Camera state switching method and device, wearable intelligent equipment and computer program product

Abstract

The invention relates to the technical field of wearable intelligent equipment, and further relates to a camera state switching method and device, wearable intelligent equipment and a computer program product. The method comprises the following steps: in a first preset time window, receiving acceleration data and angular velocity data of the wearable intelligent equipment; determining an attitude angle and stability of the wearable intelligent device according to the acceleration data and the angular velocity data; determining the state change of the wearable intelligent equipment according to a first preset time window, the angular velocity data, the attitude angle and the stability; and when the state change indicates that the wearable intelligent equipment is overturned, switching the camera state. According to the method, the problem of wrong switching of the camera state caused by dependence on data of a single sensor can be effectively avoided, the convenience and flexibility of camera use are improved, and the user experience is enhanced. Meanwhile, the method is not limited by the screen of the wearable intelligent equipment, and the interaction experience of the user is enhanced.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of wearable smart devices, and further relates to a camera state switching method and device, a wearable smart device, and a computer program product. BACKGROUND

[0002] Wearable smart devices integrate multiple functions such as health monitoring, information reminding, and motion recording, and can bring convenience to people's life. Among them, the photographing function provides a convenient recording method for users, whether it is to capture exciting moments in sports or to quickly record what is seen and felt in daily life, and it is particularly practical. As an important part of the photographing function, the camera state switching directly affects the user experience.

[0003] At present, most wearable smart devices rely only on the data of a single sensor to determine the camera state switching, which can easily cause the mis-switching of the camera state, thereby reducing the user experience. SUMMARY

[0004] To solve the above technical problems, the present application provides a camera state switching method and device, a wearable smart device, and a computer program product, which effectively avoids the mis-switching of the camera state caused by relying on the data of a single sensor, improves the convenience and flexibility of camera use, and enhances the user experience.

[0005] In a first aspect, the present application provides a camera state switching method applied to a first working mode of a wearable smart device, the first working mode being used to realize the switching of the camera state of the wearable smart device. The camera state switching method comprises: receiving acceleration data and angular velocity data of the wearable smart device within a first preset time window; determining an attitude angle and a stability of the wearable smart device according to the acceleration data and the angular velocity data; determining a state change of the wearable smart device according to the first preset time window, the angular velocity data, the attitude angle, and the stability; and switching the camera state when the state change indicates that the wearable smart device is flipped.

[0006] The above camera state switching method receives the acceleration data and the angular velocity data of the wearable smart device within the first preset time window, and then determines the attitude angle and the stability thereof, and then determines the state change of the wearable smart device by combining the angular velocity data and the attitude angle and the stability, and timely switches the camera state when the state change indicates that the device is flipped. This method can effectively avoid the mis-switching of the camera state caused by relying on the data of a single sensor, improve the convenience and flexibility of camera use, and enhance the user experience. At the same time, this method is not limited by the screen of the wearable smart device, and also enhances the user's interactive experience.

[0007] In one implementation, when the state change indicates that the wearable smart device is flipped, the state change of the wearable smart device is determined according to the first preset time window, the angular velocity data, the attitude angle, and the stability, and specifically includes: when the angular velocity data is less than a first angular velocity data threshold, the attitude angle is greater than a first attitude angle threshold, and the stability is less than a first stability threshold within the first preset time window, it is determined that the wearable smart device is flipped.

[0008] In one implementation, the stability of the wearable smart device is determined according to the acceleration data and the angular velocity data, and specifically includes: the stability is determined according to the variance of the acceleration data and the variance of the angular velocity data; or, the acceleration data and the angular velocity data are analyzed in the frequency domain to determine the spectral energy, and the spectral energy is taken as the stability.

[0009] In one implementation, it further includes: the current state of the user is determined according to the acceleration data and the angular velocity data; when the current state indicates that the user is in a motion state and / or the acceleration data is less than a first acceleration data threshold, the wearable smart device is controlled to exit the first working mode.

[0010] In one implementation, it further includes: the original acceleration data and the original angular velocity data of the wearable smart device are received; the original attitude angle of the wearable smart device is determined according to the original acceleration data and the original angular velocity data; when the original attitude angle is greater than a second attitude angle threshold and the original angular velocity data is greater than a second angular velocity data threshold, the wearable smart device is controlled to enter the first working mode.

[0011] The above camera state switching method obtains the original attitude angle by analyzing the original acceleration data and the original angular velocity data. When the original attitude angle and the original angular velocity data meet the corresponding conditions, the wearable smart device is controlled to enter the first working mode, that is, the preliminary flip detection is realized. The current state of the user is obtained by analyzing the acceleration data and the angular velocity data. When the current state and / or the acceleration data meet the corresponding conditions, the wearable smart device is controlled to exit the first working mode, that is, the false touch filtering is realized. The method combines the preliminary flip detection and the false touch filtering, which can effectively reduce the power consumption of the wearable smart device and increase the practicability and reliability of the camera state switching. The mutual cooperation of the preliminary flip detection and the first working mode can avoid the problems of long switching delay and false switching caused by manual switching of the camera state. The setting of the false touch filtering can effectively avoid the problem of false switching of the camera state caused by the wearable smart device being worn, the user wearing the wearable smart device to swing the arm, and the wearable smart device being placed in the pocket of the user, etc.

[0012] In one implementation, the method further includes: acquiring the sampling frequency of acceleration and angular velocity data; and adjusting the sampling frequency based on the active state of the target application and / or the flipping frequency of the wearable smart device.

[0013] The above camera state switching method, through strategies such as limiting foreground activity, downsampling in the background, and detecting screen shutdown, maintains the sensor at a high sampling frequency only when needed, significantly reducing the power consumption of wearable smart devices. Furthermore, the key thresholds and strategy parameters in this embodiment can be distributed via cloud policies and dynamically adjusted according to actual usage, improving the system's flexibility and adaptability.

[0014] In one implementation, the method further includes: after switching the camera state, controlling the wearable smart device to enter a second working mode, the second working mode being used to prevent the wearable smart device from performing the camera state switching step; wherein, the duration of the second working mode is adjusted based on user needs or the flipping frequency of the wearable smart device.

[0015] The above camera state switching method, by setting a second working mode, can effectively avoid frequent switching of camera state, and adaptively adjust the duration of the second working mode according to user needs and flip frequency, making the overall interaction more smooth and natural, and improving the user experience.

[0016] Secondly, this application provides a camera state switching device applied to a first working mode of a wearable smart device. The first working mode is used to switch the camera state of the wearable smart device. The camera state switching device includes: a receiving module configured to receive acceleration data and angular velocity data of the wearable smart device within a first preset time window; a processing module configured to: determine the attitude angle and stability of the wearable smart device based on the acceleration data and angular velocity data; determine the state change of the wearable smart device based on the first preset time window, angular velocity data, attitude angle, and stability; and an execution module configured to switch the camera state when the state change indicates that the wearable smart device has flipped.

[0017] Thirdly, this application provides a wearable smart device, including a memory, a processor, and a computer program stored in the memory, wherein the processor executes the computer program to implement the steps of any of the above-described camera state switching methods.

[0018] Fourthly, this application also provides a computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps of any of the above-described camera state switching methods.

[0019] Fifthly, this application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of any of the above-described camera state switching methods.

[0020] Compared with the prior art, the present invention has at least one of the following beneficial effects:

[0021] 1. By receiving acceleration and angular velocity data from the wearable smart device within a first preset time window, its attitude angle and stability are determined. Then, by combining the angular velocity data with multiple parameters such as attitude angle and stability, the state changes of the wearable smart device are determined. When the state change indicates that the device has flipped, the camera state is switched in a timely manner. This method can effectively avoid the problem of erroneous camera state switching caused by relying on data from a single sensor, improving the convenience and flexibility of camera use and enhancing the user experience. At the same time, this method is not limited by the screen of the wearable smart device and also enhances the user's interactive experience.

[0022] 2. By analyzing the raw acceleration and angular velocity data, the raw attitude angle is obtained. Under the corresponding conditions of the raw attitude angle and angular velocity data, the wearable smart device is controlled to enter the first working mode, which achieves preliminary flip detection. By analyzing the acceleration and angular velocity data, the user's current state is obtained. When the current state and / or acceleration data meet the corresponding conditions, the wearable smart device is controlled to exit the first working mode, which achieves accidental touch filtering. This method, combining preliminary flip detection and accidental touch filtering, can effectively reduce the power consumption of the wearable smart device and increase the practicality and reliability of camera state switching. The cooperation between preliminary flip detection and the first working mode can avoid the long switching delay and accidental switching problems caused by manually switching camera states. The accidental touch filtering setting can effectively avoid accidental camera state switching problems that are prone to occur in scenarios such as when the wearable smart device is worn, when the user swings their arm while wearing the wearable smart device, or when the wearable smart device is placed in the user's pocket.

[0023] 3. By setting a second working mode, frequent switching of camera states can be effectively avoided. The duration of the second working mode can be adaptively adjusted according to user needs and flip frequency, making the overall interaction more natural and improving the user experience.

[0024] 4. By employing strategies such as limiting foreground activity, downsampling in the background, and detecting screen shutdown, the sensor maintains a high sampling frequency only when needed, significantly reducing the power consumption of wearable smart devices. Furthermore, the key thresholds and strategy parameters in this application embodiment can be distributed via cloud policies and dynamically adjusted according to actual usage, improving the system's flexibility and adaptability. Attached Figure Description

[0025] The preferred embodiments will now be described in a clear and easy-to-understand manner, in conjunction with the accompanying drawings, to further explain the above-mentioned characteristics, technical features, advantages, and implementation methods of the present invention.

[0026] Figure 1 A flowchart of a camera state switching method provided in an embodiment of this application is shown;

[0027] Figure 2 A flowchart illustrating a preliminary flip detection method provided in an embodiment of this application is shown;

[0028] Figure 3 A flowchart illustrating a method for filtering accidental touches according to an embodiment of this application is shown;

[0029] Figure 4 A power consumption control timing diagram provided in an embodiment of this application is shown;

[0030] Figure 5 A structural block diagram of a camera state switching device provided in an embodiment of this application is shown.

[0031] Figure 6 A structural block diagram of a wearable smart device provided in an embodiment of this application is shown. Detailed Implementation

[0032] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the specific implementation methods of the present invention will be described below with reference to the accompanying drawings. Obviously, the drawings described below are merely some embodiments of the present invention. For those skilled in the art, other drawings and other implementation methods can be obtained based on these drawings without any creative effort.

[0033] To keep the drawings concise, each figure only schematically shows the parts relevant to the invention, and these do not represent the actual structure of the product. Furthermore, to facilitate understanding, in some figures, only one of components with the same structure or function is schematically depicted, or only one is labeled. In this document, "one" not only means "only one," but can also mean "more than one."

[0034] It should also be further understood that the term “and / or” as used in this application specification and the appended claims means any combination of one or more of the associated listed items and all possible combinations, and includes such combinations.

[0035] In this document, it should be noted that, unless otherwise explicitly specified and limited, the terms "installation," "connection," and "linking" should be interpreted broadly. For example, they can refer to fixed connections, detachable connections, or integral connections; they can refer to mechanical connections or electrical connections; they can refer to direct connections or indirect connections through an intermediate medium; and they can refer to the internal communication between two components. Those skilled in the art can understand the specific meaning of the above terms in this invention based on the specific circumstances.

[0036] Furthermore, in the description of this application, the terms "first," "second," etc., are used only to distinguish descriptions and should not be construed as indicating or implying relative importance.

[0037] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

[0038] Wearable smart devices integrate multiple functions, such as health monitoring, information reminders, and exercise tracking, bringing convenience to people's lives. Among them, the photography function provides users with a convenient way to record, whether capturing wonderful moments during exercise or quickly recording what they see and feel in daily life, it is particularly practical. And camera state switching, as an important part of the photography function, directly affects the user experience.

[0039] In one implementation of camera state switching technology, the wearable smart device relies on data from a single sensor to determine whether to switch camera states. This implementation is prone to erroneous camera state switching, thus degrading the user experience. Another implementation uses lens orientation to determine if a switch is necessary. This approach is also prone to erroneous switching in scenarios such as when the wearable device is being worn, when the user is swinging their arm while wearing the device, or when the device is in the user's pocket. A third implementation uses a button or knob on the wearable smart device for manual camera state switching. However, this approach is limited by the device's screen, further compromising the user's interactive experience.

[0040] Therefore, this application provides a camera state switching method to solve the above-mentioned technical problems.

[0041] The following explanation is based on the accompanying diagram:

[0042] Reference Appendix Figure 1The document illustrates a flowchart of a camera state switching method provided in an embodiment of this application. This method is applied to a first operating mode of a wearable smart device, which is used to switch the camera state of the wearable smart device, such as... Figure 1 As shown, it includes:

[0043] S100 receives acceleration and angular velocity data from the wearable smart device within a first preset time window.

[0044] S110 determines the attitude angle and stability of wearable smart devices based on acceleration and angular velocity data.

[0045] S120: Determine the state changes of the wearable smart device based on the first preset time window, angular velocity data, attitude angle, and stability.

[0046] S130: Switches camera status when a status change indicates that the wearable smart device has been flipped.

[0047] The wearable smart device is equipped with a sensor that collects triaxial acceleration and triaxial angular velocity data of the wearable smart device at a fixed sampling frequency. The sampling frequency is in the range of 30-150Hz, preferably 50-100Hz. The sensor may include, but is not limited to, accelerometers, angular velocity sensors, and inertial measurement units. This application does not limit the type of sensor.

[0048] When a user is using a target application such as a camera (whether the camera or the target application is in the foreground or background, it falls under the category of use), the sensor can collect three-axis acceleration data and three-axis angular velocity data from the wearable smart device in real time, and output acceleration data (or resultant acceleration data) and angular velocity data (or resultant angular velocity data) to the processor of the wearable smart device. The processor can process the received acceleration and angular velocity data according to a multi-time-window division method, and process the acceleration and angular velocity data within each time window, thereby realizing the switching of camera states.

[0049] For example, the processor can receive acceleration and angular velocity data from the wearable smart device within a first preset time window. This acceleration and angular velocity data can be pre-processed or unprocessed. This application does not limit this. A sensor fusion algorithm (such as complementary filtering, Kalman filtering, etc.) is used to fuse and calculate the received acceleration and angular velocity data to obtain the attitude angle (tilt angle relative to the direction of gravity) of the wearable smart device. Based on the acceleration and angular velocity data, the stability of the wearable smart device is calculated. For example, stability can be calculated based on the variance of the acceleration data and the variance of the angular velocity data; alternatively, frequency domain analysis can be performed on the acceleration and angular velocity data to determine the spectral energy, which can then be used as the stability (lower spectral energy indicates greater stability); furthermore, other stability calculation methods can be combined, including but not limited to stability prediction based on machine learning models and stability assessment based on motion pattern recognition. This application does not limit this.

[0050] After obtaining the attitude angle and stability, if the angular velocity data, attitude angle, and stability all meet their respective threshold conditions within the first preset time window, the state change of the wearable smart device can be determined to be a flip, and then the camera state of the wearable smart device can be switched. If any parameter of the angular velocity data, attitude angle, or stability does not meet the corresponding condition, the state change of the wearable smart device can be determined to be no flip. Camera state switching includes, but is not limited to, front and rear camera switching: switching from the front camera to the rear camera, or vice versa; framing layout switching: switching the layout mode of the viewfinder (such as full screen, 4:3, 16:9, etc.); mode switching: switching the shooting mode (such as photo mode, video mode, professional mode, etc.); combined switching: executing multiple switching actions simultaneously. After the switching action is completed, the processor can record the switching time and switching type for subsequent analysis and optimization.

[0051] This application embodiment receives acceleration and angular velocity data from a wearable smart device within a first preset time window to determine its attitude angle and stability. Then, by combining the angular velocity data with multiple parameters such as attitude angle and stability, it determines the state changes of the wearable smart device. When the state change indicates that the device has flipped, the camera state is switched promptly. This method effectively avoids the problem of erroneous camera state switching caused by relying on data from a single sensor, improving the convenience and flexibility of camera use and enhancing the user experience. Furthermore, this method is not limited by the screen of the wearable smart device and also enhances the user's interactive experience.

[0052] The threshold conditions corresponding to angular velocity data, attitude angle, and stability can all be set according to user needs. For example, angular velocity data can correspond to a first angular velocity data threshold, which is in the range of 5-30 degrees / second, preferably 10-20 degrees / second. Attitude angle can correspond to a first attitude angle threshold, which is in the range of 30-60 degrees, preferably 45 degrees. Stability can correspond to a first stability threshold, which is in the range of 0.3-2.0 m / s². 2 Within this range, the preferred value is 0.5-1.0 m / s. 2 Or equivalent angular velocity units. Therefore, within the first preset time window, if the angular velocity data (or the absolute value of the angular velocity data) is less than a first angular velocity data threshold, the attitude angle is greater than a first attitude angle threshold, and the stability is less than a first stability threshold, it can be determined that the wearable smart device has flipped. If any of these conditions is not met, it is determined that the wearable smart device has not flipped.

[0053] In one embodiment of this application, the stability of a wearable smart device is determined based on acceleration data and angular velocity data, specifically including: determining the stability based on the variance of the acceleration data and the variance of the angular velocity data; or, performing frequency domain analysis on the acceleration data and angular velocity data to determine the spectral energy, and using the spectral energy as the stability.

[0054] The formula for calculating stability based on variance can be σ = sqrt(var(ax,ay,az) + var(ωxωy,ωz)), where var represents the variance, var(ax,ay,az) is the variance of the acceleration data, and var(ωx,ωy,ωz) is the variance of the angular velocity data. Another formula for calculating stability based on variance is... Among them ā and These are the mean values ​​of acceleration and angular velocity data within the first preset time window, respectively, and Σ(ai-ā)² / n is the variance of the acceleration data. This represents the variance of the angular velocity data.

[0055] The specific process of using spectral energy as stability is as follows: Frequency domain transformation is performed on the acceleration and angular velocity data. Then, the spectral energy of the acceleration data and the spectral energy of the angular velocity data are calculated separately. The sum of the spectral energies of the acceleration data and the spectral energy of the angular velocity data yields the final spectral energy, which is then used as the stability measure.

[0056] The embodiments of this application can determine stability in various ways, such as based on variance, spectral energy, etc., or based on stability prediction from machine learning models, or stability evaluation based on motion pattern recognition. This method can effectively improve the compatibility of wearable smart devices.

[0057] This application adds preliminary flip detection and accidental touch filtering steps when implementing camera state switching, thereby reducing the power consumption of wearable smart devices and increasing the practicality and reliability of camera state switching. For example, refer to the appendix... Figure 2 This illustrates a flowchart of a preliminary flipping detection method provided in an embodiment of this application. Figure 2 As shown, it includes:

[0058] The S200 receives raw acceleration and raw angular velocity data from wearable smart devices.

[0059] S210 determines the original attitude angle of the wearable smart device based on the original acceleration data and the original angular velocity data.

[0060] S220: When the original attitude angle is greater than the second attitude angle threshold and the original angular velocity data is greater than the second angular velocity data threshold, control the wearable smart device to enter the first working mode.

[0061] For example, see the attached reference. Figure 3 The diagram illustrates a flowchart of an accidental touch filtering method provided in an embodiment of this application. Figure 3 As shown, it includes:

[0062] The S300 determines the user's current state based on acceleration and angular velocity data.

[0063] S310, when the current state indicates that the user is in motion, and / or the acceleration data is less than the first acceleration data threshold, control the wearable smart device to exit the first working mode.

[0064] The difference between the raw acceleration data and raw angular velocity data and the previously mentioned acceleration data and angular velocity data lies in the scenario. The acceleration data and angular velocity data are data within the first preset time window. The raw acceleration data and raw angular velocity data are data received by the processor when the first preset time window is not open. Similarly, the raw acceleration data and raw angular velocity data received by the processor can be pre-processed or unprocessed. The preprocessing process for the raw acceleration data and raw angular velocity data is as follows: Low-pass filtering: removing high-frequency noise, with a cutoff frequency in the range of 5-30Hz, preferably 10-20Hz; Median filtering: removing outliers, with a window size in the range of 3-15 sampling points, preferably 5-7 sampling points; Data calibration: calibration is performed according to the device installation direction and sensor offset.

[0065] Based on the original acceleration and angular velocity data, the initial attitude angle of the wearable smart device is determined. The second attitude angle threshold corresponding to the initial attitude angle is in the range of 45-75 degrees, preferably 60 degrees. The second angular velocity data threshold corresponding to the initial angular velocity data is in the range of 20-80 degrees / second, preferably 30-50 degrees / second. When the initial attitude angle is greater than the second attitude angle threshold and the initial angular velocity data (or the absolute value of the initial angular velocity data) is greater than the second angular velocity data threshold (or when the initial attitude angle changes from the initial state to a value greater than the second attitude angle threshold, where the initial state is in the range of 20-40 degrees, preferably 30 degrees), it is determined that the wearable smart device has undergone initial flipping, and then the wearable smart device is controlled to enter the first working mode.

[0066] After entering the first working mode, the wearable smart device can start a first preset time window, during which the processor receives acceleration data and angular velocity data. If the acceleration data and angular velocity data have been preprocessed, the preprocessing process is the same as that for the original acceleration data and original angular velocity data. The processor can then execute the relevant steps S110 to S130, thereby switching the camera state.

[0067] In one possible implementation, the first operating mode may also include a step of filtering for accidental touches on acceleration and angular velocity data. For example, after entering the first operating mode, the wearable smart device can determine the user's current state based on the acceleration and angular velocity data. When the user is currently in motion (e.g., running, ball sports, etc.), the wearable smart device can be controlled to exit the first operating mode. For example, the motion state could be an arm-swinging motion; the processor can identify the swing frequency through frequency domain analysis, thereby filtering such motions. Alternatively, when the acceleration data (or the amplitude of the acceleration data) is less than a first acceleration data threshold (the first acceleration data threshold is between 0.3 and 1.0 m / s²), the device can filter out accidental touches.2 Within the range, preferably 0.5 m / s 2 When the wearable smart device exits its first working mode, it may be stationary or in the user's pocket, thus not triggering the flip detection. If the wearable smart device passes the accidental touch filtering, the processor can execute the aforementioned steps S110 to S130 to switch the camera state.

[0068] This embodiment analyzes raw acceleration and angular velocity data to obtain the raw attitude angle. Under corresponding conditions of the raw attitude angle and angular velocity data, the wearable smart device is controlled to enter a first working mode, thus achieving preliminary flip detection. The user's current state is obtained by analyzing the acceleration and angular velocity data. When the current state and / or acceleration data meet corresponding conditions, the wearable smart device is controlled to exit the first working mode, thus achieving accidental touch filtering. This method, combining preliminary flip detection and accidental touch filtering, can effectively reduce the power consumption of the wearable smart device and increase the practicality and reliability of camera state switching. The cooperation between preliminary flip detection and the first working mode avoids the long switching delay and accidental switching problems caused by manually switching camera states. The accidental touch filtering effectively avoids accidental camera state switching in scenarios such as when the wearable smart device is worn, when the user swings their arm while wearing the wearable smart device, or when the wearable smart device is placed in the user's pocket.

[0069] The wearable smart device may also include a second operating mode. The second operating mode is a cooling window phase, the purpose of which is to prevent the wearable smart device from performing the camera state switching step during the duration of the second operating mode (or a cooling window timer, the duration of which is in the range of 0.5-5 seconds, preferably 1-3 seconds) after the camera state switch. For example, the preliminary flip detection and accidental touch filtering in the aforementioned embodiments are also a type of camera state switching implementation. Therefore, during the duration of the second operating mode, the wearable smart device is prohibited from performing the preliminary flip detection and accidental touch filtering steps, and is also prohibited from entering the first operating mode.

[0070] The duration of the second working mode can be adjusted according to user needs or the flipping frequency of the wearable smart device. The flipping frequency can refer to the frequency at which the wearable smart device flips, or the frequency at which the user triggers the camera state switch. For example, if the user triggers frequently (e.g., three consecutive triggers with an interval of less than 5 seconds), the duration of the second working mode will be extended to 1.5-2 times the initial duration. If the user triggers at longer intervals (e.g., an average interval greater than 10 seconds), the duration of the second working mode will be shortened to 0.8-1 times the initial duration. After the second working mode ends, the wearable smart device can return to an idle state, waiting for the next camera state switch.

[0071] This application embodiment can effectively avoid frequent switching of camera state by setting a second working mode, and adaptively adjust the duration of the second working mode according to user needs and flip frequency, making the overall interaction more natural and improving the user experience.

[0072] Wearable smart devices can also be equipped with a power control module. This module manages the power consumption of the wearable smart device. For example, it can reduce or increase power consumption by lowering or increasing the sampling frequency of the sensors (or the sampling frequency of acceleration data and angular velocity data, or the sampling frequency of raw acceleration data and raw angular velocity data), or by lowering or increasing the brightness of the display screen. Specifically, the power control module can adjust the sampling frequency based on the activity level of the target application and / or the flipping frequency of the wearable smart device. For example, refer to the attached... Figure 4 This will be illustrated using an example where the target application is a camera application and the sensor is an IMU sensor (or inertial measurement unit):

[0073] When the camera application is active in the foreground, the power control module subscribes to IMU sensor events and maintains the IMU sensor's sampling frequency at a high sampling rate (within the range of 30-150Hz, preferably 50-100Hz). When the camera application is in the background, the power control module reduces the IMU sensor's sampling frequency to 5-30Hz (preferably 10-20Hz) or stops subscribing to IMU events (or reduces the IMU sensor's sampling frequency to 0). When the screen is off, the power control module completely stops IMU listening, reducing power consumption. Alternatively, the power control module adjusts the sampling frequency based on the wearable smart device's flip frequency. For example, if no flip is detected for a long time, the sampling frequency is gradually reduced.

[0074] This application embodiment significantly reduces the power consumption of wearable smart devices by employing strategies such as limiting foreground activity, downsampling in the background, and detecting screen shutdown, maintaining a high sampling frequency for the sensor only when needed. Furthermore, the key thresholds and strategy parameters of this application embodiment can be dynamically adjusted based on actual usage through cloud-based policy distribution, enhancing the system's flexibility and adaptability.

[0075] Reference Appendix Figure 5 This illustrates a structural block diagram of a camera state switching device provided in an embodiment of this application. Figure 5 As shown, the device 500 includes: a receiving module 510 configured to receive acceleration data and angular velocity data of a wearable smart device within a first preset time window; a processing module 520 configured to: determine the attitude angle and stability of the wearable smart device based on the acceleration data and angular velocity data; and determine the state changes of the wearable smart device based on the first preset time window, angular velocity data, attitude angle, and stability; and an execution module 530 configured to switch the camera state when the state change indicates that the wearable smart device has flipped.

[0076] This application embodiment receives acceleration and angular velocity data from a wearable smart device within a first preset time window to determine its attitude angle and stability. Then, by combining the angular velocity data with multiple parameters such as attitude angle and stability, it determines the state changes of the wearable smart device. When the state change indicates that the device has flipped, the camera state is switched promptly. This method effectively avoids the problem of erroneous camera state switching caused by relying on data from a single sensor, improving the convenience and flexibility of camera use and enhancing the user experience. Furthermore, this method is not limited by the screen of the wearable smart device and also enhances the user's interactive experience.

[0077] Reference Appendix Figure 6 This application also provides a wearable smart device 600, which includes a memory 610, a processor 620, and a computer program stored in the memory 610. The processor 620 executes the computer program to implement the steps of the camera state switching method of any of the above embodiments.

[0078] The memory 610 may be non-volatile memory (NVM), such as, but not limited to, semiconductor non-volatile memory, disk storage, or optical storage. Semiconductor non-volatile memory includes, but is not limited to, read-only memory (ROM) or flash memory, such as mask ROM, programmable read-only memory (PROM), erasable programmable read-only memory (EPROM), electrically erasable programmable read-only memory (EEPROM), NAND flash memory, or NOR flash memory.

[0079] The memory 610 can also be volatile memory, such as random access memory (RAM). RAM includes, for example, static random-access memory (SRAM) or dynamic random-access memory (DRAM). DRAM includes, for example, synchronous dynamic RAM (SDRAM) or double data rate SDRAM (DDR). With the development of technology, DDR includes, but is not limited to, DDR1, DDR2, DDR3, ..., DDR5, and may also include future DDR6.

[0080] Processor 620 is a circuit with signal processing capabilities. In one example, the processor can be a circuit with instruction read and execute capabilities; such as a central processing unit (CPU), microcontroller unit (MCU), microprocessor unit (MPU), graphics processing unit (GPU), or digital signal processor (DSP). In another example, the processor can realize its processing capabilities through the logical relationships of hardware circuits, which can be fixed or reconfigurable; for example, the processor can be a dedicated processor, such as a processor implemented with an application-specific integrated circuit (ASIC), which realizes its processing capabilities through the design of the logical relationships between components within the circuit; or a processor implemented with a programmable logic device (PLD), which realizes its processing capabilities by configuring the logical relationships between logic devices through a configuration file; for example, a processor implemented with a field-programmable gate array (FPGA). In another example, the processor can be a hardware circuit designed for artificial intelligence, which can be understood as an ASIC, such as a neural network processing unit (NPU), tensor processing unit (TPU), deep learning processing unit (DPU), etc. This application is not limited to the type of processor.

[0081] In some embodiments of this application, wearable smart devices include, but are not limited to, smartwatches, smart bracelets, etc.

[0082] The wearable smart device used in this application embodiment is basically similar to the method embodiment, so the description is relatively simple. For relevant details, please refer to the description of the method embodiment.

[0083] This application also provides a computer-readable storage medium storing a computer program thereon, which, when executed by a processor, implements the steps of the camera state switching method of any of the above embodiments.

[0084] This application also provides a computer program product, including a computer program that, when executed by a processor, implements the steps of the camera state switching method of any of the above embodiments.

[0085] It should be noted that the above embodiments can be freely combined as needed. The above are merely preferred embodiments of the present invention. It should be pointed out that for those skilled in the art, several improvements and modifications can be made without departing from the principle of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A camera state switching method, characterized in that, A first working mode applied to a wearable smart device, wherein the first working mode is used to switch the camera state of the wearable smart device; The camera state switching method includes: Within a first preset time window, receive acceleration and angular velocity data from the wearable smart device; Based on the acceleration data and the angular velocity data, the attitude angle and stability of the wearable smart device are determined; The state changes of the wearable smart device are determined based on the first preset time window, the angular velocity data, the attitude angle, and the stability. When the state change indicates that the wearable smart device has been flipped, the camera state is switched.

2. The camera state switching method according to claim 1, characterized in that, When the state change indicates that the wearable smart device has flipped, determining the state change of the wearable smart device based on the first preset time window, the angular velocity data, the attitude angle, and the stability specifically includes: Within the first preset time window, if the angular velocity data is less than the first angular velocity data threshold, the attitude angle is greater than the first attitude angle threshold, and the stability is less than the first stability threshold, it is determined that the wearable smart device has flipped.

3. The camera state switching method according to claim 1, characterized in that, Determining the stability of the wearable smart device based on the acceleration data and the angular velocity data specifically includes: The stability is determined based on the variance of the acceleration data and the variance of the angular velocity data. or, Frequency domain analysis is performed on the acceleration data and the angular velocity data to determine the spectral energy, and the spectral energy is used as the stability.

4. The camera state switching method according to claim 1, characterized in that, Also includes: The user's current state is determined based on the acceleration data and the angular velocity data; When the current state indicates that the user is in motion, and / or the acceleration data is less than a first acceleration data threshold, the wearable smart device is controlled to exit the first working mode.

5. The camera state switching method according to claim 1, characterized in that, Also includes: Receive raw acceleration data and raw angular velocity data from the wearable smart device; Based on the original acceleration data and the original angular velocity data, the original attitude angle of the wearable smart device is determined; When the original attitude angle is greater than the second attitude angle threshold and the original angular velocity data is greater than the second angular velocity data threshold, the wearable smart device is controlled to enter the first working mode.

6. The camera state switching method according to claim 1, characterized in that, Also includes: The sampling frequency for obtaining the acceleration data and the angular velocity data; The sampling frequency is adjusted based on the activity level of the target application and / or the flipping frequency of the wearable smart device.

7. The camera state switching method according to any one of claims 1-6, characterized in that, Also includes: After switching the camera state, the wearable smart device is controlled to enter a second working mode. The second working mode is used to prevent the wearable smart device from performing the camera state switching step. The duration of the second working mode is adjusted based on user needs or the flipping frequency of the wearable smart device.

8. A camera state switching device, characterized in that, A first working mode applied to a wearable smart device, wherein the first working mode is used to switch the camera state of the wearable smart device; The camera state switching device includes: The receiving module is configured to receive acceleration data and angular velocity data from the wearable smart device within a first preset time window; The processing module is configured to: determine the attitude angle and stability of the wearable smart device based on the acceleration data and the angular velocity data; and determine the state changes of the wearable smart device based on a first preset time window, the angular velocity data, the attitude angle, and the stability. An execution module is configured to switch the camera state when the state change indicates that the wearable smart device has been flipped.

9. A wearable smart device, comprising a memory, a processor, and a computer program stored in the memory, characterized in that, The processor executes the computer program to implement the steps of the camera state switching method according to any one of claims 1-7.

10. A computer program product, comprising a computer program, characterized in that, When executed by a processor, the computer program implements the steps of the camera state switching method according to any one of claims 1-7.

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