Fall detection method and device and storage medium

Abstract

The invention provides a fall detection method and device and a storage medium. According to the technical scheme, the electronic device obtains a first target parameter and a second target parameter, the first target parameter is used for indicating the speed change condition of the electronic device in the movement process, and the second target parameter comprises at least one of the first parameter, the second parameter and the third parameter; the first parameter is used for indicating the law degree of the movement process, the second parameter is used for indicating the intensity degree of the movement process, and the third parameter is used for indicating the height change condition of the target part in the movement process; performing grading judgment according to the first target parameter and the second target parameter, and determining whether the user of the electronic equipment is in a falling state or not according to a grading judgment result. According to the fall detection method, the electronic device determines whether the user of the electronic device is in the fall state in a hierarchical judgment mode, the fall behavior can be well recognized even in the scene that a child does strenuous exercise, and the detection accuracy is high.

Description

Technical Field

[0001] The present application relates to the technical field of human fall detection, and in particular to a fall detection method, device and storage medium. Background Art

[0002] Falling is one of the main causes of accidental injuries to the human body in daily life, especially for the elderly and children. Therefore, how to detect human falling behavior has always been a hot issue of concern.

[0003] The current fall detection algorithms are mainly based on sensors and image recognition. However, the deployment cost of image recognition algorithms is high, and they can only identify falls that occur within the camera range. In addition, due to privacy issues, it is not convenient to set them in areas such as bedrooms and bathrooms, and their deployment has many restrictions. Sensor-based fall detection algorithms are not restricted by time and place, but currently sensor-based algorithms are only popular in fall detection devices for the elderly. If the daily activities of the wearer of the device are younger and more intense, such as children running or playing ball, the existing algorithms will find it difficult to distinguish correctly.

[0004] Compared with the elderly, children are naturally lively and active, often chasing and fighting, and are more likely to fall. Therefore, how to improve the accuracy of child fall detection has become a technical problem that needs to be solved urgently. Summary of the invention

[0005] The present application provides a fall detection method, device and storage medium, which can improve the accuracy of child fall detection.

[0006] In a first aspect, the present application provides a fall detection method, which is applied to an electronic device, the method comprising: obtaining a first target parameter and a second target parameter of the electronic device, the first target parameter being used to indicate a speed change of the electronic device during movement, the second target parameter comprising at least one of a first parameter, a second parameter and a third parameter, the first parameter being used to indicate a regularity of the movement process, the second parameter being used to indicate an intensity of the movement process, and the third parameter being used to indicate a height change of a target part during the movement; performing a graded judgment based on the first target parameter and the second target parameter, and determining whether a user of the electronic device is in a fall state based on a graded judgment result; wherein, when the graded judgment result indicates that the first target parameter satisfies a first preset condition and the second target parameter satisfies a second preset condition, it is determined that the user is in the fall state, and the first preset condition and the second preset condition are determined based on data characteristics of corresponding parameters in the fall state.

[0007] In some embodiments, the step of performing a graded judgment based on the first target parameter and the second target parameter and determining whether the user of the electronic device is in a falling state based on the graded judgment result includes: judging whether the first target parameter satisfies the first preset condition; if the first target parameter satisfies the first preset condition, judging whether the second target parameter satisfies the second preset condition; if the second target parameter satisfies the second preset condition, determining that the user is in the falling state.

[0008] In some embodiments, the first target parameter includes the acceleration of the electronic device at a current moment, and the first preset condition is that the acceleration at the current moment is greater than a preset acceleration threshold; determining whether the first target parameter satisfies the first preset condition includes: determining whether the acceleration at the current moment is greater than the preset acceleration threshold.

[0009] In some embodiments, the second target parameter includes the first parameter, the second parameter and the third parameter, the first parameter includes the rotation angular velocity of the electronic device in multiple preset directions, the second parameter includes the number of times the acceleration of the electronic device crosses zero per unit time in the multiple preset directions, and the third parameter includes the air pressure change value of the electronic device; accordingly, the second preset condition includes a first sub-condition, a second sub-condition and a third sub-condition, the first sub-condition includes that the target mutual correlation coefficient between the rotation angular velocities in each direction of the multiple preset directions is less than a preset coefficient threshold, the second sub-condition includes that the average number of zero crossings of the electronic device per unit time is greater than a preset zero crossing threshold, and the third sub-condition includes that the air pressure change value of the electronic device is greater than a preset air pressure threshold.

[0010] In some embodiments, the determining whether the second target parameter satisfies the second preset condition includes: determining whether the first parameter satisfies the first sub-condition, and whether the second parameter satisfies the second sub-condition; when it is determined that the first parameter satisfies the first sub-condition and the second parameter satisfies the second sub-condition, determining whether the third parameter satisfies the third sub-condition; when the third parameter satisfies the third sub-condition, determining that the second target parameter satisfies the second preset condition.

[0011] In some embodiments, the determining whether the first parameter satisfies the first sub-condition and whether the second parameter satisfies the second sub-condition includes: calculating the target mutual correlation coefficient between the rotation angular velocities in each of the multiple preset directions based on the rotation angular velocities in the multiple preset directions, and calculating the average number of zero crossings of the electronic device per unit time based on the number of acceleration zero crossings per unit time in the multiple preset directions; determining whether the target mutual correlation coefficient is less than the preset coefficient threshold and whether the average number of zero crossings is greater than the preset zero crossing threshold.

[0012] In some embodiments, the multiple preset directions include a first direction, a second direction and a third direction, and the angular velocities of rotation in the multiple preset directions include the angular velocities of rotation of the electronic device in the first direction, the angular velocities of rotation in the second direction and the angular velocities of rotation in the third direction; calculating the target mutual correlation coefficient between the angular velocities of rotation in each of the multiple preset directions based on the angular velocities of rotation in the multiple preset directions includes: calculating the target mutual correlation coefficient between the angular velocities of rotation in each of the multiple preset directions based on any two of the angular velocities of rotation in the first direction, the angular velocities in the second direction and the angular velocity in the third direction, and a preset mutual correlation function.

[0013] In some embodiments, the target mutual correlation coefficient between the rotation angular velocities in each of the multiple preset directions is calculated based on any two of the rotation angular velocities in the first direction, the rotation angular velocity in the second direction, and the third direction, and a preset mutual correlation function, including: calculating a first mutual correlation coefficient based on the rotation angular velocity of the electronic device at a first moment in the first direction, the rotation angular velocity at a second moment in the second direction, and the preset mutual correlation function, the first mutual correlation coefficient being used to indicate the degree of correlation between the rotation angular velocity at the first moment and the rotation angular velocity at the second moment, the first moment and the second moment being any two different moments between two time series; calculating a first mutual correlation coefficient based on the rotation angular velocity of the electronic device at a third moment in the second direction, the rotation angular velocity at a fourth moment in the third direction, and the preset mutual correlation function. Assume a mutual correlation function, calculate a second mutual correlation coefficient, the second mutual correlation coefficient is used to indicate the correlation between the rotation angular velocity at the third moment and the rotation angular velocity at the fourth moment, the third moment and the fourth moment are any two different moments between two time series; calculate a third mutual correlation coefficient according to the rotation angular velocity of the electronic device at a fifth moment in the first direction, the rotation angular velocity at a sixth moment in the third direction and the preset mutual correlation function number, the third mutual correlation coefficient is used to indicate the correlation between the rotation angular velocity at the fifth moment and the rotation angular velocity at the sixth moment, the fifth moment and the sixth moment are any two different moments between the two time series; calculate a target mutual correlation coefficient between the rotation angular velocities in each of the multiple preset directions according to the first mutual correlation coefficient, the second mutual correlation coefficient and the third mutual correlation coefficient.

[0014] In some embodiments, the formula of the preset cross-correlation function is:

[0015]

[0016] Among them, R xy is the mutual correlation coefficient, ω x (t) is the rotation angular velocity of the electronic device at the seventh moment in any one of the first direction, the second direction and the third direction, ω y (t+τ) is the rotational angular velocity of the electronic device at the eighth moment in any one of the first direction, the second direction and the third direction, and τ is a time displacement variable.

[0017] In some embodiments, the target correlation coefficient between the rotational angular velocities in each of the plurality of preset directions is an average value of the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient.

[0018] In some embodiments, the number of acceleration zero crossings per unit time in the multiple preset directions includes a first acceleration zero crossing number per unit time of the electronic device in the first direction, a second acceleration zero crossing number per unit time in the second direction, and a third acceleration zero crossing number per unit time in the third direction; accordingly, the average number of zero crossings of the electronic device per unit time is the average of the first acceleration zero crossing number, the second acceleration zero crossing number, and the third acceleration zero crossing number.

[0019] In some embodiments, the determining whether the third parameter satisfies the third sub-condition includes: obtaining an air pressure change value of the electronic device; and determining whether the air pressure change value is greater than the preset air pressure threshold.

[0020] In some embodiments, obtaining the air pressure change value of the electronic device includes: obtaining a first air pressure value of the electronic device at a current moment and a second air pressure value within a preset time period before the current moment; calculating the difference between the first air pressure value and the second air pressure value as the air pressure change value of the electronic device.

[0021] In this embodiment, the electronic device determines that the user of the electronic device is in a falling state by means of graded judgment, when the graded judgment result represents the speed change, regularity, intensity and height change of the target part of the electronic device during the movement, and satisfies the data characteristics of the corresponding parameters in the falling state. The fall detection method can also well identify the child's fall behavior through graded judgment when the child is doing intense exercise, such as running, playing ball, chasing and playing, etc., so the accuracy of child fall detection can be improved.

[0022] In a second aspect, the present application provides a fall detection device, the device comprising: an acquisition module, used to acquire a first target parameter and a second target parameter of the electronic device, the first target parameter being used to indicate a speed change of the electronic device during movement, the second target parameter comprising at least one of a first parameter, a second parameter and a third parameter, the first parameter being used to indicate a regularity of the movement process, the second parameter being used to indicate an intensity of the movement process, and the third parameter being used to indicate a height change of a target part during the movement; a processing module, used to perform a graded judgment based on the first target parameter and the second target parameter, and determine whether a user of the electronic device is in a fall state based on a graded judgment result; wherein, when the graded judgment result indicates that the first target parameter satisfies a first preset condition and the second target parameter satisfies a second preset condition, it is determined that the user is in the fall state, and the first preset condition and the second preset condition are determined based on data characteristics of corresponding parameters in the fall state.

[0023] In a third aspect, the present application provides an electronic device, comprising a processor and a memory, wherein the memory is used to store code instructions; the processor is used to execute the code instructions to implement the method in the above-mentioned first aspect and any one of the embodiments.

[0024] Optionally, there are one or more processors and one or more memories.

[0025] Optionally, the memory may be integrated with the processor, or the memory may be provided separately from the processor.

[0026] In the specific implementation process, the memory can be a non-transitory memory, such as a read-only memory (ROM), which can be integrated with the processor on the same chip or can be set on different chips respectively. The embodiments of the present application do not limit the type of memory and the setting method of the memory and the processor.

[0027] In a fourth aspect, the present application provides a computer-readable storage medium, which stores a computer program (also referred to as code, or instructions) which, when executed on a computer, enables the computer to execute the method in the first aspect and any one of the embodiments described above.

[0028] In a fifth aspect, the present application provides a computer program product, comprising: a computer program (also referred to as code, or instruction), which, when executed, enables a computer to execute the method in the first aspect and any one of the embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0029] Figure 1 A flowchart of a fall detection method provided by an embodiment of the present application;

[0030] Figure 2 A flowchart of a fall detection method provided by another embodiment of the present application;

[0031] Figure 3 A schematic diagram of the number of acceleration crossing zero in a unit time corresponding to normal walking and falling provided in one embodiment of the present application;

[0032] Figure 4 A flowchart of a fall detection method provided in yet another embodiment of the present application;

[0033] Figure 5 A schematic diagram of a display interface for outputting prompt information provided by an embodiment of the present application;

[0034] Figure 6 A structural schematic diagram of a fall detection device provided in one embodiment of the present application;

[0035] Figure 7 A structural schematic diagram of a fall detection device provided in another embodiment of the present application. DETAILED DESCRIPTION

[0036] The technical solution in this application will be described below in conjunction with the accompanying drawings.

[0037] In order to facilitate the clear description of the technical solutions of the embodiments of the present application, in the embodiments of the present application, the words "first" and "second" are used to distinguish the same items or similar items with substantially the same functions and effects. For example, the first instruction and the second instruction are to distinguish different user instructions, and their order is not limited. Those skilled in the art can understand that the words "first" and "second" do not limit the quantity and execution order, and the words "first" and "second" do not necessarily limit them to be different.

[0038] It should be noted that, in this application, words such as "exemplarily" or "for example" are used to indicate examples, illustrations or explanations. Any embodiment or design described as "exemplarily" or "for example" in this application should not be interpreted as being more preferred or more advantageous than other embodiments or designs. Specifically, the use of words such as "exemplarily" or "for example" is intended to present related concepts in a specific way.

[0039] In addition, "at least one" means one or more, and "plurality" means two or more. "And / or" describes the association relationship of associated objects, indicating that three relationships may exist. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone, where A and B can be singular or plural. The character " / " generally indicates that the previous and next associated objects are in an "or" relationship. "At least one of the following" or similar expressions refers to any combination of these items, including any combination of single or plural items. For example, at least one of a, b and c can mean: a, or b, or c, or a and b, or a and c, or b and c, or a, b and c, where a, b, c can be single or multiple.

[0040] With the advancement of sensor technology and the improvement of life needs, many health and safety monitoring tools, such as fall detection, are installed on existing smart devices. Falling is one of the main causes of accidental injuries to the human body in daily life, especially for the elderly and children. Once a fall occurs, if it is not discovered and rescued in time, it may aggravate the injury to the elderly and children. Therefore, how to detect human fall behavior has always been a hot issue of concern.

[0041] The current fall detection algorithms are mainly based on sensors and image recognition. For example, sensors collect the user's motion data in real time, and analyze and determine whether the user's current motion state is in a fall state; or image recognition devices are used to collect the user's motion images to determine whether the user's current motion state is in a fall state.

[0042] In recent years, the image recognition method based on neural network has achieved good results, but its deployment cost is high, and it can only identify falls that occur within the camera range. It is also inconvenient to set it in areas such as bedrooms and bathrooms due to privacy issues, and its deployment has many restrictions. The sensor-based fall detection algorithm is not restricted by time and place and has strong applicability. However, the sensor-based algorithm is currently only popular in fall detection devices for the elderly, because the elderly's movements are relatively slow and stable, and the sensor can keenly capture abnormal fall movements. If the daily activities of the device wearer are younger and more intense, such as in scenes such as children running and playing ball, it is difficult for existing algorithms to distinguish correctly.

[0043] At the same time, compared with the elderly, the penetration rate of smart devices among children is higher. Children are lively and active by nature, often chasing and fighting, and are more likely to fall. However, children's physical development is not complete, and falls may lead to more serious consequences. Therefore, how to improve the accuracy of children's fall detection has become a technical problem that needs to be solved urgently.

[0044] In view of this, the embodiments of the present application provide a fall detection method, device and storage medium. In the technical solution of the present application, the electronic device obtains a first target parameter and a second target parameter of the electronic device, the first target parameter is used to indicate the speed change of the electronic device during the movement, the second target parameter includes at least one of the first parameter, the second parameter and the third parameter, the first parameter is used to indicate the regularity of the movement process, the second parameter is used to indicate the intensity of the movement process, and the third parameter is used to indicate the height change of the target part during the movement; a graded judgment is performed according to the first target parameter and the second target parameter, and according to the graded judgment result, it is determined whether the user of the electronic device is in a fall state; wherein, when the graded judgment result indicates that the first target parameter satisfies the first preset condition and the second target parameter satisfies the second preset condition, it is determined that the user is in the fall state, and the first preset condition and the second preset condition are determined according to the data characteristics of the corresponding parameters in the fall state. In the fall detection method, the electronic device determines that the user of the electronic device is in a fall state through graded judgment. When the graded judgment result represents the speed change, regularity, intensity and height change of the target part of the electronic device during the movement, and satisfies the data characteristics of the corresponding parameters in the fall state, the fall detection method can also well identify the child's fall behavior through graded judgment when the child is doing strenuous exercise, such as running, playing ball, chasing and fighting, etc., thereby improving the accuracy of child fall detection.

[0045] It should be understood that the electronic devices involved in the embodiments of the present application may be mobile phones, watches, bracelets and other electronic devices that can collect user-related information. The electronic devices may also include components such as processors, memories, transceivers, display units, input units, sensors, audio circuits and power modules, but the specific embodiments of the present application do not limit this.

[0046] In order to make the purpose and technical solution of the present application clearer and more intuitive, the fall detection method, device and storage medium provided by the embodiments of the present application will be described in detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present application and are not used to limit the present application.

[0047] Please refer to Figure 1 , is a flow chart of a fall detection method provided by an embodiment of the present application. The method can be applied to electronic devices, and is described in detail below. Figure 1 The various steps in the method shown in the flowchart include:

[0048] S101, obtaining a first target parameter and a second target parameter of the electronic device, wherein the first target parameter is used to indicate a speed change of the electronic device during movement, and the second target parameter includes at least one of a first parameter, a second parameter and a third parameter, the first parameter is used to indicate a regularity of the movement process, the second parameter is used to indicate an intensity of the movement process, and the third parameter is used to indicate a height change of a target part during movement.

[0049] It should be understood that the electronic device may obtain the first target parameter and the second target parameter through the electronic device's own sensor or through interaction with other electronic devices, and this application does not limit this.

[0050] It should also be understood that the first target parameter is used to indicate the speed change of the electronic device during movement. For example, the first target parameter can be the acceleration of the electronic device at the current moment, or the speed at the current moment, or the speed within a preset time period. Then, the corresponding acceleration is calculated based on the ratio of the speed change within the preset time period to the time used. The preset time period can be flexibly set and can be a long time or a short time. This application does not limit this.

[0051] For ease of understanding, the first parameter is used to indicate the regularity of the motion process. For example, the first parameter can be the correlation of the three axes calculated by collecting the rotation angular velocity of the electronic device on the x-axis, y-axis and z-axis through a gyroscope, or it can be the correlation of the three axes calculated by collecting the acceleration of the electronic device on the x-axis, y-axis and z-axis through an accelerometer, etc. This application does not specifically limit this.

[0052] It should also be understood that the second parameter is used to indicate the intensity of the exercise process. For example, the second parameter can be the zero-crossing rate of the acceleration of the electronic device, the heart rate of the user of the electronic device, or the amplitude of the electronic device, etc. This application does not specifically limit this.

[0053] The third parameter is used to indicate the change in height of the target part during the movement. For example, the third parameter may be the change in air pressure of the target part during the movement, or the height difference obtained by directly collecting the change in height of the target part during the movement using an altitude measuring tool, etc. This application does not make any specific limitations on this.

[0054] S102, performing a graded judgment according to the first target parameter and the second target parameter, and determining whether the user of the electronic device is in a falling state according to a graded judgment result.

[0055] Among them, when the graded judgment result indicates that the first target parameter satisfies the first preset condition and the second target parameter satisfies the second preset condition, it is determined that the user is in a falling state, and the first preset condition and the second preset condition are determined based on the data characteristics of the corresponding parameters in the falling state.

[0056] It should be understood that the electronic device performs graded judgment based on the first target parameter and the second target parameter, that is, the electronic device first determines whether the first target parameter meets the first preset condition, and when the first target parameter meets the first preset condition, further determines whether the second target parameter meets the second preset condition; if the second target parameter meets the second preset condition, it is determined that the user is in a falling state.

[0057] It should also be understood that the data characteristics of the corresponding parameters in the fall state include that the speed change of the electronic device during the movement exceeds the normal range, the regularity of the movement is low, the intensity of the movement process exceeds the normal range, and the height change of the target part during the movement is abnormal, etc. Accordingly, the first preset condition and the second preset condition are determined based on the data characteristics of the corresponding parameters in the fall state.

[0058] In this embodiment, the electronic device determines that the user of the electronic device is in a falling state through graded judgment. When the graded judgment result represents the speed change, regularity, intensity and height change of the target part of the electronic device during the movement, which meets the data characteristics of the corresponding parameters in the falling state, the electronic device is determined to be in a falling state. The fall detection method collects and analyzes a large amount of data. Therefore, in situations such as children doing strenuous exercise, such as running, playing ball, chasing and fighting, etc., the child's fall behavior can be well identified, and the accuracy of fall detection is high.

[0059] Based on the above embodiments, Figure 2 A flow chart of a fall detection method provided in another embodiment of the present application is shown in FIG. Figure 2 In the embodiment shown, taking how to detect a fall state according to the first target parameter and the second target parameter as an example, the following describes in detail Figure 2 The various steps in the method shown in the flowchart include:

[0060] S201, obtaining a first target parameter and a second target parameter of the electronic device, wherein the first target parameter is used to indicate a speed change of the electronic device during movement, and the second target parameter includes at least one of the first parameter, the second parameter and a third parameter, the first parameter is used to indicate a regularity of the movement process, the second parameter is used to indicate an intensity of the movement process, and the third parameter is used to indicate a height change of a target part during movement.

[0061] This step and Figure 1Step S101 in the illustrated embodiment is similar and will not be described in detail here.

[0062] S202: Determine whether the first target parameter satisfies a first preset condition.

[0063] In a possible implementation, the first target parameter includes the acceleration of the electronic device at the current moment. When the user of the electronic device falls, the electronic device and the user will experience drastic acceleration or deceleration, and the body will lose its center of gravity and fall downward. Generally, when the user is not acted upon by external force, the acceleration is usually 1g. When the body collides with the ground, the speed returns to zero almost instantly and generates a speed in the opposite direction in a short period of time. The acceleration fluctuates violently, so acceleration can be used as the primary judgment criterion.

[0064] For example, assuming that the user of the electronic device is a child, if the child falls while running, the child's body will usually lean forward and lose its center of gravity, and then the acceleration will shake violently.

[0065] Optionally, acceleration can be collected using an acceleration sensor or other collection tools, which is not limited in this application.

[0066] Taking the first target parameter as the acceleration of the electronic device at the current moment as an example, the first preset condition is that the acceleration at the current moment is greater than the preset acceleration threshold. Accordingly, the electronic device determines whether the acceleration at the current moment is greater than the preset acceleration threshold. If the acceleration at the current moment is greater than the preset acceleration threshold, the first target parameter meets the first preset condition, otherwise it does not meet the first preset condition, and the electronic device continues to detect.

[0067] S203: When the first target parameter satisfies the first preset condition, determine whether the second target parameter satisfies the second preset condition.

[0068] It should be understood that the electronic device performs hierarchical judgment based on the first target parameter and the second target parameter, so when the first target parameter meets the first preset condition, the electronic device triggers a judgment on whether the second target parameter meets the second preset condition.

[0069] In a possible implementation, the second target parameter includes a first parameter, a second parameter, and a third parameter, the first parameter includes the rotational angular velocity of the electronic device in multiple preset directions, the second parameter includes the number of acceleration zero crossings of the electronic device in a unit time in multiple preset directions, and the third parameter includes the air pressure change value of the electronic device. Accordingly, the second preset condition includes a first sub-condition, a second sub-condition, and a third sub-condition, the first sub-condition includes that the target mutual correlation coefficient between the rotational angular velocity in each direction of the multiple preset directions is less than a preset coefficient threshold, the second sub-condition includes that the average number of zero crossings of the electronic device in a unit time is greater than a preset zero crossing number threshold, and the third sub-condition includes that the air pressure change value of the electronic device is greater than a preset air pressure threshold.

[0070] Among them, the multiple preset directions may include the x-axis direction, y-axis direction and z-axis direction in three-dimensional space. In this application, the multiple preset directions are also described as the x-axis direction, y-axis direction and z-axis direction as an example.

[0071] It should be understood that the first parameter includes the angular velocity of rotation of the electronic device in multiple preset directions, and the angular velocity of rotation refers to the angle of rotation per unit time when an object rotates around a certain axis. In general, when the user of an electronic device performs strenuous activities, the motion trajectory of the electronic device is usually regular, so the angular velocities of rotation of the three axes are usually strongly correlated; and when the user falls, especially when children fall while playing, the motion trajectory of the electronic device is usually chaotic, and the correlation between the three axes is relatively weak. Based on this principle, the correlation coefficient between the three-axis rotation angular velocities can be calculated for judgment. Specifically, the larger the absolute value of the correlation coefficient, the higher the correlation between the two. If the correlation coefficient is positive, it means that the two are positively correlated, otherwise it means negatively correlated. And it is verified by experiments that the peak value of the correlation coefficient of daily activities such as running, jumping, squatting, etc. is usually 5 to 15 times the peak value of the falling behavior, and this can be used as a basis for effective judgment of falling behavior. Correspondingly, the corresponding first sub-condition includes that the target mutual correlation coefficient between the rotation angular velocities in each direction of the plurality of preset directions is less than a preset coefficient threshold, that is, the correlation is lower than a preset threshold.

[0072] It should be noted that the above-mentioned preset coefficient threshold is obtained through a large number of fall experiment tests, and can also be preset based on experience, and this application does not limit this.

[0073] Optionally, the rotational angular velocity may be collected using a gyroscope or other sensors, which is not limited in the present application.

[0074] It should also be understood that the second parameter includes the number of acceleration zero crossings per unit time of the electronic device in multiple preset directions. Since falling is a relatively complex action, after a violent collision, the acceleration signal shows a more violent oscillation characteristic, and the number of zero crossings per unit time is significantly higher than that in normal use. For example, taking normal walking and falling as an example, the number of zero crossings per unit time is as follows: Figure 3 As shown, the number of zero crossings of acceleration when falling is significantly greater than the number of zero crossings of acceleration when walking normally. Accordingly, the corresponding second sub-condition includes that the average number of zero crossings of the electronic device per unit time is greater than a preset zero crossing threshold, that is, when the average number of zero crossings of the electronic device per unit time is greater than a certain value, it can be used as a condition for determining a falling behavior.

[0075] It should be noted that the above-mentioned preset zero-crossing number threshold is obtained through a large number of fall experiments and tests, and can also be preset based on experience, and this application does not limit this.

[0076] Optionally, acceleration may be collected using an accelerometer or other sensors, which is not limited in this application.

[0077] Understandably, the third parameter includes the air pressure change value of the electronic device, and the air pressure change of the electronic device can simply represent the height change of the electronic device. Since the relative change value is considered, the interference factors of ambient temperature, humidity, altitude, etc. on the air pressure can be ignored. When users use electronic devices, for example, children are very prone to bumps, pushing and fighting, etc., and it is difficult to distinguish them from falling actions only by acceleration and rotational angular velocity. In general, the relative height of electronic devices is kept within the normal range of activities, and the height change of the falling action is close to half the height of the user, so the use of height change can effectively detect the falling behavior. Correspondingly, the corresponding third sub-condition includes that the air pressure change value of the electronic device is greater than the preset air pressure threshold, that is, when the height difference of the electronic device changes by more than a certain value, it can be used as a condition for determining the falling behavior.

[0078] It should be noted that the above-mentioned preset air pressure threshold is obtained through a large number of fall experiment tests, and can also be preset based on experience, and this application does not limit this.

[0079] Optionally, the air pressure may be collected using a barometer or other sensors, which is not limited in the present application.

[0080] Optionally, the sampling frequency of the acceleration sensor, gyroscope and barometer mentioned above is about 50 Hz. In the embodiment of the present application, one frame is defined as 1 second, that is, one frame contains 50 sample points, and the example of calculating whether a fall occurs every 1 second is used for explanation.

[0081] Exemplarily, the first parameter is the rotational angular velocity of the electronic device in multiple preset directions, the second parameter is the number of acceleration zero crossings of the electronic device per unit time in multiple preset directions, and the third parameter is the air pressure change value of the electronic device. Accordingly, the second preset condition includes a first sub-condition, a second sub-condition, and a third sub-condition, the first sub-condition being that the target correlation coefficient between the rotational angular velocities in each direction of the multiple preset directions is less than a preset coefficient threshold, the second sub-condition being that the average number of zero crossings of the electronic device per unit time is greater than a preset zero crossing threshold, and the third sub-condition being that the air pressure change value of the electronic device is greater than a preset air pressure threshold. As an example, how the electronic device determines whether the second target parameter satisfies the second preset condition when the first target parameter satisfies the first preset condition is described in detail. For details, please refer to Figure 4 , the following details Figure 4 The various steps in the method shown in the flowchart include:

[0082] S2031, determining whether the first parameter satisfies the first sub-condition, and whether the second parameter satisfies the second sub-condition.

[0083] In a possible implementation, the electronic device calculates the target correlation coefficient between the rotation angular velocities in each of the plurality of preset directions according to the rotation angular velocities in the plurality of preset directions.

[0084] It should be understood that the multiple preset directions include a first direction, a second direction, and a third direction, and the rotational angular velocities in the multiple preset directions include the rotational angular velocity of the electronic device in the first direction, the rotational angular velocity in the second direction, and the rotational angular velocity in the third direction. Accordingly, the electronic device calculates the target cross-correlation coefficient between the rotational angular velocities in each direction in the multiple preset directions based on any two of the rotational angular velocities in the first direction, the rotational angular velocity in the second direction, and the rotational angular velocity in the third direction, and a preset cross-correlation function.

[0085] Understandably, the electronic device calculates a first correlation coefficient based on the rotation angular velocity of the electronic device at a first moment in the first direction, the rotation angular velocity at a second moment in the second direction and a preset cross-correlation function. The first correlation coefficient is used to indicate the degree of correlation between the rotation angular velocity at the first moment and the rotation angular velocity at the second moment, where the first moment and the second moment are any two different moments between the two time series.

[0086] Among them, the formula of the preset cross-correlation function is:

[0087]

[0088] Among them, R xy is the mutual correlation coefficient, ωx (t) is the rotation angular velocity of the electronic device at the seventh moment in any one of the first direction, the second direction and the third direction, ω y (t+τ) is the rotational angular velocity of the electronic device at the eighth moment in any one of the first direction, the second direction and the third direction, τ is the time displacement variable, and the seventh moment and the eighth moment are any two different moments between the two time series.

[0089] Exemplarily, taking the first direction as the x-axis direction, the second direction as the y-axis direction, and the third direction as the z-axis direction as an example, based on the angular velocity of rotation of the electronic device in the x-axis direction at the first moment, the angular velocity of rotation in the y-axis direction at the second moment, and the above-mentioned preset cross-correlation function, the correlation degree between the angular velocity of rotation of the x-axis at the first moment and the angular velocity of rotation of the y-axis at the second moment, that is, the first cross-correlation coefficient, can be calculated.

[0090] Further, the electronic device calculates a second correlation coefficient based on the rotational angular velocity of the electronic device at a third moment in the second direction, the rotational angular velocity at a fourth moment in the third direction and a preset cross-correlation function, and the second correlation coefficient is used to indicate the degree of correlation between the rotational angular velocity at the third moment and the rotational angular velocity at the fourth moment, the third moment and the fourth moment being any two different moments between the two time series.

[0091] Exemplarily, taking the first direction as the x-axis direction, the second direction as the y-axis direction, and the third direction as the z-axis direction as an example, based on the angular velocity of rotation of the electronic device in the y-axis direction at the third moment, the angular velocity of rotation in the z-axis direction at the fourth moment, and the above-mentioned preset cross-correlation function, the correlation degree between the angular velocity of rotation of the y-axis at the third moment and the angular velocity of rotation of the z-axis at the fourth moment, that is, the second cross-correlation coefficient, can be calculated.

[0092] Further, the electronic device calculates a third mutual correlation coefficient based on the rotation angular velocity of the electronic device in the first direction at a fifth moment, the rotation angular velocity of the electronic device in the third direction at a sixth moment, and a preset mutual correlation function number. The third mutual correlation coefficient is used to indicate the degree of correlation between the rotation angular velocity at the fifth moment and the rotation angular velocity at the sixth moment, the fifth moment and the sixth moment being any two different moments between the two time series.

[0093] Exemplarily, taking the first direction as the x-axis direction, the second direction as the y-axis direction, and the third direction as the z-axis direction as an example, based on the angular velocity of rotation of the electronic device in the x-axis direction at the fifth moment, the angular velocity of rotation in the z-axis direction at the sixth moment, and the above-mentioned preset cross-correlation function, the correlation degree between the angular velocity of rotation of the x-axis at the fifth moment and the angular velocity of rotation of the z-axis at the sixth moment, that is, the third cross-correlation coefficient, can be calculated.

[0094] Finally, the electronic device calculates the target mutual correlation coefficient between the rotation angular velocities in each direction of the plurality of preset directions according to the first mutual correlation coefficient, the second mutual correlation coefficient and the third mutual correlation coefficient, wherein the target mutual correlation coefficient is the average value of the first mutual correlation coefficient, the second mutual correlation coefficient and the third mutual correlation coefficient.

[0095] Furthermore, the electronic device determines whether the target mutual correlation coefficient is less than a preset coefficient threshold. If the target mutual correlation coefficient is less than the preset coefficient threshold, the first parameter satisfies the first sub-condition. Otherwise, the first parameter does not satisfy the first sub-condition, and the electronic device returns to continue detecting the falling behavior.

[0096] In another possible implementation, the electronic device calculates an average number of zero crossings of the electronic device per unit time based on the number of acceleration zero crossings per unit time in multiple preset directions.

[0097] It should be understood that the number of acceleration zero crossings per unit time in multiple preset directions includes the first number of acceleration zero crossings per unit time of the electronic device in the first direction, the second number of acceleration zero crossings per unit time in the second direction, and the third number of acceleration zero crossings per unit time in the third direction; accordingly, the average number of zero crossings per unit time of the electronic device is the average of the first number of acceleration zero crossings, the second number of acceleration zero crossings, and the third number of acceleration zero crossings.

[0098] For example, taking the first direction as the x-axis direction, the second direction as the y-axis direction, and the third direction as the z-axis direction as an example, define zero x is the number of times the acceleration passes zero per unit time T in the x-axis direction, zero y is the number of times the acceleration passes zero per unit time T in the y-axis direction, zero z is the number of times the acceleration crosses zero per unit time T in the z-axis direction, then the average number of zero crossings Zero of the electronic device per unit time is:

[0099]

[0100] Further, the electronic device determines whether the average number of zero crossings is greater than a preset zero crossing threshold. If the average number of zero crossings is greater than the preset zero crossing threshold, then the second parameter satisfies the second sub-condition. Otherwise, if the second parameter does not satisfy the second sub-condition, the electronic device returns to continue detecting the falling behavior.

[0101] S2032: When it is determined that the first parameter satisfies the first sub-condition and the second parameter satisfies the second sub-condition, determine whether the third parameter satisfies the third sub-condition.

[0102] It should be noted that the first parameter satisfying the first sub-condition and the second parameter satisfying the second sub-condition must be satisfied at the same time, neither of which can be missing. Otherwise, the electronic device will fail to detect and return to continue detecting the fall behavior.

[0103] In one possible implementation, the electronic device first obtains the air pressure change value of the electronic device, and then determines whether the air pressure change value is greater than a preset air pressure threshold. If the air pressure change value is greater than the preset air pressure threshold, the third parameter satisfies the third sub-condition. Otherwise, the third parameter does not satisfy the third sub-condition, and the electronic device returns to continue detecting the falling behavior.

[0104] Understandably, the electronic device can obtain the air pressure change value by obtaining the first air pressure value of the electronic device at the current moment and the second air pressure value in a preset time period before the current moment, and calculating the difference between the first air pressure value and the second air pressure value as the air pressure change value of the electronic device. The preset time period before the current moment can be 1 second before the current moment, or 2 seconds, 10 seconds, etc., and this application does not limit this.

[0105] S2033: When the third parameter satisfies the third sub-condition, determine that the second target parameter satisfies the second preset condition.

[0106] It should be understood that when the third parameter of the electronic device meets the third sub-condition, it means that the target correlation coefficient of the electronic device at this time is less than the preset coefficient threshold, the average number of zero crossings is greater than the preset zero crossing threshold, and the air pressure change value is greater than the preset air pressure threshold, so it is determined that the second target parameter meets the second preset condition.

[0107] S204: When the second target parameter satisfies a second preset condition, determine that the user is in a falling state.

[0108] It should be understood that when the second target parameter of the electronic device satisfies the second preset condition, the electronic device determines that the user is in a falling state. At this time, the electronic device can output a fall prompt, such as sending a notification message to the mobile phone of the user bound to the electronic device, informing the user of the electronic device that the user has fallen, so as to timely discover and rescue. Especially when the user of the electronic device is a child, timely notifying the child's parents can prevent the child from further injury.

[0109] For example, the notification message may be a text prompt or a voice prompt, etc., which is not limited in this application. Figure 5 , the words "Child has fallen, please check in time" will be output on the user's mobile phone screen that is bound to the electronic device, so as to inform the user in time and reduce the harm caused by the fall.

[0110] In this embodiment, the electronic device uses a graded judgment detection method to first judge the speed change of the user during exercise based on acceleration, and then further judge whether the user is in a falling state according to the regularity and intensity of the user's exercise process, as well as the height change of the target part. This detection method is based on the analysis of multiple data, and even when the user of the electronic device is performing high-intensity strenuous exercise, such as children running, playing ball, chasing and fighting, etc., it can better identify falling behavior and has a high detection accuracy.

[0111] In a possible implementation, the graded judgment of the above-mentioned fall detection algorithm can better identify the fall behavior, even if the user of the electronic device performs more intense activities.

[0112] Exemplarily, Table 1 shows a test result of testing the fall detection algorithm.

[0113] Table 1

[0114] Reality / Detection fall Other actions total fall 68 22 90 Other actions 5 1395 1400 total 73 1417 1490

[0115] The design of the test experiment is as follows:

[0116] Preliminary stage: 14 volunteers were recruited to simulate falling actions on a foam pad for a total of 127 times. By calculating their statistical information, a suitable threshold range was obtained, such as the preset acceleration threshold, preset coefficient threshold, preset zero-crossing threshold, and preset air pressure threshold in the above embodiment.

[0117] Later stage: 10 volunteers were recruited, each of whom performed strenuous exercise freely on a foam pad, fell from time to time, and recorded the time and number of falls, which were compared with the detection results of the fall detection method in the embodiment of the present application to judge the performance of the algorithm. Among them, a total of about 2950 seconds of motion data were collected, of which 90 falls occurred, and the remaining motions included running, high jump, long jump, squatting, boxing, slapping the table, free falling watches, waving, clapping, and going up and down stairs, totaling about 1400 times. Other data statistics are shown in Table 1.

[0118] Based on the data in Table 1, it can be calculated that the recall rate of falls is 68 / 90=75.6%, and the false alarm rate caused by other actions is 5 / 1400=0.4%. Therefore, the credibility of the detection result of the fall detection method in the embodiment of the present application is 68 / 73=93%, and the accuracy is relatively high.

[0119] It should also be understood that the above embodiments may be coupled to each other, and this application does not limit this. Moreover, the size of the sequence number of each process does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of this application.

[0120] Combined with the above Figures 1 to 5 , describes in detail the fall detection method of the embodiment of the present application, and will be combined with Figure 6 and Figure 7 , describe in detail the fall detection device of an embodiment of the present application.

[0121] Figure 6 This is a structural schematic diagram of a fall detection device 600 provided in one embodiment of the present application. The device 600 includes: an acquisition module 601 and a processing module 602.

[0122] Among them, the acquisition module 601 is used to acquire the first target parameter and the second target parameter of the electronic device, the first target parameter is used to indicate the speed change of the electronic device during the movement process, the second target parameter includes at least one of the first parameter, the second parameter and the third parameter, the first parameter is used to indicate the regularity of the movement process, the second parameter is used to indicate the intensity of the movement process, and the third parameter is used to indicate the height change of the target part during the movement; the processing module 602 is used to perform graded judgment according to the first target parameter and the second target parameter, and determine whether the user of the electronic device is in a falling state according to the graded judgment result; wherein, when the graded judgment result indicates that the first target parameter satisfies the first preset condition and the second target parameter satisfies the second preset condition, it is determined that the user is in the falling state, and the first preset condition and the second preset condition are determined according to the data characteristics of the corresponding parameters in the falling state.

[0123] In some embodiments, the processing module 602 is specifically used to: determine whether the first target parameter satisfies the first preset condition; if the first target parameter satisfies the first preset condition, determine whether the second target parameter satisfies the second preset condition; if the second target parameter satisfies the second preset condition, determine that the user is in the falling state.

[0124] In some embodiments, the first target parameter includes the acceleration of the electronic device at a current moment, and the first preset condition is that the acceleration at the current moment is greater than a preset acceleration threshold; the processing module 602 is specifically used to: determine whether the acceleration at the current moment is greater than the preset acceleration threshold.

[0125] In some embodiments, the second target parameter includes the first parameter, the second parameter and the third parameter, the first parameter includes the rotation angular velocity of the electronic device in multiple preset directions, the second parameter includes the number of times the acceleration of the electronic device crosses zero per unit time in the multiple preset directions, and the third parameter includes the air pressure change value of the electronic device; accordingly, the second preset condition includes a first sub-condition, a second sub-condition and a third sub-condition, the first sub-condition includes that the target mutual correlation coefficient between the rotation angular velocities in each direction of the multiple preset directions is less than a preset coefficient threshold, the second sub-condition includes that the average number of zero crossings of the electronic device per unit time is greater than a preset zero crossing threshold, and the third sub-condition includes that the air pressure change value of the electronic device is greater than a preset air pressure threshold.

[0126] In some embodiments, the processing module 602 is specifically used to: determine whether the first parameter satisfies the first sub-condition, and whether the second parameter satisfies the second sub-condition; when it is determined that the first parameter satisfies the first sub-condition and the second parameter satisfies the second sub-condition, determine whether the third parameter satisfies the third sub-condition; when the third parameter satisfies the third sub-condition, determine that the second target parameter satisfies the second preset condition.

[0127] In some embodiments, the processing module 602 is specifically used to: calculate the target mutual correlation coefficient between the rotation angular velocities in each of the multiple preset directions based on the rotation angular velocities in the multiple preset directions, and calculate the average number of zero crossings of the electronic device per unit time based on the number of acceleration zero crossings per unit time in the multiple preset directions; determine whether the target mutual correlation coefficient is less than the preset coefficient threshold, and whether the average number of zero crossings is greater than the preset zero crossing threshold.

[0128] In some embodiments, the multiple preset directions include a first direction, a second direction and a third direction, and the angular velocities of rotation in the multiple preset directions include the angular velocities of rotation of the electronic device in the first direction, the angular velocities of rotation in the second direction and the angular velocities of rotation in the third direction; the processing module 602 is specifically used to: calculate the target mutual correlation coefficient between the angular velocities of rotation in each direction in the multiple preset directions according to any two of the angular velocities of rotation in the first direction, the angular velocities of rotation in the second direction and the angular velocities in the third direction, and a preset mutual correlation function.

[0129] In some embodiments, the processing module 602 is specifically used to: calculate a first correlation coefficient based on the rotation angular velocity of the electronic device at a first moment in the first direction, the rotation angular velocity at a second moment in the second direction, and the preset cross-correlation function, wherein the first correlation coefficient is used to indicate the degree of correlation between the rotation angular velocity at the first moment and the rotation angular velocity at the second moment, wherein the first moment and the second moment are any two different moments between two time series; calculate a second correlation coefficient based on the rotation angular velocity of the electronic device at a third moment in the second direction, the rotation angular velocity at a fourth moment in the third direction, and the preset cross-correlation function, wherein the second correlation coefficient is used to indicate the degree of correlation between the rotation angular velocity at the third moment and The correlation degree between the rotation angular velocity at the fourth moment, the third moment and the fourth moment are any two different moments between the two time series; according to the rotation angular velocity of the electronic device at the fifth moment in the first direction, the rotation angular velocity at the sixth moment in the third direction and the preset cross-correlation function number, a third cross-correlation coefficient is calculated, and the third cross-correlation coefficient is used to indicate the correlation degree between the rotation angular velocity at the fifth moment and the rotation angular velocity at the sixth moment, the fifth moment and the sixth moment are any two different moments between the two time series; according to the first cross-correlation coefficient, the second cross-correlation coefficient and the third cross-correlation coefficient, a target cross-correlation coefficient between the rotation angular velocities in each direction of the multiple preset directions is calculated.

[0130] In some embodiments, the formula of the preset cross-correlation function is:

[0131]

[0132] Among them, R xy is the mutual correlation coefficient, ω x (t) is the rotation angular velocity of the electronic device at the seventh moment in any one of the first direction, the second direction and the third direction, ω y (t+τ) is the rotational angular velocity of the electronic device at the eighth moment in any one of the first direction, the second direction and the third direction, and τ is a time displacement variable.

[0133] In some embodiments, the target correlation coefficient between the rotational angular velocities in each of the plurality of preset directions is an average value of the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient.

[0134] In some embodiments, the number of acceleration zero crossings per unit time in the multiple preset directions includes a first acceleration zero crossing number per unit time of the electronic device in the first direction, a second acceleration zero crossing number per unit time in the second direction, and a third acceleration zero crossing number per unit time in the third direction; accordingly, the average number of zero crossings of the electronic device per unit time is the average of the first acceleration zero crossing number, the second acceleration zero crossing number, and the third acceleration zero crossing number.

[0135] In some embodiments, the processing module 602 is specifically used to: obtain the air pressure change value of the electronic device; and determine whether the air pressure change value is greater than the preset air pressure threshold.

[0136] In some embodiments, the processing module 602 is specifically used to: obtain a first air pressure value of the electronic device at a current moment and a second air pressure value within a preset time period before the current moment, and calculate the difference between the first air pressure value and the second air pressure value as the air pressure change value of the electronic device.

[0137] It should be understood that the device 600 here is embodied in the form of a functional module. The term "module" here may refer to an application specific integrated circuit (ASIC), an electronic circuit, a processor (such as a shared processor, a proprietary processor or a group processor, etc.) and a memory for executing one or more software or firmware programs, a merged logic circuit and / or other suitable components that support the described functions. In an optional example, those skilled in the art will understand that the device 600 may be specifically an electronic device in the above-mentioned embodiment, or the functions of the electronic device in the above-mentioned embodiment may be integrated in the device 600, and the device 600 may be used to execute the various processes and / or steps corresponding to the electronic device in the above-mentioned method embodiment, and to avoid repetition, it will not be repeated here.

[0138] The above-mentioned device 600 has the function of implementing the corresponding steps executed by the electronic device in the above-mentioned method; the above-mentioned function can be implemented by hardware, or by hardware executing corresponding software. The hardware or software includes one or more modules corresponding to the above-mentioned function.

[0139] Figure 7 A structural schematic diagram of a fall detection device provided in another embodiment of the present application. Figure 7 The fall detection device shown can be used to execute the method of any of the aforementioned embodiments.

[0140] like Figure 7As shown, the fall detection device 700 of this embodiment includes: a memory 701, a processor 702, a communication interface 703 and a bus 704. The memory 701, the processor 702 and the communication interface 703 are connected to each other through the bus 704.

[0141] The memory 701 may be a read only memory (ROM), a static storage device, a dynamic storage device or a random access memory (RAM). The memory 701 may store a program. When the program stored in the memory 701 is executed by the processor 702, the processor 702 is used to execute each step of the method shown in the above embodiment.

[0142] The processor 702 can adopt a general-purpose central processing unit (CPU), a microprocessor, an application specific integrated circuit (ASIC), or one or more integrated circuits to execute relevant programs to implement the various methods shown in the embodiments of the present application.

[0143] The processor 702 may also be an integrated circuit chip with signal processing capability. In the implementation process, each step of the method of the embodiment of the present application may be completed by an integrated logic circuit of hardware in the processor 702 or by instructions in the form of software.

[0144] The processor 702 may also be a general purpose processor, a digital signal processor (DSP), an ASIC, a field programmable gate array (FPGA) or other programmable logic device, a discrete gate or transistor logic device, or a discrete hardware component. The methods, steps, and logic block diagrams disclosed in the embodiments of the present application may be implemented or executed. The general purpose processor may be a microprocessor or the processor may be any conventional processor, etc.

[0145] The steps of the method disclosed in the embodiment of the present application can be directly embodied as being executed by a hardware decoding processor, or being executed by a combination of hardware and software modules in the decoding processor. The software module can be located in a mature storage medium in the art such as a random access memory, a flash memory, a read-only memory, a programmable read-only memory, or an electrically erasable programmable memory, a register, etc. The storage medium is located in the memory 701, and the processor 702 reads the information in the memory 701, and completes the functions required to be performed by the units included in the device of the present application in combination with its hardware.

[0146] The communication interface 703 may use, but is not limited to, a transceiver or other transceiver device to implement communication between the apparatus 700 and other devices or a communication network.

[0147] The bus 704 may include a path for transmitting information between various components of the device 700 (eg, the memory 701 , the processor 702 , and the communication interface 703 ).

[0148] It should be understood that the device 700 shown in the embodiment of the present application may be an electronic device, or may also be a chip configured in the electronic device.

[0149] It should be understood that in the various embodiments of the present application, the size of the serial numbers of the above-mentioned processes does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present application.

[0150] Those of ordinary skill in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professional and technical personnel can use different methods to implement the described functions for each specific application, but such implementation should not be considered to be beyond the scope of this application.

[0151] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the systems, devices and units described above can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0152] In the several embodiments provided in the present application, it should be understood that the disclosed systems, devices and methods can be implemented in other ways. For example, the device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0153] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0154] In addition, each functional unit in each embodiment of the present application may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit.

[0155] If the functions are implemented in the form of software functional units and sold or used as independent products, they can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present application can be essentially or partly embodied in the form of a software product that contributes to the prior art. The computer software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the methods described in the various embodiments of the present application. The aforementioned storage media include: various media that can store program codes, such as USB flash drives, mobile hard disks, read-only memories (ROM), random access memories (RAM), magnetic disks or optical disks.

[0156] The above is only a specific implementation of the present application, but the protection scope of the present application is not limited thereto. Any person skilled in the art who is familiar with the present technical field can easily think of changes or substitutions within the technical scope disclosed in the present application, which should be included in the protection scope of the present application. Therefore, the protection scope of the present application should be based on the protection scope of the claims.

Claims

1. A fall detection method, characterized in that: Applied to electronic equipment, the method comprises: Acquire a first target parameter and a second target parameter of the electronic device, wherein the first target parameter is used to indicate a speed change of the electronic device during movement, and the second target parameter includes at least one of a first parameter, a second parameter, and a third parameter, wherein the first parameter is used to indicate a regularity of the movement process, the second parameter is used to indicate a severity of the movement process, and the third parameter is used to indicate a height change of a target part during the movement; performing a graded judgment according to the first target parameter and the second target parameter, and determining whether the user of the electronic device is in a falling state according to a graded judgment result; Among them, when the graded judgment result represents that the first target parameter satisfies the first preset condition and the second target parameter satisfies the second preset condition, it is determined that the user is in the fall state, and the first preset condition and the second preset condition are determined according to the data characteristics of the corresponding parameters in the fall state.

2. The method according to claim 1, characterized in that The step of performing a graded judgment according to the first target parameter and the second target parameter, and determining whether the user of the electronic device is in a falling state according to a graded judgment result, includes: Determining whether the first target parameter meets the first preset condition; In the case where the first target parameter satisfies the first preset condition, determining whether the second target parameter satisfies the second preset condition; When the second target parameter satisfies the second preset condition, it is determined that the user is in the falling state.

3. The method according to claim 2, characterized in that The first target parameter includes the acceleration of the electronic device at a current moment, and the first preset condition is that the acceleration at the current moment is greater than a preset acceleration threshold; The determining whether the first target parameter satisfies the first preset condition includes: Determine whether the acceleration at the current moment is greater than the preset acceleration threshold.

4. The method according to claim 2, characterized in that: The second target parameter includes the first parameter, the second parameter and the third parameter, the first parameter includes the rotation angular velocity of the electronic device in multiple preset directions, the second parameter includes the number of times the acceleration of the electronic device passes through zero per unit time in the multiple preset directions, and the third parameter includes the air pressure change value of the electronic device; Correspondingly, the second preset condition includes a first sub-condition, a second sub-condition and a third sub-condition, the first sub-condition includes that the target mutual correlation coefficient between the rotational angular velocities in each of the multiple preset directions is less than a preset coefficient threshold, the second sub-condition includes that the average number of zero crossings of the electronic device per unit time is greater than a preset zero crossing number threshold, and the third sub-condition includes that the air pressure change value of the electronic device is greater than a preset air pressure threshold.

5. The method according to claim 4, characterized in that The determining whether the second target parameter satisfies the second preset condition includes: Determining whether the first parameter satisfies the first sub-condition, and whether the second parameter satisfies the second sub-condition; In the case where it is determined that the first parameter satisfies the first sub-condition and the second parameter satisfies the second sub-condition, determining whether the third parameter satisfies the third sub-condition; When the third parameter satisfies the third sub-condition, it is determined that the second target parameter satisfies the second preset condition.

6. The method according to claim 5, characterized in that The determining whether the first parameter satisfies the first sub-condition, and whether the second parameter satisfies the second sub-condition, includes: Calculating target correlation coefficients between the rotational angular velocities in each of the plurality of preset directions according to the rotational angular velocities in the plurality of preset directions, and calculating an average number of zero crossings of the electronic device in a unit time according to the number of zero crossings of the acceleration in a unit time in the plurality of preset directions; It is determined whether the target correlation coefficient is less than the preset coefficient threshold and whether the average number of zero crossings is greater than the preset number of zero crossings threshold.

7. The method according to claim 6, characterized in that The multiple preset directions include a first direction, a second direction and a third direction, and the rotation angular velocities in the multiple preset directions include the rotation angular velocity of the electronic device in the first direction, the rotation angular velocity in the second direction and the rotation angular velocity in the third direction; The step of calculating the target correlation coefficient between the rotation angular velocities in each of the plurality of preset directions according to the rotation angular velocities in the plurality of preset directions comprises: According to any two of the rotation angular velocity in the first direction, the rotation angular velocity in the second direction and the rotation angular velocity in the third direction, and a preset cross-correlation function, a target cross-correlation coefficient between the rotation angular velocities in each of the multiple preset directions is calculated.

8. The method according to claim 7, characterized in that The step of calculating a target mutual correlation coefficient between the rotational angular velocities in each of the plurality of preset directions according to any two of the rotational angular velocities in the first direction, the rotational angular velocity in the second direction, and the rotational angular velocity in the third direction, and a preset mutual correlation function, comprises: Calculating a first cross-correlation coefficient according to the rotation angular velocity of the electronic device in the first direction at a first moment, the rotation angular velocity in the second direction at a second moment, and the preset cross-correlation function, wherein the first cross-correlation coefficient is used to indicate the degree of correlation between the rotation angular velocity at the first moment and the rotation angular velocity at the second moment, wherein the first moment and the second moment are any two different moments between two time series; calculating a second cross-correlation coefficient according to the rotational angular velocity of the electronic device in the second direction at a third moment, the rotational angular velocity in the third direction at a fourth moment, and the preset cross-correlation function, wherein the second cross-correlation coefficient is used to indicate the degree of correlation between the rotational angular velocity at the third moment and the rotational angular velocity at the fourth moment, wherein the third moment and the fourth moment are any two different moments between two time series; Calculating a third cross-correlation coefficient according to the rotational angular velocity of the electronic device in the first direction at a fifth moment, the rotational angular velocity in the third direction at a sixth moment, and the preset cross-correlation function number, wherein the third cross-correlation coefficient is used to indicate the degree of correlation between the rotational angular velocity at the fifth moment and the rotational angular velocity at the sixth moment, wherein the fifth moment and the sixth moment are any two different moments between two time series; A target mutual correlation coefficient between the rotation angular velocities in each of the plurality of preset directions is calculated based on the first mutual correlation coefficient, the second mutual correlation coefficient, and the third mutual correlation coefficient.

9. The method according to claim 8, characterized in that The formula of the preset cross-correlation function is: Among them, R xy is the mutual correlation coefficient, ω x (t) is the rotation angular velocity of the electronic device at the seventh moment in any one of the first direction, the second direction and the third direction, ω y (t+τ) is the rotational angular velocity of the electronic device at the eighth moment in any one of the first direction, the second direction and the third direction, and τ is a time displacement variable.

10. The method according to claim 9, characterized in that The target correlation coefficient between the rotational angular velocities in each of the plurality of preset directions is an average value of the first correlation coefficient, the second correlation coefficient, and the third correlation coefficient.

11. The method according to claim 7, characterized in that The acceleration zero crossing times per unit time in the plurality of preset directions include a first acceleration zero crossing times per unit time of the electronic device in the first direction, a second acceleration zero crossing times per unit time in the second direction, and a third acceleration zero crossing times per unit time in the third direction; Correspondingly, the average number of zero crossings of the electronic device per unit time is the average of the first acceleration zero crossing number, the second acceleration zero crossing number, and the third acceleration zero crossing number.

12. The method according to claim 5, characterized in that The determining whether the third parameter satisfies the third sub-condition includes: Obtaining an air pressure change value of the electronic device; Determine whether the air pressure change value is greater than the preset air pressure threshold.

13. The method according to claim 12, characterized in that The obtaining of the air pressure change value of the electronic device includes: Acquire a first air pressure value of the electronic device at a current moment and a second air pressure value within a preset time period before the current moment; A difference between the first air pressure value and the second air pressure value is calculated and used as an air pressure change value of the electronic device.

14. A fall detection device, characterized in that: The device comprises: an acquisition module, configured to acquire a first target parameter and a second target parameter of the electronic device, wherein the first target parameter is used to indicate a speed change of the electronic device during movement, and the second target parameter includes at least one of a first parameter, a second parameter, and a third parameter, wherein the first parameter is used to indicate a regularity of the movement process, the second parameter is used to indicate a severity of the movement process, and the third parameter is used to indicate a height change of a target part during the movement; a processing module, configured to perform a graded judgment according to the first target parameter and the second target parameter, and determine whether the user of the electronic device is in a falling state according to a graded judgment result; Among them, when the graded judgment result represents that the first target parameter satisfies the first preset condition and the second target parameter satisfies the second preset condition, it is determined that the user is in the fall state, and the first preset condition and the second preset condition are determined according to the data characteristics of the corresponding parameters in the fall state.

15. An electronic device, characterized in that: The system comprises a processor and a memory, wherein the memory is used to store code instructions; and the processor is used to run the code instructions to execute the method according to any one of claims 1 to 13.

16. A computer-readable storage medium, characterized in that: Used to store a computer program, the computer program comprising instructions for implementing the method according to any one of claims 1 to 13.

Images