CN115844377A - Human body sleeping posture pressure distribution testing method and device and mattress recommendation system - Google Patents
Human body sleeping posture pressure distribution testing method and device and mattress recommendation system Download PDFInfo
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Abstract
The invention provides a method and a device for testing human body sleeping posture pressure distribution and a mattress recommendation system, wherein the method comprises the following steps: controlling a pressure acquisition device to acquire first pressure data of a human body; generating a human body pressure distribution map according to the first human body pressure data; identifying, based on the human body pressure profile: a back region, a waist region, and a hip region; and respectively obtaining second human body pressure data of the back area, the waist area and the hip area based on the first human body pressure data, and obtaining pressure grade data by combining with a human body capillary vessel closing pressure threshold value. The method and the device for testing the human body sleeping posture pressure distribution and the mattress recommendation system provided by the invention provide accurate data analysis for mattress selection, and improve user experience.
Description
Technical Field
The invention relates to the technical field of sleep, in particular to a method and a device for testing human body sleeping posture pressure distribution and a mattress recommendation system.
Background
At present, mattress sales are carried out in mattress sales shops by adopting a sales mode of user experience and shopping guide introduction, and the mattress sales can not accurately help users to quickly select proper mattresses.
In order to better serve a user, a more appropriate mattress is selected for the user, the user can be assisted to select an appropriate mattress according to a pressure distribution map generated when the human body of the user lies on mattresses with different hardness and materials, pressure data of the user on the mattresses with different hardness are displayed to the user, and the appropriate mattress is selected for the user according to feedback of the user and in combination with the pressure data collected when the user experiences on different mattresses.
However, in the prior art, the pressure distribution map is only displayed, the user mainly selects according to the experience and feel of the user, and the intelligent scheme of automatically recommending the mattress by the system cannot be achieved.
Disclosure of Invention
The invention provides a method and a device for testing human body sleeping posture pressure distribution and a mattress recommendation system, which aim to solve the problem of automatically analyzing human body pressure data to provide a judgment basis for mattress recommendation.
The invention provides a method for testing the distribution of human body sleeping posture pressure, which comprises the following steps:
controlling a pressure acquisition device to acquire first pressure data of a human body; wherein, pressure acquisition device includes: a plurality of pressure sensors arranged in an array;
generating a human body pressure distribution map according to the first human body pressure data;
identifying, based on the human body pressure profile: a back region, a waist region, and a hip region;
and respectively obtaining second human body pressure data of the back area, the waist area and the hip area based on the first human body pressure data, and obtaining pressure grade data by combining with a human body capillary vessel closing pressure threshold value.
Further, the method for testing the distribution of the human body sleeping posture pressure according to the present invention includes the steps of obtaining second human body pressure data of the back region, the waist region and the hip region based on the first human body pressure data, and obtaining pressure grade data by combining the capillary vessel closure pressure threshold of the human body, including:
deriving maximum pressures of the back region, the waist region and the hip region, respectively, based on the human body first pressure data;
respectively deriving average pressures of the back region, the waist region and the hip region based on the human body first pressure data;
obtaining maximum pressure gradients and average pressure gradients of the back region, the waist region and the hip region respectively based on the human body first pressure data;
and obtaining the pressure grade data based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient with the human capillary vessel closing pressure threshold value.
Further, the method for testing the distribution of the human body sleeping posture pressure according to the present invention, wherein the step of obtaining the pressure grade data based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient, the average pressure gradient and the human body capillary vessel occlusion pressure threshold value comprises:
setting the weight of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient respectively;
calculating human body pressure weight calculation data based on the maximum pressure, the average pressure, the maximum pressure gradient, the average pressure gradient and the weight thereof and combining area ratio of the back area, the waist area and the hip area;
and comparing the calculated data of the human body pressure weight with the human body capillary vessel closing pressure threshold value to obtain the pressure grade data.
Further, the method for testing the distribution of the human body sleeping posture pressure obtains the human body pressure weight calculation data by calculation based on the following formula (1):
S＝(P1 m ×QPM+P1 v ×QPV+G1 m ×QGM+G1 v ×QGV)×A+(P2 m ×QPM+P2 v ×QPV+G2 m ×QGM+G2 v ×QGV)×B+(P3 m ×QPM+P3 v ×QPV+G3 m ×QGM+G3 v ×QGV)×C (1)；
wherein S represents the human body pressure weight calculation data, A represents an area ratio of a back region, B represents an area ratio of a waist region, and C represents an area ratio of a hip region;
wherein, P1 m Denotes the maximum pressure in the back region, P1 v Denotes the average pressure in the back region, G1 m Denotes the maximum pressure gradient in the back region, G1 v Represents the mean pressure gradient of the back region;
wherein, P2 m Representing the maximum pressure in the lumbar region, P2 v Denotes the mean pressure in the lumbar region, G2 m Maximum pressure gradient in the lumbar region, G2 v Representing the mean pressure gradient in the lumbar region;
wherein, P3 m Representing the maximum pressure in the buttocks region, P3 v Mean pressure in the buttocks area, G3 m Maximum pressure gradient in the hip region, g3 v Represents the mean pressure gradient in the hip region;
where QPM represents the weight of the maximum pressure, QPV represents the weight of the mean pressure, QGM represents the weight of the maximum pressure gradient, and QGV represents the weight of the mean pressure gradient.
Further, the pressure distribution testing method for the human body sleeping posture, provided by the invention, comprises the following pressure grades: a first pressure level, a second pressure level, a third pressure level;
the first pressure level corresponds to the state that the comprehensive pressure of the human body is more than 30 mmHg;
the second pressure level corresponds to a state where the integrated pressure of the human body is between 20 and 30 mmHg;
the third pressure level corresponds to a state where the integrated pressure of the human body is less than 20 mmHg.
Further, the method for testing the distribution of the human body sleeping posture pressure further comprises the following steps:
outputting mattress recommended firmness data based on the pressure rating data.
The invention provides a human body sleeping posture pressure distribution testing device, which comprises:
the acquisition module is used for controlling the pressure acquisition device to acquire first pressure data of the human body; wherein, pressure acquisition device includes: a plurality of pressure sensors arranged in an array;
the human body pressure distribution diagram module is used for generating a human body pressure distribution diagram according to the first human body pressure data;
the region identification module is used for identifying the following components based on the human body pressure distribution diagram: a back region, a waist region, and a hip region;
and the grade calculation module is used for respectively obtaining second human body pressure data of the back area, the waist area and the hip area based on the first human body pressure data and obtaining pressure grade data by combining with a human body capillary vessel closing pressure threshold value.
Further, the human body sleeping posture pressure distribution testing device of the invention, the grade calculating module comprises:
the first calculation submodule is used for respectively obtaining the maximum pressure of the back region, the waist region and the hip region based on the first human body pressure data;
the second calculation submodule is used for respectively obtaining the average pressure of the back region, the waist region and the hip region based on the first human body pressure data;
a third calculation submodule for respectively deriving maximum pressure gradients and average pressure gradients of the back region, the waist region and the hip region based on the human body first pressure data;
and the weight calculation submodule is used for obtaining the pressure grade data based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient with the human body capillary vessel closing pressure threshold value.
Further, the human body sleeping posture pressure distribution testing device of the invention, the weight calculation submodule includes:
a weight setting submodule for setting weights of the maximum pressure, the average pressure, the maximum pressure gradient, and the average pressure gradient, respectively;
the human body pressure weight calculation data calculation submodule is used for calculating human body pressure weight calculation data on the basis of the maximum pressure, the average pressure, the maximum pressure gradient, the average pressure gradient and the weight thereof and in combination with the area proportion of the back area, the waist area and the hip area;
and the comparison submodule is used for comparing the human body pressure weight calculation data with the human body capillary vessel closing pressure threshold value to obtain the pressure grade data.
Further, the human body sleeping posture pressure distribution testing device of the invention obtains the human body pressure weight calculation data based on the following formula (1):
S＝(P1 m ×QPM+P1 v ×QPV+G1 m ×QGM+G1 v ×QGV)×A+(P2 m ×QPM+P2 v ×QPV+G2 m ×QGM+G2 v ×QGV)×B+(P3 m ×QPM+P3 v ×QPV+G3 m ×QGM+G3 v ×QGV)×C (1)；
wherein S represents the human body pressure weight calculation data, A represents an area ratio of a back region, B represents an area ratio of a waist region, and C represents an area ratio of a hip region;
wherein, P1 m Denotes the maximum pressure in the back region, P1 v Denotes the average pressure in the back region, G1 m Denotes the maximum pressure gradient in the back region, G1 v Represents the mean pressure gradient of the back region;
wherein, P2 m Representing the maximum pressure in the lumbar region, P2 v Denotes the mean pressure in the lumbar region, G2 m Maximum pressure gradient in the lumbar region, G2 v Representing the mean pressure gradient in the lumbar region;
wherein, P3 m Representing the maximum pressure in the buttocks region, P3 v Mean pressure in the buttocks area, G3 m Indicating the maximum of the hip regionPressure gradient, G3 v Represents the mean pressure gradient in the hip region;
where QPM represents the weight of the maximum pressure, QPV represents the weight of the mean pressure, QGM represents the weight of the maximum pressure gradient, and QGV represents the weight of the mean pressure gradient.
Further, the pressure distribution testing device for the sleeping posture of the human body comprises the following pressure grades: a first pressure level, a second pressure level, a third pressure level;
the first pressure level corresponds to the state that the comprehensive pressure of the human body is more than 30 mmHg;
the second pressure level corresponds to a state where the integrated pressure of the human body is between 20 and 30 mmHg;
the third pressure level corresponds to a state where the integrated pressure of the human body is less than 20 mmHg.
Further, the device for testing the distribution of the human body sleeping posture pressure further comprises:
and the output module is used for outputting the recommended hardness data of the mattress based on the pressure grade data.
The invention provides a mattress recommendation system, which comprises: the invention relates to a human body sleeping posture pressure distribution testing device;
further comprising: the flexible pressure acquisition device and the display device;
the flexible pressure acquisition device is arranged on the mattress; the flexible pressure acquisition device comprises: a plurality of flexible pressure sensors arranged in an array;
the human body sleeping posture pressure distribution testing device controls the flexible pressure acquisition device to acquire human body pressure data; the display device displays the pressure grade data and the human body pressure distribution diagram obtained by the human body sleeping posture pressure distribution testing device.
The present invention provides a storage medium storing computer program instructions for performing a method according to the present invention.
The present invention provides a computing device comprising: a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the computing device to perform the method of the invention.
According to the method and the device for testing the human body sleeping posture pressure distribution and the mattress recommendation system, the human body is divided into the back area, the waist area and the hip area, the three areas are comprehensively analyzed to obtain the pressure grade, a user can more accurately select the mattress based on the pressure grade, the user is prevented from selecting the mattress only depending on experience feeling, accurate data analysis is provided for mattress selection, and user experience is improved.
Drawings
Other features, objects and advantages of the invention will become more apparent upon reading of the detailed description of non-limiting embodiments made with reference to the following drawings:
fig. 1 is a schematic flow chart of a human body sleeping posture pressure distribution testing method according to a first embodiment of the present invention;
fig. 2 is a schematic structural diagram of a pressure acquisition device according to a first embodiment of the present invention;
FIG. 3 is a display image of a human body pressure distribution diagram according to an embodiment of the present invention;
FIG. 4 is a diagram of the back region, the waist region and the hip region of a human pressure profile according to a first embodiment of the present invention;
fig. 5 is a schematic flow chart of a human body sleeping posture pressure distribution testing method according to a second embodiment of the present invention;
FIG. 6 is a schematic flow chart of calculating pressure level data according to a second embodiment of the present invention;
fig. 7 is a schematic flow chart of the comprehensive weight calculation according to the second embodiment of the present invention;
fig. 8 is a schematic structural view of a human body sleeping posture pressure distribution testing device according to a third embodiment of the present invention;
fig. 9 is a schematic structural diagram of a mattress recommendation system according to an embodiment of the invention.
Detailed Description
The present invention is described in further detail below with reference to the attached drawing figures.
Fig. 1 is a schematic flow chart of a method for testing human body sleeping posture pressure distribution according to a first embodiment of the present invention, and as shown in fig. 1, the method for testing human body sleeping posture pressure distribution according to the first embodiment of the present invention includes:
s101, controlling a pressure acquisition device to acquire first pressure data of a human body; wherein, pressure acquisition device includes: and the pressure sensors are arranged in an array.
Fig. 2 is a schematic structural diagram of a pressure acquisition device according to an embodiment of the present invention, and as shown in fig. 2, the pressure acquisition device may adopt a flexible pressure acquisition pad 22, the flexible pressure acquisition pad is provided with a plurality of flexible pressure sensors 21 arranged in an array, each flexible pressure sensor 21 independently acquires a pressure signal, and after processing a plurality of independently acquired pressure signals, the pressure signals form original data of the flexible pressure acquisition pad, that is, first pressure data of a human body. When the pressure distribution test is performed on the sleeping posture of the human body, the human body can lie flat on the flexible pressure collection mat 22. Besides lying sleeping positions, the human body can also adopt various sleeping positions such as lying on one side, lying prone, and the like. Aiming at different sleeping postures, a pressure acquisition mode corresponding to the sleeping postures needs to be adopted so as to obtain first pressure data of the human body corresponding to the sleeping postures.
And S102, generating a human body pressure distribution map according to the first human body pressure data.
Fig. 3 is a display image of a human body pressure distribution diagram according to an embodiment of the present invention, and as shown in fig. 3, a corresponding relationship between a numerical value of first human body pressure data and brightness and/or color of an image is constructed, so that a human body pressure distribution diagram can be obtained to reflect a pressure distribution state of a human body in a certain sleeping posture, for example, in fig. 3, an area with highest brightness is an area with highest pressure. In addition, red can be used to represent the area with the maximum pressure, blue or green can be used to represent the area with the minimum pressure, and the corresponding relation is established by the light wave frequency, the intensity and the pressure value.
Step S103, identifying based on the human body pressure distribution diagram: the back region, the waist region and the hip region.
Fig. 4 is a schematic diagram of a back region, a waist region and a hip region of a human body pressure distribution diagram according to a first embodiment of the present invention, as shown in fig. 4, training is performed based on a sample image by using a machine learning technique, and a training model can distinguish the back region, the waist region and the hip region of a human body based on the schematic diagram of the human body pressure distribution diagram shown in fig. 3. Establish sample image database, carry out the back region to the sample image, the regional division of waist region and buttock, the sample image input training model after will dividing the region trains and debugs, obtain the training model after the training, the training model can be deployed in the high in the clouds server, after the human pressure distribution diagram that waits to detect is generated based on the first pressure data of pressure acquisition device collection, send this human pressure distribution diagram to the training model and match the judgement, the training model after the training can carry out the feature matching according to this human pressure distribution diagram, obtain the back region of this human pressure distribution diagram, waist region and buttock region. The distinction of the regions, which are mainly used for calculation, does not necessarily need to be reflected on the human body pressure distribution diagram. Wherein the back region may or may not include a head. The hip region may or may not include the under-thigh portion. Considering that the diseases mainly occur in the shoulders, back and waist of the human body when the human body sleeps, preferably, the back area does not include the head, and the hip area does not include the parts below the thighs.
And step S104, respectively obtaining second human body pressure data of the back area, the waist area and the hip area based on the first human body pressure data, and obtaining pressure grade data by combining with a human body capillary vessel closing pressure threshold value.
Wherein, the capillary vessel closing pressure threshold of the human body can be set to be 20mmHg and 30mmHg, and the pressure grades comprise: a first pressure level, a second pressure level, and a third pressure level. The first pressure level corresponds to a pressure greater than 30mmHg and is a high pressure zone. The second pressure level corresponds to a pressure between 20 and 30mmHg, which is a mid-pressure region. The third pressure level corresponds to a pressure of less than 20mmHg, which is a low pressure region. For the human body, in the medium pressure region, it is the most healthy region. At low pressure areas, it is desirable to increase the firmness of the mattress. In high pressure areas, it is desirable to reduce mattress firmness. The hardness of the mattress can be divided into 9 grades, and whether the human body is in a high pressure area or a low pressure area is judged based on the comprehensive pressure of the human body on the flexible pressure acquisition mat, so that the mattress which can enable the human body to be in a middle pressure area is selected. The second human body pressure data are intermediate data obtained by dividing and calculating the original first human body pressure data and the back region, the waist region and the hip region, the comprehensive human body pressure can be reflected more accurately, and then whether the human body is in a high pressure region, a middle pressure region or a low pressure region is judged based on the human body capillary vessel closing pressure threshold value, so that the hardness of a mattress suitable for the human body pressure is recommended in an auxiliary mode. The calculation and use process of the second pressure data of the human body will be described in detail in the second embodiment, and will not be described herein.
Because the human body is in a sleeping posture, the back area, the waist area and the hip area have different pressure characteristics based on different characteristics of bones, muscles, fat, the gravity center of the human body and the like, such as different conditions of stress size, stress area, pressure distribution and the like. Therefore, based on the originally acquired data, the intermediate data capable of more accurately reflecting the human body pressure distribution is obtained by respectively calculating the three divided regions, and then the intermediate data is compared with the human body capillary vessel closing pressure threshold capable of reflecting the human body pressure stimulation, so that the mattress with proper hardness can be conveniently recommended to the user according to different conditions that the human body comprehensive pressure is positioned in a high-pressure region, a medium-pressure region, a low-pressure region and the like, and the user is assisted in selecting the mattress suitable for the health condition of the user.
Fig. 5 is a schematic flow chart of a method for testing human body sleeping posture pressure distribution according to a second embodiment of the present invention, and as shown in fig. 5, the method for testing human body sleeping posture pressure distribution according to the second embodiment of the present invention includes:
step S201, controlling a pressure acquisition device to acquire first pressure data of a human body; wherein, pressure acquisition device includes: and the pressure sensors are arranged in an array.
And step S202, generating a human body pressure distribution map according to the first human body pressure data.
Step S203, identifying, based on the human body pressure distribution diagram: the back region, the waist region and the hip region.
And step S204, respectively obtaining second human body pressure data of the back area, the waist area and the hip area based on the first human body pressure data, and obtaining pressure grade data by combining with a human body capillary vessel closing pressure threshold value.
And S205, outputting recommended hardness data of the mattress based on the pressure grade data.
If the pressure grade data is in the first pressure grade, reducing the hardness information of the mattress recommendation information by one hardness grade, replacing the mattress with the corresponding hardness, and performing the pressure test again until the pressure grade data is in the second pressure grade; if the pressure grade data is in the third pressure grade, the hardness information of the mattress recommendation information is improved by one hardness grade, the mattress with the corresponding hardness is replaced, and the pressure test is carried out again until the pressure grade data is in the second pressure grade; if the pressure level data is in the second pressure level, the hardness information of the recommended information of the mattress is kept unchanged. Wherein, the mattress recommendation information can also include, besides the mattress hardness information: mattress type, mattress brand, mattress material, mattress characteristics, and the like. In addition, 9 pressure levels can be pre-established, the pressure levels and the recommended hardness of the mattress have one-to-one correspondence, and the recommended hardness data of the mattress corresponding to the pressure levels can be output according to the pressure levels of the pressure level data. And then, the pressure grade data, the mattress recommendation information and the human body pressure distribution map can be displayed in a display device, so that a friendly mattress recommendation experience is provided for a user.
Fig. 6 is a schematic flow chart of calculating pressure level data according to the second embodiment of the present invention, and as shown in fig. 6, in step S204 of the second embodiment of the present invention, the step of obtaining second human pressure data of the back region, the waist region and the hip region respectively based on the first human pressure data, and obtaining the pressure level data by combining with a capillary vessel closure pressure threshold of a human body includes:
step S301, obtaining maximum pressures of the back region, the waist region, and the hip region based on the first human body pressure data, respectively.
Wherein, for three large intervals of the back, the waist and the hip, the following formula (2) can be used:
P m ＝Max(P 1 ，P 2 ，P 3 ，...，P n ，) (2)
respectively calculating to obtain the maximum pressure P1 of the back area m Maximum pressure in lumbar region P2 m Maximum pressure in the hip region P3 m . In the formula (2), P m Represents the maximum pressure, P, of the corresponding region (back, waist, hip) 1 、P 2 、P 3 、……、P n The measured pressure values measured by the flexible pressure sensors 21 of the n test points in the corresponding region are respectively represented, and n represents the number of test points in the corresponding segment interval of the corresponding region. Equation (2) may refer directly to the functional model of MATLAB software. Maximum pressure P m The maximum pressure applied by the mattress to a human body is reflected, the maximum pressure is generally positioned at the positions of blood vessels, less nerve distribution and bearing capacity of the human body, such as the positions of waist and hip, and when the pressure is overlarge, the spine is easy to deform due to the serious compression of the human body, so that the discomfort of the human body is caused. The maximum pressure can reflect the hardness degree of the mattress, under the condition that the material and the structure of the mattress are determined, the hard mattress represents that the maximum pressure is larger, the soft mattress represents that the maximum pressure is smaller, and under the condition that the maximum pressure is larger, the capillary vessels of the human body are pressed and pressed for a long time, so that the discomfort of the human body is caused, and the health and the sleeping quality of the human body are influenced.
Step S302, respectively obtaining average pressures of the back region, the waist region and the hip region based on the first human body pressure data.
Wherein, for three intervals of the back, the waist and the hip, the following formula (3) can be used:
respectively calculating to obtain the average pressure P1 of the back area v Mean pressure in lumbar region P2 v Maximum pressure in the hip region P3 v . In the formula (3), pv represents the average value of the pressure in the corresponding region (back, waist, hip), P i The pressure measurement value measured by the flexible pressure sensor 21 representing the i-th test point of the corresponding area, i representing the corresponding areaAnd the ith test point in the corresponding segmentation interval of the domain, wherein N represents the number of the test points in the corresponding region. Equation (3) may refer directly to the functional model of MATLAB software. The mean pressure may reflect the distribution of the individual regions to the human body pressure. The average pressure represents the supporting effect of the mattress on the human body, the smaller the numerical value is, the better the effect of the mattress on dispersing the weight of the human body is, and the smaller the pressure stimulation of the mattress sensed by the human body is.
Step S303, respectively obtaining a maximum pressure gradient and an average pressure gradient of the back region, the waist region, and the hip region based on the first human body pressure data.
Wherein, for three large intervals of the back, the waist and the hip, the following formula (4) can be used:
G m ＝Max(gradG 1 ，gradG 2 ，gradG 3 ，...，gradG n ) (4)；
respectively calculating to obtain the maximum pressure gradient G1 of the back area m Maximum pressure gradient G2 in the lumbar region m Maximum pressure gradient G3 in the hip region m . In the formula (4), G m Representing the maximum pressure gradient, gradG, of the corresponding region (back, waist, hip) 1 、gradG 2 、gradG 3 、……、gradG n And respectively representing the pressure gradient obtained by the pressure measurement value measured by each flexible pressure sensor 21 of the n test points of the corresponding area, wherein n represents the number of the test points in the corresponding subsection interval of the corresponding area. The grad function model of MATLAB software can be referenced directly in equation (4) to calculate the pressure gradient.
Wherein, for three large intervals of the back, the waist and the hip, the following formula (5) can be used:
respectively calculating to obtain the average pressure gradient G1 of the back area v Mean pressure gradient G2 in the lumbar region v Average pressure gradient G3 in the hip region v . In the formula (5), G v Indicating the mean pressure of the corresponding region (back, waist, hip)Force gradient, gradG i And a pressure gradient obtained by a pressure measurement value measured by the flexible pressure sensor 21 of the ith test point of the corresponding area is represented, i represents the ith test point in the corresponding subsection interval of the corresponding area, and N represents the number of the test points of the corresponding area. The grad function model of MATLAB software can be directly referenced in equation (5) to calculate the pressure gradient.
Maximum pressure gradient G m And the mean pressure gradient G v Reflecting the change of pressure values of different test points, the maximum pressure gradient is generally positioned near the joint position of the human body, and the average pressure gradient G v Smaller values indicate more uniform and gentle pressure increase and decrease, and the human body has less response to pressure stimulation.
Step S304, the pressure grade data is obtained based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient with the human body capillary vessel closing pressure threshold value.
Fig. 7 is a schematic flow chart of the comprehensive weight calculation according to the second embodiment of the present invention, and as shown in fig. 7, in step S304 according to the second embodiment of the present invention, the step of obtaining the pressure level data based on the comparison between the maximum pressure, the average pressure, the maximum pressure gradient, and the comprehensive weight of the average pressure gradient and the human capillary vessel occlusion pressure threshold value includes:
step S401, setting the weights of the maximum pressure, the average pressure, the maximum pressure gradient, and the average pressure gradient, respectively.
Step S402, calculating to obtain human body pressure weight calculation data based on the maximum pressure, the average pressure, the maximum pressure gradient, the average pressure gradient and the weight thereof and combining the area ratio of the back area, the waist area and the hip area.
Wherein, the human body pressure weight calculation data can be calculated based on the following formula (1):
S＝(P1 m ×QPM+P1 v ×QPV+G1 m ×QGM+G1 v ×QGV)×A+(P2 m ×QPM+P2 v ×QPV+G2 m ×QGM+G2 v ×QGV)×B+(P3 m ×QPM+P3 v ×QPV+G3 m ×QGM+G3 v ×QGV)×C (1)；
wherein S represents the human body pressure weight calculation data, A represents an area ratio of a back region, B represents an area ratio of a waist region, and C represents an area ratio of a hip region;
wherein, P1 m Denotes the maximum pressure in the back region, P1 v Denotes the average pressure in the back region, G1 m Denotes the maximum pressure gradient in the back region, G1 v Represents the mean pressure gradient of the back region;
wherein, P2 m Representing the maximum pressure in the lumbar region, P2 v Denotes the mean pressure in the lumbar region, G2 m Maximum pressure gradient in the lumbar region, G2 v Representing the mean pressure gradient in the lumbar region;
wherein, P3 m Representing the maximum pressure in the buttocks region, P3 v Mean pressure in the buttocks area, G3 m Representing the maximum pressure gradient in the hip region, G3 v Represents the mean pressure gradient in the hip region;
where QPM represents the weight of the maximum pressure, QPV represents the weight of the mean pressure, QGM represents the weight of the maximum pressure gradient, and QGV represents the weight of the mean pressure gradient.
And step S403, comparing the human body pressure weight calculation data with the human body capillary vessel closing pressure threshold value to obtain the pressure grade data.
For example, set maximum pressure P m Weight 20%, mean pressure P v Weight 30%, maximum pressure gradient G m Weight 30%, mean pressure gradient G v The weight is 20%, and for a certain experience user, according to the human body pressure distribution diagram, the back area ratio a of the user is 30%, the waist area ratio B is 40%, and the hip area ratio C is 30%, then the following formula (6) can be formed by formula (1):
S＝(P1 m ×20％+P1 v ×30％+G1 m ×30％+G1 v ×20％)×A+(P2 m ×20％+P2 v ×30％+G2 m ×30％+G2 v ×20％)×B+(P3 m ×20％+P3 v ×30％+G3 m ×30％+G3 v ×20％)×C (6)
according to the formula (6), the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient of each region calculated according to fig. 6 are combined, and the weight calculation data S of the human body pressure can be calculated. And then comparing the S with a human capillary vessel closing pressure threshold value to judge which pressure level the human body pressure distribution condition of the experience user belongs to from the first pressure level, the second pressure level or the third pressure level. When S is compared with a human capillary vessel closure pressure threshold value, the unit of S is Newton, and the unit of S needs to be converted into mmHg. Can be determined according to the formula: pressure = pressure/area, which is the contact area of a person on the flexible pressure acquisition pad 22 (which can be obtained from the body pressure profile), and the pressure is the body pressure weight calculation data S, which is converted into pressure units. Then, the standard atmospheric pressure (1.013X 10^5 Pa) can be converted into the following steps:
pressure P = 1.013X 10^5Pa =760mm Hg,
and then the unit of the human body pressure weight calculation data S is converted into mmHg, so that the unit of S is unified with the unit of the human body capillary vessel closing pressure threshold value, and the comparison of S and the human body capillary vessel closing pressure threshold value is facilitated.
Preferably, the area ratio A of the back area takes a specific value of 30%, the area ratio B of the waist area takes a specific value of 40%, the area ratio C of the hip area takes a specific value of 30%, and the maximum pressure P obtained by each test point in each area is obtained according to the area ratio of each area m Average pressure Pv, maximum pressure gradient G m Average pressure gradient G v And further calculating to obtain human body pressure weight calculation data S.
The lumbar region supports the upper half of the body and is the most important load-bearing part of the body. Bad habits such as working over a desk in modern life affect the structures of lumbar vertebra and the like invisibly, thereby causing pain to human bodies. In the weighted design, the waist ratio should be larger than the back and the hip, and the area ratio should be selected to be more than 40%. The weight of the buttocks required to properly release pressure due to natural protrusion of the mattress is small, and the area percentage of the buttocks is generally controlled to be below 30 percent. Nerve impulses caused by the pressure of the back can cause the hormones of the hypothalamus, the pituitary and the adrenal gland to turn up in the body, the synthesis of cortisol is increased, and the 'alert' response of the body is also mobilized, such as sharper vision, tighter muscles and the like. However, if this stress state persists, the body is unable to return to equilibrium. The muscles of the back are sore, contracted, and even cramped, and become injured parts under heavy pressure, so that the area ratio is preferably more than 30%.
In summary, the values of the area ratio A of the back region, the area ratio B of the waist region and the area ratio C of the hip region should be B ≧ 40% and B > A > C.
Preferably, the weight QPM of the maximum pressure is 20%, the weight QPV of the mean pressure is 30%, the weight QGM of the maximum pressure gradient is 30%, the weight QGV of the mean pressure gradient is 20%, can be determined according to the pressure standard deviation P sd The ratio of the maximum pressure weight to the average pressure weight is fine-tuned.
Overall, the average pressure represents the support effect of the mattress on the human body, the smaller the numerical value is, the better the dispersity is, and the smaller the stimulation of the human body sensing mattress is. The mattress with higher hardness has larger pressure gradient representation, the mattress with proper material has smaller maximum pressure gradient, and the human body feels comfortable.
Maximum pressure P m The places where the pressure points representing the highest pressure of the human body appear are generally the shoulders and the buttocks. Mean pressure value P v The characteristic of the average pressure received by the human body under the current mattress is mainly related to the hardness of the bed.
When the trunk is bent forwards, the muscles of the waist are relaxed, but the bending of the spine causes uneven pressure inside and outside the intervertebral disc, a pressure gradient is formed, and the intervertebral disc is seriously extruded from the lumbar vertebra to press the central nerve. This parameter is characterized by a maximum pressure gradient G m This feature point is generally present near the joints and lumbar vertebrae.
For different crowds, the pressure and gradient relation is related to the structure of the mattress and the surface material of the mattress, and the two parameters are equally important, so that the pressure and the gradient weight respectively account for 50 percent, namely the maximum pressure and the average pressure, and the pressure and the gradient weight respectively account for 50 percent in the whole compared with the maximum pressure gradient and the average pressure gradient; in the pressure relation, the average pressure can represent the supporting effect of the mattress, so that the weight of the average pressure is higher than that of the maximum pressure; in the gradient relation, the maximum gradient is more obvious in characteristic of the material, so that the weight of the maximum pressure gradient is higher than that of the average pressure gradient.
In summary, the values of the weight QPM of the maximum pressure, the weight QPV of the average pressure, the weight QGM of the maximum pressure gradient and the weight QGV of the average pressure gradient are compared,
QPM + QPV = QGM + QGV and QPM < QPV, QGM > QGV.
Wherein, according to the standard deviation P of the pressure sd The ratio of the maximum pressure weight to the average pressure weight is fine-tuned. The ratio of the pressure standard deviation to the maximum pressure weight is in inverse proportion, and when the pressure standard deviation exceeds a certain threshold value, the maximum pressure weight should be adjusted to be low.
Wherein, for three large intervals of the back, the waist and the hip, the following formula (7) can be used:
respectively calculating to obtain the standard deviation P1 of the pressure of the back area sd Standard deviation of pressure in lumbar region P2 sd Standard deviation of pressure P3 in hip region sd . In the formula (7), P sd Denotes the standard deviation of pressure, P, of the corresponding region (back, waist, hip) i The pressure measurement, P, measured by the flexible pressure sensor 21 representing the i-th test point of the corresponding area v And the pressure average value of the test points of the corresponding area is represented, i represents the ith test point in the corresponding segmentation interval of the corresponding area, and N represents the number of the test points of the corresponding area. The average pressure reflects the distribution of human body pressure in each region, and the standard deviation P sd Means dispersion over mean pressureP v In the surrounding pressure distribution condition, the smaller the value, the higher the concentration degree of the data, the more the data tends to be an average value, the more uniform the pressure distribution, and the larger the value, the abnormal distribution condition is indicated by the pressure distribution at the peak.
In addition, a pressure scoring system can be established according to the comprehensive comparison result of the human body pressure weight calculation data S and the human body capillary vessel closing pressure threshold value. Taking S as a main parameter of the system, providing an experience comfort questionnaire for a user to obtain a comfort feedback score of the user by combining with user feedback data, for example, establishing a sample database according to a weight comparison result, the user feedback data and basic information of the user, inputting sample data into a machine learning model for learning by using a machine learning technology to obtain the machine learning model after sample learning, inputting weight calculation data S of the experience user into the machine learning model after learning, and outputting a user pressure score through the machine model, for example, the system is divided into 100 points at the highest, an evaluation conclusion of which is not suitable for the mattress under 50 points, 50-60 points of unsuitable condition, 60-70 points of slightly unsuitable condition, 70-80 points of suitable condition, 80-90 points of suitable condition, and 90-100 points of suitable condition. .
In addition, in an implementation manner of the embodiment of the present invention, the method for testing the distribution of the human body sleeping posture pressure further includes:
step S206, recognizing the sleeping posture of the human body based on the human body pressure distribution graph;
and step S207, respectively obtaining the pressure levels of the back area, the waist area and the hip area corresponding to the sleeping posture of the human body.
Fig. 8 is a schematic structural diagram of a human body sleeping posture pressure distribution testing apparatus according to an embodiment of the present invention, and as shown in fig. 8, the human body sleeping posture pressure distribution testing apparatus 10 provided by the present invention includes:
the acquisition module 11 is used for controlling the pressure acquisition device to acquire first pressure data of the human body; wherein, pressure acquisition device includes: a plurality of pressure sensors arranged in an array;
the human body pressure distribution diagram module 12 is used for generating a human body pressure distribution diagram according to the human body pressure data;
a region identification module 13, configured to identify, based on the human body pressure distribution map: a back region, a waist region, and a hip region;
and the grade calculation module 14 is configured to obtain second human body pressure data of the back region, the waist region and the hip region respectively based on the first human body pressure data, and obtain pressure grade data by combining a human capillary vessel occlusion pressure threshold.
Further, the grade calculation module 14 includes:
the first calculation submodule is used for respectively obtaining the maximum pressure of the back region, the waist region and the hip region based on the first human body pressure data;
the second calculation submodule is used for respectively obtaining the average pressure of the back region, the waist region and the hip region based on the first human body pressure data;
a third calculation submodule for respectively deriving maximum pressure gradients and average pressure gradients of the back region, the waist region and the hip region based on the human body first pressure data;
and the weight calculation submodule is used for obtaining the pressure grade data based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient with the human body capillary vessel closing pressure threshold value.
Further, the weight calculation sub-module includes:
a weight setting submodule for setting weights of the maximum pressure, the average pressure, the maximum pressure gradient, and the average pressure gradient, respectively;
the human body pressure weight calculation data calculation submodule is used for calculating human body pressure weight calculation data based on the maximum pressure, the average pressure, the maximum pressure gradient, the average pressure gradient and the weight thereof and combining the area ratio of the back area, the waist area and the hip area;
and the comparison submodule is used for comparing the human body pressure weight calculation data with the human body capillary vessel closing pressure threshold value to obtain the pressure grade data.
Further, the human body pressure weight calculation data is calculated based on the following formula (1):
S＝(P1 m ×QPM+P1 v ×QPV+G1 m ×QGM+G1 v ×QGV)×A+(P2 m ×QPM+P2 v ×QPV+G2 m ×QGM+G2 v ×QGV)×B+(P3 m ×QPM+P3 v ×QPV+G3 m ×QGM+G3 v ×QGV)×C (1)；
wherein S represents the human body pressure weight calculation data, A represents the area proportion of the back region, B represents the area proportion of the waist region, and C represents the area proportion of the hip region;
wherein, P1 m Denotes the maximum pressure in the back region, P1 v Denotes the average pressure in the back region, G1 m Denotes the maximum pressure gradient in the back region, G1 v Represents the mean pressure gradient of the back region;
wherein, P2 m Representing the maximum pressure in the lumbar region, P2 v Denotes the mean pressure in the lumbar region, G2 m Maximum pressure gradient in the lumbar region, G2 v Representing the mean pressure gradient in the lumbar region;
wherein, P3 m Representing the maximum pressure in the buttocks region, P3 v Mean pressure in the buttocks area, G3 m Representing the maximum pressure gradient in the hip region, G3 v Represents the mean pressure gradient of the hip region;
where QPM represents the weight of the maximum pressure, QPV represents the weight of the mean pressure, QGM represents the weight of the maximum pressure gradient, and QGV represents the weight of the mean pressure gradient.
Further, the pressure distribution testing device for the human body sleeping posture of the embodiment of the invention comprises the following pressure grades: a first pressure level, a second pressure level, a third pressure level;
the first pressure level corresponds to the state that the comprehensive pressure of the human body is more than 30 mmHg;
the second pressure level corresponds to a state where the integrated pressure of the human body is between 20 and 30 mmHg;
the third pressure level corresponds to a state where the integrated pressure of the human body is less than 20 mmHg.
Further, the device for testing the distribution of the human body sleeping posture pressure in the embodiment of the invention further comprises:
and the output module 15 is used for outputting the recommended hardness data of the mattress based on the pressure grade data.
The device for testing the human body sleeping posture pressure distribution in the embodiment of the invention is an implementation device of the method for testing the human body sleeping posture pressure distribution in the embodiment of the invention, and specific principles are referred to the method for testing the human body sleeping posture pressure distribution in the embodiment of the invention, and are not described herein again.
Fig. 9 is a schematic structural diagram of a mattress recommendation system according to an embodiment of the present invention, and as shown in fig. 9, the mattress recommendation system according to the embodiment of the present invention includes: fig. 8 shows a human body sleeping posture pressure distribution testing device 10.
The human body sleeping posture pressure distribution testing device 10 further comprises: a flexible pressure acquisition device 91 and a display device 92.
The flexible pressure acquisition device 91 is arranged on the mattress; the flexible pressure acquisition device comprises: a plurality of flexible pressure sensors arranged in an array;
the human body sleeping posture pressure distribution testing device controls the flexible pressure acquisition device to acquire human body pressure data; the display device 92 displays the pressure grade data and the human body pressure distribution map obtained by the human body sleeping posture pressure distribution testing device 10.
In an embodiment of the present invention, a storage medium is further provided, where the storage medium stores computer program instructions, and the computer program instructions are executed according to the method for testing distribution of human body sleeping posture pressure described in the first embodiment or the second embodiment.
In a typical configuration of the invention, the storage media include permanent and non-permanent, removable and non-removable media implemented in any method or technology for storage of information. The information may be computer readable instructions, data structures, programmed devices, or other data. Examples of computer storage media include, but are not limited to, phase change memory (PRAM), static Random Access Memory (SRAM), dynamic Random Access Memory (DRAM), other types of Random Access Memory (RAM), read Only Memory (ROM), electrically Erasable Programmable Read Only Memory (EEPROM), flash memory or other memory technology, compact disc read only memory (CD-ROM), digital Versatile Disks (DVD) or other optical storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transmission medium which can be used to store information and which can be accessed by a computing device.
In one embodiment of the present invention, there is also provided a computing device comprising: a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the computing device to execute the method for testing distribution of human sleep posture pressure according to the first embodiment or the second embodiment of the present invention.
In one exemplary configuration of the invention, the computing devices each include one or more processors (CPUs), input/output interfaces, network interfaces, and memory.
The memory may include forms of volatile memory in a computer readable medium, random Access Memory (RAM) and/or non-volatile memory, such as Read Only Memory (ROM) or flash memory (flash RAM). Memory is an example of a computer-readable medium.
The computing device includes, but is not limited to, any electronic product that can perform human-computer interaction with a user (e.g., human-computer interaction through a touch panel), such as a mobile electronic product, e.g., a smart phone, a tablet computer, and the like, and the mobile electronic product may employ any operating system, e.g., an android operating system, an iOS operating system, and the like.
In the embodiments of the present application, the sequence numbers of the processes do not mean the execution sequence, and the execution sequence of the processes should be determined by the functions and the inherent logic of the processes, and should not limit the implementation processes of the present application.
In addition, the term "and/or" herein is only one kind of association relationship describing the association object, and means that there may be three kinds of relationships, for example, a and/or B, and may mean: a exists alone, A and B exist simultaneously, and B exists alone. In addition, the character "/" herein generally indicates that the former and latter related objects are in an "or" relationship.
The steps of a method or algorithm described in connection with the disclosure herein may be embodied in hardware or in software instructions executed by a processor. The software instructions may be comprised of corresponding software modules that may be stored in Random Access Memory (RAM), flash memory, read Only Memory (ROM), erasable Programmable ROM (EPROM), electrically Erasable Programmable ROM (EEPROM), registers, a hard disk, a removable disk, a compact disc read only memory (CD-ROM), or any other form of storage medium known in the art. An exemplary storage medium is coupled to the processor such the processor can read information from, and write information to, the storage medium. Of course, the storage medium may also be integral to the processor. The processor and the storage medium may reside in an ASIC. In addition, the ASIC may reside in a terminal device or a core network element. Of course, the processor and the storage medium may reside as discrete components in a terminal device or a core network element.
In the above embodiments, the implementation may be wholly or partially realized by software, hardware, firmware, or any combination thereof. When implemented in software, may be implemented in whole or in part in the form of a computer program product. The computer program product includes one or more computer instructions. When the computer program instructions are loaded and executed on a computer, the procedures or functions described in accordance with the present application are generated, in whole or in part. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable device. The computer instructions may be stored on or transmitted over a computer-readable storage medium. The computer instructions may be transmitted from one website, computer, server, or data center to another website, computer, server, or data center by wire (e.g., coaxial cable, fiber optic, digital Subscriber Line (DSL)), or wirelessly (e.g., infrared, wireless, microwave, etc.). The computer-readable storage medium can be any available medium that can be accessed by a computer or a data storage device, such as a server, a data center, etc., that incorporates one or more of the available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., digital Versatile Disk (DVD), or a semiconductor medium (e.g., solid State Disk (SSD)), etc.
The above-mentioned embodiments, objects, technical solutions and advantages of the present application are further described in detail, it should be understood that the above-mentioned embodiments are only examples of the present application, and are not intended to limit the scope of the present application, and any modifications, equivalent substitutions, improvements and the like made on the basis of the technical solutions of the present application should be included in the scope of the present application.
Claims (15)
1. A method for testing the pressure distribution of human body sleeping postures is characterized by comprising the following steps:
controlling a pressure acquisition device to acquire first pressure data of a human body; wherein, pressure acquisition device includes: a plurality of pressure sensors arranged in an array;
generating a human body pressure distribution map according to the first human body pressure data;
identifying, based on the human body pressure profile: a back region, a waist region, and a hip region;
and respectively obtaining second human body pressure data of the back area, the waist area and the hip area based on the first human body pressure data, and obtaining pressure grade data by combining with a human body capillary vessel closing pressure threshold value.
2. The method for testing the pressure distribution of the sleeping postures of the human body according to the claim 1, wherein the step of obtaining the second pressure data of the human body in the back area, the waist area and the hip area respectively based on the first pressure data of the human body and obtaining the pressure grade data by combining with the capillary vessel closure pressure threshold of the human body comprises the following steps:
deriving maximum pressures of the back region, the waist region and the hip region, respectively, based on the human body first pressure data;
respectively deriving average pressures of the back region, the waist region and the hip region based on the human body first pressure data;
obtaining maximum pressure gradients and average pressure gradients of the back region, the waist region and the hip region respectively based on the human body first pressure data;
and obtaining the pressure grade data based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient with the human capillary vessel closing pressure threshold value.
3. The method for testing the pressure distribution of human sleeping postures according to the claim 2, wherein the step of obtaining the pressure grade data based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient with the threshold value of the capillary vessel closing pressure of the human body comprises:
setting the weight of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient respectively;
calculating human body pressure weight calculation data based on the maximum pressure, the average pressure, the maximum pressure gradient, the average pressure gradient and the weight thereof and combining area ratio of the back area, the waist area and the hip area;
and comparing the calculated data of the human body pressure weight with the human body capillary vessel closing pressure threshold value to obtain the pressure grade data.
4. The human body sleeping posture pressure distribution test method according to claim 3, wherein the human body pressure weight calculation data is calculated based on the following formula (1):
S＝(P1 m ×QPM+P1 v ×QPV+G1 m ×QGM+G1 v ×QGV)×A+(P2 m ×QPM+P2 v ×QPV+G2 m ×QGM+G2 v ×QGV)×B+(P3 m ×QPM+P3 v ×QPV+G3 m ×QGM+G3 v ×QGV)×C (1)；
wherein S represents the human body pressure weight calculation data, A represents an area ratio of a back region, B represents an area ratio of a waist region, and C represents an area ratio of a hip region;
wherein, P1 m Denotes the maximum pressure in the back region, P1 v Denotes the average pressure in the back region, G1 m Denotes the maximum pressure gradient in the back region, G1 v Represents the mean pressure gradient of the back region;
wherein, P2 m Representing the maximum pressure in the lumbar region, P2 v Denotes the mean pressure in the lumbar region, G2 m Indicating the maximum pressure gradient in the lumbar region, G2 v Representing the mean pressure gradient in the lumbar region;
wherein, P3 m Representing the maximum pressure in the buttocks region, P3 v Mean pressure in the buttocks area, G3 m Representing the maximum pressure gradient in the hip region, G3 v Represents the mean pressure gradient in the hip region;
where QPM represents the weight of the maximum pressure, QPV represents the weight of the mean pressure, QGM represents the weight of the maximum pressure gradient, and QGV represents the weight of the mean pressure gradient.
5. The human body sleeping posture pressure distribution testing method according to any one of claims 1 to 4, wherein the pressure grade comprises: a first pressure level, a second pressure level, a third pressure level;
the first pressure level corresponds to the state that the comprehensive pressure of the human body is more than 30 mmHg;
the second pressure level corresponds to a state where the integrated pressure of the human body is between 20 and 30 mmHg;
the third pressure level corresponds to a state where the integrated pressure of the human body is less than 20 mmHg.
6. The human body sleeping posture pressure distribution testing method according to any one of claims 1 to 4, further comprising:
outputting mattress recommended firmness data based on the pressure rating data.
7. The utility model provides a human appearance pressure distribution testing arrangement of sleeping which characterized in that includes:
the acquisition module is used for controlling the pressure acquisition device to acquire first pressure data of the human body; wherein, pressure acquisition device includes: a plurality of pressure sensors arranged in an array;
the human body pressure distribution diagram module is used for generating a human body pressure distribution diagram according to the first human body pressure data;
the region identification module is used for identifying the following components based on the human body pressure distribution diagram: a back region, a waist region, and a hip region;
and the grade calculation module is used for respectively obtaining second human body pressure data of the back area, the waist area and the hip area based on the first human body pressure data and obtaining pressure grade data by combining with a human capillary vessel closing pressure threshold value.
8. The human body sleeping posture pressure distribution testing device according to claim 7, wherein the grade calculating module comprises:
the first calculation submodule is used for respectively obtaining the maximum pressure of the back region, the waist region and the hip region based on the first human body pressure data;
the second calculation submodule is used for respectively obtaining the average pressure of the back region, the waist region and the hip region based on the first human body pressure data;
a third calculation submodule, configured to derive maximum pressure gradients and average pressure gradients of the back region, the waist region, and the hip region, respectively, based on the first human body pressure data;
and the weight calculation submodule is used for obtaining the pressure grade data based on the comprehensive weight comparison of the maximum pressure, the average pressure, the maximum pressure gradient and the average pressure gradient with the human body capillary vessel closing pressure threshold.
9. The human body sleeping posture pressure distribution testing device according to claim 8, wherein the weight calculating submodule comprises:
a weight setting submodule for setting weights of the maximum pressure, the average pressure, the maximum pressure gradient, and the average pressure gradient, respectively;
the human body pressure weight calculation data calculation submodule is used for calculating human body pressure weight calculation data based on the maximum pressure, the average pressure, the maximum pressure gradient, the average pressure gradient and the weight thereof and combining the area ratio of the back area, the waist area and the hip area;
and the comparison submodule is used for comparing the human body pressure weight calculation data with the human body capillary vessel closing pressure threshold value to obtain the pressure grade data.
10. The human body sleeping posture pressure distribution testing device according to claim 9, wherein the human body pressure weight calculation data is calculated based on the following formula (1):
S＝(P1 m ×QPM+P1 v ×QPV+G1 m ×QGM+G1 v ×QGV)×A+(P2 m ×QPM+P2 v ×QPV+G2 m ×QGM+G2 v ×QGV)×B+(P3 m ×QPM+P3 v ×QPV+G3 m ×QGM+G3 v ×QGV)×C (1)；
wherein S represents the human body pressure weight calculation data, A represents an area ratio of a back region, B represents an area ratio of a waist region, and C represents an area ratio of a hip region;
wherein, P1 m Denotes the maximum pressure in the back region, P1 v Denotes the average pressure in the back region, G1 m Denotes the maximum pressure gradient in the back region, G1 v Represents the mean pressure gradient of the back region;
wherein, P2 m Representing the maximum pressure in the lumbar region, P2 v Denotes the mean pressure in the lumbar region, G2 m Indicating the maximum pressure gradient in the lumbar region, G2 v Representing the mean pressure gradient in the lumbar region;
wherein, P3 m Representing the maximum pressure in the buttocks region, P3 v Mean pressure in the buttocks area, G3 m Representing the maximum pressure gradient in the hip region, G3 v Represents the mean pressure gradient in the hip region;
where QPM represents the weight of the maximum pressure, QPV represents the weight of the mean pressure, QGM represents the weight of the maximum pressure gradient, and QGV represents the weight of the mean pressure gradient.
11. The human body sleeping posture pressure distribution testing device according to any one of the claims 7 to 10, wherein the pressure grade comprises: a first pressure level, a second pressure level, a third pressure level;
the first pressure level corresponds to the state that the comprehensive pressure of the human body is more than 30 mmHg;
the second pressure level corresponds to a state where the integrated pressure of the human body is between 20 and 30 mmHg;
the third pressure level corresponds to a state where the integrated pressure of the human body is less than 20 mmHg.
12. The human body sleeping posture pressure distribution testing device according to any one of claims 7 to 10, further comprising:
and the output module is used for outputting the recommended hardness data of the mattress based on the pressure grade data.
13. A mattress recommendation system, comprising: the human sleeping posture pressure distribution test device of any one of claims 7 to 12;
further comprising: the flexible pressure acquisition device and the display device;
the flexible pressure acquisition device is arranged on the mattress; the flexible pressure acquisition device comprises: a plurality of flexible pressure sensors arranged in an array;
the human body sleeping posture pressure distribution testing device controls the flexible pressure acquisition device to acquire human body pressure data; the display device displays the pressure grade data and the human body pressure distribution diagram obtained by the human body sleeping posture pressure distribution testing device.
14. A storage medium storing computer program instructions for performing a method according to any one of claims 1 to 6.
15. A computing device, comprising: a memory for storing computer program instructions and a processor for executing the computer program instructions, wherein the computer program instructions, when executed by the processor, trigger the computing device to perform the method of any of claims 1 to 6.
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| CN116703532A (en) * | 2023-08-04 | 2023-09-05 | 湖南星港家居发展有限公司 | Mattress recommendation method and system based on user personalized data analysis |
| CN118402783A (en) * | 2024-07-01 | 2024-07-30 | 中国人民解放军联勤保障部队第九〇〇医院 | Sleeping posture detection system based on human body pressure distribution |
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|---|---|---|---|---|
| CN116703532A (en) * | 2023-08-04 | 2023-09-05 | 湖南星港家居发展有限公司 | Mattress recommendation method and system based on user personalized data analysis |
| CN116703532B (en) * | 2023-08-04 | 2023-10-27 | 湖南星港家居发展有限公司 | Mattress recommendation method and system based on user personalized data analysis |
| CN118402783A (en) * | 2024-07-01 | 2024-07-30 | 中国人民解放军联勤保障部队第九〇〇医院 | Sleeping posture detection system based on human body pressure distribution |
| CN118402783B (en) * | 2024-07-01 | 2024-09-20 | 中国人民解放军联勤保障部队第九〇〇医院 | Sleeping posture detection system based on human body pressure distribution |
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