Control method and device for linkage of wearable equipment and limit scooter and storage medium

Abstract

The invention discloses a control method and device for linkage of a wearable device and a limit scooter and a storage medium, and belongs to the technical field of motion data processing.The method comprises the steps that a motion trigger signal is obtained, and the wearable device is connected with the limit scooter through a near-field communication module to obtain and record motion parameters of the limit scooter; key actions are recognized, the motion parameters are analyzed, the key actions and key action moments of the limit scooter are recognized, and key motion indexes of the limit scooter are obtained; comparing a motion target with a historical record, obtaining the motion target, comparing a proportional relation between the motion parameter or the key motion index and the motion target, and / or comparing a size relation between the motion parameter or the key motion index and the best score of the historical record; according to the control method combining the wearable device and the extreme scooter, the exercise data can be collected, analyzed, fed back and visualized, and the healthy development of extreme exercise is promoted.

Description

Technical Field

[0001] The present invention belongs to the technical field of motion data processing, and in particular relates to a control method, device and storage medium for a wearable device linked to an extreme scooter. Background Art

[0002] With the popularity of extreme sports, extreme scooters (also known as scooters, sliding cars, etc.) have gradually become a popular choice for young people seeking excitement and fun. Extreme scooters not only require superb skills and flexible control capabilities, but also involve sports safety and performance monitoring. Therefore, it is particularly important to develop an intelligent wearable device to enhance the sports experience and ensure safety.

[0003] Traditional extreme scooter motion monitoring methods often rely on manual observation and subjective judgment, which is not only easily affected by environmental factors, but also difficult to achieve accurate data recording. With the help of wearable devices, the athlete's motion parameters (such as speed, acceleration, tilt angle, etc.) can be obtained in real time, and key movements can be identified and analyzed through advanced algorithms. Wearable devices can obtain the athlete's physiological and motion parameters in real time by integrating a variety of sensors (such as accelerometers, gyroscopes, heart rate sensors, etc.). The application of near-field communication modules (NFC, Bluetooth, etc.) enables wearable devices to seamlessly connect with other smart devices (such as extreme scooters) to achieve real-time data exchange and analysis.

[0004] For extreme scooter enthusiasts, it is crucial to set personal sports goals and get timely feedback. Wearable devices can not only record the user's historical sports data, but also compare the current sports status with the established goals. The introduction of this process helps to enhance the user's sports motivation and improve the training effect. In addition, by comparing with historical results, users can clearly understand their progress and shortcomings, providing a reference for subsequent training. Summary of the invention

[0005] In order to solve the technical problems that current extreme scooters lack effective targeted motion analysis means and have a low level of intelligence, the present invention provides a control method, device and storage medium for a wearable device-linked extreme scooter.

[0006] First aspect

[0007] The present invention provides an electrode clamp power indication method, which specifically comprises:

[0008] S1: Obtain a motion trigger signal. The wearable device establishes a connection with the extreme scooter through a near field communication module to obtain and record the motion parameters of the extreme scooter.

[0009] By integrating appropriate sensors (such as accelerometers, gyroscopes, etc.) into wearable devices, the user's motion status can be monitored in real time. In addition, by embedding near-field communication (NFC / Bluetooth, etc.) modules in wearable devices, a connection with the extreme scooter can be established. The wearable device is connected to the extreme scooter with the help of near-field communication technology to achieve real-time acquisition and recording of motion parameters, and further data analysis and feedback, thereby improving the user's sports experience.

[0010] Furthermore, the extreme scooter further includes a motion sensor, and obtaining the motion trigger signal in S1 specifically means that the data obtained by the motion sensor is not zero, or the wearable device receives a start motion signal sent by the mobile terminal or the extreme scooter.

[0011] S2: Identify key actions, analyze the motion parameters, identify key actions and key action moments of the extreme scooter, determine key action periods based on the key action moments, analyze the motion parameters during the key action periods, and obtain key motion indicators of the extreme scooter.

[0012] Identify key actions in extreme scooter sports, such as starting, accelerating, braking, turning, rotating, jumping, etc., establish corresponding standard features (such as acceleration changes, tilt angles, etc.) for each key action for subsequent recognition, and collect a large amount of user motion data in different environments and conditions for training and verification to improve recognition accuracy.

[0013] Use the established motion parameter model to analyze the motion data of the scooter, determine its key action moments (such as the start of acceleration, the highest point, the landing time, the rotation lap speed, etc.), calculate the main motion parameters during the key actions, such as average speed, peak acceleration, deceleration, energy consumption, and heart rate changes, etc., integrate the above analysis results, and generate a key motion indicator report, including but not limited to the following: acceleration ability (such as the time required for 0-30km / h), braking distance, maximum tilt angle, rotation angle and number of laps, jump height and landing stability, etc.

[0014] Furthermore, the extreme scooter further includes an angle sensor and a posture sensor disposed between the handlebar and the front fork of the extreme scooter, wherein by analyzing the motion parameters, identifying the key actions and key action moments of the extreme scooter includes:

[0015] Identify the time period when the Z-axis acceleration of the attitude sensor is less than a preset threshold, and take the moment when the Z-axis acceleration of the attitude sensor is less than the preset threshold as the key action moment. For example, in the air state, the acceleration of the Z axis is usually close to zero, indicating that the gravity effect is temporarily eliminated.

[0016] Wherein, the angle sensor is a magnetic encoder, the attitude sensor is a six-axis gyroscope, and the attitude sensor is arranged on the bottom plate of the extreme scooter.

[0017] Furthermore, identifying key actions also includes:

[0018] Collect the acceleration data of the attitude sensor in all directions (a x , a y , a z ), calculate the acceleration modulus of the attitude sensor:

[0019]

[0020] Calculate the roll angle (Roll) and pitch angle (Pitch):

[0021]

[0022] When the acceleration modulus A is less than Ta, and the roll angle Roll is between 0°-110° or the pitch angle Pitch is between 0°-150°, the action generated during the key action period corresponding to the key action moment is identified as a key action, wherein Ta=1g, a x represents the acceleration of the extreme scooter along the X-axis, a y represents the acceleration of the extreme scooter along the y-axis, a z Represents the acceleration of the extreme scooter along the z-axis.

[0023] The modulus of acceleration (or the magnitude of acceleration) is a scalar value that represents the overall strength of the acceleration vector. It is obtained by calculating the components of the acceleration on the three coordinate axes (x, y, z).

[0024] In some embodiments, in each data collection cycle, if a key action is successfully identified, the time obtained by extending 250 ms before and after the key action moment is used as the key action period T.

[0025] In some embodiments, the length L of the time window T during the critical action is determined by the flight height h[i], and the flight height h[i] is calculated as follows:

[0026]

[0027] L = k × h[i]

[0028] Where v[i] represents the speed at the (i)th moment, v[i-1] represents the speed at the (i-1)th moment, that is, the speed before the current moment speed calculation, and a z,corrected [i-1] and a z,corrected[i-1] represents the corrected vertical acceleration, Δt represents the time interval, i.e., the time difference from moment (i-1) to moment (i), h[i] and h[i-1] represent the heights at moment (i) and moment (i-1) (i.e., the heights before the height at the current moment is calculated), v[i-1] and v[i] represent the velocities at moment (i-1) and moment (i), respectively, k is a calculation coefficient, which is used to convert the duration proportionally according to the flight height h[i], and k is between 75-200ms / m, and T represents the time obtained by extending L before and after the critical action moment as the critical action period T.

[0029] At the beginning of the flight, the instantaneous speed can be considered to be zero. If the time point of the start of the flight is known, the initial speed can be set to 0. However, since there may be no continuous time series in the actual acquisition process, we can use the discrete time integration method (for example, the trapezoidal rule) to effectively calculate the flight height of the extreme scooter by combining the data of the accelerometer, performing appropriate correction and integration processing.

[0030] Furthermore, the motion parameters during the key action are analyzed to obtain the key motion indicators of the extreme scooter, which also include:

[0031] Calculate the cumulative rotation angle α of the angle sensor during the critical action period T and the maximum roll angle (Roll max ) and pitch angle (Pitch max ) as the key sports indicator of extreme scooters.

[0032] S3: Compare the sports target and the historical records, obtain the sports target, compare the proportional relationship between the sports parameter or the key sports index and the sports target, and / or compare the size relationship between the sports parameter or the key sports index and the best result in the historical records.

[0033] Users can set personalized sports goals through wearable devices or mobile terminals, such as acceleration time, maximum speed, jump height, riding distance, energy consumption, etc. It can also automatically recommend appropriate goals based on the user's historical sports data and ability level to ensure that the goals are both challenging and achievable.

[0034] Wearable devices can be used to monitor the user's motion parameters (such as acceleration, speed, heart rate, etc.) and key motion indicators (such as acceleration ability, jumping height, etc.) in real time, associate the real-time monitored data with the user's motion goals, and continuously update the stored historical records.

[0035] The collected motion parameters and key motion indicators are pre-processed, such as noise removal and standardization, for effective comparison. Each motion parameter or key motion indicator is compared with the set motion goal, and the proportional relationship is calculated. For example, if the user's acceleration time is 5 seconds and the target acceleration time is 4 seconds, the ratio is 125%. This means that the user has not reached the goal. In addition, based on the comparison results, real-time feedback is provided to the user. For example, if the user's acceleration time does not reach the target, the system can prompt the user to "try to optimize the starting technique." If the user has achieved significant progress over multiple training cycles, the user can be advised to gradually increase the motion goal to maintain challenge and motivation.

[0036] S4: Abnormal state identification. After identifying the key action, record the sensor data of the wearable device within a predetermined time window after identifying the key action, analyze the sensor data to identify whether a fall or a severe impact occurs, and identify the fall or severe impact as an abnormal state, wherein the sensor data includes at least acceleration sensor data.

[0037] According to the preset motion model and algorithm (such as machine learning or threshold method), the user's key actions, such as sharp turns, jumps, sudden braking, etc., are identified in real time. After the key actions are identified, the abnormal state identification process is immediately started. When a fall or severe collision is confirmed, the system should immediately trigger the alarm mechanism: send a vibration reminder or audio alarm to the user to remind the user to pay attention to safety. If the networking function is realized, send help information and location data to the user's emergency contact or medical service.

[0038] Through the above steps, not only can abnormal conditions such as falls or severe collisions that may occur during the use of extreme scooters be effectively identified and responded to, but also necessary safety protection and post-event analysis can be provided to users, thereby improving user experience and safety.

[0039] S5: Data visualization, displaying the proportional relationship, the size relationship or the abnormal state through the wearable device.

[0040] Specifically, calculate the proportional relationship between different sensor data. For example, the relationship between acceleration and heart rate can help users understand the body's response to different exercise intensities. Analyze and express the size relationship of various data. For example, compare the current acceleration value with the historical average, and use a chart to display data that exceeds the normal range. Collect and mark abnormal events (such as falls, severe collisions), and display them on wearable devices. The visual effect can be enhanced by color coding (for example, red for abnormality and green for normal).

[0041] Through the above steps, you can achieve efficient data visualization of the linkage between wearable devices and extreme scooters. This can not only enhance the user experience, but also provide practical information and feedback to help users better monitor their exercise status and safety.

[0042] Second aspect

[0043] The present invention provides a control device for a wearable device linked to an extreme scooter, comprising:

[0044] An acquisition module is used to establish a connection between the wearable device and the extreme scooter through the near field communication module to acquire and record the motion parameters of the extreme scooter;

[0045] an identification module, configured to analyze the motion parameters, identify key actions and key action moments of the extreme scooter, determine a key action period based on the key action moment, analyze the motion parameters during the key action period, and obtain key motion indicators of the extreme scooter;

[0046] A comparison module, used for obtaining a sports target, comparing the proportional relationship between the sports parameter or the key sports index and the sports target, and / or comparing the size relationship between the sports parameter or the key sports index and the best performance in the historical record;

[0047] an abnormal state identification module, configured to, after identifying a key action, record sensor data of the wearable device within a predetermined time window after identifying the key action, analyze the sensor data to identify whether a fall or a severe impact occurs, and identify the fall or severe impact as an abnormal state, wherein the sensor data at least includes acceleration sensor data;

[0048] A visualization module is used to display the proportional relationship, the size relationship or the abnormal state through the wearable device.

[0049] The third aspect

[0050] The present invention provides a computer-readable storage medium having a computer program stored thereon, characterized in that when the program is executed by a processor, the control method of the wearable device linked to an extreme scooter as described in the first aspect is implemented.

[0051] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0052] In the present invention, by combining the wearable device with the control method of the extreme scooter, it is possible to not only improve the efficiency and fun of exercise, but also effectively ensure the safety of exercise. Through the collection, analysis, feedback and visualization of exercise data, this solution will provide extreme scooter enthusiasts with a more intelligent and personalized sports experience and promote the healthy development of extreme sports. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] The preferred implementation modes will be described below in a clear and understandable manner with reference to the accompanying drawings to further illustrate the above-mentioned characteristics, technical features, advantages and implementation methods of the present invention.

[0054] Figure 1 It is a flow chart of a control method of a wearable device linked to an extreme scooter provided by the present invention.

[0055] Figure 2 It is a structural schematic diagram of a control device for a wearable device linked to an extreme scooter provided by the present invention.

[0056] Figure 3 It is a structural schematic diagram of a wearable device linked extreme scooter provided by the present invention.

[0057] Figure 4 It is a structural schematic diagram of a wearable device linked to a rotating part of a front fork of an extreme scooter provided by the present invention. DETAILED DESCRIPTION

[0058] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings and other implementation methods can be obtained based on these drawings without creative work.

[0059] In order to simplify the drawings, only the parts related to the invention are schematically shown in each figure, and they do not represent the actual structure of the product. In addition, in order to simplify the drawings and facilitate understanding, in some figures, only one of the parts with the same structure or function is schematically drawn or marked. In this article, "one" not only means "only one", but also means "more than one".

[0060] It should be further understood that the term “and / or” used in the present specification and the appended claims refers to any combination and all possible combinations of one or more of the associated listed items, and includes these combinations.

[0061] In this document, it should be noted that, unless otherwise clearly specified and limited, the terms "installed", "connected", and "connected" should be understood in a broad sense. For example, it can be a fixed connection, a detachable connection, or an integral connection. It can be a mechanical connection or an electrical connection. It can be directly connected or indirectly connected through an intermediate medium, and it can be the internal communication of two components. For ordinary technicians in this field, the specific meanings of the above terms in the present invention can be understood according to specific circumstances.

[0062] In addition, in the description of the present invention, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0063] Example 1

[0064] In one embodiment, the reference specification Figure 1 , showing a flow chart of a control method of a wearable device linked to an extreme scooter provided by the present invention.

[0065] S1: Obtain a motion trigger signal. The wearable device establishes a connection with the extreme scooter through a near field communication module to obtain and record the motion parameters of the extreme scooter.

[0066] By integrating suitable sensors (such as accelerometers, gyroscopes, etc.) into wearable devices, the user's motion state can be monitored in real time. In addition, by embedding a near field communication (NFC / Bluetooth, etc.) module in the wearable device, a connection can be established with the extreme scooter. Specifically, when the user uses it for the first time, it is necessary to pair the wearable device and the extreme scooter through a mobile phone application or manual operation. When the user performs a specific action (such as jumping, tilting or turning), the motion data is collected through the sensor, and through a pre-set threshold algorithm, when the motion data exceeds the preset threshold, a motion trigger signal is output. At this time, after the wearable device receives the motion trigger signal, the motion parameters of the extreme scooter (such as speed, acceleration, driving distance, etc.) can be obtained through the near field communication module. In this way, the wearable device can be connected to the extreme scooter through the wearable device, and connected to the extreme scooter with the help of near field communication technology, so as to realize the real-time acquisition and recording of motion parameters, and further perform data analysis and feedback, thereby improving the user's sports experience.

[0067] Furthermore, the extreme scooter further includes a motion sensor, and obtaining the motion trigger signal in S1 specifically means that the data obtained by the motion sensor is not zero, or the wearable device receives a start motion signal sent by the mobile terminal or the extreme scooter.

[0068] S2: Identify key actions, analyze the motion parameters, identify key actions and key action moments of the extreme scooter, determine key action periods based on the key action moments, analyze the motion parameters during the key action periods, and obtain key motion indicators of the extreme scooter.

[0069] Determine the key actions in the movement of extreme scooters, such as starting, accelerating, braking, turning, rotating, jumping, etc., establish corresponding standard features (such as acceleration changes, tilt angles, etc.) for each key action for subsequent recognition, and collect a large amount of user movement data in different environments and conditions for training and verification to improve recognition accuracy. The specific data collected comes from the sensors installed on the extreme scooters, and the collected data is processed in real time, and the noise is removed through the filtering algorithm to extract effective information.

[0070] Use the established motion parameter model to analyze the scooter's motion data, determine its key action moments (such as the start of acceleration, the highest point, the landing moment, the rotation lap speed, etc.), calculate the main motion parameters during the key actions, such as average speed, peak acceleration, deceleration, energy consumption, and heart rate changes, etc., integrate the above analysis results, and generate key motion indicator reports, including but not limited to the following: acceleration ability (such as 0-30km / h required time), braking distance, maximum tilt angle, rotation angle and number of laps, jump height and landing stability. Capture motion parameters through wearable devices, identify the scooter's key actions and moments, and analyze key motion indicators based on this information, so as to help users optimize scooter performance and enhance sports safety.

[0071] Furthermore, the extreme scooter further includes an angle sensor and a posture sensor disposed between the handlebar and the front fork of the extreme scooter, wherein by analyzing the motion parameters, identifying the key actions and key action moments of the extreme scooter includes:

[0072] Identify the time period when the Z-axis acceleration of the attitude sensor is less than a preset threshold, and take the moment when the Z-axis acceleration of the attitude sensor is less than the preset threshold as the key action moment. For example, in the air state, the acceleration of the Z axis is usually close to zero, indicating that the gravity effect is temporarily eliminated.

[0073] Wherein, the angle sensor is a magnetic encoder, the attitude sensor is a six-axis gyroscope, and the attitude sensor is arranged on the bottom plate of the extreme scooter.

[0074] Furthermore, identifying key actions also includes:

[0075] Collect the acceleration data of the attitude sensor in all directions (a x , a y , a z ), calculate the acceleration modulus of the attitude sensor:

[0076]

[0077] Calculate the roll angle (Roll) and pitch angle (Pitch):

[0078]

[0079]

[0080] When the acceleration modulus A is less than Ta, and the roll angle Roll is between 0°-110° or the pitch angle Pitch is between 0°-150°, the action generated during the key action period corresponding to the key action moment is identified as a key action, wherein Ta=1g, a x represents the acceleration of the extreme scooter along the X-axis, a y represents the acceleration of the extreme scooter along the y-axis, a z Represents the acceleration of the extreme scooter along the z-axis.

[0081] The modulus of acceleration (or the magnitude of acceleration) is a scalar value that represents the overall strength of the acceleration vector. It is obtained by calculating the components of the acceleration on the three coordinate axes (x, y, z).

[0082] In the airborne state, the acceleration of the Z axis is generally close to zero, but before entering the airborne state, there may still be the influence of gravity. Therefore, the gravity component needs to be subtracted from the acceleration data. Static calibration or low-pass filtering can be used to estimate the influence of gravity.

[0083] In some embodiments, in each data collection cycle, if a key action is successfully identified, the time obtained by extending 250 ms before and after the key action moment is used as the key action period T.

[0084] In some embodiments, the length L of the time window T during the critical action is determined by the flight height h[i], and the flight height h[i] is calculated as follows:

[0085]

[0086] L = k × h[i]

[0087] Where v[i] represents the speed at the (i)th moment, v[i-1] represents the speed at the (i-1)th moment, that is, the speed before the current moment speed calculation, and a z,corrected [i-1] and a z,corrected[i-1] represents the corrected vertical acceleration, Δt represents the time interval, i.e., the time difference from moment (i-1) to moment (i), h[i] and h[i-1] represent the heights at moment (i) and moment (i-1) (i.e., the heights before the height at the current moment is calculated), v[i-1] and v[i] represent the velocities at moment (i-1) and moment (i), respectively, k is a calculation coefficient, which is used to convert the duration proportionally according to the flight height h[i], and k is between 75-200ms / m, and T represents the time obtained by extending L before and after the critical action moment as the critical action period T.

[0088] At the beginning of the flight, the instantaneous speed can be considered to be zero. If the time point when the flight starts is known, the initial speed can be set to 0. However, since there may be no continuous time series in the actual acquisition process, we can use the discrete time integration method (for example, the trapezoidal rule). By combining the data of the accelerometer, appropriate correction and integration processing, the flight height of the extreme scooter can be effectively calculated. This formula uses the trapezoidal rule to approximate the integration. The height change during this period is estimated by averaging the speeds of the two moments before and after. The trapezoidal rule believes that during this period of time, the height change of an object can be obtained by multiplying the average speed during this period by the time. Although this method is affected by noise and errors, it can provide sufficient accuracy after reasonable design and verification.

[0089] Furthermore, the motion parameters during the key action are analyzed to obtain the key motion indicators of the extreme scooter, which also include:

[0090] Calculate the cumulative rotation angle α of the angle sensor during the critical action period T and the maximum roll angle (Roll max ) and pitch angle (Pitch max ) as the key sports indicator of extreme scooters.

[0091] S3: Compare the sports target and the historical records, obtain the sports target, compare the proportional relationship between the sports parameter or the key sports index and the sports target, and / or compare the size relationship between the sports parameter or the key sports index and the best result in the historical records.

[0092] Users can set personalized sports goals through wearable devices or mobile terminals, such as acceleration time, maximum speed, jump height, riding distance, energy consumption, etc. It can also automatically recommend appropriate goals based on the user's historical sports data and ability level to ensure that the goals are both challenging and achievable.

[0093] Wearable devices can be used to monitor the user's motion parameters (such as acceleration, speed, heart rate, etc.) and key motion indicators (such as acceleration ability, jumping height, etc.) in real time, associate the real-time monitored data with the user's motion goals, and continuously update the stored historical records.

[0094] The collected motion parameters and key motion indicators are pre-processed, such as noise removal and standardization, for effective comparison. Each motion parameter or key motion indicator is compared with the set motion goal, and the proportional relationship is calculated. For example, if the user's acceleration time is 5 seconds and the target acceleration time is 4 seconds, the ratio is 125%. This means that the user has not reached the goal. In addition, based on the comparison results, real-time feedback is provided to the user. For example, if the user's acceleration time does not reach the target, the system can prompt the user to "try to optimize the starting technique." If the user has achieved significant progress over multiple training cycles, the user can be advised to gradually increase the motion goal to maintain challenge and motivation.

[0095] Through the above steps, users can not only clearly understand the relationship between their sports performance and set goals, but also track their progress, which motivates users to constantly challenge themselves and improve the user experience of extreme scooters.

[0096] S4: Abnormal state identification. After identifying the key action, record the sensor data of the wearable device within a predetermined time window after identifying the key action, analyze the sensor data to identify whether a fall or a severe impact occurs, and identify the fall or severe impact as an abnormal state, wherein the sensor data includes at least acceleration sensor data.

[0097] According to the preset motion model and algorithm (such as machine learning or threshold method), the user's key actions, such as sharp turns, jumps, sudden braking, etc., are identified in real time. After the key actions are identified, the abnormal state identification process is immediately started. When a fall or severe collision is confirmed, the system should immediately trigger the alarm mechanism: send a vibration reminder or audio alarm to the user to remind the user to pay attention to safety. If the networking function is realized, send help information and location data to the user's emergency contact or medical service.

[0098] Through the above steps, not only can abnormal conditions such as falls or severe collisions that may occur during the use of extreme scooters be effectively identified and responded to, but also necessary safety protection and post-event analysis can be provided to users, thereby improving user experience and safety.

[0099] S5: Data visualization, displaying the proportional relationship, the size relationship or the abnormal state through the wearable device.

[0100] Specifically, calculate the proportional relationship between different sensor data. For example, the relationship between acceleration and heart rate can help users understand the body's response to different exercise intensities. Analyze and express the size relationship of various data. For example, compare the current acceleration value with the historical average, and use a chart to display data that exceeds the normal range. Collect and mark abnormal events (such as falls, severe collisions), and display them on wearable devices. The visual effect can be enhanced by color coding (for example, red for abnormality and green for normal).

[0101] Through the above steps, you can achieve efficient data visualization of the linkage between wearable devices and extreme scooters. This can not only enhance the user experience, but also provide practical information and feedback to help users better monitor their exercise status and safety.

[0102] Example 2

[0103] In one embodiment, the reference specification Figure 2 , showing a structural schematic diagram of a control device for a wearable device linked to an extreme scooter provided by the present invention.

[0104] The present invention provides a control device for a wearable device linked to an extreme scooter, comprising:

[0105] An acquisition module is used to establish a connection between the wearable device and the extreme scooter through the near field communication module to acquire and record the motion parameters of the extreme scooter;

[0106] an identification module, configured to analyze the motion parameters, identify key actions and key action moments of the extreme scooter, determine a key action period based on the key action moment, analyze the motion parameters during the key action period, and obtain key motion indicators of the extreme scooter;

[0107] A comparison module, used for obtaining a sports target, comparing the proportional relationship between the sports parameter or the key sports index and the sports target, and / or comparing the size relationship between the sports parameter or the key sports index and the best performance in the historical record;

[0108] an abnormal state identification module, configured to, after identifying a key action, record sensor data of the wearable device within a predetermined time window after identifying the key action, analyze the sensor data to identify whether a fall or a severe impact occurs, and identify the fall or severe impact as an abnormal state, wherein the sensor data at least includes acceleration sensor data;

[0109] A visualization module is used to display the proportional relationship, the size relationship or the abnormal state through the wearable device.

[0110] In a possible implementation, the acquisition module 201 is specifically used to: acquire a motion trigger signal, and the wearable device establishes a connection with the extreme scooter through the near field communication module to acquire and record motion parameters of the extreme scooter;

[0111] By integrating suitable sensors (such as accelerometers, gyroscopes, etc.) into wearable devices, the user's motion state can be monitored in real time. In addition, by embedding a near field communication (NFC / Bluetooth, etc.) module in the wearable device, a connection can be established with the extreme scooter. Specifically, when the user uses it for the first time, it is necessary to pair the wearable device and the extreme scooter through a mobile phone application or manual operation. When the user performs a specific action (such as jumping, tilting or turning), the motion data is collected through the sensor, and through a pre-set threshold algorithm, when the motion data exceeds the preset threshold, a motion trigger signal is output. At this time, after the wearable device receives the motion trigger signal, the motion parameters of the extreme scooter (such as speed, acceleration, driving distance, etc.) can be obtained through the near field communication module. In this way, the wearable device can be connected to the extreme scooter through the wearable device, and connected to the extreme scooter with the help of near field communication technology, so as to realize the real-time acquisition and recording of motion parameters, and further perform data analysis and feedback, thereby improving the user's sports experience.

[0112] Furthermore, the extreme scooter further includes a motion sensor, and obtaining the motion trigger signal in S1 specifically means that the data obtained by the motion sensor is not zero, or the wearable device receives a start motion signal sent by the mobile terminal or the extreme scooter.

[0113] The recognition module 202 is specifically used to: identify key actions, analyze the motion parameters, identify the key actions and key action moments of the extreme scooter, determine the key action period based on the key action moment, analyze the motion parameters during the key action period, and obtain the key motion indicators of the extreme scooter; determine the key actions in the movement of the extreme scooter, such as starting, accelerating, braking, turning, rotating, jumping, etc., establish corresponding standard features (such as acceleration changes, tilt angles, etc.) for each key action for subsequent recognition, and collect a large number of user motion data under different environments and conditions, and conduct training and verification to improve the recognition accuracy. The specific collected data comes from the sensors set on the extreme scooter, and the collected data is processed in real time, and the noise is removed by the filtering algorithm to extract effective information.

[0114] Use the established motion parameter model to analyze the scooter's motion data, determine its key action moments (such as the start of acceleration, the highest point, the landing moment, the rotation lap speed, etc.), calculate the main motion parameters during the key actions, such as average speed, peak acceleration, deceleration, energy consumption, and heart rate changes, etc., integrate the above analysis results, and generate key motion indicator reports, including but not limited to the following: acceleration ability (such as 0-30km / h required time), braking distance, maximum tilt angle, rotation angle and number of laps, jump height and landing stability. Capture motion parameters through wearable devices, identify the scooter's key actions and moments, and analyze key motion indicators based on this information, so as to help users optimize scooter performance and enhance sports safety.

[0115] Furthermore, the extreme scooter further includes an angle sensor and a posture sensor disposed between the handlebar and the front fork of the extreme scooter, wherein by analyzing the motion parameters, identifying the key actions and key action moments of the extreme scooter includes:

[0116] Identify the time period when the Z-axis acceleration of the attitude sensor is less than a preset threshold, and take the moment when the Z-axis acceleration of the attitude sensor is less than the preset threshold as the key action moment. For example, in the air state, the acceleration of the Z axis is usually close to zero, indicating that the gravity effect is temporarily eliminated.

[0117] Wherein, the angle sensor is a magnetic encoder, the attitude sensor is a six-axis gyroscope, and the attitude sensor is arranged on the bottom plate of the extreme scooter.

[0118] Furthermore, identifying key actions also includes:

[0119] Collect the acceleration data of the attitude sensor in all directions (a x , a y , a z ), where x, y, and z are three mutually orthogonal directions, and the acceleration modulus of the attitude sensor is calculated:

[0120]

[0121] Calculate the roll angle (Roll) and pitch angle (Pitch):

[0122]

[0123] When the acceleration modulus A is less than Ta, and the roll angle Roll is between 0°-110° or the pitch angle Pitch is between 0°-150°, the action generated during the key action period corresponding to the key action moment is identified as a key action, wherein Ta=1g, a x represents the acceleration of the extreme scooter along the X-axis, a yrepresents the acceleration of the extreme scooter along the y-axis, a z Represents the acceleration of the extreme scooter along the z-axis.

[0124] The modulus of acceleration (or the magnitude of acceleration) is a scalar value that represents the overall strength of the acceleration vector. It is obtained by calculating the components of the acceleration on the three coordinate axes (x, y, z).

[0125] In the airborne state, the acceleration of the Z axis is generally close to zero, but before entering the airborne state, there may still be the influence of gravity. Therefore, the gravity component needs to be subtracted from the acceleration data. Static calibration or low-pass filtering can be used to estimate the influence of gravity.

[0126] In some embodiments, in each data collection cycle, if a key action is successfully identified, the time obtained by extending 250 ms before and after the key action moment is used as the key action period T.

[0127] In some embodiments, the length L of the time window T during the critical action is determined by the flight height h[i], and the flight height h[i] is calculated as follows:

[0128]

[0129] L = k × h[i]

[0130] Where v[i] represents the speed at the (i)th moment, v[i-1] represents the speed at the (i-1)th moment, that is, the speed before the current moment speed calculation, and a z,corrected [i-1] and a z,corrected [i-1] represents the corrected vertical acceleration, Δt represents the time interval, that is, the time difference from moment (i-1) to moment (i), h[i] and h[i-1] represent the heights at moment (i) and moment (i-1) (that is, the heights before the current moment height calculation), v[i-1] and v[i] represent the velocities at moment (i-1) and moment (i), respectively, k is a calculation coefficient, which is used to convert the duration proportionally according to the flight height h[i], and k is between 75-200ms / m, and T represents the time obtained by extending L before and after the critical action moment as the critical action period T.

[0131] At the beginning of the flight, the instantaneous speed can be considered to be zero. If the time point when the flight starts is known, the initial speed can be set to 0. However, since there may be no continuous time series in the actual acquisition process, we can use the discrete time integration method (for example, the trapezoidal rule). By combining the data of the accelerometer, appropriate correction and integration processing, the flight height of the extreme scooter can be effectively calculated. This formula uses the trapezoidal rule to approximate the integration. The height change during this period is estimated by averaging the speeds of the two moments before and after. The trapezoidal rule believes that during this period of time, the height change of an object can be obtained by multiplying the average speed during this period by the time. Although this method is affected by noise and errors, it can provide sufficient accuracy after reasonable design and verification.

[0132] Furthermore, the motion parameters during the key action are analyzed to obtain the key motion indicators of the extreme scooter, which also include:

[0133] Calculate the cumulative rotation angle α of the angle sensor during the critical action period T and the maximum roll angle (Roll max ) and pitch angle (Pitch max ) as the key sports indicator of extreme scooters.

[0134] The comparison module 203 is specifically used to: compare the sports target and the historical records, obtain the sports target, compare the proportional relationship between the sports parameter or the key sports index and the sports target, and or compare the size relationship between the sports parameter or the key sports index and the best performance in the historical records;

[0135] Users can set personalized sports goals through wearable devices or mobile terminals, such as acceleration time, maximum speed, jump height, riding distance, energy consumption, etc. It can also automatically recommend appropriate goals based on the user's historical sports data and ability level to ensure that the goals are both challenging and achievable.

[0136] Wearable devices can be used to monitor the user's motion parameters (such as acceleration, speed, heart rate, etc.) and key motion indicators (such as acceleration ability, jumping height, etc.) in real time, associate the real-time monitored data with the user's motion goals, and continuously update the stored historical records.

[0137] The collected motion parameters and key motion indicators are pre-processed, such as noise removal and standardization, for effective comparison. Each motion parameter or key motion indicator is compared with the set motion goal, and the proportional relationship is calculated. For example, if the user's acceleration time is 5 seconds and the target acceleration time is 4 seconds, the ratio is 125%. This means that the user has not reached the goal. In addition, based on the comparison results, real-time feedback is provided to the user. For example, if the user's acceleration time does not reach the target, the system can prompt the user to "try to optimize the starting technique." If the user has achieved significant progress over multiple training cycles, the user can be advised to gradually increase the motion goal to maintain challenge and motivation.

[0138] Through the above steps, users can not only clearly understand the relationship between their sports performance and set goals, but also track their progress, which motivates users to constantly challenge themselves and improve the user experience of extreme scooters.

[0139] The abnormal state identification module 205 is specifically used to: after identifying the key action, record the sensor data of the wearable device within a predetermined time window after identifying the key action, analyze the sensor data to identify whether a fall or severe impact occurs, and identify the fall or severe impact as an abnormal state, wherein the sensor data at least includes acceleration sensor data.

[0140] According to the preset motion model and algorithm (such as machine learning or threshold method), the user's key actions, such as sharp turns, jumps, sudden braking, etc., are identified in real time. After the key actions are identified, the abnormal state identification process is immediately started. When a fall or severe collision is confirmed, the system should immediately trigger the alarm mechanism: send a vibration reminder or audio alarm to the user to remind the user to pay attention to safety. If the networking function is realized, send help information and location data to the user's emergency contact or medical service.

[0141] Through the above steps, not only can abnormal conditions such as falls or severe collisions that may occur during the use of extreme scooters be effectively identified and responded to, but also necessary safety protection and post-event analysis can be provided to users, thereby improving user experience and safety.

[0142] The visualization module 206 is specifically used to display the proportional relationship, the size relationship or the abnormal state through the wearable device.

[0143] Specifically, calculate the proportional relationship between different sensor data. For example, the relationship between acceleration and heart rate can help users understand the body's response to different exercise intensities. Analyze and express the size relationship of various data. For example, compare the current acceleration value with the historical average, and use a chart to display data that exceeds the normal range. Collect and mark abnormal events (such as falls, severe collisions), and display them on wearable devices. The visual effect can be enhanced by color coding (for example, red for abnormality and green for normal).

[0144] Example 3

[0145] The present invention provides a computer-readable storage medium on which a computer program is stored, characterized in that when the program is executed by a processor, the control method of the wearable device linked to an extreme scooter described in any one of Example 1 is implemented.

[0146] A computer-readable storage medium provided by the present invention can implement the steps and effects of the control method of the wearable device linked to the extreme scooter in the above-mentioned embodiment 1. To avoid repetition, the present invention will not go into details.

[0147] Compared with the prior art, the present invention has at least the following beneficial technical effects:

[0148] In the present invention, by combining the wearable device with the control method of the extreme scooter, it is possible to not only improve the efficiency and fun of exercise, but also effectively ensure the safety of exercise. Through the collection, analysis, feedback and visualization of exercise data, this solution will provide extreme scooter enthusiasts with a more intelligent and personalized sports experience and promote the healthy development of extreme sports.

[0149] Extreme scooters are relatively risky, especially when performing stunts, where the probability of falling and collision increases. Wearable devices collect data from athletes after key actions through built-in accelerometers and other sensors to identify abnormal conditions (such as falls or severe collisions). The real-time abnormal state recognition mechanism can quickly alert users and even trigger an emergency rescue function, greatly improving sports safety.

[0150] Ultimately, presenting exercise data to users in an intuitive way can effectively improve the use value of the device. Wearable devices display exercise parameters, goal achievement, historical record comparison, abnormal status prompts and other information through display screens or mobile phone applications, helping users better understand their own exercise status, adjust training strategies, and motivate their sports enthusiasm.

[0151] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0152] The above embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for those of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A control method for a wearable device linked to an extreme scooter, characterized in that: include: S1: Obtaining a motion trigger signal, the wearable device establishes a connection with the extreme scooter through a near field communication module to obtain and record the motion parameters of the extreme scooter; S2: identifying key actions, analyzing the motion parameters, identifying key actions and key action moments of the extreme scooter, determining a key action period based on the key action moment, analyzing the motion parameters during the key action period, and obtaining key motion indicators of the extreme scooter; S3: Compare the sports target and the historical records, obtain the sports target, compare the proportional relationship between the sports parameter or the key sports index and the sports target, and / or compare the size relationship between the sports parameter or the key sports index and the best performance in the historical records; S4: abnormal state identification, after identifying the key action, recording the sensor data of the wearable device within a predetermined time window after identifying the key action, analyzing the sensor data to identify whether a fall or a severe impact occurs, and identifying the fall or severe impact as an abnormal state, wherein the sensor data at least includes acceleration sensor data; S5: Data visualization, displaying the proportional relationship, the size relationship or the abnormal state through the wearable device.

2. The control method of the wearable device linked extreme scooter according to claim 1, characterized in that: The extreme scooter also includes a motion sensor, and obtaining the motion trigger signal specifically means that the data obtained by the motion sensor is not zero, or the wearable device receives a start motion signal sent by the mobile terminal or the extreme scooter.

3. The control method of the wearable device linked extreme scooter according to claim 1, characterized in that: The extreme scooter further includes an angle sensor and a posture sensor disposed between the handlebar and the front fork of the extreme scooter, wherein the angle sensor is a magnetic encoder, the posture sensor is a six-axis gyroscope, and the posture sensor is disposed on the bottom plate of the extreme scooter; wherein, by analyzing the motion parameters, identifying the key actions and key action moments of the extreme scooter includes: A time period during which the Z-axis acceleration of the posture sensor is less than a preset threshold is identified, and a moment when the Z-axis acceleration of the posture sensor is less than the preset threshold is taken as a key action moment.

4. The control method of the wearable device linked extreme scooter according to claim 3, characterized in that: Identifying key actions also includes: Collect the acceleration data of the attitude sensor in all directions (a x , a y , a z ), calculate the acceleration modulus of the attitude sensor: Calculate the roll angle Roll and pitch angle Pitch: When the acceleration modulus A is less than Ta, and the roll angle Roll is between 0°-110° or the pitch angle Pitch is between 0°-150°, the action generated during the key action period corresponding to the key action moment is identified as a key action, wherein Ta=1g, a x represents the acceleration of the extreme scooter along the X-axis, a y represents the acceleration of the extreme scooter along the y-axis, a z Represents the acceleration of the extreme scooter along the z-axis.

5. The control method of the wearable device linked extreme scooter according to claim 4, characterized in that: In each data collection cycle, if a key action is successfully identified, the time obtained by extending 250ms before and after the key action moment is taken as the key action period T.

6. The control method of the wearable device linked extreme scooter according to claim 4, characterized in that: The length L of the time window T during the critical action is determined by the flight height h[i], and the flight height h[i] is calculated as follows: L = k × h[i] Where v[i] represents the speed at the (i)th moment, v[i-1] represents the speed at the (i-1)th moment, that is, the speed before the current moment speed calculation, and a z,corrected [i-1] and a z,corrected [i-1] represents the corrected vertical acceleration, Δt represents the time interval, i.e., the time difference from moment (i-1) to moment (i), h[i] and h[i-1] represent the heights at moment (i) and moment (i-1) (i.e., the heights before the height at the current moment is calculated), v[i-1] and v[i] represent the velocities at moment (i-1) and moment (i), respectively, k is a calculation coefficient, which is used to convert the duration proportionally according to the flight height h[i], and k is between 75-200ms / m, and T represents the time obtained by extending L before and after the critical action moment as the critical action period T.

7. The control method of the wearable device linked extreme scooter according to claim 1, characterized in that: Analyzing the sensor data to identify whether a fall or a severe impact occurs, and identifying the fall or the severe impact as an abnormal state includes: Call the fall recognition interface on the wearable device to obtain fall recognition judgment information.

8. The control method of the wearable device linked extreme scooter according to claim 5 or 6, characterized in that: The motion parameters during the key action are analyzed to obtain the key motion indicators of the extreme scooter, which also include: Calculate the cumulative rotation angle α of the angle sensor during the critical action period T and the maximum roll angle (Roll l max ) and pitch angle (Pitch max ) as the key sports indicator of extreme scooters.

9. A control device for a wearable device linked to an extreme scooter, characterized in that: include: An acquisition module is used to establish a connection between the wearable device and the extreme scooter through the near field communication module to acquire and record the motion parameters of the extreme scooter; an identification module, configured to analyze the motion parameters, identify key actions and key action moments of the extreme scooter, determine a key action period based on the key action moment, analyze the motion parameters during the key action period, and obtain key motion indicators of the extreme scooter; A comparison module, used for obtaining a sports target, comparing the proportional relationship between the sports parameter or the key sports index and the sports target, and / or comparing the size relationship between the sports parameter or the key sports index and the best performance in the historical record; an abnormal state identification module, configured to, after identifying a key action, record sensor data of the wearable device within a predetermined time window after identifying the key action, analyze the sensor data to identify whether a fall or a severe impact occurs, and identify the fall or severe impact as an abnormal state, wherein the sensor data at least includes acceleration sensor data; A visualization module is used to display the proportional relationship, the size relationship or the abnormal state through the wearable device.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the program is executed by a processor, a control method for a wearable device linked to an extreme scooter as described in any one of claims 1 to 8 is implemented.

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