Intelligent training mat, motion data processing method and system and medium

Abstract

The invention discloses an intelligent training pad, a motion data processing method and system and a medium, and is applied to the technical field of training pads. A pressure sensing unit is arranged in the jumping area and used for collecting a pressure signal; a thin film switch unit is arranged in the flat plate supporting area and used for collecting switch state signals; and the control module is electrically connected with the pressure sensing unit, the membrane switch unit and the display module, and is used for counting based on the pressure signal and displaying a counting result through the display module, or timing based on the switch state signal and displaying a timing result through the display module. Thus, by using the intelligent training pad, the sports safety and comfort can be improved, automatic timing and counting can be achieved, and therefore most sports scenes can be met; moreover, the counting result or the timing result is displayed through the display module, so that the user can conveniently master the training progress in real time, training is easier to achieve the training target, and the user experience can be improved.

Description

Technical Field

[0001] This application relates to the field of training mat technology, and in particular to an intelligent training mat and a method, system and medium for processing sports data. Background Technology

[0002] Currently, training mats have become a common auxiliary tool for people's daily exercise or sports. Most training mats in the current technology are purely structural products with limited functions, only serving as mats. During exercise or sports, manual counting or timing is usually required, and it is impossible to review past exercise or sports conditions, resulting in insufficient intelligence of existing training mats. Summary of the Invention

[0003] This application provides an intelligent training mat, a method, system, and medium for processing sports data, to address the problem of insufficient intelligence in existing training mats. The technical solution provided by this application is as follows: On the one hand, this application provides a smart training mat, including a training mat body, a multimodal sensor module and a control module disposed within the training mat body, and a display module disposed on the training mat body; The training mat is divided into a jumping area and a plank support area. The multimodal sensor module includes a pressure sensing unit disposed in the jump zone and a membrane switch unit disposed in the plate support zone; the pressure sensing unit is used to collect the pressure signal of the jump zone and transmit it to the control module; the membrane switch unit is used to collect the switch status signal of the plate support zone and transmit it to the control module. The control module is electrically connected to the multimodal sensor module and the display module. The control module is used to count the jumping motion based on the pressure signal using a three-dimensional jump state model and display the counting result through the display module; or, using a three-dimensional plank support state model, timing the plank support motion based on the switch state signal and displaying the timing result through the display module.

[0004] Optionally, the pressure sensing unit includes multiple pressure sensors, which are arranged in an array within the jump zone.

[0005] Optionally, the flat plate support area includes two elbow positioning areas; the membrane switch unit includes two membrane switch sensors, which are respectively disposed in the two elbow positioning areas.

[0006] Optionally, the display module is located on the side of the training pad body.

[0007] Optionally, the surface of the training mat may be marked with jump zone markings corresponding to the jump zone and plank zone markings corresponding to the plank zone.

[0008] Optionally, the jumping area and the plank area together form a yoga area; the jumping area, the plank area, and the yoga area together form a multifunctional area.

[0009] Optionally, the control module is also used to provide standardized reminders for jumping movements based on pressure signals; or to provide standardized reminders for plank support movements based on switch status signals.

[0010] Optionally, the smart training mat provided in this application also includes a storage module and a communication module disposed within the main body of the training mat; The storage module is electrically connected to the control module. The storage module is used to store the pressure signals, counting results, switch status signals and timing results written by the control module. The communication module is electrically connected to the control module and the storage module. Under the control of the control module, the communication module is used to upload the pressure signal and counting results, switch status signal and timing results stored in the storage module to the user terminal device.

[0011] Optionally, the multimodal sensor module, control module, display module, storage module, and communication module are all integrated on a flexible circuit board.

[0012] On the other hand, this application provides a motion data processing method applied to the aforementioned smart training mat, comprising: If a pressure signal is received from a pressure sensor located in the jumping area of ​​the smart training mat, the current type of exercise is determined to be a jumping exercise and the counting mode is switched. In the counting mode, the three-dimensional jumping state model is used to identify the preparation state, the airborne state and the landing state of the jumping exercise based on the pressure signal, and the jumping exercise is counted based on the preparation state, the airborne state and the landing state. If a switch status signal is received from the thin-film switch sensor located in the plank support area of ​​the smart training mat, the current motion type is determined to be plank support motion and the timing mode is switched. In timing mode, the start state, support state and end state of plank support motion are identified based on the switch status signal through the three-dimensional plank support state model, and the plank support motion is timed based on the start state, support state and end state.

[0013] Optionally, a three-dimensional jump state model is used to identify the preparation state, airborne state, and landing state of the jump motion based on pressure signals, including: Using a three-dimensional jump state model, if the pressure signal exceeds the first pressure threshold for a continuous first duration, the user is determined to be in a ready state. If the pressure signal decreases to below the second pressure threshold within a second duration, the user is determined to be in an airborne state. If the pressure signal exceeds the first pressure threshold again within a third duration, the user is determined to be in a landing state.

[0014] Optionally, the jump motion is counted based on the ready state, the airborne state, and the landing state, including: When the ready state, the airborne state, and the landing state are detected consecutively, it is determined as a valid jump movement, and the count result is incremented by 1.

[0015] Optionally, using a three-dimensional plank support state model, the start state, support state, and end state of the plank support motion are identified based on the switch state signals, including: Based on the three-dimensional plank support state model, when the switch state signal changes from an off signal to an on signal and remains on for the fourth consecutive time, the user is determined to be in the start state. When the duration of the on signal continuously increases, the user is determined to be in the support state. When the switch state signal changes from an on signal to an off signal, the user is determined to be in the end state.

[0016] Optionally, the plank exercise is timed based on the start state, support state, and end state, including: The timing starts from the determination time of the start state, accumulates the duration of the support state, and stops when the determination time of the end state is reached.

[0017] Optionally, the motion data processing method provided in this application further includes: A three-dimensional jumping state model is constructed using pressure signals as the first dimension of data, jumping motion state as the second dimension of data, and time as the third dimension of data. The first-dimensional data, the second-dimensional data, and the third-dimensional data are stored in different buffers corresponding to the three-dimensional jump state model.

[0018] Optionally, the motion data processing method provided in this application further includes: A three-dimensional plank support state model is constructed using the switch state signal as the first dimension of data, the plank support motion state as the second dimension of data, and time as the third dimension of data. The first-dimensional data, the second-dimensional data, and the third-dimensional data are stored in different buffers corresponding to the three-dimensional flat plate support state model.

[0019] On the other hand, this application provides a sports data processing system, including the aforementioned smart training mat, user terminal device, and cloud device; The smart training mat communicates with the user terminal device; the smart training mat is used to generate user motion data and transmit it to the user terminal device based on the pressure signal collected by the pressure sensing unit or the switch status signal collected by the membrane switch unit; wherein, the user motion data includes at least the pressure signal and the counting result, or the switch status signal and the timing result; The user terminal device communicates with the cloud device; the user terminal device is used to upload user exercise data to the cloud device; and displays exercise data statistical reports for different time periods issued by the cloud device based on the user's exercise data. Cloud devices are used to generate statistical reports on exercise data for different time periods based on user exercise data and distribute them to user devices.

[0020] Optionally, the cloud device is also used to generate exercise strategy suggestion reports based on exercise data statistics reports for different time periods, and to distribute the exercise strategy suggestion reports to user devices; The user-side device is also used to display exercise strategy recommendation reports.

[0021] Optionally, the user terminal device is also used to obtain the exercise type selected by the user and transmit it to the smart training mat in response to the exercise type selection operation; The smart training mat is also used to receive the user's selected exercise type and use the selected exercise type as the current exercise type; when the current exercise type is jumping exercise, it switches to counting mode, in which the jumping exercise is counted based on pressure signals through a three-dimensional jumping state model; when the current exercise type is plank exercise, it switches to timing mode, in which the plank exercise is timed based on switch state signals through a three-dimensional plank state model.

[0022] Optionally, the user terminal device is also used to respond to the family competition mode configuration operation, obtain and save the user-configured family user identifiers and user competition order; when it receives user exercise data transmitted from the smart training mat, it associates the user exercise data with the corresponding family user identifier according to the user competition order, uploads the user exercise data corresponding to each family user identifier to the cloud device, and displays the family competition results sent by the cloud device. The cloud-based devices are also used to perform family-wide comparison statistics on the user activity data corresponding to each family user identifier uploaded by the user terminal devices, and to send the family comparison results to the user terminal devices.

[0023] Optionally, the user terminal device is also used to obtain the user information to be networked as specified by the user and send it to the cloud device; when it receives the network success message sent by the cloud device, it displays the network success message; when it receives the user's exercise data transmitted by the smart training pad, it uploads the user's exercise data to the cloud device and displays the network competition results sent by the cloud device. The cloud-based devices are also used to create virtual gaming rooms, associate user-end devices and user-end devices corresponding to the information of users to be networked with the virtual gaming room, and send a network success message to each user-end device in the virtual gaming room; when receiving user motion data uploaded by each user-end device in the virtual gaming room, the devices perform network comparison statistics on the user motion data uploaded by each user-end device in the virtual gaming room, and distribute the network comparison results to each user-end device in the virtual gaming room.

[0024] Optionally, if the user terminal device is a game running terminal, the user terminal device is also used to generate game control commands based on the user motion data transmitted by the smart training pad, and adjust the game running process based on the game control commands.

[0025] On the other hand, this application provides a computer-readable storage medium storing computer instructions, which, when executed by a processor, implement the above-described motion data processing method.

[0026] The beneficial effects of this application are as follows: (1) The smart training mat can be laid flat and rolled up or folded, making it easy to store and carry. It can be used indoors and outdoors, and has a jumping area and a plank support area. The jumping area and the plank support area can form a yoga area, which can meet most types of exercise and thus improve its practicality. (2) By using a smart training mat, the safety and comfort of exercise can be improved, and the timing and counting can be automatically completed, which can meet most exercise scenarios and thus improve the level of intelligence. (3) Displaying the counting or timing results through the display module makes it easier for users to keep track of the training progress in real time, making it easier to achieve the training goal and thus improving the user experience. (4) By using a three-dimensional jump state model for counting and a three-dimensional plank support state model for timing, misjudgments can be effectively reduced, thereby improving the accuracy of timing and counting and further enhancing the user experience. (5) By generating statistical reports of exercise data for different time periods through cloud devices and sending them to user devices for display, users can easily view their daily exercise records, enhance their personal exercise confidence, and improve product usage.

[0027] Other features and advantages of this application will be set forth in the following description, and will be apparent in part from the description, or may be learned by practicing the application. The objectives and other advantages of this application may be realized and obtained by means of the structures particularly pointed out in the written description, claims, and drawings. Attached Figure Description

[0028] The accompanying drawings, which are included to provide a further understanding of this application and form part of this application, illustrate exemplary embodiments and are used to explain this application, but do not constitute an undue limitation of this application. In the drawings: Figure 1 This is a schematic diagram of the product structure of the smart training mat in the embodiments of this application; Figure 2 This is a schematic diagram showing the functional area division of the smart training mat in the embodiments of this application; Figure 3 This is a schematic diagram outlining the motion data processing method in the embodiments of this application; Figure 4 This is a schematic diagram of the system composition of the motion data processing system in the embodiments of this application; Figure 5 This is a schematic diagram of the hardware structure of the control module in an embodiment of this application. Detailed Implementation

[0029] To make the objectives, technical solutions, and beneficial effects of this application clearer, the technical solutions in the embodiments of this application 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 this application, and not all of the embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0030] The terms "first," "second," and similar words used in this application do not indicate any order, quantity, or importance, but are merely used to distinguish different components. Words such as "including" or "comprising" mean that the element or object preceding the word encompasses the elements or objects listed after the word and their equivalents, without excluding other elements or objects. Words such as "connected," "coupled," or "linked" are not limited to physical or mechanical connections, but can include electrical connections, whether direct or indirect. Terms such as "upper," "lower," "left," and "right" are used only to indicate relative positional relationships; these relative positional relationships may change accordingly when the absolute position of the described object changes.

[0031] The preferred embodiments of this application are described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit this application. Furthermore, the embodiments and features in the embodiments of this application can be combined with each other without conflict.

[0032] This application provides an embodiment of a smart training mat, see below. Figure 1As shown, the smart training mat provided in this application embodiment includes a training mat body, a multimodal sensor module and a control module disposed within the training mat body, and a display module disposed on the training mat body; The training mat is divided into a jumping area and a plank support area. The multimodal sensor module includes a pressure sensing unit disposed in the jump zone and a membrane switch unit disposed in the plate support zone; the pressure sensing unit is used to collect the pressure signal of the jump zone and transmit it to the control module; the membrane switch unit is used to collect the switch status signal of the plate support zone and transmit it to the control module. The control module is electrically connected to the multimodal sensor module and the display module. The control module is used to count the jumping motion based on the pressure signal using a three-dimensional jump state model and display the counting result through the display module; or, using a three-dimensional plank support state model, timing the plank support motion based on the switch state signal and displaying the timing result through the display module.

[0033] In this embodiment, the smart training mat can be laid flat and rolled up or folded for easy storage and portability. It can be used indoors and outdoors and features a jumping area and a plank support area, which can be combined to form a yoga area, thus meeting the needs of most types of exercise and improving its practicality. Moreover, by using the smart training mat, exercise safety and comfort can be improved, and automatic timing and counting can be performed to meet the needs of most exercise scenarios, thereby improving its level of intelligence. In addition, the display module shows the counting or timing results, which allows users to easily monitor their training progress in real time, making it easier to achieve training goals and thus improving the user experience. Furthermore, by using a three-dimensional jumping state model for counting and a three-dimensional plank support state model for timing, misjudgments can be effectively reduced, thereby improving the accuracy of timing and counting and further enhancing the user experience.

[0034] In one possible implementation, see [reference] Figure 1 As shown, the pressure sensing unit includes multiple pressure sensors, which are arranged in an array within the jump zone.

[0035] In this embodiment, multiple pressure sensors are arranged in an array within the jump zone, which can efficiently and accurately identify the user's pressure signals in the jump zone and improve the accuracy of jump motion counting.

[0036] In one possible implementation, see [reference] Figure 1 As shown, the flat plate support area includes two elbow positioning areas; the membrane switch unit includes two membrane switch sensors, which are respectively located in the two elbow positioning areas.

[0037] In this embodiment, two thin-film switch sensors are respectively set in two elbow positioning areas, which can efficiently and accurately identify the user's switch status signal on the plank support area, thereby improving the accuracy of plank support exercise timing.

[0038] In one possible implementation, see [reference] Figure 1 As shown, the display module is located on the side of the main body of the training pad.

[0039] In this embodiment of the application, by placing the training pad on the side of the main body, such as in the middle of the side, it does not affect the user's exercise and movement, but also makes it easy for the user to keep track of the exercise and movement progress in real time, making it easier to achieve the training goal, thereby improving the user experience.

[0040] In one possible implementation, see [reference] Figure 2 As shown, the surface of the training mat has markings for the jump zone and the plank zone.

[0041] In this embodiment, by setting jump zone markings corresponding to the jump zone and plank support markings corresponding to the plank support zone on the surface of the training mat, users can easily adjust their movement position according to different types of exercise, thereby improving the effectiveness and accuracy of sensor signal acquisition.

[0042] In one possible implementation, see [reference] Figure 2 As shown, the jumping area and the plank area together form the yoga area; the jumping area, the plank area, and the yoga area together form the multifunctional area.

[0043] In this embodiment, by setting up a jumping area, a plank support area, and a yoga area, a multi-functional area can be formed, which makes it convenient for users to flexibly choose the type of exercise according to their own needs, and has high adaptability and strong practicality.

[0044] In one possible implementation, see [reference] Figure 1 As shown, the smart training mat provided in this application embodiment also includes a storage module and a communication module disposed within the main body of the training mat; The storage module is electrically connected to the control module. The storage module is used to store the pressure signals, counting results, switch status signals and timing results written by the control module. The communication module is electrically connected to the control module and the storage module. Under the control of the control module, the communication module is used to upload the pressure signal and counting results, switch status signal and timing results stored in the storage module to the user terminal device.

[0045] In this embodiment, the communication module can be a Bluetooth module and / or a WiFi module, which can connect to user terminal devices such as mobile phones, tablets, and smart TVs, or networks such as local area networks, to enable real-time uploading of user exercise data to user terminals such as mini programs or apps. On the mini program or app, user exercise data statistics for different time periods such as daily, weekly, monthly, quarterly, and yearly can be realized, allowing users to see their daily exercise records and enhance their personal exercise confidence.

[0046] In one possible implementation, the multimodal sensor module, control module, display module, storage module, and communication module are all integrated on a flexible circuit board.

[0047] In this embodiment, the multimodal sensor module, control module, display module, storage module and communication module are all integrated on a flexible circuit board. Since the flexible circuit board has a certain degree of extensibility, it can be further facilitated to store and is not easily damaged.

[0048] In one possible implementation, the multimodal sensor module further includes an accelerometer, a gyroscope, and a sound sensor disposed within the training pad body; the accelerometer is used to collect the take-off acceleration; the gyroscope is used to collect the body tilt angle; and the sound sensor is used to collect the landing sound pattern.

[0049] In this embodiment, the multimodal sensor module integrates pressure sensors, thin-film switch sensors, accelerometers, gyroscopes, sound sensors, and other multimodal sensors, enabling the control module to perform Bayesian inference based on multimodal sensor data, predict the start / end of motion in advance, and achieve zero-delay start / stop determination.

[0050] In one possible implementation, the control module is also used to provide standardized reminders for jumping movements based on pressure signals and for standardized reminders for plank support movements based on switch status signals.

[0051] In this embodiment, the control module provides standardized reminders for jumping exercises based on pressure signals, and for plank exercises based on switch status signals, enabling real-time early warnings (such as voice prompts like "Movement deviation, please adjust"), while simultaneously marking and uploading abnormal movement data to the user's device. The user's device can perform in-depth analysis based on the marked abnormal movement data, generating a detailed report on the causes of deviations and improvement plans (such as "Three instances of lower back collapse occurred during this plank exercise; due to insufficient core strength, it is recommended to combine it with side plank training"). This report is then pushed to the user after training, forming a closed loop of immediate correction and in-depth optimization, satisfying both real-time requirements and providing in-depth guidance.

[0052] In one possible implementation, the control module is used to identify non-standard movements during the jump motion by employing a jump normality evaluation algorithm based on the dynamic trajectory similarity of the pressure center, specifically including: First, during the initial calibration phase, the user is guided to perform multiple (e.g., 3-10) standard jump maneuvers to collect the spatial pressure distribution matrix of the pressure sensor array; the pressure center coordinates (COP) of each landing are calculated using formula (1). x COP y The pressure center coordinates (COP) of each landing will be determined. x COP y The average value of ) is used as the standard COP trajectory; the standard deviation of curvature σ of the standard COP trajectory is calculated using formula (2); the standard COP trajectory and its standard deviation of curvature σ are saved to the storage module;

[0053] Formula (1) Among them, COP x COP is the x-axis of the pressure center. y P is the ordinate of the pressure center. i Let x be the pressure value corresponding to the pressure signal collected by the i-th pressure sensor. i ,y i ) represents the physical coordinates of the i-th pressure sensor, and M represents the number of pressure sensors.

[0054]

[0055] Formula (2) Where σ is the standard deviation of curvature; κ i Let κ be the curvature of the standard COP trajectory at the i-th sampling time. i μ is the mean curvature; N is the number of samples; COP i The standard COP trajectory is the i-th sampling time; COP i+1 The standard COP trajectory at the (i+1)th sampling time; COP i+2 This is the standard COP trajectory at the (i+2)th sampling time.

[0056] Then, during the jumping motion, pressure signals are collected for each jumping cycle, and the actual COP trajectory is calculated in real time. The Dynamic Time Warping (DTW) algorithm is used to calculate the similarity coefficient S (0≤S≤1) between the actual COP trajectory and the standard COP trajectory. When the similarity coefficient S is less than the similarity threshold (e.g., S<0.7), it is determined to be a trajectory deviation.

[0057] Secondly, if the actual COPx With standard COP x If the offset is consistently greater than the offset threshold (e.g., >2cm), it is determined to be uneven force exerted by the left and right feet; if the actual COP y With standard COP y If the offset is consistently greater than the offset threshold, it is determined to be an abnormal weight-bearing condition of the forefoot and heel; if the curvature of the actual COP trajectory changes abruptly by more than a multiple of the standard deviation σ of the curvature of the standard COP trajectory (such as 3σ), it is determined to be a landing imbalance.

[0058] Finally, the display module flashes a red warning icon, and the communication module sends a standard reminder message to the user device. The standard reminder message includes the type of non-compliant action, the degree of deviation, and corrective suggestions (such as "Please keep your feet exerting force symmetrically").

[0059] In one possible implementation, the control module is used to identify non-standard movements during the plank support motion by employing a plank support posture detection algorithm based on dual-switch timing synchronization deviation, specifically including: First, during the initial calibration phase, the user is guided to perform a standard plank exercise, and the standard conduction time difference Δt between the left and right elbow switch state signals is recorded when the user performs the standard plank exercise. ideal (Ideally, it should be <50ms), and stored as a synchronization reference.

[0060] Then, during the timing process, each switch state signal change event is captured in interrupt mode, and the left elbow conduction time t is recorded. L and the conduction time t of the right elbow R Calculate the real-time conduction time difference Δt real =|t R -t L |

[0061] Secondly, if the real-time conduction time difference Δt real Greater than the standard conduction time difference Δt ideal If the sum of the sum with the set tolerance threshold (e.g., 50ms) and the set duration (e.g., 2s) is used, it is judged as alternating support (non-standard action); if a single elbow switch shows a pulse sequence of on-off-on, and the pulse width is less than the width threshold (e.g., 500ms), it is judged as single elbow shaking (body tilting); if the difference in the on-time of the two elbow switches exceeds the difference threshold (e.g., 20%), it is judged as lateral shift of the center of gravity.

[0062] Finally, the display module flashes the left or right warning light (the left light flashes for left elbow problems) and sends posture correction instructions to the user's device via Bluetooth, triggering voice guidance (such as "Please keep your elbows stable and avoid rotating your body").

[0063] Based on the above embodiments, this application provides a motion data processing method applied to the above-mentioned smart training mat, see below. Figure 3 As shown, the general flow of the motion data processing method provided in this application embodiment is as follows: Step 301: If a pressure signal is received from the pressure sensor located in the jumping area of ​​the smart training mat, the current motion type is determined to be jumping motion and the counting mode is switched. In the counting mode, the three-dimensional jumping state model is used to identify the preparation state, the airborne state and the landing state of the jumping motion based on the pressure signal, and the jumping motion is counted based on the preparation state, the airborne state and the landing state.

[0064] In this embodiment, the user can select an exercise type in a mini-program or APP on the user's terminal device, and the user can send the information to the smart training mat as the current exercise type. The user can also select an automatic recognition mode, and send the information to the smart training mat through the user's terminal device. In the automatic recognition mode, the smart training mat automatically recognizes the current exercise type based on the sensor signal type. Specifically, if a pressure signal is received from a pressure sensor located in the jump area of ​​the smart training mat, the current exercise type is determined to be a jump exercise and the system switches to counting mode. If a switch status signal is received from a membrane switch sensor located in the plank support area of ​​the smart training mat, the current exercise type is determined to be a plank support exercise and the system switches to timing mode.

[0065] In practical applications, during jumping exercises such as rope skipping, jumping jacks, and running, each complete cycle of the body and the smart training mat is divided into three states: the preparatory state upon contact with the mat, the airborne state after jumping off the mat, and the landing state when the user falls from the air onto the mat. Based on this, in this embodiment, three-dimensional data is used to construct a three-dimensional jump state model. The first dimension data is a sequence of pressure signals directly collected by pressure sensors; the second dimension data is a set of data calculated by an intelligent judgment algorithm, i.e., discrete state labels obtained after processing the first dimension data; the third dimension data is a timestamp sequence, providing an absolute time reference for the pressure signals and discrete state labels, and is a decisive factor in determining the duration of the state and the number of effective jumps, thus having a significant weight in the three-dimensional jump state model. Specifically, a three-dimensional jump state model is constructed, using pressure signal as the first dimension, jump motion state as the second dimension, and time as the third dimension. The first, second, and third dimension data are stored in different buffers corresponding to the three-dimensional jump state model. For example, the first dimension data is stored in the X-dimensional buffer of the three-dimensional jump state model, storing the raw pressure time-series signal for subsequent filtering and feature extraction; the second dimension data is stored in the Y-dimensional buffer of the three-dimensional jump state model, used to trigger the counting logic; and the third dimension data is stored in the Z-dimensional buffer of the three-dimensional jump state model, with each timestamp strictly aligned with the X and Y dimension data. In this way, in counting mode, the three-dimensional jump state model can be used to identify the preparation state, airborne state, and landing state of the jump motion based on the pressure signal, and the jump motion can be counted based on these three states.

[0066] In practical implementation, when identifying the preparation state, airborne state, and landing state of a jump motion based on pressure signals using a three-dimensional jump state model, the following methods can be adopted, but are not limited to: Using a three-dimensional jump state model, if the pressure signal continuously exceeds a first pressure threshold for a first duration, the user is determined to be in a ready state. If the pressure signal decreases to below a second pressure threshold within a second duration, the user is determined to be in the air. If the pressure signal exceeds the first pressure threshold again within a third duration, the user is determined to be in the landing state. The first, second, and third durations are set values ​​based on experience; for example, preparation time is typically over 1000ms, so the first duration is 1000ms; the jump height during rope skipping is typically no more than 20cm. Where h is the standard takeoff height and g is the standard gravitational acceleration, the airtime is approximately 202ms, and the second duration is also 202ms. The time on the smart training mat is typically no more than 800ms, and the third duration is 800ms. The first and second pressure thresholds are adaptively adjusted based on the user's vital signs; for example, beginners experience greater impact upon landing, so the first and second pressure thresholds are automatically widened, while experienced users move more lightly, so the first and second pressure thresholds are tightened. Simultaneously, the first and second pressure thresholds are adaptively adjusted based on the user's historical data.

[0067] In practice, when counting jumps based on the ready state, the airborne state, and the landing state, the following methods can be used, but are not limited to: When the ready state, the airborne state, and the landing state are detected consecutively, it is determined as a valid jump movement, and the count result is incremented by 1.

[0068] Step 302: If a switch status signal is received from the thin-film switch sensor located in the plank support area of ​​the smart training mat, the current motion type is determined to be plank support motion and the timing mode is switched. In the timing mode, the start state, support state and end state of the plank support motion are identified based on the switch status signal through the three-dimensional plank support state model, and the plank support motion is timed based on the start state, support state and end state.

[0069] In practical applications, during a plank exercise, each complete cycle of the body and the smart training mat is divided into three states: the initial state when one or both elbows (determined by the user's choice of single-arm or double-arm plank) touch the mat; the supporting state when the body is suspended; and the final state when one or both elbows (determined by the user's choice of single-arm or double-arm plank) leave the mat. Based on this, in this embodiment, three-dimensional data is used to construct a three-dimensional plank state model. The first dimension data is the sequence of switch state signals directly acquired by a thin-film switch sensor; the second dimension data is a set of data calculated by an intelligent judgment algorithm, i.e., discrete state labels obtained after processing the first dimension data; the third dimension data is a timestamp sequence, providing an absolute time reference for the switch state signals and discrete state labels, and is a decisive factor in determining the start / end time and the duration of the state, thus having a significant weight in the three-dimensional plank state model. Specifically, a three-dimensional plank support state model is constructed using the switch state signal as the first dimension of data, the plank support motion state as the second dimension of data, and time as the third dimension of data. The first, second, and third dimension data are stored in different buffers corresponding to the three-dimensional plank support state model. For example, the first dimension data is stored in the X-dimensional buffer of the three-dimensional plank support state model, storing the original switch state timing signal for subsequent filtering and feature extraction; the second dimension data is stored in the Y-dimensional buffer of the three-dimensional plank support state model, used to trigger timing logic; and the third dimension data is stored in the Z-dimensional buffer of the three-dimensional plank support state model, with each timestamp strictly aligned with the X and Y dimension data. In this way, in timing mode, the start, support, and end states of the plank support motion can be identified based on the switch state signal using the three-dimensional plank support state model, and the plank support motion can be timed based on these start, support, and end states.

[0070] In practical implementation, when identifying the start, support, and end states of the plate support movement based on the switch state signals using a three-dimensional plate support state model, the following methods can be adopted, but are not limited to: Using a 3D plank support state model, the system determines the user is in the "starting" state when the switch signal changes from "off" to "on" and remains on for the fourth consecutive time period. It determines the user is in the "supporting" state when the duration of the on-state signal continuously increases, and it determines the user is in the "ending" state when the switch signal changes from "on" to "off". The fourth time period is adaptively adjusted based on the user's physical characteristics; for example, the fourth time period is automatically increased for beginners with longer preparation times and automatically decreased for experienced users with shorter preparation times. Furthermore, the fourth time period is adaptively adjusted based on historical user data.

[0071] In practice, when timing the plank support motion based on the start state, support state, and end state, the following methods can be used, but are not limited to: The timing starts from the determination time of the start state, accumulates the duration of the support state, and stops when the determination time of the end state is reached.

[0072] In this embodiment of the application, in order to further improve the level of intelligence and user experience, during the user's exercise, a pressure distribution map can be generated based on the pressure signal collected by the pressure sensor as a spatiotemporal sequence input to train the action fine classification model. It can not only distinguish the main action mode such as rope skipping / plank support, but also distinguish the sub-action modes such as single-leg jump, alternating double-leg jump, and double-swing in rope skipping, as well as incorrect postures such as hip sinking and waist collapse in plank support, so as to achieve error correction level detection.

[0073] Based on the above embodiments, this application provides a motion data processing system, see below. Figure 4 As shown, the sports data processing system provided in this application embodiment includes the aforementioned smart training mat, user terminal device, and cloud device; The smart training mat communicates with the user terminal device; the smart training mat is used to generate user motion data and transmit it to the user terminal device based on the pressure signal collected by the pressure sensing unit or the switch status signal collected by the membrane switch unit; wherein, the user motion data includes at least the pressure signal and the counting result, or the switch status signal and the timing result; The user terminal device communicates with the cloud device; the user terminal device is used to upload user exercise data to the cloud device; and displays exercise data statistical reports for different time periods issued by the cloud device based on the user's exercise data. Cloud devices are used to generate statistical reports on exercise data for different time periods based on user exercise data and distribute them to user devices.

[0074] In this embodiment, before establishing a communication connection with the user's device, the smart training mat stores the user's exercise data in its local storage area. After establishing a communication connection with the user's device, such as through Bluetooth or WiFi, the locally stored user exercise data is transmitted to the user's device. The user's device receives and displays the user's exercise data transmitted by the smart training mat, and simultaneously establishes a TCP / IP communication connection with the cloud device via WiFi or 4G / 5G networks to upload user exercise data and receive exercise data statistical reports from the cloud device.

[0075] The user's device has an app or a mini-program installed. The app or mini-program has built-in data receiving program, report display program and user interaction interface. It supports users to view real-time exercise data, set training goals (such as 50 jump ropes in a single session, 30 seconds of plank support), select exercise modes (such as personal exercise mode, family competition mode, network competition mode, game mode, etc.), and also supports the visualization of exercise data statistics reports, family competition results and network competition results sent from cloud devices in the form of charts (such as line charts, bar charts, progress bars).

[0076] In practical applications, when users perform jumping exercises such as rope skipping, jumping jacks, and running, the array-type pressure sensors on the smart training mat collect pressure / no-pressure signals in real time. The control module processes the signals through a three-dimensional jumping state model: it filters valid pressure signals with a duration ≥ 50ms, determines the complete cycle from the preparation state (t1 > 1000ms) to the airborne state (t2 < 300ms) and then to the landing state (t3 < 800ms), generates a counting result, and synchronously stores it in the storage module. When users perform plank exercises such as single-arm plank and double-arm plank, the membrane switch sensor collects single / double elbow contact / disengagement signals. When the control module detects that the duration of single / double elbow contact is ≥ 500ms, it determines that it is in the start state and starts timing. When it detects that the duration of continuous contact is continuously increasing, it determines that it is in the support state. When it detects that the duration of single / double elbow disengagement is > 200ms, it determines that it is in the end state and stops timing, generates a timing result, and synchronously stores it in the storage module.

[0077] The smart training mat establishes a communication connection with the user's device via a Bluetooth or WiFi module, transmitting locally stored user motion data such as pressure signal sequences, counting results, switch status signal sequences, and timing results to the user's device. If the device is offline, the storage module caches the data and automatically retransmits it to the user's device via Bluetooth or WiFi once connected to the network.

[0078] User devices upload user activity data to cloud devices via WiFi or 4G / 5G networks; the cloud devices then perform aggregation calculations on the user activity data according to preset time periods such as daily, weekly, monthly, quarterly, and yearly. Daily statistics report: Calculate the cumulative number of jump ropes, total time spent in plank position, average time per jump rope session, and target completion rate (e.g., if the target for the day is 300 jump ropes and 240 jump ropes are actually completed, the target completion rate is 80%). Weekly Statistical Report: Calculates the usage percentage of each exercise mode this week (e.g., rope skipping 60%, plank 30%, yoga 10%), cumulative exercise duration, and average daily exercise intensity; Monthly / Quarterly / Yearly Statistical Report: Year-on-year / month-on-month analysis of newly added exercise data (e.g., the average duration of plank exercises this month increased by 25% compared to last month), cumulative exercise volume and progress trend curve; The cloud-based device generates a sports data statistics report, which is then sent to the user's device via TCP / IP protocol. The app or mini-program on the user's device displays the sports data statistics report in chart form, and allows users to filter and view data for specific periods.

[0079] In one possible implementation, the cloud device is also used to generate a motion strategy suggestion report based on motion data statistical reports for different time periods, and to send the motion strategy suggestion report to the user's terminal device; The user-side device is also used to display exercise strategy suggestion reports sent from the cloud device.

[0080] In practical applications, cloud-based devices conduct in-depth analysis based on users' historical statistical reports and a pre-set database of population exercise standards (such as a recommended duration of ≥60 seconds / session for plank exercises for young people aged 18-30 and a recommended frequency of 3 times / week for jump rope). If it is detected that the user's single plank duration is consistently below the duration threshold (e.g., 40 seconds) and the core training ratio is less than the set ratio (e.g., 20%), then the user is judged to have weak core strength, and a plank exercise strategy suggestion is generated, such as recommending 3 side plank training sessions per week (e.g., 30 seconds per session, 3 sets) and standard plank progressive training (e.g., increasing the duration by 10% per week). If it is detected that the user's jump rope goal completion rate is consistently lower than the completion rate threshold (e.g., 70%), and the maximum number of consecutive jump ropes in a single session is less than or equal to the number threshold (e.g., 50 times), it is determined that the user has insufficient lower limb endurance. Jump rope exercise strategy suggestions are generated, such as recommending two jump rope interval training sessions per week (e.g., 3 sets × 60 times, with 1 minute rest between sets) and leg stretching training. The cloud-based device sends exercise strategy recommendations to the user's device. The app or mini-program on the user's device displays the exercise strategy recommendations in the form of text and step-by-step illustrations, and supports users in marking completed training tasks.

[0081] In this embodiment, through local data collection and preprocessing of the smart training mat, interactive transmission of user terminal devices, and in-depth analysis of cloud devices, a closed-loop management of the entire chain of exercise data is realized. This not only meets the user's need for multi-cycle exercise data statistics, but also provides personalized exercise strategy suggestions, effectively supporting the training needs of users of different ages and exercise levels in the context of national fitness, and improving the scientific nature and sustainability of exercise.

[0082] In one possible implementation, the user terminal device is also used to obtain the exercise type selected by the user and transmit it to the smart training mat in response to the exercise type selection operation. The smart training mat is also used to receive the user's selected exercise type and use the selected exercise type as the current exercise type; when the current exercise type is jumping exercise, it switches to counting mode, in which the jumping exercise is counted based on pressure signals through a three-dimensional jumping state model; when the current exercise type is plank exercise, it switches to timing mode, in which the plank exercise is timed based on switch state signals through a three-dimensional plank state model.

[0083] In this embodiment, users can select a type of exercise as the current exercise type in various exercise modes such as personal exercise mode, family competition mode, network competition mode, and game interaction mode. The smart training mat can adaptively switch between counting mode and timing mode based on the current exercise type, thereby adapting to different groups of people and scenarios and improving the user's exercise experience and training effect.

[0084] In one possible implementation, the cloud device is also used to predict multidimensional motion index values ​​based on pressure signals and counting results, as well as switch status signals and timing results, through a multidimensional motion index evaluation model, and write the multidimensional motion index values ​​into motion data statistical reports for different time periods.

[0085] In this embodiment, the cloud device uses a multi-dimensional exercise index evaluation model to predict multi-dimensional exercise index values ​​(such as jump stability, core strength, endurance level, flexibility, endurance, calorie consumption, rope skipping frequency, running frequency, etc.) based on pressure signals (such as jump pressure peak reflecting jump force, pressure distribution reflecting movement standardization) and counting results, as well as switch status signals (such as switch on / off stability during plank) and timing results. The index values ​​and interpretations are written into a statistical report, which can help users understand their own exercise status and improve their exercise performance.

[0086] In one possible implementation, the cloud device is also used to generate statistical reports on exercise data based on different time periods, match exercise course information, and send the exercise course information to the user's terminal device. The user-side device is also used to display exercise course information sent from the cloud device.

[0087] In this embodiment, the cloud device generates a sports strategy suggestion report based on sports data statistical reports at different time periods (e.g., if jumping stability is insufficient, it is recommended to adjust the landing posture; if the plank support duration is short, it is recommended to strengthen core training). At the same time, it matches suitable courses (e.g., core strength training courses, jumping movement standardization courses) from the course resource library and sends the suggestion report and course information (including course name, duration, difficulty, and playback link) to the APP or mini-program for relevant content display. This allows users to easily click on the course link to learn online, realizing the linkage between user sports data and online course learning, improving user experience and sports effects.

[0088] In one possible implementation, the cloud device is also used to maintain sports equipment product information and to distribute the sports equipment product information to the user's terminal device; The user-end device is also used to render the sports equipment product information sent from the cloud device into a sports equipment mall page for display.

[0089] In this embodiment, the cloud device maintains information on sports equipment products (including name, specifications, price, suitable scenarios, and pictures), and filters product information according to the user's sports type and needs (e.g., recommending dedicated anti-slip mats based on training mat models, and recommending ankle supports based on jumping sports). The information is then sent to the user's app or mini-program to be rendered into a visual mall page, which supports category browsing, searching, and clicking to view details. This makes it convenient for users to purchase sports equipment and realizes the linkage between user sports data and sports equipment consumption.

[0090] In one possible implementation, the user terminal device is also used to respond to the family competition mode configuration operation, obtain and save the user-configured family user identifiers and user competition order; each time it receives user exercise data transmitted by the smart training mat, it associates the user exercise data with the corresponding family user identifier according to the user competition order, uploads the user exercise data corresponding to each family user identifier to the cloud device, and displays the family competition results sent by the cloud device. The cloud-based devices are also used to perform family-wide comparison statistics on the user activity data corresponding to each family user identifier uploaded by the user terminal devices, and to send the family comparison results to the user terminal devices.

[0091] In this embodiment, the family competition mode mainly includes three steps: 1. Mode configuration: Users can initiate a configuration operation on the family competition mode interface of the APP or mini-program, input family member identifiers (such as father, child, mother, etc.), and set the competition order (such as child → father → mother). The configuration information is synchronized to the local storage of the user's terminal device and the backup storage of the cloud device. 2. Data association and upload: Family members exercise using the smart training mat in a preset order. When the APP or mini-program receives a set of user exercise data, it associates the user exercise data with the corresponding family member identifier according to the competition order (such as the first group is bound to "child", the second group is bound to "father", and the third group is bound to "mother"), and uploads it to the cloud device in batches. 3. Generation of family competition results: The cloud device calculates the user exercise data corresponding to the family user identifier according to the same type of exercise rules (such as comparing the count if all are jumping, and comparing the time if all are doing plank exercises), generates the family internal ranking and cumulative data (such as the total family exercise time and average count), and sends it to the user's terminal device; the APP or mini-program on the user's terminal device displays the results in the form of a family leaderboard, and supports saving screenshots for sharing.

[0092] In one possible implementation, the user terminal device is also used to respond to the network competition mode configuration operation, obtain the network conditions configured by the user and send them to the cloud device; when receiving the network success message sent by the cloud device, display the network success message; when receiving the user's exercise data transmitted by the smart training pad, upload the user's exercise data to the cloud device, and display the network competition results sent by the cloud device. The cloud device is also used to create at least one virtual gaming room and associate two or more user-end devices that meet the networking conditions with the same virtual gaming room, and send a networking success message to each user-end device in the same virtual gaming room; when it receives user motion data uploaded by each user-end device in the same virtual gaming room, it performs networking comparison statistics on the user motion data uploaded by each user-end device in the same virtual gaming room, and distributes the networking comparison results to each user-end device in the same virtual gaming room.

[0093] In this embodiment, the network competition mode mainly includes three steps: 1. Network establishment: Users initiate a network request through an APP or mini-program on their user terminal device, enter a group identifier, and invite users to join the network. After receiving the network request, the cloud device creates a dedicated competition group, synchronously records the binding information and user identifiers of each user terminal device, and completes the network configuration. 2. Data collection and transmission: Each user performs jumping or plank exercises on the smart training mat. The smart training mat collects signals through corresponding sensors, processes them through a three-dimensional state model to generate timing / counting results, combines the original signals to form user motion data, and transmits it to the APP or mini-program on their respective user terminal devices; the APP or mini-program displays the user motion data in real time and uploads it to the cloud device. 3. Competitive Statistics and Feedback: Cloud devices perform real-time statistics on user exercise data uploaded by each user terminal device according to preset rules (default sorting by count / time duration, with the option for users to select rules within the APP), generate a network competition ranking (including user ID, corresponding exercise data, and ranking order), and distribute it to the APP or mini-program on each user terminal device; the APP or mini-program highlights the group ranking and individual ranking in the data display area, and can also provide sound alerts (when rankings change).

[0094] In addition, in the network competition mode, the APP or mini-program on the user's terminal device can also display a chat input box in the interaction area, supporting text input and sending emoticons; after the user enters the chat information, the APP or mini-program uploads the chat information (with user identification and sending time) to the cloud device; after receiving the chat information, the cloud device filters out other user terminal devices in the group and sends the chat information to the corresponding user terminal devices; the APP or mini-program on other user terminal devices displays the chat information in the interaction area in chronological order, marking the sender identification, realizing real-time interaction among multiple users, and synchronously sharing exercise status and competition experience.

[0095] In one possible implementation, if the user terminal device is a game running terminal, the user terminal device is also used to generate game control commands based on the user motion data transmitted by the smart training pad, and adjust the game running process based on the game control commands.

[0096] In this embodiment, when the user's device is a game running terminal (such as a mobile phone or smart screen), the APP or mini-program automatically associates with built-in games (such as parkour or adventure games, supporting the download of expanded game resources); after the smart training mat collects user motion data and uploads it to the APP or mini-program, the APP or mini-program converts the user's motion data into game control commands: for example, jump count corresponds to the jumping action of the game character (1 count triggers 1 jump), and plank support timer corresponds to the shield duration of the game character (the longer the timer, the longer the shield duration); the APP or mini-program adjusts the game running process in real time based on the control commands, and the user controls the game character's actions and level progression by adjusting their own movement (such as increasing the jumping frequency and extending the plank support time), realizing deep interaction between exercise and game.

[0097] After introducing the smart training mat and its motion data processing method and system provided in the embodiments of this application, the control module in the smart training mat provided in the embodiments of this application will be briefly introduced next.

[0098] See Figure 5 As shown, the control module 500 provided in this application embodiment includes at least a processor 501, a memory 502, and a computer program stored in the memory 502 and executable on the processor 501. When the processor 501 executes the computer program, it implements the motion data processing method provided in this application embodiment.

[0099] In one possible implementation, processor 501 can be a single processing element or a collective term for multiple processing elements. For example, processor 501 can be a central processing unit (CPU), or one or more integrated circuits configured to implement the motion data processing method described in the embodiments of this application. Specifically, processor 501 can be a general-purpose processor, including but not limited to CPUs, application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc.

[0100] In one possible implementation, memory 502 may include a readable medium in the form of volatile memory, such as random access memory (RAM) 5021 and / or cache memory 5022, and may further include read-only memory (ROM) 5023; memory 502 may also include a program tool 5025 having a set (at least one) of program modules 5024, including but not limited to: operating subsystem, one or more application programs, other program modules, and program data, each or some combination of these examples may include an implementation of a network environment.

[0101] In one possible implementation, the control module 500 provided in this application embodiment may further include a bus 503 connecting different components (including processor 501 and memory 502). The bus 503 represents one or more types of bus structures, including memory bus, peripheral bus, local area bus, etc.

[0102] In one possible implementation, the control module 500 can also communicate with one or more devices that allow a user to interact with it (e.g., mobile phones, computers, etc.), and / or with external devices 504 that enable it to communicate with one or more other devices (e.g., routers, modems, etc.). This communication can be performed via an input / output (I / O) interface 505. Furthermore, the control module 500 can also communicate with one or more networks (e.g., local area networks (LANs), wide area networks (WANs), and / or public networks, such as the Internet) via a network adapter 506. Figure 5As shown, network adapter 506 communicates with other modules of control module 500 via bus 503. It should be understood that, although... Figure 5 As not shown in the diagram, other hardware and / or software modules can be used in conjunction with the control module 500, including but not limited to microcode, device drivers, redundant processors, external disk drive arrays, Redundant Arrays of Independent Disks (RAID) subsystems, tape drives, and data backup storage subsystems.

[0103] It should be noted that, Figure 5 The control module 500 shown is merely an example and should not impose any limitations on the functionality and scope of use of the embodiments of this application.

[0104] Furthermore, embodiments of this application also provide a computer-readable storage medium storing computer instructions that, when executed by a processor, implement the motion data processing method described above in embodiments of this application. Specifically, the computer instructions may be built into or installed in a processor, enabling the processor to implement the motion data processing method described above in embodiments of this application by executing the built-in or installed computer instructions.

[0105] The computer-readable storage medium provided in the embodiments of this application may be, but is not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. Specifically, more specific examples of computer-readable storage media (a non-exhaustive list) include: electrical connections having one or more wires, portable disks, hard disks, RAM, ROM, erasable programmable read-only memory (EPROM), optical fibers, portable compact disc read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0106] It should be noted that although several units or sub-units of the device have been mentioned in the detailed description above, this division is merely exemplary and not mandatory. In fact, according to embodiments of this application, the features and functions of two or more units described above can be embodied in one unit. Conversely, the features and functions of one unit described above can be further divided and embodied by multiple units.

[0107] Furthermore, although the operations of the method of this application are described in a specific order in the accompanying drawings, this does not require or imply that these operations must be performed in that specific order, or that all the operations shown must be performed to achieve the desired result. Additionally or alternatively, certain steps may be omitted, multiple steps may be combined into one step, and / or one step may be broken down into multiple steps.

[0108] Although preferred embodiments of this application have been described, those skilled in the art, upon learning the basic inventive concept, can make other changes and modifications to these embodiments. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments as well as all changes and modifications falling within the scope of this application.

[0109] Obviously, those skilled in the art can make various modifications and variations to the embodiments of this application without departing from the spirit and scope of the embodiments of this application. Therefore, if these modifications and variations to the embodiments of this application fall within the scope of the claims of this application and their equivalents, this application also intends to include these modifications and variations.

Claims

1. A smart training mat, characterized in that, It includes a training pad body, a multimodal sensor module and a control module disposed within the training pad body, and a display module disposed on the training pad body; The training mat is divided into a jumping area and a plank support area. The multimodal sensor module includes a pressure sensing unit disposed in the jump zone and a membrane switch unit disposed in the plate support zone; the pressure sensing unit is used to collect the pressure signal of the jump zone and transmit it to the control module; the membrane switch unit is used to collect the switch status signal of the plate support zone and transmit it to the control module. The control module is electrically connected to the multimodal sensor module and the display module. The control module is used to count the jumping motion based on the pressure signal using a three-dimensional jumping state model and display the counting result through the display module; or, using a three-dimensional plank support state model, timing the plank support motion based on the switch state signal and displaying the timing result through the display module.

2. The smart training mat as described in claim 1, characterized in that, The pressure sensing unit includes multiple pressure sensors, which are arranged in an array within the jump zone.

3. The smart training mat as described in claim 1, characterized in that, The flat plate support area includes two elbow positioning areas; the membrane switch unit includes two membrane switch sensors, which are respectively disposed in the two elbow positioning areas.

4. The smart training mat as described in claim 1, characterized in that, The display module is located on the side of the main body of the training pad.

5. The smart training mat as described in claim 1, characterized in that, The training mat body has a jump zone mark corresponding to the jump zone and a plank support zone mark corresponding to the plank support zone.

6. The smart training mat as described in claim 1, characterized in that, The jumping area and the plank area together form a yoga area; the jumping area, the plank area, and the yoga area together form a multi-functional area.

7. The smart training mat as described in claim 1, characterized in that, The control module is also used to provide a standardized reminder for the jumping motion based on the pressure signal; or, to provide a standardized reminder for the plank support motion based on the switch status signal.

8. The smart training mat as described in any one of claims 1-7, characterized in that, It also includes a storage module and a communication module disposed within the main body of the training pad; The storage module is electrically connected to the control module, and the storage module is used to store the pressure signal, the counting result, the switch status signal, and the timing result written by the control module. The communication module is electrically connected to the control module and the storage module. Under the control of the control module, the communication module is used to upload the pressure signal, the counting result, the switch status signal and the timing result stored in the storage module to the user terminal device.

9. The intelligent training mat as described in claim 8, characterized in that, The multimodal sensor module, the control module, the display module, the storage module, and the communication module are all integrated on a flexible circuit board.

10. A motion data processing method, characterized in that, Applied to the smart training mat as described in any one of claims 1-9, comprising: If a pressure signal is received from a pressure sensor located in the jumping area of ​​the smart training mat, the current movement type is determined to be a jumping movement and the counting mode is switched. In the counting mode, the preparation state, the airborne state and the landing state of the jumping movement are identified based on the pressure signal through a three-dimensional jumping state model, and the jumping movement is counted based on the preparation state, the airborne state and the landing state. If a switch status signal is received from a thin-film switch sensor located in the plank support area of ​​the smart training mat, the current motion type is determined to be plank support motion and the timing mode is switched. In the timing mode, the start state, support state, and end state of the plank support motion are identified based on the switch status signal using a three-dimensional plank support state model, and the plank support motion is timed based on the start state, support state, and end state.

11. The motion data processing method as described in claim 10, characterized in that, Using a three-dimensional jump state model, the preparation state, airborne state, and landing state of the jump motion are identified based on the pressure signal, including: Using a three-dimensional jump state model, if the pressure signal is detected to exceed the first pressure threshold for a continuous first duration, the user is determined to be in a ready state. If the pressure signal is detected to decrease to below the second pressure threshold within a second duration, the user is determined to be in an airborne state. If the pressure signal is detected to exceed the first pressure threshold again within a third duration, the user is determined to be in a landing state.

12. The motion data processing method as described in claim 10, characterized in that, The jump motion is counted based on the preparation state, the airborne state, and the landing state, including: When the preparation state, the airborne state, and the landing state are detected consecutively, it is determined to be a valid jump, and the count result is incremented by 1.

13. The motion data processing method as described in claim 10, characterized in that, Using a three-dimensional plank support state model, the start state, support state, and end state of the plank support motion are identified based on the switch state signal, including: Based on the three-dimensional plank support state model, when the switch state signal changes from an off signal to an on signal and remains on for a fourth consecutive duration, the user is determined to be in the start state. When the duration of the on signal continuously increases, the user is determined to be in the support state. When the switch state signal changes from an on signal to an off signal, the user is determined to be in the end state.

14. The motion data processing method as described in claim 10, characterized in that, Timing the plank support motion based on the start state, the support state, and the end state includes: The timing begins with the determination time of the start state and accumulates the duration of the support state, and stops with the determination time of the end state.

15. The motion data processing method as described in claim 10, characterized in that, Also includes: The three-dimensional jump state model is constructed using pressure signal as the first dimension data, jump motion state as the second dimension data, and time as the third dimension data. The first dimension data, the second dimension data, and the third dimension data are stored in different buffers corresponding to the three-dimensional jump state model, respectively.

16. The motion data processing method as described in claims 10-15, characterized in that, Also includes: The three-dimensional plank support state model is constructed using the switch state signal as the first dimension data, the plank support motion state as the second dimension data, and time as the third dimension data. The first dimension data, the second dimension data, and the third dimension data are stored in different buffers corresponding to the three-dimensional flat plate support state model.

17. A motion data processing system, characterized in that, Includes the smart training mat, user terminal device, and cloud device as described in any one of claims 1-9; The smart training mat is communicatively connected to the user terminal device; the smart training mat is used to generate user motion data and transmit it to the user terminal device based on the pressure signal collected by the pressure sensing unit or the switch status signal collected by the membrane switch unit; wherein, the user motion data includes at least the pressure signal and the counting result, or the switch status signal and the timing result; The user terminal device is communicatively connected to the cloud device; the user terminal device is used to upload the user's motion data to the cloud device; and to display motion data statistical reports for different time periods issued by the cloud device based on the user's motion data. The cloud device is used to generate statistical reports on exercise data for different time periods based on the user's exercise data and send them to the user's terminal device.

18. The motion data processing system as described in claim 17, characterized in that, The cloud device is also used to generate a sports strategy suggestion report based on the sports data statistical reports of different time periods, and to send the sports strategy suggestion report to the user terminal device; The user terminal device is also used to display the exercise strategy suggestion report.

19. The motion data processing system as described in claim 17, characterized in that, The user terminal device is also used to respond to the exercise type selection operation, obtain the exercise type selected by the user, and transmit it to the smart training mat; The smart training mat is also used to, when receiving the exercise type selected by the user, take the exercise type selected by the user as the current exercise type; when the current exercise type is jumping exercise, switch to counting mode, in which the jumping exercise is counted based on the pressure signal using a three-dimensional jumping state model; and when the current exercise type is plank exercise, switch to timing mode, in which the plank exercise is timed based on the switch state signal using a three-dimensional plank state model.

20. The motion data processing system as described in claim 17, characterized in that, The user terminal device is also used to respond to the family competition mode configuration operation, obtain and save the user-configured family user identifiers and user competition order; each time it receives user exercise data transmitted by the smart training mat, it associates the user exercise data with the corresponding family user identifier according to the user competition order, uploads the user exercise data corresponding to each family user identifier to the cloud device, and displays the family competition results sent by the cloud device. The cloud device is also used to perform family comparison statistics on the user movement data corresponding to each family user identifier uploaded by the user terminal device, and to send the family comparison results to the user terminal device.

21. The motion data processing system as described in claim 17, characterized in that, The user terminal device is also used to respond to the network competition mode configuration operation, obtain the user information to be networked specified by the user and send it to the cloud device; when it receives the network success message sent by the cloud device, it displays the network success message; when it receives the user motion data transmitted by the smart training pad, it uploads the user motion data to the cloud device and displays the network competition result sent by the cloud device. The cloud device is also used to create virtual competition rooms, associate the user terminal devices and the user terminal devices corresponding to the information of users to be networked with the virtual competition rooms, and send a network success message to each user terminal device in the virtual competition room; when receiving user motion data uploaded by each user terminal device in the virtual competition room, it performs network comparison statistics on the user motion data uploaded by each user terminal device in the virtual competition room, and distributes the network comparison results to each user terminal device in the virtual competition room.

22. The motion data processing system as described in claim 17, characterized in that, If the user terminal device is a game running terminal, the user terminal device is also used to generate game control commands based on the user motion data transmitted by the smart training mat, and adjust the game running process based on the game control commands.

23. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the motion data processing method as described in any one of claims 10-16.

Images