A state detection method, device, apparatus and storage medium

Abstract

Embodiments of the present application disclose a state detection method and device, equipment and a storage medium, wherein the method comprises: obtaining pressure data corresponding to each sampling time point in at least one sampling time point; wherein the pressure data comprises first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data of different parts of a user; determining a state analysis strategy from a first analysis strategy for analyzing the first pressure data and a second analysis strategy for analyzing the second pressure data based on the first pressure data corresponding to each sampling time point; and analyzing the pressure data corresponding to each sampling time point by using the state analysis strategy to determine the current state of the user; thus, the current state of the user can be accurately determined by combining the pressure data of different parts.

Description

Technical Field

[0001] This application relates to computer processing technology, and more particularly to a state detection method, apparatus, device, and storage medium. Background Technology

[0002] In related technologies, accelerometers and infrared sensors are used to detect the user's status; however, this can lead to misjudgments in scenarios such as when the user quickly crouches down or jumps from a height. Summary of the Invention

[0003] In view of this, embodiments of this application provide a state detection method, apparatus, device, and storage medium.

[0004] In a first aspect, embodiments of this application provide a state detection method, the method comprising:

[0005] Acquire pressure data corresponding to each of the at least one sampling time points; wherein the pressure data includes first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data of different parts of the user;

[0006] Based on the first pressure data corresponding to each sampling time point, a state parsing strategy is determined from a first parsing strategy for parsing the first pressure data and a second parsing strategy for parsing the second pressure data;

[0007] The pressure data corresponding to each sampling time point is analyzed using the state parsing strategy to determine the user's current state.

[0008] Secondly, embodiments of this application provide a state detection device, the device comprising:

[0009] The first acquisition module is used to acquire pressure data corresponding to each sampling time point in at least one sampling time point; the pressure data includes first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data of different parts of the user's body.

[0010] The first pressure sensor and the second pressure sensor are worn on different parts of the user's body; the pressure sensor includes the first pressure sensor and the second pressure sensor.

[0011] The first determining module is used to determine a state parsing strategy based on the first pressure data corresponding to each sampling time point, from a first parsing strategy for parsing the first pressure data and a second parsing strategy for parsing the second pressure data;

[0012] The first processing module uses the state parsing strategy to parse the pressure data corresponding to each sampling time point to determine the user's current state.

[0013] Thirdly, embodiments of this application provide an electronic device, which includes: a processor, a memory, and a communication bus;

[0014] The communication bus is used to realize the communication connection between the processor and the memory;

[0015] The processor is used to execute the program in the memory to implement the state detection method described in the first aspect.

[0016] Fourthly, embodiments of this application provide a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the state detection method described in the first aspect.

[0017] In the embodiments of this application, firstly, pressure data corresponding to each of the at least one sampling time points is acquired; wherein, the pressure data includes first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data of different parts of the user; secondly, based on the first pressure data corresponding to each sampling time point, a state parsing strategy is determined from a first parsing strategy for parsing the first pressure data and a second parsing strategy for parsing the second pressure data; finally, the pressure data corresponding to each sampling time point is parsed using the state parsing strategy to determine the user's current state; thus, the user's current state can be accurately determined by combining pressure data from different parts of the body. Attached Figure Description

[0018] Figure 1 A schematic diagram illustrating the implementation process of a state detection method provided in an embodiment of this application;

[0019] Figure 2 This application provides a flowchart illustrating a method for determining a state resolution strategy.

[0020] Figure 3 This application provides a flowchart illustrating a method for determining a user's state in a first state.

[0021] Figure 4 A flowchart illustrating a method for determining whether a user is in a first state or a second state, provided in an embodiment of this application;

[0022] Figure 5 This application provides a schematic flowchart of a method for determining the state based on pressure data of at least one drive shaft position.

[0023] Figure 6A schematic diagram illustrating the implementation process of a method for determining a user's state in a third state, provided in an embodiment of this application;

[0024] Figure 7 A flowchart illustrating a method for determining whether a user is in a third or fourth state, provided in an embodiment of this application;

[0025] Figure 8 A schematic diagram illustrating the implementation flow of a prompt message generation method provided in an embodiment of this application;

[0026] Figure 9A A schematic diagram illustrating the implementation process of a fall detection method provided in this application embodiment;

[0027] Figure 9B A schematic diagram illustrating the implementation process of a rescue method based on state detection provided in this application embodiment;

[0028] Figure 10A This is a schematic diagram of the structure of a state detection system provided in an embodiment of this application;

[0029] Figure 10B A schematic diagram showing the positions of a first device, a second device, and a third device provided in an embodiment of this application;

[0030] Figure 10C This is a schematic diagram of the position of the second device in the top view according to an embodiment of this application;

[0031] Figure 10D This is a schematic diagram showing the position of the second device in the frontal view, as provided in an embodiment of this application.

[0032] Figure 11 This is a schematic diagram of the composition structure of a state detection device provided in an embodiment of this application;

[0033] Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application. Detailed Implementation

[0034] The technical solution of this application will be further described in detail below with reference to the accompanying drawings and embodiments.

[0035] Falling is a common occurrence. Children and young people can get up and continue walking after falling, but for special groups, especially the elderly, a fall may cause them to faint or lead to life-threatening situations if they do not receive timely medical attention.

[0036] In related technologies, accelerometers and height sensors are used to detect the user's state. However, this can lead to misjudgments when the user quickly crouches down or jumps from a height. Furthermore, since state detection is based on data changes, it cannot detect the user's getting up after a fall.

[0037] To address the aforementioned technical problems, this application proposes the following technical solutions.

[0038] Figure 1 This is a schematic diagram illustrating the implementation flow of the state detection method provided in the embodiments of this application, as shown below. Figure 1 As shown, the method includes:

[0039] Step S101: Obtain pressure data corresponding to each of the at least one sampling time points.

[0040] In some embodiments, the pressure data includes first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data from different parts of the user's body; for example, the first pressure data may be pressure data from the waist, and the second pressure data may be pressure data from the knee; or, the first pressure data may be pressure data from the abdomen, and the second pressure data may be pressure data from the palm, elbow, and knee; wherein, when the second pressure data is pressure data from multiple different parts of the body, the second pressure data corresponding to each sampling time point is not a single data point, but a set of pressure data formed by pressure data from multiple different parts of the body; the first pressure data is similar, and will not be elaborated here.

[0041] In some embodiments, the first pressure data and the second pressure data are pressure data of different parts of the user at the same sampling time point; that is, the first pressure data and the second pressure data are acquired synchronously, and pressure data of one part is acquired at the same time as pressure data of another part.

[0042] In some embodiments, the first pressure data and the second pressure data can be acquired by a pressure sensor.

[0043] In some embodiments, when there are N sampling time points, pressure data corresponding to each sampling time point is acquired; that is, pressure data of different parts are acquired at N sampling time points, so that pressure data of each part at N time points can be obtained; for example, the pressure data of part A are: A1, A2...A N The pressure data for part B are B1, B2...B N Here, the pressure data of part A at any sampling time point can be regarded as the first pressure data, such as: A1, A2, ..., or A N The pressure data at any sampling time for part B is considered as the second pressure data, such as: B1, B2, ..., or B N Here, when parts A and B include multiple different sub-parts, each first pressure data becomes a first pressure data set, for example: A1 = {A 11 A12 A 13 ...A 1i}、A2={A 21 A 22 A 23 ...A 2i}….A N ={A N1 A N2 A N3 ...A Ni}; where i represents the number of different parts; each second pressure data becomes a second pressure data set, for example: B1 = {B 11 B 12 B 13 ...B 1i}、B2={B 21 B 22 B 23 ...B 2i}……B N ={B N1 B N2 B N3 ...B Ni}

[0044] Step S102: Based on the first pressure data corresponding to each sampling time point, determine the state parsing strategy from the first parsing strategy for parsing the first pressure data and the second parsing strategy for parsing the second pressure data.

[0045] In some embodiments, the state parsing strategy may include a first parsing strategy and a second parsing strategy; wherein the first parsing strategy is used to parse first stress data, and the second parsing strategy is used to parse second stress data to determine the user's current state.

[0046] In some embodiments, a state analysis strategy can be determined based on the data characteristics of the first pressure data corresponding to each sampling time point; wherein, the data characteristics can be the magnitude characteristics of the first pressure data corresponding to each sampling time point, or the distribution characteristics of the first pressure data corresponding to each sampling time point.

[0047] In some embodiments, the size feature can be determined by a reference object; in one possible implementation, it can be achieved by the following process: first, statistical analysis of the first pressure data; second, comparison of the first pressure data with a preset threshold; and finally, the size relationship between the first pressure data and the preset threshold as the size feature of the first pressure data.

[0048] In some embodiments, the distribution characteristics may be interval distribution, concentrated distribution, etc.

[0049] Step S103: Analyze the pressure data corresponding to each sampling time point using the state parsing strategy to determine the user's current state.

[0050] In some embodiments, when the first parsing strategy is determined to be a state parsing strategy, the first pressure data corresponding to each sampling time point is parsed using the first parsing strategy to determine the user's current state.

[0051] In some embodiments, when the second parsing strategy is determined to be a state parsing strategy, the second pressure data corresponding to each sampling time point is parsed using the second parsing strategy to determine the user's current state.

[0052] In this embodiment, firstly, pressure data corresponding to each of the at least one sampling time points is acquired; secondly, based on the first pressure data corresponding to each sampling time point, a state parsing strategy is determined from a first parsing strategy for parsing the first pressure data and a second parsing strategy for parsing the second pressure data; finally, the pressure data corresponding to each sampling time point is parsed using the state parsing strategy to determine the user's current state; thus, the user's current state can be accurately determined by combining pressure data from different locations.

[0053] Figure 2 This application provides a flowchart illustrating a method for determining a state resolution strategy, as shown in the embodiments below. Figure 2 As shown, step S102 includes:

[0054] Step S201: If the first pressure data corresponding to each sampling time point is less than the first pressure threshold, the second parsing strategy used to parse the second pressure data is determined as the state parsing strategy.

[0055] In some embodiments, when the first pressure data corresponding to all sampling time points is less than the first pressure threshold, the second pressure data corresponding to all sampling time points is actually analyzed using the second analysis strategy; for example, when the sampling time point is N, the pressure data of part A are A1, A2...A N Then we need A1, A2...A N All are less than the first pressure threshold; when part A includes multiple sub-parts, the pressure data for part A is A1 = {A 11 A 12 A 13 ...A 1i}、A2={A 21 A 22 A 23 ...A 2i}….AN ={A N1 A N2 A N3 ...A Ni}, then we need A1 = {A 11 A 12 A 13 ...A 1i}、A2={A 21 A 22 A 23 ...A 2i}….A N ={A N1 A N2 A N3 ...A Ni All are less than the first pressure threshold.

[0056] In some embodiments, the first pressure threshold may be determined based on historical data. If the first pressure data corresponding to all sampling time points is less than the first pressure threshold, it indicates that the part corresponding to the first pressure data has not experienced a large impact and is in a safe state.

[0057] In some embodiments, the second pressure data includes limb pressure data corresponding to each limb site in at least one limb region. Figure 3 This application provides a flowchart illustrating a method for determining a user's first state, as illustrated in the embodiments of this application. Figure 3 As shown, step S103 includes:

[0058] Step S301: If the limb pressure data corresponding to each limb part at each sampling time point is less than the second pressure threshold, determine that the user is in the first state.

[0059] In some embodiments, the first state indicates that the user's limb is in a safe state.

[0060] In some embodiments, a limb part can be a part or organ of the user's limbs, such as the palm, elbow, knee, etc.; here, a limb part can be understood as the part or organ that the user usually contacts the ground when falling.

[0061] In some embodiments, the second pressure threshold may be determined based on historical data. If the second pressure data corresponding to all sampling time points is less than the second pressure threshold, it indicates that the part corresponding to the second pressure data has not experienced excessive impact and is in a safe state.

[0062] In some embodiments, the first state can be a standing, getting up, or other states; that is, the first state can be a state other than an unsafe state such as falling down.

[0063] Step S302: If the limb pressure data corresponding to at least one limb part at at least one sampling time point is greater than or equal to the second pressure threshold, the current state of the user is determined based on the distribution characteristics of the limb pressure data of the target limb part at the at least one sampling time point using the first feature data corresponding to at least one target limb part.

[0064] In some embodiments, the case where the limb pressure data corresponding to at least one limb part at at least one sampling time point is greater than or equal to the second pressure threshold can be understood as the opposite of step S301; that is, there is a case where the limb pressure data corresponding to all limb parts at all sampling time points is greater than the second pressure threshold. In this case, the limb pressure data that is greater than or equal to the second pressure threshold is regarded as the first feature data; and the first feature data may correspond to one limb part or multiple limb parts; here, each limb part corresponding to the first feature data is regarded as a target limb part.

[0065] In some embodiments, the current state of the user is determined based on the distribution characteristics of limb pressure data at the target limb at at least one sampling time point, according to the first feature data corresponding to at least one target limb. That is, for a certain target limb, the current state of the user is determined based on the distribution characteristics of limb pressure data at all sampling time points of the target limb at which the limb pressure data corresponding to the target limb is greater than or equal to a second pressure threshold. For example, the second pressure data includes limb pressure data corresponding to limb A and limb B, and there are three possible cases:

[0066] (1) If both limb pressure data of limb part A and limb part B have limb pressure data greater than or equal to the second pressure threshold, then limb part A and limb part B are both target limb parts. At this time, the distribution characteristics are as follows: First, both satisfy the distribution characteristics; Second, one satisfies the distribution characteristics; Third, neither satisfies the distribution characteristics.

[0067] (2) If the limb pressure data of one of the limb parts A and B (limb part A or limb part B) is greater than or equal to the second pressure threshold, then limb part A or limb part B is the target limb part. At this time, the distribution characteristics are as follows: First, the distribution characteristics are satisfied; Second, the distribution characteristics are not satisfied.

[0068] (3) If there are no limb pressure data greater than or equal to the second pressure threshold for either limb A or limb B, then neither limb A nor limb B is the target limb, and there is no need to further determine the distribution characteristics.

[0069] In some embodiments, the distribution characteristics may be interval distribution, concentrated distribution, etc.

[0070] In this embodiment, if the limb pressure data corresponding to all limb parts at all sampling time points is less than the second pressure threshold, the user is directly determined to be in the first state; otherwise, for each limb part, based on the data greater than or equal to the second pressure threshold, the distribution characteristics of the limb pressure data in the data sequence formed at all sampling time points are used to determine the user's current state; thus, the user's true current state can be accurately detected from at least two states using the second parsing strategy.

[0071] Figure 4 This application provides a flowchart illustrating a method for determining whether a user is in a first or second state, as illustrated in the embodiments of this application. Figure 4 As shown, step S302 includes:

[0072] Step S401: If the distribution characteristics of the first feature data corresponding to at least one target limb part satisfy the first distribution condition, determine that the user is in the second state.

[0073] In some embodiments, the first distribution condition is that the sampling time point of the first feature data corresponding to any target limb part is located in the target location interval of the at least one sampling time point.

[0074] In some embodiments, the second state represents at least one limb of the user contacting the ground; further, the second state may be a fall.

[0075] In some embodiments, the target location interval can be the tail of the time series formed by the sampling time points; for example, if there are 9 sampling time points, the 9 sampling time points are divided into three equal parts, and then one or more of the last three sampling time points are determined as the target location interval.

[0076] In some embodiments, step S401 can be understood as follows: in the limb pressure data of at least one target limb part at all sampling times, there is data that is greater than or equal to the second pressure threshold, and the sampling time point corresponding to the data is within the target position range of all sampling time points.

[0077] Step S402: If the distribution characteristics of the first feature data corresponding to at least one target limb part do not meet the first distribution condition, determine that the user is in the first state.

[0078] In this embodiment, there are two cases: if the distribution characteristics of the first feature data corresponding to at least one target limb part meet the first distribution condition, the user is directly determined to be in the second state; otherwise, the user is determined to be in the first state. In this way, the user's true current state can be accurately detected from at least two states using the second parsing strategy.

[0079] In some embodiments, the first pressure data includes pressure data for each of at least one drive position in the drive shaft. Figure 5 This application provides a flowchart illustrating a method for determining a state based on pressure data at at least one drive shaft location, as illustrated in the embodiments of this application. Figure 5 As shown, step S102 includes:

[0080] Step S501: If the pressure data of the driving stem position corresponding to at least one driving stem position at at least one sampling time point is greater than or equal to the first pressure threshold, the first parsing strategy used to parse the first pressure data is determined as the state parsing strategy.

[0081] In some embodiments, when the pressure data at at least one driving position corresponding to at least one driving position at at least one sampling time point is greater than or equal to a first pressure threshold, the first analysis strategy is actually used to analyze the first pressure data corresponding to all sampling time points.

[0082] Figure 6 This application provides a schematic diagram of the implementation process of a method for determining a user's state in a third state, as illustrated in the embodiments of this application. Figure 6 As shown, step S103 includes:

[0083] Step S601: If the pressure data of at least one drive position corresponding to at least one drive position at each sampling time point that is greater than or equal to the first pressure threshold is located in the target position interval of the at least one sampling time point, then the user is determined to be in the third state.

[0084] In some embodiments, the third state represents the user's drive body contacting the ground in a fixed posture; further, the third state can be an inverted state.

[0085] In some embodiments, step S601, that is, determining that the user is in the third state when all the sampling time points corresponding to the pressure data of the drive trunk position that are greater than or equal to the first pressure threshold are within the target position interval of the time series formed by all the sampling time points; for example, if there are a total of 9 sampling time points and the target position area is the 7th to 9th sampling time points, then when all the time points corresponding to the pressure data of the drive trunk position that are greater than or equal to the first pressure threshold are within the 7th to 9th sampling time points, it is determined that the user is in the reversed state.

[0086] Step S602: If there is at least one driving position pressure data corresponding to at least one driving position at at least one sampling time point that is greater than or equal to the first pressure threshold, and the sampling time point is located outside the target position interval of the at least one sampling time point, the current state of the user is determined based on the distribution characteristics of the second feature data corresponding to the target driving position of the driving position pressure data at the target driving position of the at least one sampling time point.

[0087] In some embodiments, the second feature data is the pressure data of at least one driving stem position corresponding to at least one sampling time point that is greater than or equal to the first pressure threshold; the target driving stem position is the driving stem position corresponding to the second feature data.

[0088] In some embodiments, step S602 can be understood as the opposite of step S601; that is, the pressure data of the at least one driving position corresponding to at least one sampling time point that is greater than or equal to the first pressure threshold is not located in the target position interval of at least one sampling time point.

[0089] In some embodiments, the torso may be the waist, abdomen, etc.; that is, the torso may be the part of the body that the user usually contacts the ground when falling.

[0090] In some embodiments, pressure sensors can be installed at different locations on the drive shaft to detect pressure data at different locations on the drive shaft.

[0091] In some embodiments, the current state of the user is determined based on the distribution characteristics of the second feature data corresponding to the target drive position in the drive position pressure data at at least one sampling time point; that is, for a certain target drive position, the current state of the user is determined based on the distribution characteristics of the drive position pressure data corresponding to the target drive position that is greater than or equal to a first pressure threshold in the drive position pressure data at all sampling time points of the target drive position; for example, the first pressure data includes the drive position pressure data corresponding to drive position A and drive position B, and there are three cases:

[0092] (1) If the pressure data of the driving trunk position A and the driving trunk position B both have driving trunk position pressure data that are greater than or equal to the first pressure threshold, then the driving trunk position A and the driving trunk position B are both target driving trunk positions. At this time, the distribution characteristics are as follows: First, both satisfy the distribution characteristics; Second, one satisfies the distribution characteristics; Third, neither satisfies the distribution characteristics.

[0093] (2) If the limb pressure data of one of the trunk positions A and B (truncation position A or trunk position B) is greater than or equal to the first pressure threshold, then trunk position A or trunk position B is the target trunk position. At this time, the distribution characteristics are as follows: First, the distribution characteristics are satisfied; Second, the distribution characteristics are not satisfied.

[0094] (3) If there is no limb pressure data greater than or equal to the first pressure threshold at either trunk position A or trunk position B, then neither trunk position A nor trunk position B is the target trunk position, and there is no need to further determine the distribution characteristics.

[0095] In some embodiments, the distribution characteristics may be interval distribution, concentrated distribution, etc.

[0096] In this embodiment, there are two scenarios: if the pressure data of at least one drive shaft position corresponding to at least one drive shaft position at each sampling time point greater than or equal to the first pressure threshold is located within the target position interval of at least one sampling time point, the user is directly determined to be in the third state; otherwise, the current state of the user is determined based on the distribution characteristics of the second feature data corresponding to the target drive shaft position and the drive shaft position pressure data at the target drive shaft position of the at least one sampling time point. In this way, the user's true current state can be accurately detected from at least two states using the first parsing strategy.

[0097] Figure 7 A flowchart illustrating a method for determining whether a user is in a third or fourth state, as provided in this application embodiment, is shown below. Figure 7 As shown, step S602 includes:

[0098] Step S701: If the distribution characteristics of the second feature data corresponding to the target driving position satisfy the second distribution condition, determine that the user is in the fourth state.

[0099] In some embodiments, the second distribution condition is that the sampling time points of the second feature data corresponding to any target drive trunk position are distributed in the at least one sampling time interval.

[0100] In some embodiments, the second distribution condition can be understood as the sampling time points corresponding to the pressure data of the drive shaft position that are greater than or equal to the first pressure threshold corresponding to any target drive shaft position being distributed at intervals across all sampling time points.

[0101] In some embodiments, the sampling time points corresponding to the pressure data of the target drive position that are greater than or equal to the first pressure threshold are distributed at intervals across all sampling time points, indicating that all target drive positions take turns contacting the ground. Therefore, the first state can be a rolling state.

[0102] Step S702: If the distribution characteristics of the second feature data corresponding to the target drive position do not meet the second distribution condition, determine that the user is in the third state.

[0103] In this embodiment, there are two cases: if the distribution characteristics of the second feature data corresponding to the target driving position meet the second distribution condition, the user is determined to be in the fourth state; otherwise, the user is determined to be in the third state. In this way, the user's true current state can be accurately detected from at least two states using the first parsing strategy.

[0104] Figure 8 This is a schematic diagram illustrating the implementation flow of the prompt message generation method provided in the embodiments of this application, such as... Figure 8 As shown, the method also includes:

[0105] Step S801: If the user's current state is any of the second to fourth states, obtain the user's previous state.

[0106] Step S802: If the user's previous state is the same as the user's current state, determine the duration of the user's state maintenance.

[0107] Step S803: If the duration of the state exceeds a preset duration threshold, trigger the prompt message corresponding to the current state.

[0108] In some embodiments, the second to fourth states can be "falling," "falling down," or "rolling." If any state is maintained for a duration exceeding a preset threshold, it indicates that the user is unable to get up and requires assistance. Therefore, the notification message can be sent to the alarm platform, the emergency medical center, or a message sent to a user linked to an emergency call service, to provide timely assistance to the user.

[0109] The following will describe an exemplary application of the embodiments of this application in a real-world application scenario, using a state detection method and system as examples.

[0110] Figure 9AThis is a schematic diagram illustrating the implementation process of a fall detection method provided in an embodiment of this application, as shown below. Figure 9A As shown, the method is applied to a first device, and the method includes:

[0111] Step S901: Obtain pressure data of the second device at N sampling time points.

[0112] In some embodiments, the Nth sampling time point is the current time.

[0113] In some embodiments, the second device may be a device worn on the user's waist, abdomen, or other torso area. Pressure data can be acquired via a pressure sensor.

[0114] Step S902: Determine whether all pressure data of the second device are less than the first preset threshold.

[0115] In some embodiments, if all pressure data of the second device are less than a first preset threshold, proceed to step S903; otherwise, proceed to step S905.

[0116] In some embodiments, when the second device is worn on the user's waist, abdomen, or other torso, since the user's waist and abdomen usually touch the ground when falling, the pressure data from the second device can be understood as primarily used to detect the user's "falling" state.

[0117] Step S903: Obtain pressure data from the third device at N sampling time points.

[0118] In some embodiments, the third device may be a device worn on the user's limbs, such as the palm, elbow, or knee; therefore, the third device may include M sub-devices; in this case, pressure data of the third device at N sampling time points are acquired, that is, M×N pressure data are acquired.

[0119] Step S904: Determine whether the pressure data of the third device at N sampling time points are all less than the second preset threshold.

[0120] If the pressure data from the third device at N sampling time points are all less than the second preset threshold, the user's current state is marked as "rising"; otherwise, the user's current state is marked as "falling". This can be understood as the pressure data from the third device being used to detect the user's "falling" state; that is, in step S902, excluding the case where the user "falls," the methods of steps S903 and S904 are used to detect whether the user's current state is "rising" or "falling".

[0121] Step S905: Determine whether the pressure data of the device that exceeds the second preset threshold is above one position and concentrated at the end of N sampling time points.

[0122] If the pressure data of the device exceeding the second preset threshold is concentrated at the end of N sampling time points in one location, the user's current state is marked as "fall"; otherwise, the user's current state is marked as "rise".

[0123] Step S906: Determine whether the pressure data of devices with values ​​greater than the first preset threshold are all concentrated at the end of the N sampling time points.

[0124] If the pressure data of devices with values ​​greater than the first preset threshold are all concentrated at the end of N sampling time points, mark the user's current state as "down"; otherwise, proceed to step S906.

[0125] Step S907: Determine whether the pressure data of the second device that is greater than the first preset threshold appears in turn in the pressure data of multiple pressure sensors in a time sequence.

[0126] In some embodiments, if the pressure data of the second device, which is greater than a first preset threshold, appears in turn in a time sequence among the pressure data of multiple pressure sensors, the user's current state is marked as "rolling"; otherwise, the user's current state is marked as "falling".

[0127] In some embodiments, steps S901 to S906 can be used for timely processing, such as: when it is detected that the user is in a state of "falling", "rolling" or "falling", the protective equipment worn by the user can be activated to protect the user; specifically, it can be inflated using an inflation device, etc., so as to prevent the user from falling into a more serious situation.

[0128] In some embodiments, if a user's current state is any of "falling," "falling down," or "rolling," and this state persists for an extended period, the user may be in danger. Figure 9B This application provides a schematic diagram of the implementation process of a rescue method based on state detection, as illustrated in the embodiments below. Figure 9B As shown, the method also includes:

[0129] For situations where the current state is "fall":

[0130] Step S908: Determine if the previous state was "start";

[0131] If the previous state is active, record the time of the "fall"; otherwise, proceed to step S909.

[0132] Step S909: Determine whether the previous state was backward or rolling;

[0133] If the previous state was backward or rolling, it means the user is getting up, and the process ends; otherwise, proceed to step S910.

[0134] Step S910: Determine if the previous state was a fall;

[0135] If the previous state was a fall, proceed to step S911; otherwise, end the process.

[0136] Step S911: Determine the duration of the fall;

[0137] Step S912: Determine whether the duration of the fall is greater than the first preset duration threshold;

[0138] If the duration of the fall exceeds the first preset duration threshold, proceed to step S919; otherwise, end the process.

[0139] For cases where the current state is "down":

[0140] Step S913: Determine if the previous state was reversed;

[0141] If the previous state was not "falling", it means that the previous state was any of "rising", "falling", or "rolling". Regardless of the state, it means that the user's unsafe state has been alleviated, and the time of "falling" is recorded; otherwise, proceed to step S914.

[0142] Step S914: Determine the duration of the inversion;

[0143] Step S915: Determine whether the duration of the inversion is greater than the second preset threshold;

[0144] If the duration of the inversion exceeds the second preset threshold, proceed to step S919; otherwise, end the process.

[0145] For cases where the current status is "rolling":

[0146] Step S916: Determine if the previous state was rolling;

[0147] If the previous state was not rolling, it means that the previous state was any of "falling", "falling down", or "getting up", indicating that the user may or may not be in danger, and the time of "rolling" is recorded; otherwise, proceed to step S917.

[0148] Step S917: Determine the rolling duration;

[0149] Step S918: Determine whether the rolling duration is greater than the third preset duration threshold;

[0150] If the duration of the fall exceeds the third preset duration threshold, proceed to step S919; otherwise, end the process.

[0151] Step S919: Initiate the rescue process for the user.

[0152] In some embodiments, Figure 10A This is a schematic diagram of the structure of a state detection system provided in an embodiment of this application, as shown below. Figure 10A As shown, the system includes a first device 1001, a second device 1002, and a third device 1003; wherein,

[0153] The first device 1001 is used to receive the first pressure data from the second device 1002 and the second pressure data from the third device 1003, and to determine the user's current status based on the first pressure data and the second pressure data;

[0154] The second device 1002 is used to detect the first pressure data and send the first pressure data to the first device 1001;

[0155] The third device 1003 is used to detect the second pressure data and send the second pressure data to the first device 1001;

[0156] In some embodiments, such as Figure 10B As shown, the first device 1001 can be a wearable device or a portable device of any shape and worn in any position. The second device 1002 can be a device worn on the torso (waist and abdomen). The first pressure data of the second device 1002 is mainly used to detect the user's "falling" state. The second device may include first pressure sensing modules set at multiple positions on the torso. The third device 1003 can be a device worn on the hand, elbow, knee, etc. The second pressure data of the third device 1003 is mainly used to detect the user's "falling" state. The third device may include second pressure sensing modules set at multiple limb parts.

[0157] In some embodiments, the first device 1001 and the second device 1002 may be combined or separated; in order to reduce the load of the second device 1002 on the trunk (waist and abdomen), the first device 1001 and the second device 1002 may be separated.

[0158] The first device 1001 includes a communication module 111, a detection module 112, an identification module 113, and a processing module 114; wherein,

[0159] Communication module 111 is used to establish communication connections with the second device 1002 and the third device 1003;

[0160] Monitoring module 112 is used to acquire pressure data of the second and third devices at each sampling time point in at least one sampling time point;

[0161] The identification module 113 is used to identify the user's current state based on the pressure data corresponding to each sampling time point in at least one sampling time point of the second device 1002 and the third device 1003;

[0162] In some embodiments, the identification module 113 may include the first determining module and the first processing module described above.

[0163] Processing module 114 is used to initiate a rescue process for the user based on the user's current status.

[0164] In some embodiments, the module for handling falls and rolls can be any application scenario and logic that facilitates rapid rescue, such as a buzzer alarm, an internet alarm, or a 120 emergency call. This system may include such a module, but does not limit the form and processing method of the module.

[0165] The second device 1002 includes several first pressure sensing modules 121 distributed at different locations in the torso (waist and abdomen) (e.g., Figure 10C and Figure 10D (as shown) and the first communication acquisition module 122; wherein,

[0166] The first pressure sensing module 121 is used to detect the pressure data of the user at the location of the first pressure sensor;

[0167] The first communication acquisition module 122 is used to establish a communication connection with the first device, and at the same time acquire pressure data from the first pressure sensing module and transmit it to the first device.

[0168] The third device 1003 includes several sub-devices 131; each sub-device 131 includes a second pressure sensing module 132 and a second communication acquisition module 133; wherein...

[0169] The second pressure sensing module 132 is used to detect the pressure data of the user at the location of the second pressure sensor;

[0170] The second communication acquisition module 133 is used to establish a communication connection with the first device, and at the same time acquire pressure data from the second pressure sensing module and transmit it to the first device.

[0171] This application provides a state detection system. Figure 11 This is a schematic diagram of the composition structure of a signal status detection system provided in an embodiment of this application, as shown below. Figure 11 As shown, the state detection system 1100 includes:

[0172] The first piece of equipment includes:

[0173] The first acquisition module is used to acquire pressure data corresponding to each sampling time point in at least one sampling time point; the pressure data includes first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data of different parts of the user's body.

[0174] The first determining module is used to determine a state parsing strategy based on the first pressure data corresponding to each sampling time point, from a first parsing strategy for parsing the first pressure data and a second parsing strategy for parsing the second pressure data;

[0175] The first processing module uses the state parsing strategy to parse the pressure data corresponding to each sampling time point to determine the user's current state.

[0176] The second device includes:

[0177] At least one first pressure sensor is used to acquire pressure data of each of the at least one drive position of the drive shaft to obtain the first pressure data; wherein, each of the drive positions is provided with the first pressure sensor; the at least one first pressure sensor is connected to the first acquisition module.

[0178] The third piece of equipment includes:

[0179] At least one second pressure sensor is used to acquire limb pressure data corresponding to each of the at least one limb parts to obtain the second pressure data; wherein, each of the limb parts is provided with the second pressure sensor; the at least one second pressure sensor is connected to the first acquisition module.

[0180] In some embodiments, the first determining module 1102 includes:

[0181] The first determining submodule is used to determine the second parsing strategy used to parse the second pressure data as the state parsing strategy when the first pressure data corresponding to each sampling time point is less than the first pressure threshold.

[0182] In some embodiments, the second pressure data includes limb pressure data corresponding to each of at least one limb part, and the first processing module 1103 includes:

[0183] The second determining submodule is used to determine that the user is in a first state when the limb pressure data corresponding to each limb part at each sampling time point is less than a second pressure threshold.

[0184] In some embodiments, the first state indicates that each part of the user's limbs is in a safe state.

[0185] The first processing submodule is used to determine the current state of the user based on the distribution characteristics of the limb pressure data of the target limb at the at least one sampling time point, when the limb pressure data of the at least one limb at the at least one sampling time point is greater than or equal to the second pressure threshold.

[0186] In some embodiments, the first feature data is limb pressure data that is greater than or equal to the second pressure threshold, and the target limb is the limb corresponding to the first feature data.

[0187] In some embodiments, the first processing submodule includes:

[0188] The third determining submodule is used to determine that the user is in the second state when the distribution characteristics of the first feature data corresponding to the at least one target limb part meet the first distribution condition.

[0189] In some embodiments, the first distribution condition is that the sampling time point of the first feature data corresponding to any target limb part is located in the target location interval of the at least one sampling time point; the second state indicates that at least one limb part of the user is in contact with the ground.

[0190] The fourth determining submodule is used to determine that the user is in the first state when the distribution characteristics of the first feature data corresponding to at least one target limb part do not meet the first distribution condition.

[0191] In some embodiments, the first pressure data includes drive position pressure data corresponding to each of at least one drive position of the drive shaft, and the first determining module 1102 further includes:

[0192] The fifth determining submodule is used to determine the first parsing strategy for parsing the first pressure data as the state parsing strategy when the pressure data of the at least one driving position corresponding to at least one driving position at at least one sampling time point is greater than or equal to the first pressure threshold.

[0193] In some embodiments, the first processing module 1103 further includes:

[0194] The sixth determining submodule is used to determine that the user is in the third state if the pressure data of the at least one drive position corresponding to at least one drive position at each sampling time point greater than or equal to the first pressure threshold is located in the target position interval of the at least one sampling time point.

[0195] In some embodiments, the third state represents at least one drive position of the user in contact with the ground in a fixed posture.

[0196] The second processing submodule is used to determine the current state of the user based on the distribution characteristics of the second feature data corresponding to the target drive position pressure data at the target drive position of the at least one sampling time point when there is drive position pressure data corresponding to at least one drive position at at least one sampling time point that is greater than or equal to the first pressure threshold and is located outside the target position interval of the at least one sampling time point.

[0197] In some embodiments, the second feature data is the pressure data of at least one driving stem position corresponding to at least one sampling time point that is greater than or equal to the first pressure threshold; the target driving stem position is the driving stem position corresponding to the second feature data.

[0198] In some embodiments, the second processing submodule includes:

[0199] The seventh determination submodule is used to determine that the user is in the fourth state when the distribution characteristics of the second feature data corresponding to the target drive position meet the second distribution condition;

[0200] In some embodiments, the second distribution condition is that the sampling time points of the second feature data corresponding to any target drive trunk position are distributed in the at least one sampling time interval.

[0201] The eighth determination submodule is used to determine that the user is in the third state when the distribution characteristics of the second feature data corresponding to the target driving position do not meet the second distribution condition.

[0202] In some embodiments, the state detection device 1100 further includes:

[0203] The second acquisition module is used to acquire the user's previous state when the user's current state is any one of the second to fourth states;

[0204] The second determining module is used to determine the duration of the user's state when the user's previous state is the same as the user's current state.

[0205] The second processing module is used to trigger a prompt message corresponding to the current state when the duration of the state exceeds a preset duration threshold.

[0206] In some embodiments, the first acquisition module 1101, the first determination module 1102, the first processing module 1103, the second acquisition module, the second determination module, and the second processing module can all be set in the identification module of the first device.

[0207] This application provides an electronic device. Figure 12 This is a schematic diagram of the structure of an electronic device provided in an embodiment of this application, such as... Figure 12 As shown, the electronic device 1200 includes: a processor 1201, a memory 1202, and a communication bus 1203;

[0208] The communication bus 1203 is used to realize the communication connection between the processor 1201 and the memory 1202;

[0209] The processor 1201 is used to execute the program in the memory 1202 to implement the above-described state detection method.

[0210] This application provides a computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the above-described state detection method.

[0211] It should be understood that the phrase "one embodiment" or "an embodiment" throughout the specification means that a specific feature, structure, or characteristic related to the embodiment is included in at least one embodiment of this application. Therefore, "in one embodiment" or "in an embodiment" appearing throughout the specification does not necessarily refer to the same embodiment. Furthermore, these specific features, structures, or characteristics can be combined in any suitable manner in one or more embodiments. It should be understood that in the various embodiments of this application, the sequence numbers of the above-described processes do not imply a sequential 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. The sequence numbers of the above-described embodiments are merely descriptive and do not represent the superiority or inferiority of the embodiments.

[0212] It should be noted that, in this document, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Unless otherwise specified, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes that element.

[0213] In the several embodiments provided in this application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are merely illustrative. For example, the division of units is only a logical functional division, and in actual implementation, there may be other division methods, such as: multiple units or components can be combined, or integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the various components shown or discussed can be through some interfaces, and the indirect coupling or communication connection between devices or units can be electrical, mechanical, or other forms.

[0214] The units described above as separate components may or may not be physically separate. The components shown as units may or may not be physical units. They may be located in one place or distributed across multiple network units. Some or all of the units may be selected to achieve the purpose of this embodiment according to actual needs.

[0215] In addition, each functional unit in the various embodiments of this application can be integrated into one processing unit, or each unit can be a separate unit, or two or more units can be integrated into one unit; the integrated unit can be implemented in hardware or in the form of hardware plus software functional units.

[0216] Those skilled in the art will understand that all or part of the steps of the above method embodiments can be implemented by hardware related to program instructions. The aforementioned program can be stored in a computer-readable storage medium. When the program is executed, it performs the steps of the above method embodiments. The aforementioned storage medium includes various media that can store program code, such as mobile storage devices, read-only memory (ROM), magnetic disks, or optical disks.

[0217] Alternatively, if the integrated units described above are implemented as software functional modules and sold or used as independent products, they can also be stored in a computer-readable storage medium. Based on this understanding, the technical solutions of the embodiments of this application, or the parts that contribute to the prior art, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions to cause a computer device (which may be a personal computer, server, or network device, etc.) to execute all or part of the methods described in the various embodiments of this application. The aforementioned storage medium includes various media capable of storing program code, such as mobile storage devices, ROMs, magnetic disks, or optical disks.

[0218] The above description is merely a specific embodiment of this application, but the scope of protection of this application is not limited thereto. Any variations or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in this application should be included within the scope of protection of this application. Therefore, the scope of protection of this application should be determined by the scope of the claims.

Claims

1. A state detection method, the method comprising: Acquire pressure data corresponding to each of the at least one sampling time points; wherein, the pressure data includes first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data of different parts of the user; the second pressure data includes limb pressure data corresponding to each of at least one limb part; If the first pressure data corresponding to each sampling time point is less than the first pressure threshold, the second parsing strategy used to parse the second pressure data will be determined as the state parsing strategy. If the limb pressure data corresponding to each limb part at each sampling time point is less than the second pressure threshold, the user is determined to be in a first state; wherein, the first state indicates that each limb part of the user is in a safe state; If the limb pressure data corresponding to at least one limb part at at least one sampling time point is greater than or equal to the second pressure threshold, the current state of the user is determined based on the distribution characteristics of the limb pressure data of the target limb part at the at least one sampling time point using the first feature data corresponding to the target limb part; wherein, the first feature data is limb pressure data greater than or equal to the second pressure threshold, and the target limb part is the limb part corresponding to the first feature data.

2. The method according to claim 1, wherein determining the user's current state based on the distribution characteristics of limb pressure data at the target limb at the at least one sampling time point using first feature data corresponding to at least one target limb part includes: If the distribution characteristics of the first feature data corresponding to at least one target limb part satisfy a first distribution condition, the user is determined to be in a second state; wherein, the first distribution condition is that there exists a sampling time point of the first feature data corresponding to any target limb part that is located within the target position interval of the at least one sampling time point; the second state indicates that at least one limb part of the user is in contact with the ground; If the distribution characteristics of the first feature data corresponding to at least one target limb part do not meet the first distribution condition, the user is determined to be in the first state.

3. The method according to claim 1, wherein the first pressure data includes pressure data at each of at least one drive position of the drive shaft, and based on the first pressure data at each sampling time point, a state parsing strategy is determined from a first parsing strategy for parsing the first pressure data and a second parsing strategy for parsing the second pressure data, comprising: If the pressure data at at least one driving position corresponding to at least one driving position at at least one sampling time point is greater than or equal to a first pressure threshold, the first parsing strategy used to parse the first pressure data is determined as the state parsing strategy.

4. The method according to claim 3, wherein parsing the pressure data corresponding to each sampling time point using the state parsing strategy to determine the user's current state includes: If the pressure data of at least one toe position corresponding to at least one toe position at each sampling time point greater than or equal to the first pressure threshold is located within the target position range of the at least one sampling time point, the user is determined to be in a third state; wherein, the third state indicates that at least one toe position of the user is in contact with the ground in a fixed posture. If there is at least one drive shaft pressure data corresponding to at least one drive shaft position at at least one sampling time point that is greater than or equal to a first pressure threshold, and the sampling time point is located outside the target position interval of the at least one sampling time point, the current state of the user is determined based on the distribution characteristics of the drive shaft pressure data at the target drive shaft position of the at least one sampling time point, based on the second feature data corresponding to the target drive shaft position; wherein, the second feature data is the drive shaft pressure data corresponding to at least one drive shaft position at at least one sampling time point that is greater than or equal to the first pressure threshold; and the target drive shaft position is the drive shaft position corresponding to the second feature data.

5. The method according to claim 4, wherein determining the user's current state based on the distribution characteristics of the pressure data at the target drive shaft position at at least one sampling time point using the second feature data corresponding to the target drive shaft position includes: If the distribution characteristics of the second feature data corresponding to the target drive position meet the second distribution condition, the user is determined to be in the fourth state; wherein, the second distribution condition is that the sampling time points of the second feature data corresponding to any target drive position are distributed in the at least one sampling time interval. If the distribution characteristics of the second feature data corresponding to the target driving position do not meet the second distribution condition, the user is determined to be in the third state.

6. The method according to any one of claims 2 to 5, further comprising: If the user's current state is any of the second to fourth states, obtain the user's previous state; If the user's previous state is the same as the user's current state, determine the duration of the user's state. If the duration of the state exceeds a preset duration threshold, a prompt message corresponding to the current state is triggered.

7. A state detection system, the system comprising: The first piece of equipment includes: The first acquisition module is used to acquire pressure data corresponding to each sampling time point in at least one sampling time point; the pressure data includes first pressure data and second pressure data; the first pressure data and the second pressure data are pressure data of different parts of the user; the second pressure data includes limb pressure data corresponding to each of at least one limb part. The first determining module is used to determine the second parsing strategy for parsing the second pressure data as the state parsing strategy when the first pressure data corresponding to each sampling time point is less than the first pressure threshold. The first processing module is used to determine that the user is in a first state when the limb pressure data corresponding to each limb part at each sampling time point is less than a second pressure threshold; wherein the first state indicates that each limb part of the user is in a safe state. If the limb pressure data corresponding to at least one limb part at at least one sampling time point is greater than or equal to the second pressure threshold, the current state of the user is determined based on the distribution characteristics of the limb pressure data of the target limb part at the at least one sampling time point using the first feature data corresponding to the target limb part; wherein, the first feature data is the limb pressure data that is greater than or equal to the second pressure threshold, and the target limb part is the limb part corresponding to the first feature data. The second device includes: At least one first pressure sensor is used to acquire pressure data of each of the at least one drive position of the drive shaft to obtain the first pressure data; wherein, each of the drive positions is provided with the first pressure sensor; the at least one first pressure sensor is connected to the first acquisition module. The third piece of equipment includes: At least one second pressure sensor is used to acquire limb pressure data corresponding to each of the at least one limb parts to obtain the second pressure data; wherein, each of the limb parts is provided with the second pressure sensor; the at least one second pressure sensor is connected to the first acquisition module.

8. An electronic device, the electronic device comprising: Processor, memory, and communication bus; The communication bus is used to realize the communication connection between the processor and the memory; The processor is used to execute the program in the memory to implement the state detection method according to any one of claims 1 to 6.

9. A computer-readable storage medium storing one or more programs that can be executed by one or more processors to implement the state detection method according to any one of claims 1 to 6.

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