Method and system for optimizing positioning precision of smart watch

Abstract

The invention discloses a smart watch positioning precision optimization method and system, relates to the field of smart watch positioning precision optimization, is used for carrying out position calculation in an underground environment lacking an external positioning reference, and comprises the following steps: identifying a specific physical interaction action executed by a user through a smart watch; after the specific physical interaction action is recognized, static state information is obtained; the static state information is sensing information of the smart watch in a static state before the specific physical interaction action occurs; executing zero-speed updating by using the static state information, and correcting an accumulated position error; the zero-speed updating is used for calibrating the gyroscope zero-point deviation of the smart watch.

Description

Technical Field

[0001] This invention relates to the field of smartwatch positioning accuracy optimization, and more particularly to a method and system for optimizing smartwatch positioning accuracy. Background Technology

[0002] In underground environments lacking satellite positioning signals, smartwatches often rely on built-in inertial navigation sensors for location estimation. However, in special working scenarios such as tunnels, the performance of inertial sensors will continue to degrade due to physical damage caused by long-term high-intensity work, the wearer's complex and irregular movement postures, and the increased internal temperature of the device. This results in a significant decrease in the accuracy of location calculation, making it difficult to meet the requirements for centimeter-level precise marking.

[0003] For example, inspectors need to perform high-intensity work inside tunnels for extended periods, including hammering inspections of tunnel structures, using power tools for localized repairs, and moving heavy equipment components. These continuous, high-frequency mechanical shocks and vibrations can cause cumulative micro-damage to the delicate physical structure of the microelectromechanical system (MEMS) accelerometer inside a smartwatch. This damage doesn't cause immediate sensor failure, but rather, at the microscopic level, such as at the sensor's suspension springs or the connection points of sensitive elements, it leads to material fatigue or micro-cracks. This cumulative effect significantly increases the accelerometer's inherent zero-point bias and noise level. This increased zero-point bias and noise level directly affect the accelerometer's raw readings when acquiring data. These readings become contaminated with more unpredictable error signals, especially during gravity component stripping processes, where these error signals are misinterpreted as part of the actual motion and are difficult to filter out effectively.

[0004] Because the gravity component is not completely removed and additional errors are introduced, the subsequent dual-integration module for position calculation treats these biased acceleration data as actual motion accelerations and accumulates them. In inertial navigation, acceleration needs to be integrated twice to obtain position information. Even small, seemingly insignificant acceleration errors are rapidly amplified after two integrations, causing systematic drift in position estimation. This cumulative nature of error is an inherent challenge of inertial navigation, and the degradation of sensor performance further exacerbates this problem.

[0005] Ultimately, in the tunnel environment, where there is a lack of external reference signals for correction, the combined effects of multiple factors—including zero bias and noise accumulation caused by physical damage to the sensors, difficulties in data analysis due to the constantly changing sensor axis caused by complex motion patterns, and further deterioration of sensor performance due to high temperatures—result in a persistent, centimeter-level cumulative deviation between the location information calculated by the smartwatch and the inspector's actual location. This centimeter-level deviation severely affects the inspector's ability to accurately mark and report specific fault points, equipment numbers, or safety hazards within the tunnel. For example, it may prevent the accurate recording of the specific coordinates of a crack or the precise navigation to a valve requiring urgent repair, potentially delaying fault handling and even creating safety hazards. Summary of the Invention

[0006] This invention provides a method for optimizing the positioning accuracy of smartwatches, used for position calculation in underground environments where external positioning references are lacking.

[0007] In a first aspect, in order to solve the above-mentioned technical problems, the present invention provides a method for optimizing the positioning accuracy of a smartwatch, comprising: identifying specific physical interaction actions performed by the user through the smartwatch;

[0008] After recognizing a specific physical interaction action, static state information is obtained; static state information is the perception information of the smartwatch when it was in a static state before the specific physical interaction action occurred.

[0009] Using static state information, a zero-speed update is performed to correct accumulated position errors; the zero-speed update is used to calibrate the gyroscope zero-point deviation of the smartwatch.

[0010] Optionally, a specific physical interaction action can be a tapping action.

[0011] Optionally, the smartwatch can be used to identify specific physical interaction actions performed by the user, including:

[0012] The smartwatch's acceleration is identified using its inertial sensor.

[0013] If the acceleration of the first axis of the smartwatch is greater than or equal to the acceleration threshold within a preset time period, and the acceleration of the second and third axes is less than the acceleration threshold within a preset time period, the action performed by the user is determined to be a specific physical interaction action; the first axis can be any axis.

[0014] Optionally, obtain static state information, including:

[0015] Determine the inactive period of the smartwatch; the inactive period is located before the time period corresponding to a specific physical interaction action;

[0016] The output data of the gyroscope during the static period is determined as static state information.

[0017] Optionally, determine the inactive periods of the smartwatch, including:

[0018] Determine the variance of the accelerometer and the amplitude of the angular velocity of the gyroscope in the smartwatch;

[0019] If the variance of the accelerometer is less than the variance threshold and the angular velocity amplitude of the gyroscope is less than the angular velocity amplitude threshold, then the smartwatch is determined to be in a stationary state, and the time period during which the smartwatch is in a stationary state is determined as the stationary time period.

[0020] Optionally, perform a zero-rate update, including:

[0021] Determine the gyroscope output data during the stationary period of the smartwatch;

[0022] The average value of the gyroscope's output data during the stationary period of the smartwatch is determined as the zero-point deviation of the gyroscope.

[0023] Zero-speed updates are performed based on zero-point deviation.

[0024] Optionally, perform zero-rate updates based on zero-point deviation, including:

[0025] Determine the difference between the measured angular velocity of the gyroscope and the zero-point deviation;

[0026] The difference between the measured angular velocity of the gyroscope and the zero-point deviation is determined as the true measured angular velocity of the gyroscope.

[0027] Optionally, correcting accumulated position errors includes:

[0028] Accumulated position errors are corrected using stationary state information and the Kalman filter algorithm.

[0029] Optionally, the accumulated position error can be corrected using stationary state information and a Kalman filter algorithm, including:

[0030] The accumulated position error is corrected according to the position update formula;

[0031] The position update formula is:

[0032] x_new = x_old + K*z;

[0033] Where x_new is the corrected position of the smartwatch, x_old is the original position of the smartwatch, K represents the Kalman gain, and the filter measurement residual z = v_t - v_true; v_t is the estimated velocity at the current moment, and v_true is the actual velocity at the current moment.

[0034] Secondly, the present invention provides a smartwatch positioning accuracy optimization system, the system comprising:

[0035] The recognition module is used to identify specific physical interaction actions performed by the user through the smartwatch;

[0036] The judgment module is used to obtain static state information after recognizing a specific physical interaction action; the static state information is the perception information of the smartwatch being in a static state before the specific physical interaction action occurs;

[0037] The update module is used to perform zero-speed updates using static state information and correct accumulated position errors; zero-speed updates are used to calibrate the gyroscope zero-point deviation of the smartwatch.

[0038] Compared with the prior art, the present invention has the following beneficial effects:

[0039] The smartwatch positioning accuracy optimization method disclosed in this application identifies specific physical interaction actions performed by the user. After identifying the specific physical interaction action, it acquires the perceived information of the smartwatch in a stationary state before the specific physical interaction action as stationary state information. Subsequently, it uses this stationary state information to perform zero-speed update to calibrate the gyroscope zero-point deviation of the smartwatch and correct accumulated position errors. This method effectively solves the problem in the prior art where smartwatches in underground environments lacking satellite positioning signals suffer from inertial sensor performance degradation due to long-term high-intensity operation, complex motion postures, and increased internal temperature, leading to a significant decrease in position calculation accuracy and making it difficult to meet the requirements for centimeter-level accurate marking. By utilizing the stationary state before the user's specific physical interaction action, a reliable calibration benchmark is provided for zero-speed update, significantly reducing gyroscope zero-point drift and accumulated position errors, thereby greatly improving the positioning accuracy of the smartwatch. This enables it to meet the requirements for centimeter-level accurate marking in special operation scenarios such as tunnel inspection, effectively avoiding delays in fault handling or safety hazards caused by inaccurate positioning. Attached Figure Description

[0040] Figure 1 This is a schematic flowchart of a method for optimizing the positioning accuracy of a smartwatch provided in an embodiment of the present invention;

[0041] Figure 2 This is a schematic diagram of another method for optimizing the positioning accuracy of a smartwatch provided in an embodiment of the present invention;

[0042] Figure 3 This is a schematic diagram of a smartwatch positioning accuracy optimization system provided in an embodiment of the present invention. Detailed Implementation

[0043] The technical solutions of this application will now be clearly and completely described with reference to the accompanying drawings. Obviously, the described embodiments are merely some embodiments of this application, and not all embodiments. The components of this application described and shown in the accompanying drawings can generally be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of this application provided in the accompanying drawings is not intended to limit the scope of the claimed application, but merely to illustrate selected embodiments of this application. All other embodiments obtained by those skilled in the art based on the embodiments of this application without inventive effort are within the scope of protection of this application.

[0044] It should be noted that similar reference numerals and letters in the following figures indicate similar items; therefore, once an item is defined in one figure, it does not need to be further defined and explained in subsequent figures. Furthermore, in the description of this application, terms such as "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0045] In order to better understand the technical solution proposed in this application, it is necessary to explain some of the key terms involved.

[0046] A smartwatch is a wearable device that integrates multiple sensors (such as inertial sensors and gyroscopes) and has computing, communication, and positioning functions. Inertial sensors typically include accelerometers and gyroscopes, used to sense the device's motion state, such as acceleration and angular velocity.

[0047] Zero-velocity update (ZUPT) is a calibration technique in inertial navigation. Its basic principle is to correct the errors of inertial sensors, especially the zero-point deviation of gyroscopes and the bias of accelerometers, by using the condition that the velocity is known to be zero when the device is stationary.

[0048] Stationary state information refers to sensor data collected by a smartwatch when it is stationary before a specific physical interaction occurs. This data reflects the device's baseline state when there is no motion interference.

[0049] Existing smartwatches often rely on built-in inertial navigation sensors for location estimation in underground environments lacking satellite positioning signals. However, in special working scenarios such as tunnels, the performance of inertial sensors continuously deteriorates due to physical damage caused by prolonged high-intensity work, the wearer's complex and irregular movements, and increased internal temperatures. This leads to a significant decrease in location calculation accuracy, making it difficult to meet the requirements for centimeter-level precise marking. If these problems are not addressed, it will severely impact inspectors' ability to accurately mark and report specific fault points, equipment numbers, or safety hazards within tunnels. For example, they may be unable to accurately record the specific coordinates of a crack or precisely navigate to a valve requiring urgent repair, potentially delaying fault handling and even creating safety hazards.

[0050] Therefore, the following specific embodiments will be used to provide a detailed introduction and explanation of the smartwatch positioning accuracy optimization method provided in this application.

[0051] Reference Figure 1 This invention provides a method for optimizing the positioning accuracy of a smartwatch, comprising the following steps:

[0052] S1 uses a smartwatch to recognize specific physical interaction actions performed by the user.

[0053] Among these, the specific physical interaction action is the tapping action. A tapping action can be understood as a user briefly touching the smartwatch itself or the limb on which it is worn with their fingers, palm, or other objects, or briefly making contact between the smartwatch and a surface. This action usually produces a momentary and significant impact or vibration, which can be clearly captured by the inertial sensors inside the smartwatch.

[0054] One possible approach is to use the smartwatch's inertial sensor to identify the smartwatch's acceleration; if the smartwatch's first axial acceleration is greater than or equal to an acceleration threshold within a preset time period, and the second and third axial accelerations are less than the acceleration thresholds within the preset time period, then the user's action can be determined as a specific physical interaction action.

[0055] Understandably, when a user performs a specific physical interaction action, such as a tapping motion, a significant acceleration change typically occurs along one main axis, while the acceleration changes along other axes are relatively small. The solution presented in this application utilizes the smartwatch's built-in inertial sensor to monitor the smartwatch's acceleration changes along various axes in real time. When a user performs a tapping motion, this action generates a significant instantaneous acceleration peak along one main axis of the smartwatch, while the acceleration changes along the other two axes are relatively small or at background noise levels. By setting reasonable preset time periods and acceleration thresholds, the system can accurately capture this specific tapping motion and effectively distinguish it from specific interactive actions and random or non-specific movements generated during daily wear.

[0056] It should be noted that the first axis can be any axis. For example, it can be the x-axis, y-axis, or z-axis. The second and third axes are axes different from the first axis.

[0057] For example, the preset time period can be 50 milliseconds, and the acceleration threshold can be 10 times the gravitational acceleration.

[0058] The acceleration threshold and preset time period are calibrated and adjusted based on experimental data of actual inspectors' tapping actions to distinguish between ordinary walking or hand tremors.

[0059] In some embodiments, a specific physical interaction action may be a circling gesture in the air.

[0060] For example, if the angular velocities of the gyroscope's X and Y axes exhibit periodic changes that approximate a sine wave, with peak amplitudes ranging from 50 to 200 degrees per second and durations from 0.8 to 1.2 seconds, it can be determined that the user's action is a specific physical interaction (a circling gesture in the air).

[0061] S2. After recognizing a specific physical interaction action, obtain static state information.

[0062] Among them, the static state information is the perception information of the smartwatch when it is in a static state before a specific physical interaction occurs.

[0063] One possible approach is to determine the static time period of the smartwatch and identify the gyroscope output data during that static time period as static state information.

[0064] The static time period is located before the time period corresponding to a specific physical interaction action. For example, it could be 0.5 seconds before the specific physical interaction action is detected.

[0065] S3. Utilize the stationary state information to perform zero-speed update and correct the accumulated position error.

[0066] Zero-speed updates are used to calibrate the gyroscope zero-point deviation of smartwatches.

[0067] One possible implementation is to determine the gyroscope's output data during a static period of the smartwatch; to determine the average value of the gyroscope's output data during the static period of the smartwatch as the gyroscope's zero-point deviation; and to perform zero-speed updates based on the zero-point deviation.

[0068] For example, the output data of a gyroscope can be used to measure angular velocity.

[0069] For example, during subsequent motion, the gyroscope's output data will be subtracted from this zero-point deviation to obtain angular velocity data that is closer to the true value, effectively calibrating the gyroscope's zero-point deviation.

[0070] Correcting accumulated position errors can include using stationary state information and Kalman filtering algorithms to correct accumulated position errors.

[0071] For example, accumulated position errors can be corrected based on position update formulas;

[0072] The position update formula is:

[0073] x_new = x_old + K*z

[0074] Where x_new is the corrected position of the smartwatch, x_old is the original position of the smartwatch, K represents the Kalman gain, and the filter measurement residual z = v_t - v_true; v_t is the estimated velocity at the current moment, and v_true is the actual velocity at the current moment.

[0075] It should be noted that during the zero-speed update period, we can assume the true speed v_true = [0, 0, 0]. The speed estimate at the current moment can be the speed value output by the smartwatch.

[0076] In some preferred embodiments, a specific example is given below. Assume a smartwatch is placed on a table by the user. The system determines that the smartwatch is stationary based on the outputs of the accelerometer and gyroscope, and defines a stationary period. During this stationary period, for example, 5 seconds, the smartwatch continuously collects the three-axis angular velocity output data from the gyroscope. The system then calculates the average of all collected gyroscope output data within these 5 seconds, and determines this average as the current zero-point deviation of the gyroscope. For example, if the average output of the gyroscope at rest is 0.05 degrees / second on the X-axis, -0.03 degrees / second on the Y-axis, and 0.02 degrees / second on the Z-axis, these values ​​are determined as the zero-point deviation. Subsequent real-time gyroscope measurements will be subtracted from these zero-point deviations to obtain a more accurate true angular velocity.

[0077] The smartwatch positioning accuracy optimization method proposed in this application aims to solve the problem of decreased positioning accuracy in traditional inertial navigation under complex environments due to sensor performance degradation and error accumulation. Its core innovation lies in introducing specific physical interaction actions performed by the user as a trigger mechanism, enabling the smartwatch to actively identify and utilize its stationary state information to perform zero-speed updates and position error corrections to the inertial navigation system.

[0078] This application addresses the aforementioned problems through the following methods: First, the smartwatch can recognize specific physical interaction actions performed by the user. For example, when an inspector habitually taps the smartwatch screen after completing an inspection, this action is recognized by the smartwatch as a signal to trigger calibration. Second, after recognizing this specific physical interaction action, the system acquires sensory information about the smartwatch's stationary state before the action occurred. For example, before the tapping action, the inspector might briefly place the smartwatch on a flat surface, at which point the smartwatch is stationary, and its inertial sensor data can be accurately collected. Finally, using this stationary state information, the system performs zero-speed updates, calibrates the gyroscope's zero-point deviation, and corrects accumulated position errors. By using the average gyroscope output in a stationary state as compensation for the zero-point deviation, the systematic errors of the gyroscope can be effectively eliminated. Simultaneously, by utilizing the condition that the true velocity is zero in a stationary state, combined with algorithms such as Kalman filtering, the velocity and position estimated by the inertial navigation system are corrected, thereby significantly reducing accumulated errors.

[0079] Compared to existing technologies, the advantages of this application lie in its proactive and intelligent nature. Traditional inertial navigation systems typically rely on external reference signals (such as GPS) or preset static time periods for calibration, but these conditions are often difficult to meet in underground environments or irregular motion patterns. This application cleverly utilizes the implicit static state in user behavior by recognizing specific physical interaction actions, providing a reliable calibration opportunity for the inertial navigation system. This method not only improves the real-time performance and accuracy of calibration but also avoids dependence on external signals, enabling smartwatches to maintain high positioning accuracy even in complex environments such as tunnels. Therefore, this application effectively solves the problem of decreased position calculation accuracy caused by sensor performance degradation and error accumulation, meeting the requirements for centimeter-level precise marking and significantly enhancing the application value of smartwatches in special operational scenarios.

[0080] In one possible design, such as Figure 2 As shown, in order to obtain static state information, this application may further include the following steps:

[0081] S101. Determine the inactive period of the smartwatch.

[0082] The static time period is located before the time period corresponding to a specific physical interaction action.

[0083] As one possible approach, the static period of a smartwatch can be determined by a preset time period prior to the recognition of a specific physical interaction.

[0084] For example, the preset time period can be 0.5 seconds, 1 second, etc., before recognizing a specific physical interaction action.

[0085] As another possible implementation, the variance of the accelerometer and the angular velocity amplitude of the gyroscope of the smartwatch can be determined; if the variance of the accelerometer is less than the variance threshold and the angular velocity amplitude of the gyroscope is less than the angular velocity amplitude threshold, then the smartwatch is determined to be in a stationary state, and the time period during which the smartwatch is in a stationary state is determined as the stationary time period.

[0086] The variance of the accelerometer refers to the degree of dispersion of the acceleration value measured by the smartwatch's accelerometer relative to its average value within a preset time window. When the smartwatch is in a truly stationary state, the fluctuation of its accelerometer output data should be minimal, thus the variance value will be very small. The variance threshold is a preset critical value used to judge whether the accelerometer data is stable enough. Its setting is usually based on the analysis and empirical determination of actual data from the smartwatch under different stationary conditions.

[0087] The angular velocity amplitude of a gyroscope refers to the instantaneous speed at which a smartwatch rotates in space. When a smartwatch is stationary, its theoretical angular velocity should be zero, so the angular velocity amplitude will be close to zero. The angular velocity amplitude threshold is a preset critical value used to determine whether the gyroscope data is close enough to zero. Its setting also needs to comprehensively consider sensor accuracy, environmental noise, and the requirements of the actual application scenario.

[0088] For example, the variance threshold can be 0.02 m / s², and the angular velocity amplitude threshold can be 1 degree / s.

[0089] For example, if the accelerometer variance (e.g., less than 0.02 m / s²) and the gyroscope angular velocity amplitude (e.g., less than 1 degree / s) are both very small within a preset time window, it is confirmed that the wrist is stationary.

[0090] S102. Determine the output data of the gyroscope during the stationary period as stationary state information.

[0091] In one example, suppose the smartwatch is worn on a user's wrist. When the user keeps their wrist still on a table or part of their body, the smartwatch's internal accelerometer and gyroscope continuously collect data. The system calculates the variance of the accelerometer data and the angular velocity amplitude of the gyroscope data within a short time window (e.g., 0.5 seconds or 1 second). For example, a variance threshold of 0.05 m / s² and an angular velocity amplitude threshold of 0.1 rad / s can be set. If, for five consecutive seconds, the accelerometer variance is consistently less than 0.05 m / s² and the gyroscope angular velocity amplitude is consistently less than 0.1 rad / s, the system determines that the smartwatch is stationary during those five seconds and defines this period as the stationary time period. Subsequently, the gyroscope output data during this stationary time period is used for zero-speed updates to calibrate the gyroscope's zero-point deviation, thereby correcting accumulated position errors. This method effectively distinguishes between true stillness and slight unconscious shaking or environmental vibrations, ensuring the accuracy of zero-speed updates.

[0092] The above technical solution enables more accurate identification of stationary periods on smartwatches. Compared to methods relying solely on single sensor data or simple threshold judgments, this solution combines translational stability information from the accelerometer and rotational stability information from the gyroscope, effectively avoiding misjudgments of stationary states caused by environmental noise, slight shaking, or sensor drift. Therefore, the acquired stationary state information is more accurate, providing high-quality input data for subsequent zero-speed updates, thereby further improving the overall effectiveness of the smartwatch positioning accuracy optimization method and reducing the difficulty and uncertainty of correcting accumulated position errors.

[0093] In one possible design, in order to perform zero-rate updates, this application further includes the following steps:

[0094] S201. Determine the gyroscope output data during the stationary period of the smartwatch.

[0095] During the stationary period of the smartwatch, the output data of the gyroscope can be obtained through the historical log data of the gyroscope.

[0096] S202. The average value of the gyroscope output data during the static period of the smartwatch is determined as the zero-point deviation of the gyroscope.

[0097] One possible implementation is to determine the average value of the gyroscope's output data during the smartwatch's static period as the ratio of the number of times the output data is generated during the static period, and then define this average value as the gyroscope's zero-point deviation.

[0098] S203, Perform zero-speed update based on zero-point deviation.

[0099] One possible approach is to determine the difference between the gyroscope's measured angular velocity and the zero-point deviation, and then use this difference as the gyroscope's true measured angular velocity.

[0100] The proposed solution effectively eliminates the systematic zero-point drift error of the gyroscope by subtracting a predetermined zero-point deviation from the measured angular velocity. This calibration mechanism ensures that the angular velocity data used during zero-velocity updates is precisely corrected, thus avoiding accumulated errors caused by gyroscope zero-point deviation. It is precisely this precise zero-point calibration of the gyroscope output data that allows subsequent inertial navigation calculations to be based on more accurate angular velocity information, providing a reliable foundation for zero-velocity updates.

[0101] The above technical solution enables precise calibration of the gyroscope's zero-point deviation, resulting in more accurate angular velocity measurements. This significantly improves the accuracy and reliability of zero-velocity updates, effectively suppressing the cumulative position error caused by gyroscope zero-point drift in inertial navigation systems, thereby enhancing the overall positioning accuracy of smartwatches.

[0102] like Figure 3 As shown in the figure, this embodiment of the invention also provides a smartwatch positioning accuracy optimization system. The system includes:

[0103] The recognition module is used to identify specific physical interaction actions performed by the user through the smartwatch;

[0104] The judgment module is used to obtain static state information after recognizing a specific physical interaction action; the static state information is the perception information of the smartwatch being in a static state before the specific physical interaction action occurs;

[0105] The update module is used to perform zero-speed updates using static state information and correct accumulated position errors; zero-speed updates are used to calibrate the gyroscope zero-point deviation of the smartwatch.

[0106] This application also provides a computer-readable storage medium. All or part of the processes in the above method embodiments can be executed by a computer program instructing related hardware. This program can be stored in the computer-readable storage medium, and when executed, it can include the processes of the above method embodiments. The computer-readable storage medium can be an internal storage unit of the task execution device (including a data sending end and / or a data receiving end) of any of the foregoing embodiments, such as the hard disk or memory of the task execution device. The computer-readable storage medium can also be an external storage device of the terminal device, such as a plug-in hard disk, smart media card (SMC), secure digital (SD) card, flash card, etc., equipped on the terminal device. Further, the computer-readable storage medium can include both the internal storage unit of the task execution device and an external storage device. The computer-readable storage medium is used to store the computer program and other programs and data required by the task execution device. The computer-readable storage medium can also be used to temporarily store data that has been output or will be output.

[0107] Furthermore, the functional units in the various embodiments of this application can be integrated into one processing unit, or each unit can exist physically separately, or two or more units can be integrated into one unit. The integrated unit can be implemented in hardware or as a software functional unit.

[0108] If the integrated unit is implemented as a software functional unit and sold or used as an independent product, it can be stored in a readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, essentially, or the parts that contribute to the prior art, or all or part of the technical solutions, can be embodied in the form of a software product. This software product is stored in a storage medium and includes several instructions to cause a device (which may be a microcontroller, chip, etc.) or processor to execute all or part of the steps of the methods of the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as USB flash drives, portable hard drives, ROM, RAM, magnetic disks, or optical disks.

[0109] The above are merely specific embodiments of this application, but the scope of protection of this application is not limited thereto. Any changes or substitutions within the technical scope disclosed in this application should be covered within the scope of protection of this application.

Claims

1. A method for optimizing the positioning accuracy of a smartwatch, characterized in that, include: The smartwatch identifies specific physical interactions performed by the user. After recognizing the specific physical interaction action, obtain static state information; The static state information refers to the sensory information of the smartwatch when it was in a static state before the specific physical interaction action occurred. Using the static state information, a zero-speed update is performed to correct the accumulated position error; the zero-speed update is used to calibrate the gyroscope zero-point deviation of the smartwatch.

2. The method for optimizing the positioning accuracy of a smartwatch according to claim 1, characterized in that, The specific physical interaction action is a tapping action.

3. The method for optimizing the positioning accuracy of a smartwatch according to claim 1, characterized in that, The method of recognizing specific physical interaction actions performed by the user through a smartwatch includes: The acceleration of the smartwatch is identified using its inertial sensor. If the acceleration of the first axis of the smartwatch is greater than or equal to the acceleration threshold within a preset time period, and the acceleration of the second and third axes is less than the acceleration threshold within the preset time period, the action performed by the user is determined to be the specific physical interaction action; the first axis can be any axis.

4. The method for optimizing the positioning accuracy of a smartwatch according to claim 1, characterized in that, The acquisition of static state information includes: Determine the inactivity period of the smartwatch; the inactivity period is located before the time period corresponding to the specific physical interaction action; The output data of the gyroscope during the static time period is determined as the static state information.

5. The method for optimizing the positioning accuracy of a smartwatch according to claim 4, characterized in that, Determining the inactive period of the smartwatch includes: Determine the variance of the accelerometer and the amplitude of the angular velocity of the gyroscope in the smartwatch; If the variance of the accelerometer is less than the variance threshold and the angular velocity amplitude of the gyroscope is less than the angular velocity amplitude threshold, then the smartwatch is determined to be in a stationary state, and the time period during which the smartwatch is in a stationary state is determined as the stationary time period.

6. The method for optimizing the positioning accuracy of a smartwatch according to claim 1, characterized in that, The execution of zero-rate updates includes: Determine the output data of the gyroscope during the stationary period of the smartwatch; The average value of the output data of the gyroscope during the static period of the smartwatch is determined as the zero-point deviation of the gyroscope. The zero-speed update is performed based on the zero-point deviation.

7. The method for optimizing the positioning accuracy of a smartwatch according to claim 6, characterized in that, The zero-speed update based on the zero-point deviation includes: Determine the difference between the measured angular velocity of the gyroscope and the zero-point deviation; The difference between the measured angular velocity of the gyroscope and the zero-point deviation is determined as the true measured angular velocity of the gyroscope.

8. The method for optimizing the positioning accuracy of a smartwatch according to claim 1, characterized in that, The correction of accumulated position error includes: The accumulated position error is corrected using the static state information and the Kalman filter algorithm.

9. The method for optimizing the positioning accuracy of a smartwatch according to claim 8, characterized in that, The step of using the static state information and the Kalman filter algorithm to correct the accumulated position error includes: The accumulated position error is corrected according to the position update formula; The position update formula is: x_new = x_old + K*z Where x_new is the corrected position of the smartwatch, x_old is the original position of the smartwatch, K represents the Kalman gain, and the filter measurement residual z = v_t - v_true; v_t is the estimated velocity at the current moment, and v_true is the actual velocity at the current moment.

10. A smartwatch positioning accuracy optimization system, characterized in that, The system includes: The recognition module is used to identify specific physical interaction actions performed by the user through the smartwatch; The judgment module is used to obtain static state information after recognizing the specific physical interaction action; the static state information is the perception information of the smartwatch being in a static state before the specific physical interaction action occurs. The update module is used to perform a zero-speed update using the static state information and correct the accumulated position error; the zero-speed update is used to calibrate the gyroscope zero-point deviation of the smartwatch.

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