Equipment holding posture determination method and device, equipment and storage medium

Abstract

The invention provides an equipment holding posture determination method and device, equipment and a storage medium, and relates to the technical field of holding posture detection. In some embodiments of the present disclosure, attitude data of the mobile device and a contact capacitance value of each angle are obtained; inputting the posture data into a holding posture prediction model to obtain a first probability of each holding posture; in combination with the contact capacitance value of each angle, updating the first probability of each holding posture to obtain a relatively accurate second probability of each holding posture; and according to the second probability of each holding posture with relatively high accuracy, the current holding posture of the mobile equipment is accurately determined, and the holding posture detection accuracy of the mobile equipment is improved.

Description

Technical Field

[0001] The present disclosure relates to the technical field of grip posture detection, and particularly to a method, apparatus, device, and storage medium for determining the grip posture of a device. Background Art

[0002] Grip posture detection is a device function that uses sensors and algorithms to recognize how a user holds and operates a mobile phone. This technology can achieve a more intelligent and user-friendly mobile phone interaction experience, improving the usability and security of the device.

[0003] Grip posture detection mainly relies on various sensors and the touch screen built into the mobile phone to collect information about the user's grip posture, finger position, touch pressure, etc. By analyzing this data in real time, the mobile device can determine the user's grip method and operation intention. Grip posture detection technology can be used for anti-mis-touch, assisting single-handed operation, intelligent screen rotation, etc.

[0004] Currently, the accuracy rate of grip posture detection for mobile devices is relatively low. Summary of the Invention

[0005] The present disclosure provides a method, apparatus, device, and storage medium for determining the grip posture of a device, so as to at least solve the problem of relatively low accuracy rate of existing grip posture detection.

[0006] The technical solution of the present disclosure is as follows:

[0007] An embodiment of the present disclosure provides a method for determining the grip posture of a device, including:

[0008] Obtaining the attitude data of the mobile device and the contact capacitance values of each angle;

[0009] Inputting the attitude data into a grip posture prediction model to obtain the first probability of each grip posture;

[0010] Updating the first probability of each grip posture according to the contact capacitance values of each angle to obtain the second probability of each grip posture;

[0011] Determining the current grip posture of the mobile device according to the second probability of each grip posture.

[0012] Optionally, the attitude data includes: linear acceleration and angular velocity, and obtaining the attitude data of the mobile device and the contact capacitance values of each angle includes:

[0013] Collecting the raw acceleration and gravitational acceleration of the mobile device by using an accelerometer; collecting the angular velocity of the mobile device by using a gyroscope; determining the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration; and

[0014] Collect the contact capacitance values of each corner using the SAR sensors provided at each corner of the mobile device.

[0015] Optionally, determining the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration includes:

[0016] Determine the linear acceleration components of the raw acceleration on each axis in the device coordinate system; and

[0017] Determine the gravitational acceleration components of the gravitational acceleration on each axis in the device coordinate system;

[0018] Subtract the corresponding gravitational acceleration components from the linear acceleration components on each axis in the device coordinate system to obtain the linear acceleration of the mobile device.

[0019] Optionally, updating the first probability of each grip posture according to the contact capacitance values of each corner to obtain the second probability of each grip posture includes:

[0020] For the contact capacitance value of the first corner of the mobile device, determine the magnitude relationship between the contact capacitance value of the first corner and the capacitance threshold; wherein, the first corner is any one of the corners of the mobile device;

[0021] If the contact capacitance value of the first corner is less than the capacitance threshold, then set the first probability of the grip posture corresponding to the first corner to zero to obtain the second probability of the grip posture corresponding to the first corner.

[0022] Optionally, determining the current grip posture of the mobile device according to the second probability of each grip posture includes:

[0023] Obtain the second probability of each grip posture multiple times to obtain multiple second probabilities of each grip posture;

[0024] Determine the magnitude relationship between the probability mean of the multiple second probabilities of each grip posture and the set probability threshold;

[0025] If the probability mean of each grip posture is less than the set probability threshold, then determine that the current grip posture of the mobile device is the free state.

[0026] Optionally, the method further includes:

[0027] If the probability mean of at least one grip posture is greater than or equal to the set probability threshold, then select the grip posture with the largest probability mean from multiple grip postures as the current grip posture of the mobile device.

[0028] The embodiments of the present disclosure further provide a device grip posture determination device, including:

[0029] An acquisition module, configured to acquire the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device;

[0030] A prediction module, configured to input the attitude data into a grip posture prediction model to obtain a first probability of each grip posture;

[0031] An update module, configured to update the first probability of each grip posture according to the contact capacitance values of each corner to obtain a second probability of each grip posture;

[0032] A determination module, configured to determine the current grip posture of the mobile device according to the second probability of each grip posture.

[0033] Optionally, the attitude data includes: linear acceleration and angular velocity. When acquiring the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device, the acquisition module is configured to:

[0034] Collect the raw acceleration and gravitational acceleration of the mobile device by using an accelerometer; collect the angular velocity of the mobile device by using a gyroscope; determine the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration; and

[0035] Collect the contact capacitance values of each corner by using SAR sensors arranged at each corner of the mobile device.

[0036] Optionally, when determining the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration, the acquisition module is configured to:

[0037] Determine the linear acceleration components of the raw acceleration on each axis in the device coordinate system; and

[0038] Determine the gravitational acceleration components of the gravitational acceleration on each axis in the device coordinate system;

[0039] Subtract the corresponding gravitational acceleration components from the linear acceleration components on each axis in the device coordinate system to obtain the linear acceleration of the mobile device.

[0040] Optionally, when updating the first probability of each grip posture according to the contact capacitance values of each corner to obtain a second probability of each grip posture, the update module is configured to:

[0041] For the contact capacitance value of the first corner of the mobile device, determine the magnitude relationship between the contact capacitance value of the first corner and the capacitance threshold; wherein, the first corner is any one of the corners of the mobile device;

[0042] If the touch capacitance value at the first corner is less than the capacitance threshold, then the first probability of the grip posture corresponding to the first corner is set to zero, and the second probability of the grip posture corresponding to the first corner is obtained.

[0043] Optionally, when determining the current grip posture of the mobile device according to the second probability of each grip posture, the determining module is configured to:

[0044] Obtain the second probability of each grip posture multiple times to obtain multiple second probabilities of each grip posture;

[0045] Determine the magnitude relationship between the probability mean of the multiple second probabilities of each grip posture and the set probability threshold;

[0046] If the probability mean of each grip posture is less than the set probability threshold, then determine that the current grip posture of the mobile device is the free state.

[0047] Optionally, the determining module may further be configured to:

[0048] If the probability mean of at least one grip posture is greater than or equal to the set probability threshold, then select the grip posture with the largest probability mean from multiple grip postures as the current grip posture of the mobile device.

[0049] An embodiment of the present disclosure further provides a mobile phone, including:

[0050] A processor;

[0051] A memory for storing executable instructions of the processor;

[0052] Wherein, the processor is configured to execute the instructions to implement the steps in the above method.

[0053] An embodiment of the present disclosure further provides an electronic device, including:

[0054] A processor;

[0055] A memory for storing executable instructions of the processor;

[0056] Wherein, the processor is configured to execute the instructions to implement the steps in the above method.

[0057] An embodiment of the present disclosure further provides a computer-readable storage medium, on which a computer program is stored, and when the computer program is executed by a processor, the steps in the above method are implemented.

[0058] An embodiment of the present disclosure further provides a computer program product, including a computer program / instructions, and when the computer program / instructions are executed by a processor, the steps in the above method are implemented.

[0059] The technical solutions provided by the embodiments of the present disclosure at least bring the following beneficial effects:

[0060] In some embodiments of the present disclosure, the attitude data of the mobile device and the contact capacitance values of each angle are obtained; the attitude data is input into the grip posture prediction model to obtain the first probability of each grip posture; in combination with the contact capacitance values of each angle, the first probability of each grip posture is updated to obtain the more accurate second probability of each grip posture; according to the second probability of each grip posture with higher accuracy, the current grip posture of the mobile device is accurately determined, improving the accuracy of the grip posture detection of the mobile device.

[0061] It should be understood that the above general description and the following detailed description are only exemplary and explanatory, and cannot limit the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0062] The accompanying drawings herein are incorporated into the specification and constitute a part of this specification, showing embodiments consistent with the present disclosure and used together with the specification to explain the principles of the present disclosure, and do not constitute an improper limitation of the present disclosure.

[0063] Figure 1 A flowchart showing a method for determining the grip posture of a device provided by an exemplary embodiment of the present disclosure;

[0064] Figure 2 A schematic diagram of a lower left corner grip posture provided by an exemplary embodiment of the present disclosure;

[0065] Figure 3 A schematic diagram of a lower right corner grip posture provided by an exemplary embodiment of the present disclosure;

[0066] Figure 4 A schematic diagram of an upper left corner grip posture provided by an exemplary embodiment of the present disclosure;

[0067] Figure 5 A schematic diagram of an upper right corner grip posture provided by an exemplary embodiment of the present disclosure;

[0068] Figure 6 A schematic diagram of a lower two - corner grip posture provided by an exemplary embodiment of the present disclosure;

[0069] Figure 7 A schematic diagram of an upper two - corner grip posture provided by an exemplary embodiment of the present disclosure;

[0070] Figure 8 A schematic diagram of a left two - corner grip posture provided by an exemplary embodiment of the present disclosure;

[0071] Figure 9 A schematic diagram of a right two - corner grip posture provided by an exemplary embodiment of the present disclosure;

[0072] Figure 10 A structural schematic diagram of a device grip determination device provided by an exemplary embodiment of the present disclosure;

[0073] Figure 11 A structural schematic diagram of an electronic device provided by an exemplary embodiment of the present disclosure. Detailed implementation manners

[0074] In order to enable those of ordinary skill in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the accompanying drawings.

[0075] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that such used data may be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The implementation manners described in the following exemplary embodiments do not represent all implementation manners consistent with the present disclosure. On the contrary, they are merely examples of devices and methods consistent with some aspects of the present disclosure.

[0076] It should be noted that the user information involved in the present disclosure includes, but is not limited to: user device information and user personal information; the collection, storage, use, processing, transmission, provision, and disclosure of the user information in the present disclosure and other processing are all in compliance with the provisions of relevant laws and regulations and do not violate public order and good customs.

[0077] Grip detection is a device function that uses sensors and algorithms to recognize how a user holds and operates a mobile phone. This technology can achieve a more intelligent and user-friendly mobile phone interaction experience and improve the usability and security of the device.

[0078] Grip detection mainly relies on various sensors and touchscreens built into the mobile phone to collect information about the user's holding posture, finger position, touch pressure, etc. By analyzing this data in real time, the mobile device can determine the user's holding method and operation intention. Grip detection technology can be used for anti-misoperation, assisting single-handed operation, intelligent screen rotation, etc. Currently, the accuracy rate of grip detection for mobile devices is relatively low.

[0079] In some embodiments of the present disclosure, in view of the above technical problems, attitude data of the mobile device and contact capacitance values of each corner are obtained; the attitude data is input into a grip gesture prediction model to obtain a first probability of each grip gesture; in combination with the contact capacitance values of each corner, the first probability of each grip gesture is updated to obtain a more accurate second probability of each grip gesture; based on the second probability of each grip gesture with higher accuracy, the current grip gesture of the mobile device is accurately determined, improving the accuracy of grip gesture detection of the mobile device.

[0080] The following will, with reference to the accompanying drawings, detail the technical solutions provided by the various embodiments of the present disclosure.

[0081] Figure 1 It is a flowchart of a method for determining the grip gesture of a device provided by an exemplary embodiment of the present disclosure. As Figure 1 shown, the method includes:

[0082] S101: Obtain the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device;

[0083] S102: Input the attitude data into a grip gesture prediction model to obtain a first probability of each grip gesture;

[0084] S103: Update the first probability of each grip gesture according to the contact capacitance values of each corner to obtain a second probability of each grip gesture;

[0085] S104: Determine the current grip gesture of the mobile device according to the second probability of each grip gesture.

[0086] In this embodiment, the execution subject of the above method is a mobile device with a display screen. The mobile devices of the present disclosure include but are not limited to the following: smart phones, tablet computers, and e-book readers.

[0087] In the embodiments of the present disclosure, the attitude data of the mobile device and the contact capacitance values of each corner are obtained; the attitude data is input into a grip gesture prediction model to obtain a first probability of each grip gesture; in combination with the contact capacitance values of each corner, the first probability of each grip gesture is updated to obtain a more accurate second probability of each grip gesture; based on the second probability of each grip gesture with higher accuracy, the current grip gesture of the mobile device is accurately determined, improving the accuracy of grip gesture detection of the mobile device.

[0088] In some embodiments of the present disclosure, attitude data of a mobile device and contact capacitance values of each angle are obtained. One achievable way is to use an accelerometer to collect the raw acceleration and gravitational acceleration of the mobile device; use a gyroscope to collect the angular velocity of the mobile device; determine the linear acceleration of the mobile device according to the raw acceleration and gravitational acceleration; and use SAR sensors provided at each angle of the mobile device to collect the contact capacitance values of each angle. In the embodiments of the present disclosure, the built-in accelerometer of the mobile device is used to collect the raw acceleration and gravitational acceleration of the mobile device, and the built-in gyroscope of the mobile device is used to collect the angular velocity of the mobile device, without adding other hardware, thus saving costs. In the embodiments of the present disclosure, SAR sensors provided at each angle of the mobile device are used to detect the contact state between the user and each angle of the mobile device, thereby improving the accuracy of the grip posture detection. Among them, the SAR sensor is an electromagnetic wave absorption rate sensor.

[0089] In some exemplary embodiments of the present disclosure, the linear acceleration of the mobile device is determined according to the raw acceleration and gravitational acceleration. One achievable way is to determine the linear acceleration components of the raw acceleration on each axis in the device coordinate system; and determine the gravitational acceleration components of the gravitational acceleration on each axis in the device coordinate system; subtract the gravitational acceleration components corresponding to each axis in the device coordinate system from the linear acceleration components on each axis to obtain the linear acceleration of the mobile device. In the embodiments of the present disclosure, in order to obtain a pure linear acceleration, the gravitational component is removed from the raw acceleration to improve the accuracy of the linear acceleration.

[0090] Among them, the linear acceleration and angular velocity are stored in a buffer for input to the subsequent grip posture prediction model.

[0091] In some exemplary embodiments of the present disclosure, an SAR sensor can be provided at each of the four corners of the mobile device to detect the contact state between the user and the four corners of the mobile device, and the contact state between the user and the corners of the mobile device can be accurately detected.

[0092] In some embodiments of the disclosure, the SAR sensor is used to collect the contact capacitance value, and the contact situation between the user and the angle is determined by determining the magnitude relationship between the contact capacitance value collected by the SAR sensor at any angle and the capacitance threshold. One achievable way is that when the contact capacitance value at the first angle is greater than or equal to the capacitance threshold, it is determined that the user is in contact with the first angle; when the contact capacitance value at the first angle is less than the capacitance threshold, it is determined that the user is not in contact with the first angle. It should be noted that the capacitance threshold is not limited in the embodiments of the present disclosure, and the capacitance threshold can be adjusted according to the actual situation.

[0093] In some exemplary embodiments of the present disclosure, in order to better enhance the user experience, the present disclosure makes a more comprehensive classification of the holding postures of mobile devices. The holding postures of the present disclosure include: bottom left holding posture, bottom right holding posture, top left holding posture, top right holding posture, bottom two - corner holding posture, top two - corner holding posture, left two - corner holding posture, right two - corner holding posture, and free state. The bottom left holding posture, bottom right holding posture, top left holding posture, top right holding posture, bottom two - corner holding posture, top two - corner holding posture, left two - corner holding posture, and right two - corner holding posture are described below in the form of schematic diagrams.

[0094] Among them, Figure 2 is a schematic diagram of a bottom left holding posture provided by an exemplary embodiment of the present disclosure. Figure 3 is a schematic diagram of a bottom right holding posture provided by an exemplary embodiment of the present disclosure. Figure 4 is a schematic diagram of a top left holding posture provided by an exemplary embodiment of the present disclosure. Figure 5 is a schematic diagram of a top right holding posture provided by an exemplary embodiment of the present disclosure. Figure 6 is a schematic diagram of a bottom two - corner holding posture provided by an exemplary embodiment of the present disclosure. Figure 7 is a schematic diagram of a top two - corner holding posture provided by an exemplary embodiment of the present disclosure. Figure 8 is a schematic diagram of a left two - corner holding posture provided by an exemplary embodiment of the present disclosure. Figure 9 is a schematic diagram of a right two - corner holding posture provided by an exemplary embodiment of the present disclosure. The holding postures other than the above eight schematic - diagram holding postures are in the free state.

[0095] In some embodiments of the present disclosure, the attitude data is input into the holding - posture prediction model to obtain the first probability of each holding posture. One achievable way is to input the linear acceleration and angular velocity of the mobile device into the holding - posture prediction model to obtain the first probability of each holding posture. Among them, the holding - posture prediction model can predict the probabilities of the bottom left holding posture, bottom right holding posture, top left holding posture, top right holding posture, bottom two - corner holding posture, top two - corner holding posture, left two - corner holding posture, and right two - corner holding posture. The present disclosure uses the holding - posture prediction model to predict the holding - posture probabilities, improving the accuracy of the holding - posture probability prediction. It should be noted that the holding - posture prediction model can be a temporal convolutional network.

[0096] For example, when the linear acceleration and angular velocity of the mobile phone are input into the holding - posture prediction model, the probability of the bottom left holding posture is 0.9; the probability of the bottom right holding posture is 0.4; the probability of the top left holding posture is 0.2; the probability of the top right holding posture is 0.1; the probability of the bottom two - corner holding posture is 0.5; the probability of the top two - corner holding posture is 0.3; the probability of the left two - corner holding posture is 0.6; the probability of the right two - corner holding posture is 0.1.

[0097] In the above embodiments of the present disclosure, the mobile device reads data from the buffer at regular intervals to capture the linear acceleration and angular velocity generated by the user's micro-movements, and inputs the linear acceleration and angular velocity into the grip gesture prediction model to obtain the probability of each grip gesture. For example, the mobile device reads data from the buffer every 0.5 seconds. It should be noted that the present disclosure does not limit the set duration, which can be adjusted according to the actual situation.

[0098] In some embodiments of the present disclosure, the first probability of each grip gesture is updated according to the contact capacitance values of each angle to obtain the second probability of each grip gesture. One achievable way is to determine the magnitude relationship between the contact capacitance value of the first angle of the mobile device and the capacitance threshold; wherein, the first angle is any one of the angles of the mobile device; when the contact capacitance value of the first angle is less than the capacitance threshold, the first probability of the grip gesture corresponding to the first angle is set to zero to obtain the second probability of the grip gesture corresponding to the first angle; when the contact capacitance value of the first angle is greater than or equal to the capacitance threshold, the first probability of the grip gesture corresponding to the first angle is retained. The probability value of the grip gesture corresponding to the first angle of the mobile device is updated by using the contact capacitance value of the first angle of the mobile device, so as to improve the accuracy of the probability value of the grip gesture corresponding to the first angle. The first probability of the grip gestures corresponding to other angles is updated in the above manner in sequence to obtain the second probability of each grip gesture.

[0099] It should be noted that the grip gestures corresponding to the lower left corner of the mobile device are: lower left corner grip gesture, lower two corners grip gesture, and left two corners grip gesture; the grip gestures corresponding to the upper left corner of the mobile device are: upper left corner grip gesture, upper two corners grip gesture, and left two corners grip gesture; the grip gestures corresponding to the lower right corner of the mobile device are: lower right corner grip gesture, lower two corners grip gesture, and right two corners grip gesture; the grip gestures corresponding to the upper right corner of the mobile device are: upper right corner grip gesture, upper two corners grip gesture, and right two corners grip gesture.

[0100] For example, the linear acceleration and angular velocity of the mobile phone are input into the grip gesture prediction model, and the probability of the lower left corner grip gesture is 0.9; the probability of the lower right corner grip gesture is 0.4; the probability of the upper left corner grip gesture is 0.2; the probability of the upper right corner grip gesture is 0.1; the probability of the lower two corners grip gesture is 0.5; the probability of the upper two corners grip gesture is 0.3; the probability of the left two corners grip gesture is 0.6; the probability of the right two corners grip gesture is 0.1. If the contact capacitance value of the lower left corner of the mobile phone is collected and is less than the capacitance threshold, the probability 0.9 of the lower left corner grip gesture is set to 0, the probability 0.6 of the left two corners grip gesture is set to 0, and the probability 0.5 of the lower two corners grip gesture is set to 0. The first probability of the grip gestures corresponding to other angles is updated in the above manner in sequence to obtain the second probability of each grip gesture.

[0101] In some embodiments of the present disclosure, the current grip posture of the mobile device is determined according to the second probability of each grip posture. One achievable way is to obtain the second probability of each grip posture multiple times to obtain multiple second probabilities of each grip posture; determine the magnitude relationship between the probability mean of the multiple second probabilities of each grip posture and the set probability threshold; when the probability mean of each grip posture is less than the set probability threshold, determine the current grip posture of the mobile device as the free state; when the probability mean of at least one grip posture is greater than or equal to the set probability threshold, select the grip posture with the largest probability mean from multiple grip postures as the current grip posture of the mobile device. It should be noted that the set probability threshold is not limited in the embodiments of the present disclosure and can be adjusted according to the actual situation. The set probability threshold, for example, is 0.5, 0.6. Through the second probabilities of the grip postures obtained multiple times in the present disclosure, according to the magnitude relationship between the probability mean of the multiple second probabilities and the set probability threshold, the corresponding grip posture determination method is adopted to determine the current grip posture, improving the accuracy of the current grip posture confirmation.

[0102] In the above embodiments, the second probability of the target grip posture can be obtained multiple times by using the above method for obtaining the second probability of the target grip posture. For example, the second probability of the target grip posture can be obtained 5 times by using the above method for obtaining the second probability of the target grip posture, and then the probability mean of the 5 second probabilities is calculated. Among them, the time interval between multiple acquisitions of the second probability of the target grip posture can be 0.3 seconds, 0.5 seconds, etc.

[0103] For example, at an interval of 0.5 seconds, the second probability of the grip posture is obtained 5 times by using the above method for obtaining the second probability of the grip posture, and then the probability mean of the 5 second probabilities of the grip posture is calculated. The probability means of the second probabilities of eight grip postures are obtained in sequence. If the probability means of the eight grip postures are all less than the set probability threshold of 0.5, the current grip posture of the mobile device is determined as the free state. If the probability mean of the lower left corner grip posture is 0.9 and the probability mean of the lower two corner grip posture is 0.8, which are greater than the set probability threshold of 0.5, the lower left corner grip posture with a larger probability mean is selected as the current grip posture.

[0104] In the embodiments of the present disclosure, the grip posture detection has a high accuracy and high sensitivity. When the user changes the grip posture, no additional action is required, and the correct switching can be completed by the output result. Real-time detection can be achieved throughout the stage when the user holds the mobile device. It has a high prosperity and good detection results for different users. In different scenarios, the accuracy of the output result is also relatively stable; the grip posture classification is more refined, which can indirectly improve the user experience of some functions (such as anti-mis-touch, etc.).

[0105] In the above method embodiment of the present disclosure, attitude data of the mobile device and contact capacitance values of each angle are obtained; the attitude data is input into the grip pose prediction model to obtain the first probability of each grip pose; in combination with the contact capacitance values of each angle, the first probability of each grip pose is updated to obtain the more accurate second probability of each grip pose; according to the second probability of each grip pose with higher accuracy, the current grip pose of the mobile device is accurately determined, improving the accuracy of grip pose detection of the mobile device.

[0106] Figure 10 FIG. 4 is a schematic structural diagram of a device grip pose determination device 100 provided by an exemplary embodiment of the present disclosure. As Figure 10 shown, the device grip pose determination device 100 includes: an acquisition module 1001, a prediction module 1002, an update module 1003, and a determination module 1004.

[0107] Among them, the acquisition module 1001 is configured to acquire attitude data of the mobile device and contact capacitance values of each angle of the mobile device;

[0108] The prediction module 1002 is configured to input the attitude data into the grip pose prediction model to obtain the first probability of each grip pose;

[0109] The update module 1003 is configured to update the first probability of each grip pose according to the contact capacitance values of each angle to obtain the second probability of each grip pose;

[0110] The determination module 1004 is configured to determine the current grip pose of the mobile device according to the second probability of each grip pose.

[0111] Optionally, the attitude data includes: linear acceleration and angular velocity. When the acquisition module 1001 acquires the attitude data of the mobile device and the contact capacitance values of each angle of the mobile device, it is configured to:

[0112] Collect the raw acceleration and gravitational acceleration of the mobile device by using an accelerometer; collect the angular velocity of the mobile device by using a gyroscope; determine the linear acceleration of the mobile device according to the raw acceleration and gravitational acceleration; and

[0113] Collect the contact capacitance values of each angle by using SAR sensors provided at each angle of the mobile device.

[0114] Optionally, when the acquisition module 1001 determines the linear acceleration of the mobile device according to the raw acceleration and gravitational acceleration, it is configured to:

[0115] Determine the linear acceleration components of the raw acceleration on each axis in the device coordinate system; and

[0116] Determine the gravitational acceleration components of the gravitational acceleration on each axis in the device coordinate system;

[0117] Subtract the linear acceleration components on each axis in the device coordinate system from the corresponding gravitational acceleration components to obtain the linear acceleration of the mobile device.

[0118] Optionally, when the updating module 1003 updates the first probability of each grip posture according to the contact capacitance values of each angle to obtain the second probability of each grip posture, it is used for:

[0119] For the contact capacitance value of the first angle of the mobile device, determine the magnitude relationship between the contact capacitance value of the first angle and the capacitance threshold; wherein, the first angle is any one of the angles of the mobile device;

[0120] In the case where the contact capacitance value of the first angle is less than the capacitance threshold, set the first probability of the grip posture corresponding to the first angle to zero to obtain the second probability of the grip posture corresponding to the first angle.

[0121] Optionally, when the determining module 1004 determines the current grip posture of the mobile device according to the second probability of each grip posture, it is used for:

[0122] Obtain the second probabilities of each grip posture multiple times to obtain multiple second probabilities of each grip posture;

[0123] Determine the magnitude relationship between the probability mean of the multiple second probabilities of each grip posture and the set probability threshold;

[0124] In the case where the probability mean of each grip posture is less than the set probability threshold, determine that the current grip posture of the mobile device is the free state.

[0125] Optionally, the determining module 1004 can also be used for:

[0126] In the case where the probability mean of at least one grip posture is greater than or equal to the set probability threshold, select the grip posture with the largest probability mean from the multiple grip postures as the current grip posture of the mobile device.

[0127] Regarding the device in the above embodiments, the specific manners in which each module performs operations have been described in detail in the embodiments related to the method, and will not be elaborated herein.

[0128] Figure 11 This is a schematic structural diagram of an electronic device provided by an exemplary embodiment of the present disclosure. As Figure 11 shown, the electronic device includes: a memory 111 and a processor 112. Additionally, the electronic device further includes a power supply component 113 and a communication component 114.

[0129] The memory 111 is used to store computer programs and can be configured to store various other data to support operations on the electronic device. Examples of these data include instructions for any application program or method for operating on the electronic device.

[0130] A memory 111, which can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic memory, flash memory, magnetic disk or optical disk.

[0131] A communication component 114 for data transmission with other devices.

[0132] A processor 112 that can execute computer instructions stored in the memory 111 for:

[0133] Obtaining the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device;

[0134] Inputting the attitude data into a grip gesture prediction model to obtain the first probability of each grip gesture;

[0135] Updating the first probability of each grip gesture according to the contact capacitance values of each corner to obtain the second probability of each grip gesture;

[0136] Determining the current grip gesture of the mobile device according to the second probability of each grip gesture.

[0137] Optionally, the attitude data includes linear acceleration and angular velocity. When the processor 112 obtains the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device, it is used for:

[0138] Collecting the raw acceleration and gravitational acceleration of the mobile device using an accelerometer; collecting the angular velocity of the mobile device using a gyroscope; determining the linear acceleration of the mobile device according to the raw acceleration and gravitational acceleration; and

[0139] Collecting the contact capacitance values of each corner using SAR sensors provided at each corner of the mobile device.

[0140] Optionally, when the processor 112 determines the linear acceleration of the mobile device according to the raw acceleration and gravitational acceleration, it is used for:

[0141] Determining the linear acceleration components of the raw acceleration on each axis in the device coordinate system; and

[0142] Determining the gravitational acceleration components of the gravitational acceleration on each axis in the device coordinate system;

[0143] Subtracting the corresponding gravitational acceleration components from the linear acceleration components on each axis in the device coordinate system to obtain the linear acceleration of the mobile device.

[0144] Optionally, when the processor 112 updates the first probability of each grip posture according to the contact capacitance values of each corner to obtain the second probability of each grip posture, it is used for:

[0145] For the contact capacitance value of the first corner of the mobile device, determine the magnitude relationship between the contact capacitance value of the first corner and the capacitance threshold; wherein, the first corner is any one of the corners of the mobile device;

[0146] In the case where the contact capacitance value of the first corner is less than the capacitance threshold, set the first probability of the grip posture corresponding to the first corner to zero to obtain the second probability of the grip posture corresponding to the first corner.

[0147] Optionally, when the processor 112 determines the current grip posture of the mobile device according to the second probability of each grip posture, it is used for:

[0148] Obtain the second probability of each grip posture multiple times to obtain multiple second probabilities of each grip posture;

[0149] Determine the magnitude relationship between the probability mean of the multiple second probabilities of each grip posture and the set probability threshold;

[0150] In the case where the probability mean of each grip posture is less than the set probability threshold, determine that the current grip posture of the mobile device is the free state.

[0151] Optionally, the processor 112 can also be used for:

[0152] In the case where the probability mean of at least one grip posture is greater than or equal to the set probability threshold, select the grip posture with the largest probability mean from multiple grip postures as the current grip posture of the mobile device.

[0153] Correspondingly, an embodiment of the present disclosure also provides a computer-readable storage medium storing a computer program. When the computer-readable storage medium stores the computer program and the computer program is executed by one or more processors, it causes one or more processors to execute Figure 1 Each step in the method embodiment.

[0154] Correspondingly, an embodiment of the present disclosure also provides a computer program product, the computer program product includes a computer program / instructions, and the computer program / instructions are executed by a processor Figure 1 Each step in the method embodiment.

[0155] The above Figure 11The communication component therein is configured to facilitate communication, either wired or wireless, between the device where the communication component is located and other devices. The device where the communication component is located can access a wireless network based on communication standards, such as WiFi, 2G, 3G, 4G / LTE, 5G and other mobile communication networks, or a combination thereof. In an exemplary embodiment, the communication component receives a broadcast signal or broadcast-related information from an external broadcast management system via a broadcast channel. In an exemplary embodiment, the communication component further includes a Near Field Communication (NFC) module to facilitate short-range communication. For example, the NFC module can be implemented based on Radio Frequency Identification (RFID) technology, Infrared Data Association (IrDA) technology, Ultra Wideband (UWB) technology, Bluetooth (BT) technology and other technologies.

[0156] The above-mentioned Figure 11 The power supply component therein provides power for various components of the device where the power supply component is located. The power supply component can include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power for the device where the power supply component is located.

[0157] The above-mentioned electronic device further includes an audio component and a display screen.

[0158] The display screen includes a screen, and the screen can include a Liquid Crystal Display (LCD) and a Touch Panel (TP). If the screen includes a touch panel, the screen can be implemented as a touch screen to receive input signals from users. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors can not only sense the boundaries of touch or swipe actions, but also detect the duration and pressure associated with touch or swipe operations.

[0159] The audio component can be configured to output and / or input audio signals. For example, the audio component includes a microphone (MIC), and when the device where the audio component is located is in an operating mode, such as a call mode, a recording mode, and a voice recognition mode, the microphone is configured to receive external audio signals. The received audio signals can be further stored in the memory or sent via the communication component. In some embodiments, the audio component further includes a speaker for outputting audio signals.

[0160] In the above-mentioned embodiments of the device, equipment, storage medium, and computer program product of the present disclosure, the attitude data of the mobile device and the contact capacitance values of each angle are obtained; the attitude data is input into the grip gesture prediction model to obtain the first probability of each grip gesture; in combination with the contact capacitance values of each angle, the first probability of each grip gesture is updated to obtain the more accurate second probability of each grip gesture; according to the second probability of each grip gesture with higher accuracy, the current grip gesture of the mobile device is accurately determined, improving the accuracy of the grip gesture detection of the mobile device.

[0161] Those skilled in the art will understand that the embodiments of the present disclosure may be provided as a method, a system, or a computer program product. Therefore, the present disclosure may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present disclosure may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk memory, CD-ROM, optical memory, etc.) that contain computer-usable program code.

[0162] The present disclosure is described with reference to the flowcharts and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the present disclosure. It should be understood that each flow and / or block in the flowcharts and / or block diagrams, and combinations of flows and / or blocks in the flowcharts and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to the processors of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to produce a machine, such that the instructions executed by the processors of the computer or other programmable data processing devices produce means for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0163] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing device to work in a specific manner, such that the instructions stored in the computer-readable memory produce a manufacture including instruction means that implement the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0164] These computer program instructions can also be loaded onto a computer or other programmable data processing device, such that a series of operation steps are performed on the computer or other programmable device to produce a computer-implemented process, and thus the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in Figure 1 one or more of the flows Figure 1 or blocks or combinations of blocks.

[0165] In a typical configuration, a computing device includes one or more processors (CPUs), an input / output interface, a network interface, and memory.

[0166] The memory may include non-permanent memory in the form of computer-readable media, random access memory (RAM), and / or non-volatile memory, such as read-only memory (ROM) or flash memory (flash RAM). The memory is an example of computer-readable media.

[0167] A computer-readable medium includes both permanent and non-permanent, removable and non-removable media that can store information by any method or technology. The information can be computer-readable instructions, data structures, program modules, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, compact disc read-only memory (CD-ROM), digital versatile disc (DVD) or other optical storage, magnetic cassettes, magnetic tape magnetic disk storage or other magnetic storage devices, or any other non-transitory medium that can be used to store information that can be accessed by a computing device. As defined herein, a computer-readable medium does not include transitory computer-readable media, such as modulated data signals and carrier waves.

[0168] It should be noted that in this document, relational terms such as "first" and "second" are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any actual relationship or order between these entities or operations. Moreover, the term "comprising", "including" or any other variant thereof is intended to cover non-exclusive inclusion, so that a process, method, article or device comprising a series of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such process, method, article or device. Without further limitation, an element defined by the statement "comprising an..." does not exclude the presence of additional identical elements in the process, method, article or device comprising the element.

[0169] The above are only specific embodiments of the present disclosure, enabling those skilled in the art to understand or implement the present disclosure. Various modifications to these embodiments will be obvious to those skilled in the art, and the general principles defined herein can be implemented in other embodiments without departing from the spirit or scope of the present disclosure. Therefore, the present disclosure will not be limited to these embodiments herein, but rather to the broadest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for determining the holding posture of a device, characterized in that, Comprising: Obtaining the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device; Inputting the attitude data into a grip posture prediction model to obtain the first probability of each grip posture; Updating the first probability of each grip posture according to the contact capacitance values of each corner to obtain the second probability of each grip posture; Determining the current grip posture of the mobile device according to the second probability of each grip posture.

2. The method according to claim 1, characterized in that, The attitude data includes: linear acceleration and angular velocity. The obtaining of the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device includes: Collecting the raw acceleration and gravitational acceleration of the mobile device by using an accelerometer; collecting the angular velocity of the mobile device by using a gyroscope; determining the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration; and Collecting the contact capacitance values of each corner by using SAR sensors arranged at each corner of the mobile device.

3. The method according to claim 2, characterized in that, The determining of the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration includes: Determining the linear acceleration components of the raw acceleration on each axis in the device coordinate system; and Determining the gravitational acceleration components of the gravitational acceleration on each axis in the device coordinate system; Subtracting the corresponding gravitational acceleration components from the linear acceleration components on each axis in the device coordinate system to obtain the linear acceleration of the mobile device.

4. The method according to claim 1, characterized in that, The updating of the first probability of each grip posture according to the contact capacitance values of each corner to obtain the second probability of each grip posture includes: For the contact capacitance value of the first corner of the mobile device, determining the magnitude relationship between the contact capacitance value of the first corner and a capacitance threshold; wherein, the first corner is any one of the corners of the mobile device; In the case where the contact capacitance value of the first corner is less than the capacitance threshold, setting the first probability of the grip posture corresponding to the first corner to zero to obtain the second probability of the grip posture corresponding to the first corner.

5. The method according to claim 1, characterized in that, The determining of the current grip posture of the mobile device according to the second probability of each grip posture includes: Obtaining the second probability of each grip posture multiple times to obtain multiple second probabilities of each grip posture; Determining the magnitude relationship between the probability mean of the multiple second probabilities of each grip posture and a set probability threshold; In the case where the probability mean of each grip posture is less than the set probability threshold, determining the current grip posture of the mobile device as the free state.

6. The method according to claim 5, characterized in that, The method further includes: In the case where the probability mean of at least one grip posture is greater than or equal to the set probability threshold, selecting the grip posture with the maximum probability mean from multiple grip postures as the current grip posture of the mobile device.

7. A device for determining the holding posture of a device, characterized in that, Comprising: An obtaining module, configured to obtain the attitude data of the mobile device and the contact capacitance values of each corner of the mobile device; A prediction module, configured to input the attitude data into a grip posture prediction model to obtain the first probability of each grip posture; An updating module, configured to update the first probability of each grip posture according to the contact capacitance values of each corner to obtain the second probability of each grip posture; A determination module, configured to determine the current grip posture of the mobile device according to the second probability of each of the grip postures.

8. The device according to claim 7, characterized in that, The posture data includes linear acceleration and angular velocity. When acquiring the posture data of the mobile device and the contact capacitance values of each angle of the mobile device, the acquisition module is configured to: Collect the raw acceleration and gravitational acceleration of the mobile device by using an accelerometer; collect the angular velocity of the mobile device by using a gyroscope; determine the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration; and Collect the contact capacitance values of each angle by using SAR sensors provided at each angle of the mobile device.

9. The device according to claim 8, characterized in that, When determining the linear acceleration of the mobile device according to the raw acceleration and the gravitational acceleration, the acquisition module is configured to: Determine the linear acceleration components of the raw acceleration on each axis in the device coordinate system; And Determine the gravitational acceleration components of the gravitational acceleration on each axis in the device coordinate system; Subtract the corresponding gravitational acceleration components from the linear acceleration components on each axis in the device coordinate system to obtain the linear acceleration of the mobile device.

10. The device according to claim 7, wherein When updating the first probability of each of the grip postures according to the contact capacitance values of each angle to obtain the second probability of each of the grip postures, the update module is configured to: For the contact capacitance value of the first angle of the mobile device, determine the magnitude relationship between the contact capacitance value of the first angle and the capacitance threshold; wherein, the first angle is any one of the angles of the mobile device; If the contact capacitance value of the first angle is less than the capacitance threshold, then set the first probability of the grip posture corresponding to the first angle to zero to obtain the second probability of the grip posture corresponding to the first angle.

11. The device according to claim 7, wherein When determining the current grip posture of the mobile device according to the second probability of each of the grip postures, the determination module is configured to: Acquire the second probability of each of the grip postures multiple times to obtain multiple second probabilities of each of the grip postures; Determine the magnitude relationship between the probability mean of the multiple second probabilities of each of the grip postures and the set probability threshold; If the probability mean of each of the grip postures is less than the set probability threshold, then determine that the current grip posture of the mobile device is the free state.

12. The device according to claim 7, wherein The determination module may further be configured to: If the probability mean of at least one of the grip postures is greater than or equal to the set probability threshold, then select the grip posture with the maximum probability mean from multiple grip postures as the current grip posture of the mobile device.

13. A mobile phone, wherein Comprising: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the instructions to implement the steps in the method according to any one of claims 1-6.

14. An electronic device, wherein Comprising: A processor; A memory for storing instructions executable by the processor; Wherein, the processor is configured to execute the instructions to implement the steps in the method according to any one of claims 1-6.

15. A computer-readable storage medium having a computer program stored thereon, wherein When the computer program is executed by a processor, it implements the steps in the method according to any one of claims 1-6.

16. A computer program product comprising a computer program / instructions, wherein When the computer program / instructions are executed by a processor, each step in the method according to any one of claims 1-6 is implemented.

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