A method and device for determining a bed exit state, an intelligent mattress and a medium

Abstract

The application discloses a bed leaving bed state determination method and device, an intelligent mattress and a medium. The method is applied to the intelligent mattress and comprises the following steps: acquiring physiological parameter information corresponding to each preset area in the intelligent mattress to form a physiological parameter information group of the intelligent mattress; determining twin information and independent parameter information in the physiological parameter information group according to a preset range threshold, wherein the twin information comprises at least two pieces of physiological parameter information; and determining number information, position information of target objects on the intelligent mattress and leaving bed states of each target object according to a preset state threshold range, the twin information and the independent parameter information. Whether the physiological parameter information is similar is determined by the range threshold, the twin information and the independent parameter information are determined, and then the number of the target objects and the leaving bed states of each target object are determined. The number of the target objects on the intelligent mattress is accurately determined, and the accuracy of the leaving bed state determination is improved.

Description

Technical Field

[0001] This invention relates to the field of intelligent control technology, and in particular to a method, device, intelligent mattress, and medium for determining the state of being on or off the bed. Background Technology

[0002] With economic development and improved living standards, people are paying increasing attention to their health in daily life. Sleep monitoring is a crucial aspect of health monitoring, and the detection of sleep patterns while out of bed is a vital component of sleep monitoring.

[0003] There are many technologies for detecting whether a user is getting into or out of bed. A commonly used method is to judge the size of the air pressure fluctuation in the airbags of the smart mattress. When the air pressure fluctuation is large, it is considered that the user is getting into or out of bed.

[0004] However, some barometric pressure sensors are quite sensitive. When a person gets into bed, the movement is large or the position is not standard. When a single person is in bed, multiple sensors may detect the user's data, leading to a false alarm that multiple people are in bed. Similarly, the detection of multiple people leaving the bed is also prone to misjudgment due to mutual interference between the signals of multiple people. Therefore, the accuracy is generally not high. Summary of the Invention

[0005] This invention provides a method, device, smart mattress, and medium for determining the state of being in and out of bed, so as to achieve accurate judgment of the state of being in and out of bed.

[0006] According to a first aspect of the present invention, a method for determining a bed-off-bed state is provided, applied to a smart mattress, comprising:

[0007] Obtain physiological parameter information corresponding to each preset area in the smart mattress to form a physiological parameter information group for the smart mattress;

[0008] Based on a preset range threshold, the twin information and independent parameter information in the physiological parameter information group are determined, wherein the twin information includes at least two physiological parameter information.

[0009] Based on the preset state threshold range, the twin information, and the independent parameter information, the number of target objects on the smart mattress, their location information, and the off-bed status of each target object are determined.

[0010] According to a second aspect of the present invention, a device for determining the state of being out of bed is provided, characterized in that it comprises:

[0011] The information group acquisition module is used to acquire physiological parameter information corresponding to each preset area in the smart mattress, and form a physiological parameter information group of the smart mattress.

[0012] The information determination module is used to determine the twin information and independent parameter information in the physiological parameter information group according to a preset range threshold, wherein the twin information includes at least two physiological parameter information.

[0013] The status determination module is used to determine the number of target objects on the smart mattress, their location information, and the off-bed status of each target object based on a preset status threshold range, the twin information, and the independent parameter information.

[0014] According to a third aspect of the present invention, a smart mattress is provided, the smart mattress comprising:

[0015] At least one processor; and

[0016] A memory communicatively connected to the at least one processor; wherein,

[0017] The memory stores a computer program that can be executed by the at least one processor, which enables the at least one processor to perform the method for determining the bed-in / bed-out state as described in any embodiment of the present invention.

[0018] According to a fourth aspect of the present invention, a computer-readable storage medium is provided, the computer-readable storage medium storing computer instructions, the computer instructions being configured to cause a processor to execute and implement the method for determining the in-bed and out-of-bed states as described in any embodiment of the present invention.

[0019] The technical solution of this invention involves acquiring physiological parameter information corresponding to each preset area in a smart mattress to form a physiological parameter information group for the smart mattress. Based on a preset range threshold, it determines the twin information and independent parameter information within the physiological parameter information group, wherein the twin information includes at least two physiological parameter information. Based on a preset state threshold range, the twin information, and the independent parameter information, it determines the number of target objects on the smart mattress, their location information, and the on-and-off state of each target object. By judging the similarity of physiological parameter information through the range threshold, the twin information and independent parameter information are determined, thereby determining the number of target objects and the on-and-off state of each target object. This achieves accurate determination of the number of target objects on the smart mattress and improves the accuracy of determining the on-and-off state.

[0020] It should be understood that the description in this section is not intended to identify key or essential features of the embodiments of the present invention, nor is it intended to limit the scope of the invention. Other features of the invention will become readily apparent from the following description. Attached Figure Description

[0021] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0022] Figure 1 This is a flowchart of a method for determining the state of being in and out of bed according to Embodiment 1 of the present invention;

[0023] Figure 2 This is a flowchart of a method for determining the state of being in and out of bed according to Embodiment 2 of the present invention;

[0024] Figure 3 This is a schematic diagram of the structure of a device for determining the state of being in and out of bed according to Embodiment 3 of the present invention;

[0025] Figure 4 This is a structural schematic diagram of the smart mattress that implements an embodiment of the present invention. Detailed Implementation

[0026] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0027] It should be noted that the terms "first," "second," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish similar objects and are not necessarily used to describe a specific order or sequence. It should be understood that such data can be interchanged where appropriate so that the embodiments of the invention described herein can be implemented in orders other than those illustrated or described herein. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover a non-exclusive inclusion; for example, a process, method, system, product, or apparatus that comprises a series of steps or units is not necessarily limited to those steps or units explicitly listed, but may include other steps or units not explicitly listed or inherent to such processes, methods, products, or apparatus.

[0028] Example 1

[0029] Figure 1This is a flowchart illustrating a method for determining the state of being in and out of bed, as provided in Embodiment 1 of the present invention. This embodiment is applicable to determining the state of being in and out of bed. The method can be executed by a device for determining the state of being in and out of bed, which can be implemented in hardware and / or software and can be configured in a smart mattress. Figure 1 As shown, the method includes:

[0030] S110. Obtain physiological parameter information corresponding to each preset area in the smart mattress to form a physiological parameter information group for the smart mattress.

[0031] In this embodiment, a smart mattress can be understood as a mattress that includes sensors to determine whether the user is in an un-bed state. A preset area can be understood as a defined detection range; for example, a smart mattress for a double bed can have two sensors set on each side, making the left and right sides the preset areas. Physiological parameter information can be understood as including multiple parameters characterizing signs of vital activities, such as heart rate, respiration, and body movement. A physiological parameter information group can be understood as a collection of physiological parameter information from different preset areas.

[0032] Specifically, the smart mattress is equipped with sensors for detecting physiological parameters in different preset areas. The processor can acquire the physiological parameter data detected by the sensors in each preset area, form the corresponding physiological parameter information according to the identifier of each preset area, and integrate them into the physiological parameter information group corresponding to the smart mattress.

[0033] S120. Based on a preset range threshold, determine the twin information and independent parameter information in the physiological parameter information group, wherein the twin information includes at least two physiological parameter information.

[0034] In this embodiment, the range threshold can be understood as a parameter used to determine whether different physiological parameter information is similar. Twin information can be understood as multiple similar physiological parameter information within a group of physiological parameter information. Independent parameter information can be understood as parameter information that is not similar to any other physiological parameter information within the same group of physiological parameter information.

[0035] Specifically, the processor can first determine the similarity assessment parameters between the physiological parameters in the physiological parameter information group, such as the mean difference and Pearson correlation coefficient. It then compares the range threshold with each similarity assessment parameter to determine the similarity assessment parameters within the range threshold. This means that these physiological parameters are considered to belong to the same object, and the physiological parameter information corresponding to the similarity assessment parameter is regarded as twin information. Physiological parameters other than twin information are regarded as independent parameter information.

[0036] For example, the physiological parameter information group includes three physiological parameter information, namely parameter A detected by the left sensor of the smart mattress and parameter B detected by the right sensor. The similarity evaluation parameter between parameter A and parameter B is r2, and the range threshold is [r1-r5]. If r2 is within the range threshold, then parameter A and parameter B are considered to be twin information; if the similarity evaluation parameter between parameter A and parameter B is r7, and r7 is not within the range threshold, then parameter A and parameter B are considered to be independent parameter information.

[0037] S130. Based on the preset state threshold range, twin information, and independent parameter information, determine the number of target objects on the smart mattress, their location information, and the on-and-off status of each target object.

[0038] In this embodiment, the state threshold range can be understood as the threshold range used to determine the state of being off-bed. The target object can be understood as the user on the smart mattress. The number information can be understood as the number of objects on the smart mattress. The location information can be understood as the position of the target object on the smart mattress, such as on the left or right side. The state of being off-bed can be understood as the state information used to indicate whether the target user is on the smart mattress or has left the smart mattress.

[0039] Specifically, the twin information and independent parameter information include the location information of the target object determined by the preset area detection position of the corresponding sensor. The processor can determine the number of target objects based on the number of twin information and independent parameter information. It compares the twin information and independent parameter information with the preset state threshold to determine whether the twin information and independent parameter information are within the state threshold range. If they are, the corresponding in-bed / out-of-bed state is identified as in bed; otherwise, it is identified as out of bed. Combining the corresponding detection position, the in-bed / out-of-bed state of the target user corresponding to different sides can be determined.

[0040] The technical solution of this invention involves acquiring physiological parameter information corresponding to each preset area in a smart mattress to form a physiological parameter information group for the smart mattress. Based on a preset range threshold, it determines the twin information and independent parameter information within the physiological parameter information group, wherein the twin information includes at least two physiological parameter information. Based on a preset state threshold range, the twin information, and the independent parameter information, it determines the number of target objects on the smart mattress, their location information, and the on-and-off state of each target object. By judging the similarity of physiological parameter information through the range threshold, the twin information and independent parameter information are determined, thereby determining the number of target objects and the on-and-off state of each target object. This achieves accurate determination of the number of target objects on the smart mattress and improves the accuracy of determining the on-and-off state.

[0041] As a first optional embodiment of this embodiment, based on the above embodiment, after determining the number of target objects on the smart mattress, their location information, and the off-bed state of each target object according to a preset state threshold range, twin information, and independent parameter information, it may further include:

[0042] When the detection end conditions are met, a sleep state curve for each target object is generated based on their in-bed and out-of-bed status and location information.

[0043] In this embodiment, the detection end condition can be understood as reaching the detection stop time, which can be set by the user. For example, if the user sets the detection time to 22:00-7:00, then the detection end condition is met when 7:00 is reached. The sleep state curve can be understood as a curve used to display the state of being out of bed within a certain period of time.

[0044] Specifically, the processor detects the time. When the time reaches the detection time set by the user, the detection end condition is met. The processor can generate a sleep state curve for each target object according to the time based on the on-bed status and corresponding location information, and send the generated sleep state curve to the user device.

[0045] For example, if only one target is detected on the smart mattress, the target is in bed from 10 PM to 4 AM and is on the left side of the smart mattress; from 2:01 AM to 2:04 AM, the target is out of bed; from 2:05 AM to 4:24 AM, the target is in bed and is on both the left and right sides of the smart mattress (the target may be sleeping at an angle); from 4:25 AM to 4:40 AM, the target is out of bed; and from 4:41 AM to 7:00 AM, the target is in bed and is on the right side of the smart mattress. The sleep state curve can be represented by time on the horizontal axis and being out of bed on the vertical axis. Different colors can also be used on the curve to represent the target's position when in bed, such as blue for the left side, green for the right side, and yellow for both sides.

[0046] Example 2

[0047] Figure 2 This is a flowchart illustrating a method for determining the state of being in and out of bed according to Embodiment 2 of the present invention. This embodiment is a further refinement based on the above embodiments. Figure 2 As shown, the method includes:

[0048] S210. Obtain physiological parameter information corresponding to each preset area in the smart mattress to form a physiological parameter information group for the smart mattress.

[0049] S220. Based on the physiological parameter information included in the physiological parameter information group, determine other physiological parameter information in the physiological parameter information group.

[0050] In this embodiment, other physiological parameter information can be understood as the remaining physiological parameter information after selecting any one of the physiological parameter information groups.

[0051] Specifically, the processor can poll the physiological parameter information included in the physiological parameter information group, select one physiological parameter information each time, and determine the other physiological parameter information in the physiological parameter information group.

[0052] S230. Determine the similarity assessment parameters between physiological parameter information and other physiological parameter information.

[0053] In this embodiment, similarity evaluation parameters can be understood as parameters used to reflect the similarity between physiological parameter information, such as mean difference, Pearson correlation coefficient, etc.

[0054] Specifically, the processor can evaluate the similarity between physiological parameter information and other physiological parameter information according to the determination method corresponding to the similarity evaluation parameters.

[0055] For example, the physiological parameter information group includes three physiological parameter information, physiological parameter information A, and other physiological parameter information B and C. Then, the mean difference between A and B is calculated as a1, the mean difference between A and C is a2, and the mean difference between B and C is a3.

[0056] S240. Compare each similarity evaluation parameter with the range threshold to obtain the comparison result.

[0057] In this embodiment, the comparison result can be understood as a result that reflects whether the similarity evaluation parameter is within the range threshold.

[0058] Specifically, the processor can compare each similarity evaluation parameter with a range threshold. If the similarity evaluation parameter is within the range threshold, the comparison result is determined to be parameter similar, and if the similarity evaluation parameter is not within the range threshold, the comparison result is determined to be parameter dissimilar.

[0059] S250. Based on the comparison results, determine the twin information and independent parameter information.

[0060] Specifically, the processor can use physiological parameter information that is similar to the comparison result as twin information, and physiological parameter information that is dissimilar to the comparison result as independent parameter information.

[0061] Furthermore, the steps for determining twin information and independent parameter information based on the comparison results can be further optimized as follows:

[0062] a1. Determine the first parameter information group whose comparison results are similar, and use the first parameter information group as twin information.

[0063] In this embodiment, the first parameter information group can be understood as several physiological parameter information that are similar in comparison results.

[0064] Specifically, the processor can compare the results with physiological parameter information that is similar to the parameters to find the first parameter information group, and use the first parameter information group as twin information.

[0065] For example, if the comparison results show that the first parameter information groups with similar parameters are AB and BC, then physiological parameters A, B, and C are used as twin information; if the comparison results show that the first parameter information groups with similar parameters are AB and CD, then physiological parameters A and B are used as twin information, and physiological parameters C and D are used as another twin information.

[0066] b1. Determine the second parameter information as the parameter dissimilarity in the comparison result, and treat the second parameter information as independent parameter information.

[0067] In this embodiment, the second parameter information can be understood as physiological parameter information for which there is no similar parameter information.

[0068] Specifically, the processor can search for physiological parameter information that is dissimilar in comparison results, obtain two physiological parameter information, determine whether there are other comparison results for these two physiological parameter information, and take the physiological parameter information whose comparison results are all dissimilar in parameters as the first parameter information.

[0069] For example, if the comparison results show that physiological parameter A and physiological parameter B are dissimilar, the comparison results of physiological parameter B and physiological parameter C are similar, and the comparison results of physiological parameter A and physiological parameter C are dissimilar, then physiological parameter A is independent parameter information, and physiological parameters B and C are twin information.

[0070] S260. Determine the number of information groups corresponding to twin information and the number of independent parameter information.

[0071] In this embodiment, the number of information groups can be understood as the number of twin information. The number of information items can be understood as the number of independent parameter information items.

[0072] Specifically, the processor can determine the number of information groups based on the number of twin information determined in this iteration, and determine the number of information items based on the number of independent parameter information determined in this iteration.

[0073] S270. Based on the number of information groups and the number of information items, determine the number of target objects included on the smart mattress.

[0074] Specifically, the processor can add the number of information groups and the number of information items to obtain the number of target objects included on the smart mattress.

[0075] S280. Determine the location information and target parameter information of the first target object to which the twin information belongs.

[0076] In this embodiment, the target parameter information can be understood as the most accurate information of the signal in the twin information. The first target object can be understood as the object to which the twin information belongs.

[0077] Specifically, the twin information includes multiple psychological parameter information. Each psychological parameter information includes not only the parameter value but also the corresponding acquisition location. The processor can determine the location information of the first target object based on the twin information, and determine the best psychological parameter information as the target parameter information based on the signal strength of each psychological parameter information.

[0078] Furthermore, the steps for determining the location information and target parameter information of the first target object to which the twin information belongs can be further optimized, including:

[0079] a2. Determine the detection area information and signal strength value of each twin in the twin information.

[0080] In this embodiment, the detection area information can be understood as the detection area corresponding to each psychological parameter in the twin information. The signal strength value can be understood as the value used to reflect the intensity of the psychological parameter information.

[0081] Specifically, the processor can obtain the detection area information and signal strength value corresponding to the psychological parameter information included in the twin information.

[0082] For example, the twin information includes psychological parameter information A and B. The detection area corresponding to A is the left side of the smart mattress with a signal strength value of s1, and B is the middle side of the smart mattress with a signal strength value of s2.

[0083] b2. Determine the location information of the first target object based on the information of each detection area.

[0084] Specifically, the processor can merge the information from each detection area to obtain the location information of the first target object, thereby determining the location of the first target object.

[0085] For example, if the detection area information corresponding to twin information 1 includes the left and right sides of the smart mattress, then the leftmost end of the left side to the rightmost end of the right side will be used as the location information of the first target object.

[0086] c2. Determine the maximum signal strength among all signal strength values, and use the psychological parameter information corresponding to the maximum signal strength as the target parameter information.

[0087] Specifically, the processor can determine the maximum signal strength among all signal strength values ​​and use the psychological parameter information corresponding to the maximum signal strength as the target parameter information.

[0088] S290. Based on the state threshold range, determine the off-bed status of the first target object to which the target parameter information belongs, and the off-bed status of the second target object to which the independent parameter information belongs.

[0089] In this embodiment, the state threshold range can be understood as a threshold used to determine the state of being out of bed. The second target object can be understood as the object to which the independent parameter information belongs.

[0090] Specifically, the processor can compare the target parameter information with the state threshold range. If the target parameter information is within the state threshold range, it means that the parameter is within the normal range of the human body and has a certain fluctuation, which is in line with human physiology. If it is determined that someone is in bed on that side, the first target object's "out-of-bed" state is determined as "in bed" state; otherwise, it is determined as "out of bed" state. The processor can also compare the independent parameter information with the state threshold range. If the independent parameter information is within the state threshold range, the second target object's "out-of-bed" state is determined as "in bed" state; otherwise, it is determined as "out of bed" state.

[0091] a3. If the target parameter information is within the state threshold range, then the off-bed state of the first target object is determined as the in-bed state.

[0092] Specifically, the processor can compare the parameter values ​​and trends in the target parameter information with the corresponding items in the state threshold range. If all items in the target parameter information are within the state threshold range, the off-bed state of the first target object is determined as the in-bed state.

[0093] b3. Otherwise, the out-of-bed state of the first target object shall be determined as the out-of-bed state.

[0094] Specifically, the processor can compare the mean and trend of the target parameter information with the corresponding items in the state threshold range. If any item in the target parameter information is not in the corresponding state threshold range, the first target object is determined to be in the out-of-bed state.

[0095] c3. If the independent parameter information is within the state threshold range, then the off-bed state of the second target object is determined as the in-bed state.

[0096] Specifically, the processor can compare the parameter values ​​and trends in the independent parameter information with the corresponding items in the state threshold range. If all items in the independent parameter information are within the state threshold range, the off-bed state of the second target object is determined as the in-bed state.

[0097] d3. Otherwise, determine the second target object's out-of-bed state as an out-of-bed state.

[0098] Specifically, the processor can compare the mean and trend of the independent parameter information with the corresponding items in the state threshold range. If any item in the independent parameter information is not in the corresponding state threshold range, the second target object is determined to be in the out-of-bed state.

[0099] The technical solution of this invention determines similarity evaluation parameters between different physiological parameters and, in conjunction with a preset range threshold, identifies twin information and independent parameter information. This achieves the segmentation of physiological parameter information belonging to the same target object. The physiological parameter information with the highest signal strength among the twin information is identified as the target parameter information. The number and location of target objects on the smart mattress are determined using the target parameter information and the twin information. Furthermore, the state of each target object in its off-bed state is determined using a state threshold range. This achieves accurate determination of the number of target objects on the smart mattress, avoids mutual interference between multiple physiological parameter information, and improves the accuracy of determining the off-bed state.

[0100] Example 3

[0101] Figure 3 This is a schematic diagram of a device for determining the state of being in and out of bed, provided in Embodiment 3 of the present invention. Figure 3 As shown, the device includes: an information group acquisition module 31, an information determination module 32, and a status determination module 33. Among them,

[0102] The information group acquisition module 31 is used to acquire physiological parameter information corresponding to each preset area in the smart mattress, and form a physiological parameter information group of the smart mattress.

[0103] The information determination module 32 is used to determine the twin information and independent parameter information in the physiological parameter information group according to a preset range threshold, wherein the twin information includes at least two physiological parameter information.

[0104] The state determination module 33 is used to determine the number of target objects on the smart mattress, their location information, and the off-bed status of each target object based on a preset state threshold range, the twin information, and the independent parameter information.

[0105] The technical solution of this invention involves acquiring physiological parameter information corresponding to each preset area in a smart mattress to form a physiological parameter information group for the smart mattress. Based on a preset range threshold, it determines the twin information and independent parameter information within the physiological parameter information group, wherein the twin information includes at least two physiological parameter information. Based on a preset state threshold range, the twin information, and the independent parameter information, it determines the number of target objects on the smart mattress, their location information, and the on-and-off state of each target object. By judging the similarity of physiological parameter information through the range threshold, the twin information and independent parameter information are determined, thereby determining the number of target objects and the on-and-off state of each target object. This achieves accurate determination of the number of target objects on the smart mattress and improves the accuracy of determining the on-and-off state.

[0106] Furthermore, the information determination module 32 includes:

[0107] The first determining unit is used to determine other physiological parameter information in the physiological parameter information group based on the physiological parameter information included in the physiological parameter information group;

[0108] A parameter determination unit is used to determine similarity evaluation parameters between the physiological parameter information and each of the other physiological parameter information;

[0109] The result determination unit is used to compare each of the similarity evaluation parameters with the range threshold to obtain the comparison result;

[0110] The second determining unit is used to determine the twin information and independent parameter information based on each of the comparison results.

[0111] Specifically, the second determining unit is used for:

[0112] The comparison result is determined to be a first parameter information group with similar parameters, and the first parameter information group is used as the twin information;

[0113] The comparison result is determined to be second parameter information with dissimilar parameters, and the second parameter information is used as the independent parameter information.

[0114] Furthermore, the state determination module 33 includes:

[0115] The number determination unit is used to determine the number of information groups corresponding to the twin information and the number of information of the independent parameter information;

[0116] The third determining unit is used to determine the number of target objects included on the smart mattress based on the number of information groups and the number of information items.

[0117] The fourth determining unit is used to determine the location information and target parameter information of the first target object to which the twin information belongs;

[0118] A status determination unit is used to determine, based on the status threshold range, the off-bed status of the first target object to which the target parameter information belongs, and the off-bed status of the second target object to which the independent parameter information belongs.

[0119] Specifically, the fourth determining unit is used for:

[0120] Determine the detection area information and signal strength value of each twin in the twin information;

[0121] Based on the detection area information, the location information of the first target object is determined;

[0122] The maximum signal strength among the various signal strength values ​​is determined, and the psychological parameter information corresponding to the maximum signal strength is used as the target parameter information.

[0123] Specifically, the state determination unit is used for:

[0124] If the target parameter information is within the state threshold range, then the first target object's out-of-bed state is determined as its in-bed state; otherwise,

[0125] The first target object's out-of-bed state is defined as the out-of-bed state;

[0126] If the independent parameter information is within the state threshold range, then the second target object's out-of-bed state is determined as in-bed state; otherwise,

[0127] The second target object is defined as being out of bed.

[0128] Optionally, the device further includes:

[0129] The curve generation module is used to determine the number of target objects on the smart mattress, their location information, and the out-of-bed state of each target object based on a preset state threshold range, the twin information, and the independent parameter information. When the detection end condition is met, it generates a sleep state curve for each target object based on its out-of-bed state and its location information.

[0130] The device for determining the state of being in bed and out of bed provided in this embodiment of the invention can execute the method for determining the state of being in bed and out of bed provided in any embodiment of the invention, and has the corresponding functional modules and beneficial effects of executing the method.

[0131] Example 4

[0132] Figure 4A schematic diagram of a smart mattress 40, which can be used to implement embodiments of the present invention, is shown. The smart mattress is intended to represent various forms of digital computers, such as laptops, desktop computers, workbenches, personal digital assistants, servers, blade servers, mainframes, and other suitable computers. The smart mattress can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices (such as helmets, glasses, watches, etc.), and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely illustrative and are not intended to limit the implementation of the invention described and / or claimed herein.

[0133] like Figure 4 As shown, the smart mattress 40 includes at least one processor 41 and a memory, such as a read-only memory (ROM) 42 and a random access memory (RAM) 43, communicatively connected to the at least one processor 41. The memory stores computer programs executable by the at least one processor. The processor 41 can perform various appropriate actions and processes based on the computer program stored in the ROM 42 or loaded from storage unit 48 into the RAM 43. The RAM 43 can also store various programs and data required for the operation of the smart mattress 40. The processor 41, ROM 42, and RAM 43 are interconnected via a bus 44. An input / output (I / O) interface 45 is also connected to the bus 44.

[0134] Multiple components in the smart mattress 40 are connected to the I / O interface 45, including: an input unit 46, such as a keyboard or mouse; an output unit 47, such as various types of displays or speakers; a storage unit 48, such as a disk or optical disc; and a communication unit 49, such as a network card, modem, or wireless transceiver. The communication unit 49 allows the smart mattress 40 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks.

[0135] Processor 41 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of processor 41 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various processors running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. Processor 41 performs the various methods and processes described above, such as methods for determining the bed-off-bed state.

[0136] In some embodiments, the method for determining the state of being in and out of bed may be implemented as a computer program tangibly contained in a computer-readable storage medium, such as storage unit 48. In some embodiments, part or all of the computer program may be loaded and / or installed on the smart mattress 40 via ROM 42 and / or communication unit 49. When the computer program is loaded into RAM 43 and executed by processor 41, one or more steps of the method for determining the state of being in and out of bed described above may be performed. Alternatively, in other embodiments, processor 41 may be configured to perform the method for determining the state of being in and out of bed by any other suitable means (e.g., by means of firmware).

[0137] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), payload-programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0138] Computer programs used to implement the methods of the present invention may be written in any combination of one or more programming languages. These computer programs may be provided to a processor of a general-purpose computer, a special-purpose computer, or other programmable data processing device, such that when executed by the processor, the computer programs cause the functions / operations specified in the flowcharts and / or block diagrams to be performed. The computer programs may be executed entirely on a machine, partially on a machine, or as a standalone software package, partially on a machine and partially on a remote machine, or entirely on a remote machine or server.

[0139] In the context of this invention, a computer-readable storage medium can be a tangible medium that may contain or store a computer program for use by or in conjunction with an instruction execution system, apparatus, or device. A computer-readable storage medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination thereof. Alternatively, a computer-readable storage medium may be a machine-readable signal medium. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination thereof.

[0140] To provide interaction with the user, the systems and technologies described herein can be implemented on a smart mattress, which includes: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the smart mattress. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0141] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as data servers), or computing systems that include middleware components (e.g., application servers), or computing systems that include frontend components (e.g., user computers with graphical user interfaces or web browsers through which users can interact with implementations of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., communication networks). Examples of communication networks include local area networks (LANs), wide area networks (WANs), blockchain networks, and the Internet.

[0142] A computing system can include clients and servers. Clients and servers are generally located far apart and typically interact through communication networks. The client-server relationship is created by computer programs running on the respective computers and having a client-server relationship with each other. The server can be a cloud server, also known as a cloud computing server or cloud host, which is a hosting product within the cloud computing service system to address the shortcomings of traditional physical hosts and VPS services, such as high management difficulty and weak business scalability.

[0143] It should be understood that the various forms of processes shown above can be used, with steps reordered, added, or deleted. For example, the steps described in this invention can be executed in parallel, sequentially, or in different orders, as long as the desired result of the technical solution of this invention can be achieved, and this is not limited herein.

[0144] The specific embodiments described above do not constitute a limitation on the scope of protection of this invention. Those skilled in the art should understand that various modifications, combinations, sub-combinations, and substitutions can be made according to design requirements and other factors. Any modifications, equivalent substitutions, and improvements made within the spirit and principles of this invention should be included within the scope of protection of this invention.

Claims

1. A method for determining the state of being in and out of bed, characterized in that, Applications in smart mattresses include: Obtain physiological parameter information corresponding to each preset area in the smart mattress to form a physiological parameter information group for the smart mattress; Based on a preset range threshold, the twin information and independent parameter information in the physiological parameter information group are determined, wherein the twin information includes at least two physiological parameter information; the twin information is multiple similar physiological parameter information in the physiological parameter information group, and the independent parameter information is parameter information that is not similar to physiological parameter information in the physiological parameter information group; Based on the preset state threshold range, the twin information, and the independent parameter information, the number of target objects on the smart mattress, their location information, and the off-bed status of each target object are determined. The step of determining the twin information and independent parameter information in the physiological parameter information group according to a preset range threshold includes: Based on the physiological parameter information included in the physiological parameter information group, determine other physiological parameter information in the physiological parameter information group; For the physiological parameter information included in the physiological parameter information group, determining other physiological parameter information in the physiological parameter information group includes: polling the physiological parameter information included in the physiological parameter information group, selecting one physiological parameter information each time, and determining the other physiological parameter information in the physiological parameter information group; Determine the similarity evaluation parameters between the physiological parameter information and each of the other physiological parameter information; Each of the aforementioned similarity evaluation parameters is compared with the aforementioned range threshold to obtain the comparison result; Based on the comparison results, the twin information and independent parameter information are determined; The step of determining the number of target objects on the smart mattress, their location information, and the off-bed status of each target object based on a preset state threshold range, the twin information, and the independent parameter information includes: Determine the number of information groups corresponding to the twin information and the number of information items in the independent parameter information; Based on the number of information groups and the number of information items, determine the number of target objects included on the smart mattress; Determine the location information and target parameter information of the first target object to which the twin information belongs; Based on the state threshold range, determine the off-bed status of the first target object to which the target parameter information belongs, and the off-bed status of the second target object to which the independent parameter information belongs; The determination of the location information and target parameter information of the first target object to which the twin information belongs includes: Determine the detection area information and signal strength value of each twin in the twin information; Based on the detection area information, the location information of the first target object is determined; Determine the maximum signal strength among all the signal strength values, and use the psychological parameter information corresponding to the maximum signal strength as the target parameter information; The step of determining the off-bed status of the first target object to which the target parameter information belongs, and the off-bed status of the second target object to which the independent parameter information belongs, based on the state threshold range, includes: If the target parameter information is within the state threshold range, then the first target object's out-of-bed state is determined as its in-bed state; otherwise, The first target object's out-of-bed state is defined as the out-of-bed state; If the independent parameter information is within the state threshold range, then the second target object's out-of-bed state is determined as in-bed state; otherwise, The second target object is defined as being out of bed.

2. The method according to claim 1, characterized in that, The step of determining the twin information and independent parameter information based on each comparison result includes: The comparison result is determined to be a first parameter information group with similar parameters, and the first parameter information group is used as the twin information; The comparison result is determined to be second parameter information with dissimilar parameters, and the second parameter information is used as the independent parameter information.

3. The method according to claim 1, characterized in that, After determining the number of target objects on the smart mattress, their location information, and the off-bed state of each target object based on a preset state threshold range, the twin information, and the independent parameter information, the method further includes: When the detection end condition is met, a sleep state curve for each target object is generated based on the described out-of-bed state and the described location information.

4. A device for determining the state of being in bed or out of bed, characterized in that, include: The information group acquisition module is used to acquire physiological parameter information corresponding to each preset area in the smart mattress, and form the physiological parameter information group of the smart mattress. The information determination module is used to determine the twin information and independent parameter information in the physiological parameter information group according to a preset range threshold. The twin information includes at least two physiological parameter information; the twin information is multiple similar physiological parameter information in the physiological parameter information group; and the independent parameter information is parameter information that does not have similar physiological parameter information in the physiological parameter information group. The status determination module is used to determine the number of target objects on the smart mattress, their location information, and the off-bed status of each target object based on a preset status threshold range, the twin information, and the independent parameter information. The information determination module includes: The first determining unit is used to determine other physiological parameter information in the physiological parameter information group based on the physiological parameter information included in the physiological parameter information group; For the physiological parameter information included in the physiological parameter information group, determining other physiological parameter information in the physiological parameter information group includes: polling the physiological parameter information included in the physiological parameter information group, selecting one physiological parameter information each time, and determining the other physiological parameter information in the physiological parameter information group; A parameter determination unit is used to determine similarity evaluation parameters between the physiological parameter information and each of the other physiological parameter information; The result determination unit is used to compare each of the similarity evaluation parameters with the range threshold to obtain the comparison result; The second determining unit is used to determine the twin information and independent parameter information based on each of the comparison results; The status determination module includes: The number determination unit is used to determine the number of information groups corresponding to the twin information and the number of information of the independent parameter information; The third determining unit is used to determine the number of target objects included on the smart mattress based on the number of information groups and the number of information items. The fourth determining unit is used to determine the location information and target parameter information of the first target object to which the twin information belongs; A status determination unit is used to determine, based on the status threshold range, the off-bed status of the first target object to which the target parameter information belongs, and the off-bed status of the second target object to which the independent parameter information belongs; The fourth determining unit is specifically used for: Determine the detection area information and signal strength value of each twin in the twin information; Based on the detection area information, the location information of the first target object is determined; Determine the maximum signal strength among all the signal strength values, and use the psychological parameter information corresponding to the maximum signal strength as the target parameter information; The state determination unit is specifically used for: If the target parameter information is within the state threshold range, then the first target object's out-of-bed state is determined as its in-bed state; otherwise, The first target object's out-of-bed state is defined as the out-of-bed state; If the independent parameter information is within the state threshold range, then the second target object's out-of-bed state is determined as in-bed state; otherwise, The second target object is defined as being out of bed.

5. A smart mattress, characterized in that, The smart mattress includes: At least one processor; and A memory communicatively connected to the at least one processor; wherein, The memory stores a computer program executable by the at least one processor, which enables the at least one processor to perform the method for determining the in-bed and out-of-bed status as described in any one of claims 1-3.

6. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores computer instructions that, when executed by a processor, implement the method for determining the in-bed and out-of-bed state as described in any one of claims 1-3.

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