor, and early warning information containing a posture correction suggestion and a fatigue recovery scheme is sent to the user terminal. The system comprises a sensing module, a processing analysis module, a feedback execution module and a user terminal interface module, can realize integration of exercise data acquisition, analysis and early warning feedback, provides personalized exercise protection guidance for a user, and is suitable for daily exercise training and rehabilitation scenes.

Description

Technical Field

[0001] This invention relates to the field of computer science and technology, specifically providing a method and system for posture perception and fatigue early warning based on smart sportswear. Background Technology

[0002] With the increasing demand for national fitness and sports rehabilitation, users are increasingly seeking standardized posture and muscle fatigue monitoring during exercise. Traditional exercise posture monitoring relies heavily on on-site guidance from professionals, which is costly and has limited coverage, making it difficult to meet the real-time monitoring needs of daily exercise. Muscle fatigue assessment, on the other hand, typically depends on subjective user feelings, lacking objective data support and prone to causing sports injuries due to delayed judgment. While existing wearable devices can collect some exercise data, they mostly use single sensors, resulting in insufficient data accuracy and limited functionality. They cannot simultaneously and accurately analyze posture and assess fatigue, and their warning methods are mostly visual or auditory alerts, which are easily ignored in exercise scenarios, making timely and effective protection difficult. Furthermore, existing technologies lack personalized adaptation capabilities, failing to adjust monitoring standards and warning thresholds according to individual user differences, thus limiting the applicability of monitoring results and failing to meet the sports protection needs of different users. Therefore, there is an urgent need for a technical solution that can achieve real-time, accurate, and personalized posture perception and fatigue warning. Summary of the Invention

[0003] To address the limitations of existing technologies that struggle to achieve accurate real-time monitoring of posture and muscle fatigue simultaneously during exercise, and the tendency for warning methods to be easily overlooked, thus failing to provide users with timely and personalized exercise protection guidance, this invention provides a posture perception and fatigue warning method and system based on smart sportswear.

[0004] In a first aspect, the present invention provides a posture perception and fatigue early warning method based on smart sportswear, comprising the following steps: By using flexible stretch sensors and electromyography sensors installed on smart sportswear, the system can collect posture data and electromyography signals from the user during exercise in real time. The collected posture data and electromyographic signals are processed, and the normality of the movement posture is analyzed based on biomechanical algorithms. The degree of muscle fatigue is also assessed based on the electromyographic signals. When the analysis results indicate an abnormal posture or muscle fatigue exceeding a preset threshold, an early warning action is triggered. The warning actions include: triggering the flexible vibration motor built into the smart sportswear to output tactile feedback, and / or sending warning information containing posture correction suggestions and fatigue recovery plans to the associated user terminal.

[0005] Furthermore, the standardization of the analysis of motion posture specifically includes: calculating the actual motion angle of the user's joints based on the data from the flexible stretch sensor through an algorithm model; The actual motion angle is compared with the standard angle range in the standard motion model database; if the actual motion angle deviates from the standard angle range, the posture is determined to be abnormal.

[0006] Furthermore, the assessment of muscle fatigue specifically includes: extracting the time-domain and frequency-domain features of the electromyographic signal; Monitor the decreasing trend of the median frequency in the frequency domain features over time; The degree of muscle fatigue is quantitatively assessed based on the slope of the downward trend or the duration during which the median frequency is below a preset threshold.

[0007] Furthermore, the condition for triggering the warning action, which is that the degree of muscle fatigue exceeds a preset threshold, is a dynamic threshold that is personalized based on the user's historical exercise data or personal physiological parameters.

[0008] Furthermore, the tactile feedback is hierarchical: for abnormal postures, the triggering position of the tactile feedback is associated with the specific joint position where the abnormality occurs; for muscle fatigue, the vibration intensity or frequency of the tactile feedback is positively correlated with the degree of fatigue.

[0009] Furthermore, the method also includes a step: during the initial user phase, recording sensor data when the user performs standard actions to establish a user-personalized benchmark model for comparison in subsequent posture standardization analysis.

[0010] Furthermore, the method also includes the steps of: storing the posture analysis results, fatigue assessment data and early warning records from multiple exercise processes to form a long-term exercise health record, and generating a trend analysis report on muscle strength imbalance or posture abnormality based on the record.

[0011] Secondly, the present invention also provides a posture perception and fatigue warning system based on smart sportswear for implementing any of the posture perception and fatigue warning methods based on smart sportswear, comprising: The sensing module includes multiple flexible stretch sensors and electromyography sensors integrated into the smart sportswear, used to collect the user's posture data and electromyography signals; The processing and analysis module is communicatively connected to the sensing module and is used to process the data collected by the sensing module to perform posture normalization analysis and muscle fatigue assessment. A feedback execution module is communicatively connected to the processing and analysis module. The feedback execution module includes a flexible vibration motor array integrated on the smart sportswear, used to output tactile feedback according to the instructions of the processing and analysis module. The user terminal interface module is used to communicate with the user terminal and send the warning information to the user terminal for display.

[0012] Furthermore, the processing and analysis module includes an embedded microprocessor mounted on the smart sportswear body and a data analysis unit mounted on a cloud server or user terminal; the embedded microprocessor is used to perform preliminary processing and forwarding of sensor data, and the data analysis unit is used to run complex biomechanical analysis algorithms.

[0013] Furthermore, the sensing module also includes an inertial measurement unit for data fusion with the flexible tensile sensor, thereby improving the accuracy of joint angle estimation through a sensor fusion algorithm.

[0014] Beneficial effects The posture perception and fatigue early warning method and system based on smart sportswear of this invention have significant benefits in multiple dimensions. At the data acquisition and analysis level, the combined application of flexible stretch sensors and electromyography (EMG) sensors, along with optional inertial measurement units for data fusion, effectively improves the acquisition accuracy of posture data and EMG signals. Combined with biomechanical algorithms, it achieves precise analysis of posture regularity and muscle fatigue levels, solving the problems of insufficient data accuracy and limited functionality of traditional single sensors. At the early warning feedback level, a flexible vibration motor outputs tactile feedback, and the tactile feedback is correlated with abnormal joint positions, with vibration intensity or frequency matched to fatigue levels. Simultaneously, combined with the display of early warning information on the user terminal, it achieves multimodal early warning, avoiding the problem of traditional visual or auditory reminders being easily ignored in sports scenarios, ensuring timely and effective early warning. At the level of personalized adaptation, a personalized benchmark model for users is established during the initial use phase. Combined with dynamic threshold settings, the monitoring standards and warning thresholds can be matched with individual user differences, improving the applicability of monitoring results. At the same time, by establishing long-term sports and health records and generating trend analysis reports, continuous sports optimization suggestions are provided to users, helping them to gradually improve their exercise posture, reasonably avoid fatigue risks, effectively reduce the probability of sports injuries, meet the personalized protection needs of different users in daily sports training and rehabilitation scenarios, and improve the overall sports experience and health protection level. Attached Figure Description

[0015] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the accompanying drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the structures shown in these drawings without creative effort.

[0016] Figure 1A flowchart illustrating the working steps of the posture perception and fatigue early warning method based on smart sportswear provided in an embodiment of the present invention; Figure 2 A block diagram of a posture perception and fatigue warning system based on smart sportswear, provided for another embodiment of the present invention. Detailed Implementation

[0017] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those of ordinary skill in the art without creative effort are within the scope of protection of the present invention.

[0018] It should be noted that if the embodiments of the present invention involve directional indicators (such as up, down, left, right, front, back, etc.), the directional indicators are only used to explain the relative positional relationship and movement of the components in a specific posture. If the specific posture changes, the directional indicators will also change accordingly.

[0019] Furthermore, if the embodiments of this invention involve descriptions such as "first" or "second," these descriptions are for descriptive purposes only and should not be construed as indicating or implying their relative importance or implicitly specifying the number of technical features indicated. Thus, features defined with "first" or "second" may explicitly or implicitly include at least one of those features. Additionally, the use of "and / or" or "and / or" throughout the text implies three parallel solutions. For example, "A and / or B" includes solution A, solution B, or a solution where both A and B are satisfied. Furthermore, the technical solutions of the various embodiments can be combined, but this must be based on the ability of those skilled in the art to implement them. When the combination of technical solutions is contradictory or impossible to implement, it should be considered that such a combination of technical solutions does not exist and is not within the scope of protection claimed by this invention.

[0020] In existing technologies, with the increasing demand for fitness and sports rehabilitation, traditional posture monitoring relies on on-site guidance from professionals, which suffers from high costs and limited scenario coverage, making it difficult to achieve real-time monitoring during daily exercise. Current wearable devices mostly use single sensors, resulting in insufficient data accuracy and limited functionality. They cannot simultaneously analyze posture and assess fatigue, and their warning methods are mostly visual or auditory alerts, which are easily ignored by environmental interference during exercise. Furthermore, the lack of personalized adaptation capabilities means that monitoring standards and warning thresholds cannot be adjusted according to individual user differences, limiting their applicability.

[0021] To address the aforementioned issues, existing technologies cannot accurately monitor movement posture and muscle fatigue in real time, and their early warning methods are unsuitable for dynamic scenarios. To resolve this, the inventors considered how to improve the comprehensiveness of data acquisition through multimodal sensor fusion, while combining biomechanical analysis and fatigue assessment algorithms to achieve accurate judgment. Furthermore, designing an effective early warning mechanism to adapt to the real-time needs of movement scenarios and establishing personalized threshold models became key considerations. By integrating flexible sensors and electromyography (EMG) signal acquisition, combined with tactile feedback and terminal information push, a multi-dimensional solution was formed.

[0022] Therefore, this application proposes to collect user posture data and electromyography (EMG) signals in real time during exercise by using flexible stretch sensors and EMG sensors installed on smart sportswear; process the collected data and signals, analyze the standardization of the exercise posture based on biomechanical algorithms, and assess the degree of muscle fatigue based on EMG signals; when the analysis results indicate the presence of abnormal posture or the degree of muscle fatigue exceeds a preset threshold, trigger an early warning action; the early warning action includes triggering the flexible vibration motor built into the smart sportswear to output tactile feedback, and / or sending early warning information containing posture correction suggestions and fatigue recovery plans to the associated user terminal.

[0023] refer to Figure 1 This embodiment provides a posture perception and fatigue warning method based on smart sportswear, comprising the following steps: S1. By using flexible stretch sensors and electromyography sensors installed on smart sportswear, the user's posture data and electromyography signals during exercise can be collected in real time. S2. The collected posture data and electromyographic signals are processed, the normality of the movement posture is analyzed based on biomechanical algorithms, and the degree of muscle fatigue is assessed based on electromyographic signals. S3. When the analysis results indicate an abnormal posture or muscle fatigue level exceeding the preset threshold, an early warning action is triggered. S4. The warning action includes: triggering the flexible vibration motor built into the smart sportswear to output tactile feedback, and / or sending warning information containing posture correction suggestions and fatigue recovery plans to the associated user terminal.

[0024] According to step S1, flexible stretching sensors and electromyography sensors installed on the smart sportswear are used to collect the user's posture data and electromyography signals in real time during the exercise process.

[0025] It should be noted that the smart sportswear uses a close-fitting knitted fabric and integrates two types of core sensors in key areas to achieve high-precision acquisition of motion data. Among them, the flexible stretch sensor uses a polyimide-based resistive sensor, sewn into six locations: the shoulder, elbow, knee, and outer waist. The sampling frequency is set to 100Hz. By monitoring the change in resistance when the fabric is stretched, it outputs posture data in real time. Taking the sensor at the knee joint as an example, its resistance change... With knee flexion angle The linear relationship is satisfied, as shown in the following formula:

[0026] In the above formula, The sensor sensitivity coefficient (in this embodiment) ), The initial resistance of the sensor is 100Ω. This formula can be used to directly convert the resistance signal into joint angle data, enabling real-time monitoring of the knee flexion and extension range during running.

[0027] The electromyography (EMG) sensor uses dry Ag / AgCl electrodes attached to the surface of the quadriceps femoris, gastrocnemius, and gluteus maximus muscles. It comprises three dual-channel acquisition units with a sampling frequency of 2000Hz. The surface EMG signals (sEMG) during muscle contraction are extracted using a differential amplifier circuit. During acquisition, the sensor automatically filters out 50Hz power frequency interference and motion artifacts, outputting raw EMG signals with a voltage range of -1.5V to 1.5V, providing a data foundation for subsequent fatigue assessment.

[0028] In some embodiments, a flexible tensile sensor refers to a flexible electronic device that can be fitted to a human joint, specifically a strain sensor based on conductive nanomaterials or an elastic substrate, used to capture joint deformation data during movement. An electromyography (EMG) sensor is a device used to detect muscle electrical activity, specifically a surface electrode or fabric-integrated electrode, used to collect electrical signals generated by muscle contraction. A biomechanical algorithm is a mathematical analysis method based on human kinematics models, specifically an algorithm that calculates joint angles and matches them to a standard movement library, used to evaluate postural correctness. Tactile feedback refers to the transmission of information through mechanical vibration, specifically implemented using a micro linear motor or piezoelectric ceramic element, used to provide real-time tactile cues during movement.

[0029] Flexible stretch sensors are distributed at key locations such as the shoulders, elbows, and knees of the smart sportswear to monitor the stretching deformation caused by joint movement in real time, converting it into electrical signals and transmitting them to the processing unit. Electromyography (EMG) sensors are placed on the surface of the target muscle group to collect changes in electrical signals during muscle contraction. After filtering and feature extraction of the sensor data, the processing unit calculates the actual joint movement angle using a biomechanical model and compares it with the standard range of movement angles to determine whether the posture is abnormal. At the same time, the time and frequency domain characteristics of the EMG signals are extracted, and the degree of muscle fatigue is assessed by analyzing the median frequency decline trend. When an abnormal posture or fatigue exceeding a threshold is detected, a flexible vibration motor triggers a vibration prompt at the corresponding joint position, and the user terminal receives a warning message containing a diagram of corrective movements or rest suggestions.

[0030] The acquired posture data and electromyographic signals are processed according to the method described in step S2. The normality of the movement posture is analyzed based on the biomechanical algorithm, and the degree of muscle fatigue is assessed based on the electromyographic signals.

[0031] It should be noted that the running posture evaluation model, built based on biomechanical algorithms, selects three key evaluation indicators: maximum knee flexion angle (...). Forward tilt angle of the torso ( ) and stride deviation rate ( In the preprocessing stage, the posture data is first filtered by a sliding window (window size set to 50ms) to eliminate instantaneous noise; then, the actual values ​​of each indicator within the running cycle are calculated using a joint angle coupling algorithm and compared with the preset standard range.

[0032] Taking stride deviation rate as an example, its calculation method is as follows: In the formula, S_std represents the current stride length (calculated using displacement data from the waist sensor), while S_std represents the standard stride length set based on the user's height H. ).when If the gait is greater than 15%, it is considered an abnormal stride. If the maximum knee flexion angle is consistently less than 60° or the trunk forward tilt angle is consistently greater than 15°, it is considered an insufficient knee joint force and a trunk posture imbalance, respectively, both of which are classified as abnormal postures.

[0033] In some embodiments, the frequency domain characteristics of electromyography (EMG) signals are used to assess muscle fatigue. First, a Fast Fourier Transform (FFT) is performed on the raw EMG signal to convert the time-domain signal into a frequency-domain signal. Then, two core indicators, median frequency (MF) and mean power frequency (MPF), are extracted. During muscle fatigue, the muscle fiber recruitment type changes, leading to a decrease in high-frequency components and an increase in low-frequency components, which is manifested as a continuous decrease in MF and MPF.

[0034] In this embodiment, the fatigue assessment window is set to 30 seconds, and the rate of change of MF within the window is calculated by sliding the sensor. Determining the degree of fatigue: In the formula, The median frequency at the start of the window. This represents the median frequency at the end of the window. When When the fatigue level is greater than 20%, the corresponding muscle (such as the quadriceps) is determined to have exceeded the preset threshold, triggering a fatigue warning.

[0035] According to step S3, when the analysis results indicate an abnormal posture or muscle fatigue exceeding a preset threshold, an early warning action is triggered. The specific implementation steps are as follows: When the microprocessor determines that there is an abnormal posture or excessive muscle fatigue, it will simultaneously activate two types of early warning mechanisms to ensure that the user receives timely feedback: Firstly, it triggers the built-in flexible vibration motors. These motors are made of thin piezoelectric ceramic material and are embedded in the inner sides of the shoulders and waist. When an abnormal posture occurs, the motors in the corresponding parts will output vibration signals of different frequencies—for example, when the torso leans forward abnormally, the waist motor vibrates at a frequency of 2Hz; when the knee joint posture is abnormal, the shoulder motor vibrates at a frequency of 4Hz, directly prompting the user to adjust their posture through tactile feedback.

[0036] Secondly, the system generates and sends warning messages to user terminals (such as smartphones). These warning messages contain two parts: posture correction suggestions in text and diagram format, such as a message indicating abnormal stride length: "Current stride length is too large; it is recommended to reduce the landing distance and maintain a stride frequency of 180 steps / minute"; and a fatigue recovery plan generated based on the location of fatigued muscles, such as recommending "pause exercise and perform wall squats for 30 seconds each time, repeating 3 sets" for quadriceps fatigue. The information is transmitted via Bluetooth Low Energy, and the user terminal app will simultaneously display a pop-up reminder and save historical warning records for subsequent analysis of exercise habits.

[0037] According to step S4, the warning action includes: triggering the flexible vibration motor built into the smart sportswear to output tactile feedback, and / or sending warning information containing posture correction suggestions and fatigue recovery plans to the associated user terminal.

[0038] In some embodiments, the condition for triggering the warning action, where the degree of muscle fatigue exceeds a preset threshold, is a dynamic threshold, which is personalized based on the user's historical movement data or personal physiological parameters. Preferably, the tactile feedback is hierarchical: for abnormal postures, the triggering location of the tactile feedback is associated with the specific joint location where the abnormality occurs; for muscle fatigue, the vibration intensity or frequency of the tactile feedback is positively correlated with the degree of fatigue.

[0039] Among these, graded feedback refers to a differentiated feedback mechanism based on the type of abnormality. This can be achieved using independent control modules for flexible vibration motor arrays, with vibration units arranged in zones corresponding to different joint regions. The association between trigger position and joint position refers to the spatial mapping between the physical location of the vibration feedback and the joint where the posture abnormality was detected. This can be achieved by establishing a mapping table between sensor data and the position of the vibration motor, creating a direct correspondence between the vibration source and the abnormal joint. The positive correlation between vibration intensity or frequency and fatigue level refers to dynamically adjusting vibration parameters based on the fatigue level derived from electromyography signal analysis. This can be achieved by using pulse width modulation (PWM) technology to control the motor drive signal, causing the vibration amplitude to increase linearly with the fatigue index.

[0040] Specifically, flexible vibration motor arrays are integrated into the shoulder, elbow, and knee joint areas of the smart sportswear. When the knee joint angle deviates from the standard range, specific motor groups located in the knee area are activated, generating local vibration alerts. When the median frequency drop slope of the back muscle groups exceeds a threshold, the motor groups distributed in the lower back increase the vibration intensity according to the fatigue level. The vibration intensity is divided into three levels; for example, low-frequency vibration is activated when the fatigue index reaches 50%, and high-frequency strong vibration mode is switched when it exceeds 80%.

[0041] refer to Figure 2 The present invention also provides an embodiment of a posture perception and fatigue warning system based on intelligent sportswear for implementing any of the posture perception and fatigue warning methods based on intelligent sportswear, comprising: The sensing module includes multiple flexible stretch sensors and electromyography sensors integrated into the smart sportswear, used to collect the user's posture data and electromyography signals; The processing and analysis module is communicatively connected to the sensing module and is used to process the data collected by the sensing module to perform posture normalization analysis and muscle fatigue assessment. A feedback execution module is communicatively connected to the processing and analysis module. The feedback execution module includes a flexible vibration motor array integrated on the smart sportswear, used to output tactile feedback according to the instructions of the processing and analysis module. The user terminal interface module is used to communicate with the user terminal and send the warning information to the user terminal for display.

[0042] Preferably, the processing and analysis module includes an embedded microprocessor mounted on the smart sportswear body and a data analysis unit mounted on a cloud server or user terminal; the embedded microprocessor is used to perform preliminary processing and forwarding of sensor data, and the data analysis unit is used to run complex biomechanical analysis algorithms.

[0043] The embedded microprocessor refers to a small computing unit integrated within the garment, typically implemented using low-power chips such as the ARM Cortex-M series processors. It is used to process raw sensor data in real time and perform basic filtering operations. The data analysis unit refers to a processing module deployed on a remote computing node, which can be implemented using cloud computing resources or a mobile terminal processor. It is used to perform computationally intensive biomechanical model calculations. Preliminary sensor data processing includes data denoising, feature extraction, and format conversion. Data forwarding is achieved via Bluetooth or Wi-Fi modules. Complex biomechanical analysis algorithms involve computationally intensive tasks such as joint dynamics modeling and muscle activation pattern recognition.

[0044] Specifically, during exercise, the flexible stretching sensor and electromyography (EMG) sensor continuously generate raw data streams. The embedded microprocessor first adjusts the sampling rate, corrects the baseline, and filters outliers from the raw data. Then, it compresses and packages the pre-processed data and transmits it to a cloud server or the user's mobile phone via a wireless communication module. After receiving the data, the data analysis unit calls a pre-trained deep learning model to perform three-dimensional reconstruction of the joint angle trajectory and calculates the muscle fatigue index based on the spectral characteristics of the EMG signal. Once the processing results are returned to the local system, a corresponding early warning mechanism is triggered.

[0045] In some embodiments, the embedded microprocessor can be configured to process only angle data of key joints, such as the bending angles of the knee and elbow joints, while forwarding sensor data from other parts directly to the cloud. The data analysis unit can automatically switch algorithm models according to the user's exercise type, for example, prioritizing the analysis of lower limb muscle fatigue in running mode and focusing on monitoring spinal posture stability in weightlifting mode.

[0046] Preferably, the sensing module further includes an inertial measurement unit for data fusion with the flexible tensile sensor, thereby improving the accuracy of joint angle estimation through a sensor fusion algorithm.

[0047] An inertial measurement unit (IMU) is a sensor assembly consisting of accelerometers and gyroscopes, specifically implemented using six-axis or nine-axis MEMS sensors. It is used to capture linear acceleration and angular velocity information during human movement. Sensor fusion algorithms are computational methods that perform spatiotemporal alignment and complementary processing of data from multiple sensors. Specifically, they can be implemented using Kalman filtering or extended Kalman filtering algorithms. By fusing deformation data from a flexible tensile sensor with kinematic data from the IMU, noise interference and measurement errors from individual sensors can be eliminated.

[0048] During user movement, the flexible stretch sensor indirectly reflects the joint bending angle by detecting fabric deformation, while the inertial measurement unit (IMU) directly measures limb acceleration and rotational angular velocity. The data from both sensors are timestamped and then input into a fusion algorithm for joint calculation. For example, when the knee joint is bent, the flexible stretch sensor's angle measurement may fluctuate due to clothing wrinkles. In this case, the motion acceleration data provided by the IMU can help correct dynamic errors, thus outputting a more stable joint angle estimate. The fused angle data is then transmitted to the processing and analysis module as a basis for determining posture conformity.

[0049] As can be seen from the above, the embodiments provided in this application collect data in collaboration with flexible sensors and electromyography sensors, and combine them with biomechanical algorithms to achieve real-time analysis of posture and fatigue, triggering graded tactile feedback and personalized early warning. This solves the problems of traditional motion monitoring relying on manual labor, lacking objective indicators, and having delayed early warnings. It has the advantages of real-time monitoring of the standardization of movement posture and the degree of muscle fatigue, providing accurate early warning feedback, and improving exercise safety and training effect.

[0050] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (devices), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0051] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0052] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1The steps of the function specified in one or more boxes.

[0053] Specific embodiments have been used to illustrate the principles and implementation methods of this invention. The descriptions of the embodiments above are only for the purpose of helping to understand the method and core ideas of this invention. At the same time, for those skilled in the art, there will be changes in the specific implementation methods and application scope based on the ideas of this invention. Therefore, the content of this specification should not be construed as a limitation of this invention.

Claims

1. A method for posture perception and fatigue early warning based on smart sportswear, characterized in that, Includes the following steps: By using flexible stretch sensors and electromyography sensors installed on smart sportswear, the system can collect posture data and electromyography signals from the user during exercise in real time. The collected posture data and electromyographic signals are processed, and the normality of the movement posture is analyzed based on biomechanical algorithms. The degree of muscle fatigue is also assessed based on the electromyographic signals. When the analysis results indicate an abnormal posture or muscle fatigue exceeding a preset threshold, an early warning action is triggered. The warning actions include: triggering the flexible vibration motor built into the smart sportswear to output tactile feedback, and / or sending warning information containing posture correction suggestions and fatigue recovery plans to the associated user terminal.

2. The posture perception and fatigue early warning method based on intelligent sportswear according to claim 1, characterized in that, The standardization of the analysis of motion posture specifically includes: calculating the actual motion angle of the user's joints based on the data from the flexible stretch sensor through an algorithm model; The actual motion angle is compared with the standard angle range in the standard motion model database; if the actual motion angle deviates from the standard angle range, the posture is determined to be abnormal.

3. The posture perception and fatigue early warning method based on intelligent sportswear according to claim 1, characterized in that, The assessment of muscle fatigue specifically includes: extracting the time-domain and frequency-domain features of the electromyographic signals; Monitor the decreasing trend of the median frequency in the frequency domain features over time; The degree of muscle fatigue is quantitatively assessed based on the slope of the downward trend or the duration during which the median frequency is below a preset threshold.

4. The posture perception and fatigue early warning method based on intelligent sportswear according to claim 1, characterized in that, The condition for triggering the warning action, which is that the degree of muscle fatigue exceeds a preset threshold, is a dynamic threshold that is personalized based on the user's historical exercise data or personal physiological parameters.

5. The posture perception and fatigue early warning method based on intelligent sportswear according to claim 1, characterized in that, The tactile feedback is tiered: for abnormal postures, the triggering location of the tactile feedback is associated with the specific joint location where the abnormality occurs; for muscle fatigue, the vibration intensity or frequency of the tactile feedback is positively correlated with the degree of fatigue.

6. The posture perception and fatigue early warning method based on intelligent sportswear according to claim 1, characterized in that, The method further includes the step of: recording sensor data when the user performs standard actions during the initial use phase, in order to establish a user-personalized benchmark model, which will serve as a comparison benchmark for subsequent posture standardization analysis.

7. The posture perception and fatigue early warning method based on intelligent sportswear according to claim 1, characterized in that, The method further includes the following steps: The results of posture analysis, fatigue assessment data and early warning records from multiple exercise sessions are stored to form a long-term exercise health record, and a trend analysis report on muscle strength imbalance or posture abnormality is generated based on this record.

8. A posture perception and fatigue warning system based on intelligent sportswear for implementing the posture perception and fatigue warning method based on intelligent sportswear as described in any one of claims 1-7, characterized in that, include: The sensing module includes multiple flexible stretch sensors and electromyography sensors integrated into the smart sportswear, used to collect the user's posture data and electromyography signals; The processing and analysis module is communicatively connected to the sensing module and is used to process the data collected by the sensing module to perform posture normalization analysis and muscle fatigue assessment. A feedback execution module is communicatively connected to the processing and analysis module. The feedback execution module includes a flexible vibration motor array integrated on the smart sportswear, used to output tactile feedback according to the instructions of the processing and analysis module. The user terminal interface module is used to communicate with the user terminal and send the warning information to the user terminal for display.

9. The posture perception and fatigue early warning system based on intelligent sportswear according to claim 8, characterized in that, The processing and analysis module includes an embedded microprocessor mounted on the smart sportswear body and a data analysis unit mounted on a cloud server or user terminal; the embedded microprocessor is used to perform preliminary processing and forwarding of sensor data, and the data analysis unit is used to run complex biomechanical analysis algorithms.

10. The posture perception and fatigue early warning system based on intelligent sportswear according to claim 8, characterized in that, The sensing module also includes an inertial measurement unit for data fusion with the flexible tensile sensor, thereby improving the accuracy of joint angle estimation through sensor fusion algorithms.

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