Body action threshold value adjustment method, human body behavior detection method, and apparatus and human body detection device

Abstract

A body action threshold value adjustment method, a human body behavior detection method, and an apparatus and a human body detection device, which aim to solve the technical problem of how to more precisely adjust a body action threshold value of a human body detection device in an adaptive manner. The body action threshold value adjustment method comprises: acquiring an inherent sensitivity level of a human body detection device (S101); on the basis of historical body action data and the inherent sensitivity level of the human body detection device, acquiring a population sensitivity level (S102); and on the basis of the inherent sensitivity level and the population sensitivity level, acquiring a body action threshold value of the human body detection device (S103). By means of the configuration mode, a body action threshold value of a human body detection device can be more accurately adjusted in an adaptive manner.

Description

Body motion threshold adjustment, human behavior detection method, device and human detection equipment

[0001] This application claims priority to Chinese patent application CN202311248973.2, filed on September 22, 2023, entitled “Body motion threshold adjustment, human behavior detection method, device and human detection equipment”. The entire contents of the above Chinese patent application are incorporated into this application by reference. Technical Field

[0002] The present invention relates to the technical field of control of human body detection equipment, and specifically provides a body motion threshold adjustment, human behavior detection method, device and human body detection equipment. Background Art

[0003] Sleep detection products include wristbands, millimeter-wave radars, sleep belts, and sleep monitors. Some of these products, such as millimeter-wave radars, are contactless. When identifying a user, the strength of the received signal is affected by the detection distance. By detecting the distance of the target, the signal strength is increased when it is far away and decreased when it is close.

[0004] When there are signal differences between the same devices that acquire data, for example, the same sleep belt product, but due to differences in quality control or usage environment (for example, the signal sensitivity is different due to the sleep belt being laid under mats of different thicknesses), it will directly manifest as different report results when user A uses products W1 and W2.

[0005] In practical applications, it is difficult to ensure the reliability of the output results by extracting the body motion threshold through the original signal. What is the appropriate radar output intensity, or what body motion threshold adjustment for piezoelectric sleep belts to achieve the optimal (accurate) output result?

[0006] Accordingly, the art needs a new solution for adjusting the body motion threshold of human detection equipment to solve the above problems.

[0007] Summary of the Invention

[0008] In order to overcome the above-mentioned defects, the present invention is proposed to provide a solution or at least a partial solution to the technical problem of how to more accurately and adaptively adjust the body motion threshold of a human body detection device.

[0009] In a first aspect, the present invention provides a method for adjusting a body motion threshold, the method comprising:

[0010] Obtaining an inherent sensitivity level of the human body detection device;

[0011] Obtaining a crowd sensitivity level based on historical body motion data of the human body detection device and the inherent sensitivity level;

[0012] A body motion threshold of the human detection device is obtained based on the inherent sensitivity level and the crowd sensitivity level.

[0013] In one technical solution of the above-mentioned method for adjusting the body motion threshold, the inherent sensitivity level includes a real-time inherent sensitivity level, and obtaining the inherent sensitivity level of the human body detection device includes:

[0014] Segmenting the historical body movement data according to a first preset time length to obtain a plurality of historical body movement sub-data;

[0015] Obtaining a range difference value of the signal strength amplitude of each preset unit time in the historical body motion sub-data, recording it as a range difference value array, and obtaining multiple range difference arrays based on multiple historical body motion sub-data;

[0016] Acquire multiple first variables and multiple second variables based on the multiple extreme difference value arrays and the multiple background noises of the historical body movement sub-data; wherein the first variable is the number of large movements of the user in the historical body movement sub-data, and the second variable is the number of small movements of the user in the historical body movement sub-data;

[0017] Obtain a first variable array based on the plurality of first variables, and obtain a second variable array based on the plurality of second variables;

[0018] Acquire characteristic parameters of the historical body motion data based on the first variable array and the second variable array;

[0019] The real-time inherent sensitivity level of the human body detection device is obtained based on the characteristic parameters of the historical body motion data.

[0020] In one technical solution of the above-mentioned method for adjusting the body motion threshold, obtaining the plurality of first variables and the plurality of second variables based on the plurality of extreme difference value arrays and the background noise of the plurality of historical body motion sub-data includes:

[0021] Counting the number of the range values ​​in the range value array that are greater than the background noise multiplied by a fifth preset threshold, recording the number as the first variable, and obtaining multiple first variables based on multiple range value arrays;

[0022] The number of the range values ​​in the range value array that is less than or equal to the background noise multiplied by the fifth preset threshold and greater than the background noise multiplied by the sixth preset threshold is counted and recorded as the second variable. Multiple second variables are obtained based on multiple range value arrays.

[0023] In one technical solution of the above-mentioned method for adjusting the body motion threshold, the inherent sensitivity level includes an initial inherent sensitivity level; and obtaining the population sensitivity level based on the historical body motion data of the human body detection device and the inherent sensitivity level includes:

[0024] determining whether the real-time inherent sensitivity level is the same as the initial inherent sensitivity level;

[0025] If so, obtaining characteristic parameters of the background noise based on the background noise of the historical body motion data;

[0026] Obtaining a body motion threshold based on an initial inherent sensitivity level and an initial crowd sensitivity level of the human body detection device;

[0027] Acquire an array of body movement states based on the characteristic parameters of the background noise, the background noise, the extreme difference value array, and the body movement threshold;

[0028] acquiring a rate of change of the body motion state based on an array of the body motion state;

[0029] Obtaining the crowd sensitivity level based on the rate of change of the body motion state;

[0030] If not, obtaining a body motion threshold based on the real-time inherent sensitivity level and the initial population sensitivity level;

[0031] Acquire an array of body movement states based on the characteristic parameters of the background noise, the background noise, the extreme difference value array, and the body movement threshold;

[0032] acquiring a rate of change of the body motion state based on an array of the body motion state;

[0033] The crowd sensitivity level is obtained based on the rate of change of the body motion state.

[0034] In one technical solution of the above-mentioned method for adjusting the body motion threshold, obtaining the crowd sensitivity level based on the rate of change of the body motion state includes:

[0035] When the initial crowd sensitivity level is greater than a first preset threshold and the rate of change of the body motion state is less than a second preset threshold, lowering the initial crowd sensitivity level by one level to obtain the crowd sensitivity level;

[0036] When the initial crowd sensitivity is less than a third preset threshold and the rate of change of the body motion state is greater than a fourth preset threshold, increasing the initial crowd sensitivity level by one level to obtain the crowd sensitivity level;

[0037] Wherein, the fourth preset threshold is greater than the second preset threshold, and the third preset threshold is greater than the first preset threshold; and / or,

[0038] The body movement threshold includes a small body movement threshold and a large body movement threshold; the array for obtaining the body movement state based on the characteristic parameter of the background noise, the background noise, the extreme difference value array and the body movement threshold includes:

[0039] When the extreme difference value in the extreme difference value array is greater than the background noise multiplied by the small body movement threshold, assigning the body movement state at the corresponding position in the body movement state array to a ninth preset threshold;

[0040] When the extreme difference value in the extreme difference value array is greater than the characteristic parameter of the background noise multiplied by the large body movement threshold, assigning the body movement state at the corresponding position in the body movement state array to a tenth preset threshold;

[0041] When the extreme difference value in the extreme difference value array is less than or equal to the background noise multiplied by the small body movement threshold and less than or equal to the characteristic parameter of the background noise multiplied by the large body movement threshold, assigning the body movement state at the corresponding position in the body movement state array to an eleventh preset threshold;

[0042] Wherein, the ninth preset threshold is greater than the tenth preset threshold and greater than the eleventh preset threshold, the small body movement threshold is the body movement threshold for small movements, and the large body movement threshold is the body movement threshold for large movements; and / or,

[0043] The acquiring the rate of change of the body motion state based on the array of the body motion state includes:

[0044] Acquire an array of change rates of the body motion states based on the array of the body motion states;

[0045] Acquiring the rate of change of the body motion state based on an array of the rate of change of the body motion state; and / or,

[0046] After obtaining the crowd sensitivity level based on the rate of change of the body motion state, the method further includes:

[0047] When the change rate of the body motion state is less than the second threshold or greater than the fourth threshold within the preset number of days, the population sensitivity level is updated.

[0048] In one technical solution of the above-mentioned method for adjusting the body motion threshold, obtaining the characteristic parameters of the historical body motion data based on the first variable array and the second variable array includes obtaining the characteristic parameters of the historical body motion data according to the following formula:

[0049] Wherein, VT is the characteristic parameter of the historical body motion data, C1 is the first variable array, C2 is the second variable array, S is the time parameter related to the historical body motion data, and sum is the summation function;

[0050] The obtaining of the inherent sensitivity level of the human body detection device based on the characteristic parameters of the historical body motion data includes:

[0051] When the characteristic parameter of the historical body motion data is greater than a seventh preset threshold, adjusting the inherent sensitivity level of the current device to a third preset level;

[0052] When the characteristic parameter of the historical body motion data is greater than an eighth preset threshold and less than or equal to the seventh preset threshold, adjusting the inherent sensitivity level of the current device to a second preset level;

[0053] When the characteristic parameter of the historical body motion data is less than or equal to an eighth preset threshold, adjusting the inherent sensitivity level of the current device to a first preset level;

[0054] The sensitivity of the third preset level is greater than the sensitivity of the second preset level, which is greater than the sensitivity of the first preset level.

[0055] In a second aspect, the present invention provides a method for detecting human behavior, the method comprising:

[0056] Applying the body motion threshold adjustment method according to any one of the above claims to obtain the body motion threshold of a human body detection device;

[0057] The human body detection device is used to detect the human body behavior based on the body movement threshold to obtain a detection result.

[0058] In a third aspect, a control device is provided, which includes at least one processor and at least one storage device, wherein the storage device is suitable for storing multiple program codes, and the program codes are suitable for being loaded and run by the processor to execute the human behavior detection method described in any one of the technical solutions of the above-mentioned human behavior detection method.

[0059] In a fourth aspect, a computer-readable storage medium is provided, which stores a plurality of program codes, wherein the program codes are suitable for being loaded and run by a processor to execute the human behavior detection method described in any one of the technical solutions of the above-mentioned human behavior detection method.

[0060] In a fifth aspect, a human body detection device is provided, which includes the control device described in the above-mentioned control device technical solution.

[0061] The above one or more technical solutions of the present invention have at least one or more of the following Beneficial effects:

[0062] In the technical solution for implementing the present invention, the present invention obtains the inherent sensitivity level of the human body detection device, obtains the crowd sensitivity level based on the historical body motion data and the inherent sensitivity level of the human body detection device, and obtains the body motion threshold of the human body detection device based on the inherent sensitivity level and the crowd sensitivity level. Through the above configuration, the historical body motion data obtained by the human body detection device in different usage environments are different. Due to the different historical body motion data, the crowd sensitivity level obtained based on the historical body motion data and the inherent sensitivity level changes. Due to the change in the crowd sensitivity level, the body motion threshold of the human body detection device obtained based on the crowd sensitivity level and the inherent sensitivity level changes. That is, the body motion threshold will change with the change of the usage environment of the human body detection device. Different body motion thresholds can be obtained in different usage environments through the above configuration, so as to more accurately and adaptively adjust the body motion threshold of the human body detection device in different usage environments. BRIEF DESCRIPTION OF THE DRAWINGS

[0063] The disclosure of the present invention will become more easily understood with reference to the accompanying drawings. It will be readily understood by those skilled in the art that these drawings are for illustrative purposes only and are not intended to limit the scope of protection of the present invention.

[0064] FIG1 is a flow chart showing the main steps of a method for adjusting a body motion threshold according to an embodiment of the present invention;

[0065] FIG2 is a flow chart showing the main steps of a method for detecting human behavior according to an embodiment of the present invention. DETAILED DESCRIPTION

[0066] Some embodiments of the present invention are described below with reference to the accompanying drawings. Those skilled in the art should understand that these embodiments are only used to explain the technical principles of the present invention and are not intended to limit the scope of protection of the present invention.

[0067] In the description of the present invention, "module" and "processor" may include hardware, software, or a combination of both. A module may include hardware circuitry, various suitable sensors, communication ports, and memory. It may also include software components, such as program code, or a combination of software and hardware. A processor may be a central processing unit, a microprocessor, an image processor, a digital signal processor, or any other suitable processor. A processor has data and / or signal processing capabilities. A processor may be implemented in software, hardware, or a combination of both. Non-transitory computer-readable storage media include any suitable medium capable of storing program code, such as magnetic disks, hard disks, optical disks, flash memory, read-only memory, random access memory, and the like. The term "A and / or B" refers to all possible combinations of A and B, such as only A, only B, or both A and B. The terms "at least one of A or B" or "at least one of A and B" have similar meanings to "A and / or B" and may include only A, only B, or both A and B. The singular forms "one" and "the" may also include the plural forms.

[0068] 1, which is a flow chart showing the main steps of a method for adjusting a body motion threshold according to an embodiment of the present invention. As shown in FIG1, the method for adjusting a body motion threshold in the embodiment of the present invention mainly includes the following steps S101 to S103.

[0069] Step S101: Acquire the inherent sensitivity level of the human body detection device.

[0070] In this embodiment, the inherent sensitivity level of the human body detection device is obtained, wherein the inherent sensitivity level is the level of sensitivity of the human body detection device in sensing the user's body movement state, that is, the higher the inherent sensitivity level, the easier it is for the human body detection device to detect slight changes in the human body movement state.

[0071] In one embodiment, the human body detection device may be a sleep detection device (eg, a wristband, a millimeter-wave radar, a sleep belt, or a sleep monitor).

[0072] In one embodiment, the human body detection device may be a non-sleep detection device, which may be used to detect a person's running posture, working posture, driving posture, and the like.

[0073] In one embodiment, the inherent sensitivity level may include a real-time inherent sensitivity level, and step S101 may include steps S1011 to S1016:

[0074] Step S1011: Segment the historical body movement data according to a first preset time length to obtain a plurality of historical body movement sub-data.

[0075] In this embodiment, the historical body movement data is first segmented according to a first preset time period to obtain a plurality of historical body movement sub-data. The historical body movement data can reflect differences in the use environment of the human body detection device, such as different users (e.g., adults or children) and different locations (e.g., distance, etc.) of the human body detection device.

[0076] In one embodiment, the first preset duration may be 8 hours.

[0077] Step S1012: obtaining the range difference value of the signal strength amplitude at each preset unit time in the historical body motion sub-data, recording it as a range difference value array, and obtaining multiple range difference arrays based on multiple historical body motion sub-data.

[0078] In this embodiment, the range difference value of the signal strength amplitude for each preset unit time in the historical body motion sub-data is obtained and recorded as a range difference value array, where the range difference value array includes multiple range difference values, and the range difference value is the maximum value minus the minimum value of the signal strength amplitude for the preset unit time. Then, multiple range difference arrays are obtained based on the multiple historical body motion sub-data.

[0079] In one embodiment, the preset unit time may be 1 minute.

[0080] Step S1013: Acquire multiple first variables and multiple second variables based on multiple extreme value arrays and background noise of multiple historical body movement sub-data; wherein the first variable is the number of large movements of the user in the historical body movement sub-data, and the second variable is the number of small movements of the user in the historical body movement sub-data.

[0081] In this embodiment, multiple first variables and multiple second variables are obtained based on multiple extreme value arrays and background noise of multiple historical body movement sub-data, wherein the first variable is the number of large movements (for example, turning over) of the user in the historical body movement sub-data, and the second variable is the number of small movements (for example, blinking) of the user in the historical body movement sub-data.

[0082] In one embodiment, step S1013 may include steps S10131 to S10132:

[0083] Step S10131: Count the number of range values ​​in the range value array that are greater than the background noise multiplied by the fifth preset threshold, record it as a first variable, and obtain multiple first variables based on multiple range value arrays.

[0084] Step S10132: Count the number of range values ​​in the range value array that are less than or equal to the background noise multiplied by the fifth preset threshold and greater than the background noise multiplied by the sixth preset threshold, record them as second variables, and obtain multiple second variables based on multiple range value arrays.

[0085] In this embodiment, the number of range values ​​in the range value array that are greater than the background noise multiplied by the fifth preset threshold value is counted as a first variable, and multiple first variables are obtained based on the multiple range value arrays. The number of range values ​​in the range value array that are less than or equal to the background noise multiplied by the fifth preset threshold value and greater than the background noise multiplied by the sixth preset threshold value is counted as a second variable, and multiple second variables are obtained based on the multiple range value arrays.

[0086] In one implementation, the fifth preset threshold may be 15.

[0087] In one implementation, the sixth preset threshold may be 5.

[0088] Step S1014: obtaining a first variable array based on the plurality of first variables, and obtaining a second variable array based on the plurality of second variables.

[0089] In this embodiment, a plurality of first variables constitute a first variable array, and a plurality of second variables constitute a second variable array.

[0090] In one example, the first variable array may be (508090100) and the second variable array may be (20020010030).

[0091] Step S1015: Acquire characteristic parameters of historical body motion data based on the first variable array and the second variable array;

[0092] In this embodiment, characteristic parameters of historical body motion data are obtained based on the first variable array and the second variable array.

[0093] In one embodiment, step S1015 may further include obtaining characteristic parameters of the historical body motion data according to the following formula (1):

[0094] Among them, VT is the characteristic parameter of the historical body motion data, C1 is the first variable array, C2 is the second variable array, S is the time parameter related to the historical body motion data, and sum is the summation function.

[0095] Step S1016: Obtaining the real-time inherent sensitivity level of the human body detection device based on the characteristic parameters of the historical body motion data.

[0096] In this embodiment, the real-time inherent sensitivity level of the human body detection device is obtained based on the characteristic parameters of the historical body motion data.

[0097] In one embodiment, step S1016 may include steps S10161 to S10163:

[0098] Step S10161: When the characteristic parameter of the historical body motion data is greater than the seventh preset threshold, the inherent sensitivity level of the current device is adjusted to the third preset level.

[0099] Step S10162: When the characteristic parameter of the historical body motion data is greater than the eighth preset threshold and less than or equal to the seventh preset threshold, the inherent sensitivity level of the current device is adjusted to the second preset level.

[0100] Step S10163: When the characteristic parameter of the historical body motion data is less than or equal to an eighth preset threshold, adjusting the inherent sensitivity level of the current device to a first preset level.

[0101] In this embodiment, when the characteristic parameter of the historical body motion data is greater than the seventh preset threshold, the inherent sensitivity level of the current device is adjusted to the third preset level. When the characteristic parameter of the historical body motion data is greater than the eighth preset threshold and less than or equal to the seventh preset threshold, the inherent sensitivity level of the current device is adjusted to the second preset level. When the characteristic parameter of the historical body motion data is less than or equal to the eighth preset threshold, the inherent sensitivity level of the current device is adjusted to the first preset level. The sensitivity of the third preset level is greater than the sensitivity of the second preset level (initial inherent sensitivity level), which is greater than the sensitivity of the first preset level.

[0102] In one implementation, under the premise that the seventh preset threshold is greater than the eighth preset threshold, the seventh preset threshold may be 90.

[0103] In one implementation, under the premise that the seventh preset threshold is greater than the eighth preset threshold, the eighth preset threshold may be 60.

[0104] Step S102: obtaining a crowd sensitivity level based on historical body motion data and inherent sensitivity levels of the human body detection device, wherein the crowd sensitivity level is a subdivision level under the inherent sensitivity level, and there are multiple crowd sensitivity levels under one inherent sensitivity level.

[0105] In this embodiment, the crowd sensitivity level is obtained based on the historical body motion data and the inherent sensitivity level of the human body detection device.

[0106] In one embodiment, the inherent sensitivity level includes an initial inherent sensitivity level, and step S102 may include steps S10201 to S10210:

[0107] Step S10201: Determine whether the real-time inherent sensitivity level is the same as the initial inherent sensitivity level. If so, execute step S10202; if not, execute step S10207.

[0108] In this embodiment, it is determined whether the real-time inherent sensitivity level is the same as the initial inherent sensitivity level. If so, characteristic parameters of the background noise are obtained based on the background noise of the historical body motion data. If not, a body motion threshold is obtained based on the real-time inherent sensitivity level and the initial crowd sensitivity level.

[0109] Step S10202: Acquire characteristic parameters of background noise based on the background noise of historical body motion data.

[0110] In this embodiment, characteristic parameters of the background noise are obtained based on the background noise of the historical body motion data.

[0111] In one embodiment, step S10202 may further include obtaining characteristic parameters of background noise based on the background noise of historical body motion data according to the following formula (2):

[0112] BT=round(B×h) (2)

[0113] Among them, BT is the characteristic parameter of background noise, B is the background noise of historical body motion data, h is a constant, and round is a basic function in EXCEL that can round the specified value.

[0114] Step S10203: Obtaining a body motion threshold based on the initial inherent sensitivity level of the human body detection device and the initial crowd sensitivity level.

[0115] In this embodiment, the body motion threshold is obtained by referring to the data code according to the initial inherent sensitivity level and the initial crowd sensitivity level of the human body detection device.

[0116] Step S10204: Obtain an array of body movement states based on the characteristic parameters of the background noise, the background noise, the extreme difference value array, and the body movement threshold.

[0117] In this embodiment, an array of body movement states is obtained based on the characteristic parameters of background noise, the background noise, the extreme difference array, and the body movement threshold.

[0118] In one embodiment, the calculation method of step S10208 may be referred to to obtain an array of body movement states based on the characteristic parameters of the background noise, the background noise, the extreme difference value array, and the body movement threshold.

[0119] Step S10205: Obtain the rate of change of the body motion state based on the array of body motion states.

[0120] In this embodiment, the change rate of the body motion state is obtained based on the array of body motion states.

[0121] In one embodiment, the change rate of the body motion state may be obtained according to the array of body motion states by referring to the calculation method of step S10209.

[0122] Step S10206: Obtain the crowd sensitivity level based on the rate of change of body motion state.

[0123] In this embodiment, the crowd sensitivity level is obtained according to the change rate of the body motion state.

[0124] In one embodiment, the sensitivity level of the crowd may be obtained according to the rate of change of the body motion state with reference to step S10210.

[0125] Step S10207: Obtaining a body motion threshold based on the real-time inherent sensitivity level and the initial crowd sensitivity level.

[0126] In this embodiment, the body motion threshold is obtained by referring to the data code based on the real-time inherent sensitivity level and the initial population sensitivity level.

[0127] Step S10208: Obtain an array of body movement states based on the characteristic parameters of the background noise, the background noise, the extreme difference value array, and the body movement threshold.

[0128] In this embodiment, an array of body movement states is obtained based on the characteristic parameters of background noise, the background noise, the extreme difference array, and the body movement threshold.

[0129] In one embodiment, the body movement threshold includes a small body movement threshold and a large body movement threshold, and step S10208 may include steps S102081 to S102083:

[0130] Step S102081: When the extreme difference value in the extreme difference value array is greater than the background noise multiplied by the small body movement threshold, the body movement state at the corresponding position in the body movement state array is assigned a ninth preset threshold.

[0131] Step S102082: When the extreme difference value in the extreme difference value array is greater than the characteristic parameter of the background noise multiplied by the body movement threshold, the body movement state at the corresponding position in the body movement state array is assigned a tenth preset threshold.

[0132] Step S102083: When the extreme difference value in the extreme difference value array is less than or equal to the background noise multiplied by the small body movement threshold and less than or equal to the characteristic parameter of the background noise multiplied by the large body movement threshold, the body movement state at the corresponding position in the body movement state array is assigned the eleventh preset threshold.

[0133] In this embodiment, when the extreme difference value in the extreme difference value array is greater than the background noise multiplied by the small body movement threshold, the body movement state at the corresponding position in the array of body movement states is assigned to the ninth preset threshold. When the extreme difference value in the extreme difference value array is greater than the characteristic parameter of the background noise multiplied by the large body movement threshold, the body movement state at the corresponding position in the array of body movement states is assigned to the tenth preset threshold. When the extreme difference value in the extreme difference value array is less than or equal to the background noise multiplied by the small body movement threshold and less than or equal to the characteristic parameter of the background noise multiplied by the large body movement threshold, the body movement state at the corresponding position in the array of body movement states is assigned to the eleventh preset threshold. Among them, the ninth preset threshold is greater than the tenth preset threshold, greater than the eleventh preset threshold, the small body movement threshold is the body movement threshold for small movements, and the large body movement threshold is the body movement threshold for large movements.

[0134] In one implementation, the ninth preset threshold may be 5.

[0135] In one implementation, the tenth preset threshold may be 3.

[0136] In one implementation, the eleventh preset threshold may be 0.

[0137] Step S10209: Obtain the rate of change of the body motion state based on the array of body motion states.

[0138] In this embodiment, the rate of change of the body motion state is acquired based on the array of body motion states.

[0139] In one embodiment, step S10209 may include steps S102091 to S102092:

[0140] Step S102091: Obtain an array of the change rate of the body movement state based on the array of the body movement state.

[0141] Step S102092: Obtain the rate of change of the body motion state based on the array of the rate of change of the body motion state.

[0142] In this embodiment, an array of change rates of body motion states is acquired based on an array of body motion states.

[0143] In one example, for each (i-th minute) BodyStatus (body motion status) = 5 or 3, the corresponding position (i-th minute) in variable N3 (array of body motion status change rate) is set to 1. Then, for the case where BodyStatus(i) = 5, N3(i) i-2 to i+2 are assigned 1, and for the case where BodyStatus(i) = 3, N3(i) i-1 to i+1 are assigned 1. Among them, i+2, i-2, i+1, and i-1 must all be within a reasonable value range, with i-2 being a number greater than zero and i+1 not being greater than the array length of BodyStatus. The array of the calculated body movement state can be (5300003000000500000000300000), and the corresponding array of the change rate of the body movement state can be (1110011100011111000001110000. The array length of the change rate of the body movement state is 28, of which the number of 0s is 14. Therefore, the change rate of the body movement state is 14 divided by 28, which is equal to 0.5.

[0144] Step S10210: Obtaining the sensitivity level of the population based on the rate of change of the body motion state.

[0145] In this embodiment, the crowd sensitivity level is obtained based on the rate of change of the body motion state.

[0146] In one embodiment, step S10210 may include steps S102101 to S102102:

[0147] Step S102101: When the initial crowd sensitivity level is greater than the first preset threshold and the rate of change of the body motion state is less than the second preset threshold, the initial crowd sensitivity level is reduced by one level to obtain the crowd sensitivity level.

[0148] Step S102102: When the default crowd sensitivity is less than the third preset threshold and the rate of change of the body motion state is greater than the fourth preset threshold, the initial crowd sensitivity level is increased by one level to obtain the crowd sensitivity level.

[0149] In this embodiment, when the initial crowd sensitivity level is greater than the first preset threshold and the rate of change of the body motion state is less than the second preset threshold, the initial crowd sensitivity level is reduced by one level to obtain the crowd sensitivity level. When the default crowd sensitivity is less than the third preset threshold and the rate of change of the body motion state is greater than the fourth preset threshold, the initial crowd sensitivity level is increased by one level to obtain the crowd sensitivity level. Under the premise that the fourth preset threshold is greater than the second preset threshold, those skilled in the art may customize the fourth preset threshold and the second preset threshold according to actual use, and the third preset threshold is greater than the first preset threshold.

[0150] In one embodiment, the initial population sensitivity levels may be divided into 0, 1, 2, 3, 4, 5, and 6.

[0151] In one implementation, the first preset threshold may be 0.

[0152] In one implementation, the third preset threshold may be 6.

[0153] In one embodiment, step S10210 may further include step S10211:

[0154] Step S10211: When the rate of change of the body motion state within the preset number of days is less than the second threshold or greater than the fourth threshold, the crowd sensitivity level is updated.

[0155] In this embodiment, when the rate of change of the body motion state within the preset number of days is less than the second threshold or greater than the fourth threshold, the population sensitivity can be updated according to the method of the previous step S10210.

[0156] In one implementation, the preset number of days may be 5 days.

[0157] Step S103: obtaining a body motion threshold of the human detection device based on the inherent sensitivity level and the crowd sensitivity level.

[0158] In this embodiment, the body motion threshold of the human body detection device can be obtained through the inherent sensitivity level and the crowd sensitivity level according to the preset comparison relationship table of the inherent sensitivity level, the crowd sensitivity level and the body motion threshold. Among them, those skilled in the art can customize the preset comparison relationship table of the inherent sensitivity level, the crowd sensitivity level and the body motion threshold according to actual use.

[0159] In one embodiment, the body motion threshold may be the number of times the user moves during sleep as detected by the human body detection device.

[0160] Based on the above steps S101 to S103, the present invention obtains the inherent sensitivity level of the human body detection device, obtains the crowd sensitivity level based on the historical body motion data and the inherent sensitivity level of the human body detection device, and obtains the body motion threshold of the human body detection device based on the inherent sensitivity level and the crowd sensitivity level. Through the above configuration, the historical body motion data obtained by the human body detection device in different usage environments are different. Due to the different historical body motion data, the crowd sensitivity level obtained based on the historical body motion data and the inherent sensitivity level changes. Due to the change in the crowd sensitivity level, the body motion threshold of the human body detection device obtained based on the crowd sensitivity level and the inherent sensitivity level changes. That is, the body motion threshold will change with the change of the usage environment of the human body detection device. Different body motion thresholds can be obtained in different usage environments through the above configuration, so as to more accurately and adaptively adjust the body motion threshold of the human body detection device in different usage environments.

[0161] Furthermore, the present invention also provides a method for detecting human behavior.

[0162] Refer to FIG2 , which is a schematic flow chart of the main steps of a human behavior detection method according to an embodiment of the present invention. As shown in FIG2 , the human behavior detection method in the embodiment of the present invention mainly includes the following steps S501 to S502:

[0163] Step S501: obtaining a body motion threshold of a human body detection device according to the body motion threshold adjustment method of the above method embodiment;

[0164] Step S502: Using human body detection equipment, based on the body motion threshold, detect human behavior and obtain detection results.

[0165] It should be pointed out that although the various steps in the above embodiments are described in a specific order, those skilled in the art will understand that in order to achieve the effects of the present invention, different steps do not have to be performed in such an order. They can be performed simultaneously (in parallel) or in other orders. These changes are within the scope of protection of the present invention.

[0166] Those skilled in the art will appreciate that all or part of the processes in the method for implementing the above-mentioned embodiment of the present invention may also be accomplished by instructing the relevant hardware through a computer program. The computer program may be stored in a computer-readable storage medium. When the computer program is executed by a processor, it may implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which may be in source code form, object code form, executable file, or some intermediate form. The computer-readable storage medium may include: any entity or device, medium, USB flash drive, mobile hard disk, magnetic disk, optical disk, computer memory, read-only memory, random access memory, electric carrier signal, telecommunication signal, and software distribution medium capable of carrying the computer program code. It should be noted that the content contained in the computer-readable storage medium may be appropriately increased or decreased according to the requirements of legislation and patent practice in the jurisdiction. For example, in some jurisdictions, according to legislation and patent practice, computer-readable storage media do not include electric carrier signals and telecommunication signals.

[0167] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the body motion threshold adjustment method of the above-mentioned method embodiment, and the processor can be configured to execute the program in the storage device, which includes but is not limited to a program for executing the body motion threshold adjustment method of the above-mentioned method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0168] In the embodiment of the present invention, the control device may be a control device device formed by various electronic devices. In some possible implementations, the control device may include multiple storage devices and multiple processors. The program for executing the body motion threshold adjustment method of the above method embodiment can be divided into multiple sub-programs, and each sub-program can be loaded and run by the processor to execute different steps of the body motion threshold adjustment method of the above method embodiment. Specifically, each sub-program can be stored in different storage devices respectively, and each processor can be configured to execute the programs in one or more storage devices to jointly implement the body motion threshold adjustment method of the above method embodiment, that is, each processor executes different steps of the body motion threshold adjustment method of the above method embodiment respectively to jointly implement the body motion threshold adjustment method of the above method embodiment.

[0169] Furthermore, the present invention also provides a control device. In one embodiment of the control device according to the present invention, the control device includes a processor and a storage device. The storage device can be configured to store a program for executing the human behavior detection method of the above-mentioned method embodiment, and the processor can be configured to execute the program in the storage device, which includes but is not limited to a program for executing the human behavior detection method of the above-mentioned method embodiment. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The control device can be a control device device formed by various electronic devices.

[0170] In the embodiment of the present invention, the control device may be a control device device formed by various electronic devices. In some possible implementations, the control device may include multiple storage devices and multiple processors. The program for executing the human behavior detection method of the above method embodiment can be divided into multiple subroutines, and each subroutine can be loaded and run by the processor to execute different steps of the human behavior detection method of the above method embodiment. Specifically, each subroutine can be stored in different storage devices respectively, and each processor can be configured to execute the program in one or more storage devices to jointly implement the human behavior detection method of the above method embodiment, that is, each processor executes different steps of the human behavior detection method of the above method embodiment respectively to jointly implement the human behavior detection method of the above method embodiment.

[0171] The aforementioned multiple processors may be processors deployed on the same device. For example, the aforementioned control device may be a high-performance device composed of multiple processors, and the aforementioned multiple processors may be processors configured on the high-performance device. Furthermore, the aforementioned multiple processors may also be processors deployed on different devices. For example, the aforementioned control device may be a server cluster, and the aforementioned multiple processors may be processors on different servers in the server cluster.

[0172] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the body motion threshold adjustment method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned body motion threshold adjustment method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-temporary computer-readable storage medium.

[0173] Furthermore, the present invention also provides a computer-readable storage medium. In a computer-readable storage medium embodiment according to the present invention, the computer-readable storage medium can be configured to store a program for executing the human behavior detection method of the above-mentioned method embodiment, and the program can be loaded and run by the processor to implement the above-mentioned human behavior detection method. For ease of explanation, only the parts related to the embodiment of the present invention are shown. For specific technical details not disclosed, please refer to the method part of the embodiment of the present invention. The computer-readable storage medium can be a storage device formed by various electronic devices. Optionally, the computer-readable storage medium in the embodiment of the present invention is a non-transitory computer-readable storage medium.

[0174] Furthermore, the present invention also provides a human body detection device. In an embodiment of the human body detection device according to the present invention, the human body detection device may include the control device in the control device embodiment.

[0175] Furthermore, it should be understood that since the configuration of each module is merely for the purpose of illustrating the functional units of the apparatus of the present invention, the physical devices corresponding to these modules may be the processor itself, or a portion of the software in the processor, a portion of the hardware, or a combination of software and hardware. Therefore, the number of modules in the figure is merely illustrative.

[0176] Those skilled in the art will appreciate that the various modules in the device can be adaptively split or merged. Such splitting or merging of specific modules does not cause the technical solution to deviate from the principles of the present invention. Therefore, the technical solutions after splitting or merging will fall within the scope of protection of the present invention.

[0177] Thus far, the technical solutions of the present invention have been described in conjunction with the preferred embodiments shown in the accompanying drawings. However, it will be readily understood by those skilled in the art that the scope of protection of the present invention is obviously not limited to these specific embodiments. Without departing from the principles of the present invention, those skilled in the art may make equivalent changes or substitutions to the relevant technical features, and the technical solutions after such changes or substitutions will fall within the scope of protection of the present invention.

Claims

1. A method for adjusting body motion threshold, characterized in that: The method comprises: Obtaining an inherent sensitivity level of the human body detection device; Obtaining a crowd sensitivity level based on historical body motion data of the human body detection device and the inherent sensitivity level; A body motion threshold of the human detection device is obtained based on the inherent sensitivity level and the crowd sensitivity level.

2. The method for adjusting the body motion threshold according to claim 1, characterized in that: The inherent sensitivity level includes a real-time inherent sensitivity level; The obtaining of the inherent sensitivity level of the human body detection device comprises: Segmenting the historical body movement data according to a first preset time length to obtain a plurality of historical body movement sub-data; Obtaining the extreme difference value of the signal strength amplitude per preset unit time in the historical body motion sub-data, recorded as an extreme difference value array, and obtaining multiple extreme difference arrays based on multiple historical body motion sub-data; Acquire multiple first variables and multiple second variables based on multiple extreme value arrays and multiple background noises of the historical body motion sub-data; wherein the first variable is the number of large movements of the user in the historical body motion sub-data, and the second variable is the number of small movements of the user in the historical body motion sub-data; Acquire a first variable array based on the plurality of the first variables, and acquire a second variable array based on the plurality of the second variables; Acquire characteristic parameters of the historical body motion data based on the first variable array and the second variable array; The real-time inherent sensitivity level of the human body detection device is obtained based on the characteristic parameters of the historical body motion data.

3. The method for adjusting the body motion threshold according to claim 2, characterized in that: The obtaining of a plurality of first variables and a plurality of second variables based on a plurality of extreme difference value arrays and a plurality of background noises of the historical body motion sub-data comprises: Counting the number of the extreme difference values ​​in the extreme difference value array that are greater than the background noise multiplied by a fifth preset threshold value, recording the number as the first variable, and obtaining a plurality of the first variables based on a plurality of the extreme difference value arrays; The number of the range values ​​in the range value array that are less than or equal to the background noise multiplied by the fifth preset threshold and greater than the background noise multiplied by the sixth preset threshold is counted as the second variable, and multiple second variables are obtained based on multiple range value arrays.

4. The method for adjusting the body motion threshold according to claim 2, characterized in that: The inherent sensitivity level includes an initial inherent sensitivity level; The obtaining of the crowd sensitivity level based on the historical body motion data of the human body detection device and the inherent sensitivity level includes: Determining whether the real-time inherent sensitivity level is the same as the initial inherent sensitivity level; If yes, obtaining characteristic parameters of the background noise based on the background noise of the historical body motion data; Obtaining a body motion threshold based on an initial inherent sensitivity level and an initial crowd sensitivity level of the human body detection device; Acquire an array of body movement states based on the characteristic parameters of the background noise, the background noise, the extreme difference value array and the body movement threshold; Acquire the rate of change of the body motion state based on the array of the body motion state; Acquire the population sensitivity level based on the rate of change of the body motion state; If not, obtaining a body motion threshold based on the real-time inherent sensitivity level and the initial population sensitivity level; Acquire an array of body movement states based on the characteristic parameters of the background noise, the background noise, the extreme difference value array and the body movement threshold; Acquire the rate of change of the body motion state based on the array of the body motion state; The population sensitivity level is obtained based on the rate of change of the body motion state.

5. The method for adjusting body motion threshold according to claim 4, characterized in that: The acquiring the crowd sensitivity level based on the rate of change of the body motion state includes: When the initial population sensitivity level is greater than a first preset threshold and the rate of change of the body motion state is less than a second preset threshold, the initial population sensitivity level is reduced by one level, To obtain the sensitivity level of the population; When the initial crowd sensitivity is less than a third preset threshold and the rate of change of the body motion state is greater than a fourth preset threshold, increasing the initial crowd sensitivity level by one level to obtain the crowd sensitivity level; Wherein, the fourth preset threshold is greater than the second preset threshold, and the third preset threshold is greater than the first preset threshold; and / or, The body movement threshold includes a small body movement threshold and a large body movement threshold; the array for obtaining the body movement state based on the characteristic parameter of the background noise, the background noise, the extreme difference value array and the body movement threshold includes: When the extreme difference value in the extreme difference value array is greater than the background noise multiplied by the small body movement threshold, assigning the body movement state at the corresponding position in the array of body movement states to a ninth preset threshold; When the extreme difference value in the extreme difference value array is greater than the characteristic parameter of the background noise multiplied by the large body movement threshold, assigning the body movement state at the corresponding position in the array of body movement states a tenth preset threshold; When the extreme difference value in the extreme difference value array is less than or equal to the background noise multiplied by the small body movement threshold and less than or equal to the characteristic parameter of the background noise multiplied by the large body movement threshold, assigning the body movement state at the corresponding position in the array of body movement states to an eleventh preset threshold; Wherein, the ninth preset threshold is greater than the tenth preset threshold which is greater than the eleventh preset threshold, the small body movement threshold is the body movement threshold for small movements, and the large body movement threshold is the body movement threshold for large movements; and / or, The acquiring the rate of change of the body motion state based on the array of the body motion state comprises: Acquire an array of change rates of the body movement states based on the array of the body movement states; Acquiring the rate of change of the body motion state based on an array of the rate of change of the body motion state; and / or, After obtaining the crowd sensitivity level based on the change rate of the body motion state, the method further includes: When the change rate of the body motion state within the preset number of days is less than the second threshold or greater than the fourth threshold, the population sensitivity level is updated.

6. The method for adjusting the body motion threshold according to claim 2, characterized in that: The acquiring the characteristic parameters of the historical body motion data based on the first variable array and the second variable array includes acquiring the characteristic parameters of the historical body motion data according to the following formula: Wherein, VT is the characteristic parameter of the historical body movement data, C1 is the first variable array, C2 is the second variable array, S is the time parameter related to the historical body movement data, and sum is the summation function; The obtaining the inherent sensitivity level of the human body detection device based on the characteristic parameters of the historical body motion data includes: When the characteristic parameter of the historical body motion data is greater than a seventh preset threshold, adjusting the inherent sensitivity level of the current device to a third preset level; When the characteristic parameter of the historical body motion data is greater than an eighth preset threshold and less than or equal to the seventh preset threshold, adjusting the inherent sensitivity level of the current device to a second preset level; When the characteristic parameter of the historical body motion data is less than or equal to an eighth preset threshold, adjusting the inherent sensitivity level of the current device to a first preset level; The sensitivity of the third preset level is greater than the sensitivity of the second preset level, which is greater than the sensitivity of the first preset level.

7. A human behavior detection method, characterized in that: The method comprises: Applying the body motion threshold adjustment method described in any one of claims 1 to 6 to obtain the body motion threshold of a human body detection device; The human body detection device is used to detect the human body behavior based on the body movement threshold to obtain a detection result.

8. A control device, comprising at least one processor and at least one storage device, wherein the storage device is suitable for storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by the processor to execute the body motion threshold adjustment method described in any one of claims 1 to 6 or the human behavior detection method described in claim 7.

9. A computer-readable storage medium storing a plurality of program codes, characterized in that: The program code is suitable for being loaded and run by a processor to execute the body motion threshold adjustment method described in any one of claims 1 to 6 or the human behavior detection method described in claim 7.

10. A human body detection device, characterized in that: The apparatus comprises the control device of claim 8.

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