CN113616039A - Method and device for adjusting seat device, equipment and computer readable storage medium - Google Patents
Method and device for adjusting seat device, equipment and computer readable storage medium Download PDFInfo
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47C—CHAIRS; SOFAS; BEDS
- A47C31/00—Details or accessories for chairs, beds, or the like, not provided for in other groups of this subclass, e.g. upholstery fasteners, mattress protectors, stretching devices for mattress nets
- A47C31/008—Use of remote controls
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47C—CHAIRS; SOFAS; BEDS
- A47C1/00—Chairs adapted for special purposes
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47C—CHAIRS; SOFAS; BEDS
- A47C31/00—Details or accessories for chairs, beds, or the like, not provided for in other groups of this subclass, e.g. upholstery fasteners, mattress protectors, stretching devices for mattress nets
- A47C31/12—Means, e.g. measuring means for adapting chairs, beds or mattresses to the shape or weight of persons
- A47C31/126—Means, e.g. measuring means for adapting chairs, beds or mattresses to the shape or weight of persons for chairs
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47C—CHAIRS; SOFAS; BEDS
- A47C7/00—Parts, details, or accessories of chairs or stools
- A47C7/62—Accessories for chairs
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/021—Measuring pressure in heart or blood vessels
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
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Abstract
The application provides an adjusting method of a seat device, a device, equipment and a computer readable storage medium, wherein the method comprises the following steps: acquiring an electric signal converted from a vibration signal, wherein the vibration signal is generated by a first object on a seat device; acquiring vital sign parameters of the first subject based on the electrical signals; inputting the vital sign parameters into a target neural network model, and outputting results, wherein the results are used for representing parameters to be adjusted of the seat device; adjusting an initial parameter of the seating arrangement based on the parameter to be adjusted. Through this application, need manually adjust seat device among the prior art, leaded to the more loaded down with trivial details problem of accommodation process.
Description
Technical Field
The present application relates to the field of seat devices, and in particular, to a method and apparatus for adjusting a seat device, a device, and a computer-readable storage medium.
Background
Although the conventional seat devices such as seats, vehicle seats, sofas and the like realize electric adjustment, the electric adjustment also requires a user to manually control the angle, lifting and the like of the seat device, but the manual control process of the user is relatively complicated.
Disclosure of Invention
An object of the embodiments of the present application is to provide a method and an apparatus for adjusting a seat device, a device, and a computer-readable storage medium, so as to solve the problem that an adjustment process is complicated due to the need of manually adjusting the seat device in the prior art. The specific technical scheme is as follows:
in a first aspect, there is provided a method of adjusting a seat apparatus, comprising: acquiring an electric signal converted from a vibration signal, wherein the vibration signal is generated by a first object on a seat device; acquiring vital sign parameters of the first subject based on the electrical signals; inputting the vital sign parameters into a target neural network model, and outputting results, wherein the results are used for representing parameters to be adjusted of the seat device; adjusting an initial parameter of the seating arrangement based on the parameter to be adjusted.
In a second aspect, there is provided an adjustment device for a seat device, comprising: the first acquisition module is used for acquiring an electric signal converted from a vibration signal, wherein the vibration signal is a vibration signal generated by a first object on the seat device; a second acquisition module for acquiring vital sign parameters of the first subject based on the electrical signals; the processing module is used for inputting the vital sign parameters into a target neural network model and outputting results, wherein the results are used for representing parameters to be adjusted of the seat device; an adjustment module for adjusting an initial parameter of the seat apparatus based on the parameter to be adjusted.
In a third aspect, there is also provided a computer-readable storage medium having stored therein instructions, which, when run on a computer, cause the computer to perform the method of the first aspect.
In a fourth aspect, there is also provided a computer program product comprising instructions which, when run on a computer, cause the computer to perform the method of the first aspect described above.
Through this application, can convert the vibration signal that first object produced at seat device into the signal of telecommunication, and then acquire the vital sign parameter of first object, regard the input of vital sign parameter as target neural network model, and then can obtain the parameter that seat device waited to adjust, adjust seat device's initial parameter based on this parameter of waiting to adjust, thereby can realize the intelligent regulation to seat device, need not the user and carry out manual regulation to seat device, the regulation efficiency has been promoted, thereby it needs manually to adjust seat device among the prior art to have solved, lead to the more loaded down with trivial details problem of accommodation process.
Drawings
Fig. 1 is a flowchart of an adjustment method of a seat apparatus according to an embodiment of the present application;
FIG. 2 is a schematic diagram of an intelligent adjusting seat device of a BP neural network according to an embodiment of the present application;
FIG. 3 is a schematic view of an adjustment mechanism of the seat assembly according to an embodiment of the present application;
fig. 4 is a schematic structural diagram of an apparatus according to an embodiment of the present application.
Detailed Description
The technical solutions in the embodiments of the present application will be described below with reference to the drawings in the embodiments of the present application.
The embodiment of the application provides an adjusting method of a seat device, as shown in fig. 1, the method includes the following steps:
102, acquiring an electric signal obtained by converting a vibration signal, wherein the vibration signal is a vibration signal generated by a first object on a seat device;
in an alternative implementation of the embodiment of the present application, the process of converting the vibration signal into the electrical signal may be implemented by a sensor, and specifically, the sensor may be a piezoelectric film sensor.
Furthermore, the seating device may be a sofa, a vehicle seat, or other device on which a user may sit. If the sensor is a piezoelectric film sensor, the piezoelectric film sensor can be arranged at the position of the seat device for bearing a user, and then a vibration signal generated by the user through sitting posture can be accurately acquired.
in an alternative implementation of the embodiment of the present application, the vital sign parameter may be a respiration, a heart rate, a blood pressure, a pulse, etc. of the first subject. The specific type or types of the vital sign parameters can be set according to needs in a specific application scenario, and are not limited in this application.
in an optional implementation manner of the embodiment of the present application, the target neural network model is trained based on sample data, where the sample data includes vital sign parameters of a plurality of different second objects on the seat device and parameters of the corresponding seat device. That is to say, the target neural network model can obtain the parameters to be adjusted of the seat device according to the input of the vital body signs.
Wherein the parameters may include parameters of the angle of inclination, height, corresponding sitting posture, etc. of the seating arrangement. Different parameters for different seat arrangements, for example, parameters for a car seat may include: the height of the seat, the angle of inclination of the back, the distance of the seat from the steering wheel, etc.
And step 108, adjusting initial parameters of the seat device based on the parameters to be adjusted.
In an alternative embodiment of the present application, if a car seat is taken as an example, the initial parameters may include an initial position of the seat, such as a seat height, a distance of the seat from a steering wheel, a back tilt angle, and the like.
Through the above steps 102 to 108, the vibration signal generated by the first object in the seat device can be converted into the electric signal, so that the vital sign parameter of the first object is obtained, the vital sign parameter is used as the input of the target neural network model, and further the parameter to be adjusted of the seat device can be obtained, the initial parameter of the seat device is adjusted based on the parameter to be adjusted, so that the intelligent adjustment of the seat device can be realized, the manual adjustment of the seat device by a user is not needed, the adjustment efficiency is improved, and the problem that the adjustment process is complicated due to the fact that the seat device needs to be manually adjusted in the prior art is solved.
In an optional implementation manner of the embodiment of the present application, for the vital sign parameters involved in step 106 before inputting the vital sign parameters into the target neural network model and outputting the result, the method steps of the present application may further include:
step 100, training an initial neural network model through sample data to obtain a target neural network model; wherein the sample data comprises vital sign parameters of a plurality of different second objects on the seating apparatus, and parameters of the corresponding seating apparatus.
Wherein, the vital sign parameters can include sitting posture, respiration, heart rate, etc. The parameters of the seating unit may include an angle of the seating unit, a distance from a target device, a height of the seating unit, and the like.
It should be noted that the neural network model in the embodiment of the present application may be an Error Back Propagation (BP) neural network model, where the initial neural network model and the target neural network model both refer to the same neural network model, and the difference between the two is that parameters in the neural network model are different.
Taking the BP neural network model as an example, the network structure of the BP neural network model includes: the input layer has n neurons, the hidden layer has p neurons, the output layer has q neurons, the input vector is x, the hidden layer input vector and the output vector are h respectivelyi、hoThe input vector and the output vector of the output layer are respectively yiAnd yoThe expected output is do。
(1) Initializing a neural network: setting each link weight value w (input layer and hidden layer weight values w)ihHidden layer and output layer weight value who) Each neuron threshold b (input layer and hidden layer threshold b)ihHidden layer and output layer threshold bho) An error function E, an activation function F, a calculation precision value epsilon and a maximum learning time M;
(2) randomly choosing the kth input sample (m samples) and the corresponding expected output value:
x(k)＝(x1(k),x2(k),…,xn(k))
do(k)＝(d1(k),d2(k),…,dn(k))
(3) calculating input and output values of each neuron of the hidden layer:
ho(k)＝F(hi(k))
yo(k)＝F(yi(k))
(4) calculating partial derivatives of the error function to each neuron of the output layer and the hidden layer and correcting each connection weight value by utilizing reverse transfer;
(5) calculating a global error value;
(6) judging whether the network error meets the maximum set times, if not, circularly (3) and (4) continuously correcting the network parameters to achieve the optimal effect; if so, ending the algorithm.
In the embodiment of the present application, the training of the BP neural network model may be completed based on (1) to (6) above. As shown in fig. 2, the parameters to be adjusted of the corresponding seat device can be quickly and intelligently determined according to the vital sign parameters of the user based on the BP neural network model, and the seat device can be adjusted conveniently and quickly without the need of manually trying to adjust the seat device back and forth by the user.
In an optional implementation manner of this embodiment of the present application, regarding the manner of acquiring the vital sign parameter of the first object based on the electrical signal in step 104, the method may further include:
step 11, sampling the electric signal through a preset sampling frequency to obtain a digital signal;
wherein, the specific value of the preset sampling frequency can be correspondingly set according to the requirement or the actual situation,
step 12, low-pass filtering the digital signal;
in this embodiment of the present application, the digital signal may be low-pass filtered by a low-pass filter, and a cut-off frequency of the low-pass filter may be set as needed.
Step 13, converting the digital signal after low-pass filtering into a target wave, wherein the target wave comprises at least one of the following items: rectangular wave, sine wave;
and step 14, determining the vital sign parameters based on the target wave.
For the above manner of determining the vital sign parameters based on the target wave in step 14, the following manner may be further included:
mode 1) determining vital sign parameters based on the wave crests or wave troughs of the sine waves;
mode 2) vital sign parameters are determined based on the rising or falling edges in the rectangular wave.
For the above process of determining the vital sign parameters based on the target wave, the vital sign parameters are taken as breathing, and the target wave is taken as a rectangular wave as an example, wherein the rising edge of the rectangular wave is expiration, and the falling edge of the rectangular wave is inspiration, so that the rising edge and the falling edge of the rectangular wave can be counted to determine the breathing frequency of the first object, and whether the adjustment of the seat device is in a relatively proper state currently is determined through the breathing frequency. A similar approach is also used for the sine wave, and the respiration or heart rate of the first object is determined by counting the peaks or troughs of the sine wave.
That is, according to the present application, after the first subject sits on the seat apparatus, the vibration signal generated by the first subject is converted into an electrical signal, and then the vital sign parameters of the first subject are determined according to the electrical signal and are used as the input of the target neural network model, so that the parameters of the seat apparatus to be adjusted can be determined. In a specific application scenario, a user sits on a car seat, and the car seat can be automatically adjusted to a comfortable state when the user sits on the car seat, so that the user does not need to manually adjust the car seat, and the seat device is intelligently adjusted.
In another optional implementation of the embodiment of the present application, after adjusting the initial parameter of the seat device based on the parameter to be adjusted, the method steps of the embodiment of the present application may further include:
step 110, receiving feedback information of a first object;
and 112, triggering and executing the method steps of adjusting the parameters of the seat device again according to the feedback information.
It can be seen that in the embodiments of the present application, if the first object feels that the current seat arrangement is still not suitable after the current adjustment, the adjustment can be performed again. The feedback information may be triggered by the first object by a button of the seating arrangement, i.e. the feedback information can be triggered by merely touching the button. In other examples, the first object may be controlled by voice, that is, the seat apparatus needs to be adjusted again when the voice is uttered, or the seat apparatus may be signaled at predetermined intervals to determine whether the seat apparatus needs to be adjusted again. If the parameters of the seat device need to be adjusted again, the steps in fig. 1 are executed again.
The present application will be explained below with reference to specific embodiments of examples of the present application. The specific embodiment provides a method for adjusting a smart device based on a piezoelectric thin film sensor, which comprises the following steps:
step 201, obtaining an initial position of a seat device, wherein the initial position information includes: the initial inclination angle of the seat and the sitting posture information;
step 202, converting a vibration signal generated by sitting posture into an electric signal by using a piezoelectric film sensor, sampling the electric signal by using a set sampling frequency to obtain initial data, designing a low-pass filter bank according to requirements, and obtaining vital sign parameters of a user such as respiration and heart rate at the moment by using a mean shaping and statistical method;
step 203, using the vital sign parameters as the input of a BP neural network to realize intelligent recommendation of parameters such as the height and the inclination of the intelligent equipment;
step 204, providing fine adjustment operation of the intelligent equipment by using the built network model and the comfort condition of the user on the intelligent equipment, wherein the fine adjustment operation comprises the inclination angle of the intelligent equipment (namely the comfort of the user on the intelligent equipment), whether the intelligent equipment needs to be lifted or moved back and forth, and the like;
step 205, determining whether the seat device needs to be adjusted again according to the real-time situation of the user.
Through this application, can utilize artificial intelligence degree of depth learning algorithm to realize the angle and the high nature regulation of seat device, avoid artificial complex operation nature, the sense organ inadaptability of bringing because of equipment adjustment is avoided to the indirectness.
Based on the above fig. 1, the present application further provides an adjusting device of a seat device, as shown in fig. 3, the adjusting device includes:
a first obtaining module 32, configured to obtain an electrical signal converted from a vibration signal, where the vibration signal is a vibration signal generated by the first object on the seat apparatus;
in an alternative implementation of the embodiment of the present application, the process of converting the vibration signal into the electrical signal may be implemented by a sensor, and specifically, the sensor may be a piezoelectric film sensor.
Furthermore, the seating device may be a sofa, a vehicle seat, or other device on which a user may sit. If the sensor is a piezoelectric film sensor, the piezoelectric film sensor can be arranged at the position of the seat device for bearing a user, and then a vibration signal generated by the user through sitting posture can be accurately acquired.
A second acquisition module 34 for acquiring vital sign parameters of the first subject based on the electrical signals;
in an alternative implementation of the embodiment of the present application, the vital sign parameter may be a respiration, a heart rate, a blood pressure, a pulse, etc. of the first subject. The specific type or types of the vital sign parameters can be set according to needs in a specific application scenario, and are not limited in this application.
The processing module 36 is configured to input the vital sign parameters into the target neural network model, and output a result, where the result is used to represent parameters to be adjusted of the seat apparatus;
in an optional implementation manner of the embodiment of the present application, the target neural network model is trained based on sample data, where the sample data includes vital sign parameters of a plurality of different second objects on the seat device and parameters of the corresponding seat device. That is to say, the target neural network model can obtain the parameters to be adjusted of the seat device according to the input of the vital body signs.
Wherein the parameters may include parameters of the angle of inclination, height, corresponding sitting posture, etc. of the seating arrangement. Different parameters for different seat arrangements, for example, parameters for a car seat may include: the height of the seat, the angle of inclination of the back, the distance of the seat from the steering wheel, etc.
An adjustment module 38 for adjusting an initial parameter of the seating arrangement based on the parameter to be adjusted.
In an alternative embodiment of the present application, if a car seat is taken as an example, the initial parameters may include an initial position of the seat, such as a seat height, a distance of the seat from a steering wheel, a back tilt angle, and the like.
Based on this, through the device of this application embodiment, can convert the vibration signal that first object produced at the seat device into the signal of telecommunication, and then acquire the vital sign parameter of first object, regard the vital sign parameter as the input of target neural network model, and then can obtain the parameter that the seat device is waited to adjust, adjust the initial parameter to the seat device based on this parameter of waiting to adjust, thereby can realize the intelligent regulation to the seat device, need not the user and carry out manual regulation to the seat device, the regulation efficiency has been promoted, thereby it needs manual to adjust the seat device to have solved among the prior art, lead to the more loaded down with trivial details problem of accommodation process.
Optionally, the second obtaining module in this embodiment of the application further includes: the sampling unit is used for sampling the electric signal through a preset sampling frequency to obtain a digital signal; the filtering unit is used for carrying out low-pass filtering on the digital signal; a conversion unit, configured to convert the low-pass filtered digital signal into a target wave, where the target wave includes at least one of: rectangular wave, sine wave; a determination unit for determining the vital sign parameters based on the target wave.
Optionally, the determining unit in the embodiment of the present application includes at least one of: the first determining subunit is used for determining the vital sign parameters based on the wave crests or the wave troughs of the sine waves; a second determining subunit, configured to determine the vital sign parameter based on a rising edge or a falling edge in the rectangular wave.
For the above process of determining the vital sign parameters based on the target wave, the vital sign parameters are taken as breathing, and the target wave is taken as a rectangular wave as an example, wherein the rising edge of the rectangular wave is expiration, and the falling edge of the rectangular wave is inspiration, so that the rising edge and the falling edge of the rectangular wave can be counted to determine the breathing frequency of the first object, and whether the adjustment of the seat device is in a relatively proper state currently is determined through the breathing frequency. A similar approach is also used for the sine wave, and the respiration or heart rate of the first object is determined by counting the peaks or troughs of the sine wave.
That is, according to the present application, after the first subject sits on the seat apparatus, the vibration signal generated by the first subject is converted into an electrical signal, and then the vital sign parameters of the first subject are determined according to the electrical signal and are used as the input of the target neural network model, so that the parameters of the seat apparatus to be adjusted can be determined. In a specific application scenario, a user sits on a car seat, and the car seat can be automatically adjusted to a comfortable state when the user sits on the car seat, so that the user does not need to manually adjust the car seat, and the seat device is intelligently adjusted.
Optionally, the apparatus in this embodiment of the present application further includes: the training module is used for training the initial neural network model through sample data before the vital sign parameters are input into the target neural network model and the result is output, so that the target neural network model is obtained; wherein the sample data comprises vital sign parameters of a plurality of different second objects on the seating apparatus, and parameters of the corresponding seating apparatus.
Optionally, the apparatus in this embodiment of the present application may further include: a receiving module for receiving feedback information of the first object after adjusting an initial parameter of the seat apparatus based on a parameter to be adjusted; and the triggering module is used for triggering and executing the process of adjusting the parameters of the seat device again according to the feedback information.
It can be seen that in the embodiments of the present application, if the first object feels that the current seat arrangement is still not suitable after the current adjustment, the adjustment can be performed again. The feedback information may be triggered by the first object by a button of the seating arrangement, i.e. the feedback information can be triggered by merely touching the button. In other examples, the first object may be controlled by voice, that is, the seat apparatus needs to be adjusted again when the voice is uttered, or the seat apparatus may be signaled at predetermined intervals to determine whether the seat apparatus needs to be adjusted again. If the parameters of the seat device need to be adjusted again, the modules in fig. 3 are triggered again to execute the corresponding steps.
The embodiment of the present application further provides an apparatus, as shown in fig. 4, including a processor 401, a communication interface 402, a memory 403, and a communication bus 404, where the processor 401, the communication interface 402, and the memory 403 complete mutual communication through the communication bus 404,
a memory 403 for storing a computer program;
the processor 401, when configured to execute the program stored in the memory 403, implements the method steps in fig. 1, and the functions of the method steps are similar to those of the method steps in fig. 1, and are not described again here.
The communication bus mentioned in the above terminal may be a Peripheral Component Interconnect (PCI) bus, an Extended Industry Standard Architecture (EISA) bus, or the like. The communication bus may be divided into an address bus, a data bus, a control bus, etc. For ease of illustration, only one thick line is shown in FIG. 4, but this does not indicate only one bus or one type of bus.
The communication interface is used for communication between the terminal and other equipment.
The Memory may include a Random Access Memory (RAM) or a non-volatile Memory (non-volatile Memory), such as at least one disk Memory. Optionally, the memory may also be at least one memory device located remotely from the processor.
The Processor may be a general-purpose Processor, and includes a Central Processing Unit (CPU), a Network Processor (NP), and the like; the Integrated Circuit may also be a Digital Signal Processor (DSP), an Application Specific Integrated Circuit (ASIC), a Field Programmable Gate Array (FPGA) or other Programmable logic device, a discrete Gate or transistor logic device, or a discrete hardware component.
In yet another embodiment provided by the present application, there is also provided a computer-readable storage medium having stored therein instructions, which when run on a computer, cause the computer to perform the method of adjusting a seat apparatus as described in any of the above embodiments.
In a further embodiment provided by the present application, there is also provided a computer program product containing instructions which, when run on a computer, cause the computer to perform the method of adjustment of a seating arrangement as described in any of the above embodiments.
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 loaded and executed on a computer, cause the processes or functions described in accordance with the embodiments of the application to occur, 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 in a computer readable storage medium or transmitted from one computer readable storage medium to another, for example, from one website site, computer, server, or data center to another website site, computer, server, or data center via wired (e.g., coaxial cable, fiber optic, Digital Subscriber Line (DSL)) or wireless (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., DVD), or a semiconductor medium (e.g., Solid State Disk (SSD)), among others.
It is noted that, herein, relational terms such as first and second, and the like may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
All the embodiments in the present specification are described in a related manner, and the same and similar parts among the embodiments may be referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for the system embodiment, since it is substantially similar to the method embodiment, the description is simple, and for the relevant points, reference may be made to the partial description of the method embodiment.
The above description is only for the preferred embodiment of the present application, and is not intended to limit the scope of the present application. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present application are included in the protection scope of the present application.
Claims (10)
1. A method of adjusting a seat arrangement, comprising:
acquiring an electric signal converted from a vibration signal, wherein the vibration signal is generated by a first object on a seat device;
acquiring vital sign parameters of the first subject based on the electrical signals;
inputting the vital sign parameters into a target neural network model, and outputting results, wherein the results are used for representing parameters to be adjusted of the seat device;
adjusting an initial parameter of the seating arrangement based on the parameter to be adjusted.
2. The method according to claim 1, wherein said obtaining vital sign parameters of the first subject based on the electrical signal comprises:
sampling the electric signal through a preset sampling frequency to obtain a digital signal;
low-pass filtering the digital signal;
converting the low-pass filtered digital signal into a target wave, wherein the target wave comprises at least one of: rectangular wave, sine wave;
determining the vital sign parameters based on the target wave.
3. The method according to claim 2, wherein the determining the vital sign parameters based on the target wave comprises at least one of:
determining the vital sign parameters based on peaks or troughs of the sine wave;
determining the vital sign parameter based on a rising edge or a falling edge in the rectangular wave.
4. The method of claim 1, wherein prior to inputting the vital sign parameters into a target neural network model and outputting the results, the method further comprises:
training an initial neural network model through sample data to obtain a target neural network model; wherein the sample data comprises vital sign parameters of a plurality of different second subjects on the seating apparatus, and corresponding parameters of the seating apparatus.
5. The method of claim 1, wherein after adjusting the initial parameter of the seating arrangement based on the parameter to be adjusted, the method further comprises:
receiving feedback information of the first object;
triggering the execution of the method step of readjusting the seat arrangement parameters according to the feedback information.
6. An adjustment device for a seating unit, comprising:
the first acquisition module is used for acquiring an electric signal converted from a vibration signal, wherein the vibration signal is a vibration signal generated by a first object on the seat device;
a second acquisition module for acquiring vital sign parameters of the first subject based on the electrical signals;
the processing module is used for inputting the vital sign parameters into a target neural network model and outputting results, wherein the results are used for representing parameters to be adjusted of the seat device;
an adjustment module for adjusting an initial parameter of the seat apparatus based on the parameter to be adjusted.
7. The apparatus of claim 6, wherein the second obtaining module comprises:
the sampling unit is used for sampling the electric signal through a preset sampling frequency to obtain a digital signal;
a filtering unit for low-pass filtering the digital signal;
a conversion unit, configured to convert the low-pass filtered digital signal into a target wave, where the target wave includes at least one of: rectangular wave, sine wave;
a determination unit for determining the vital sign parameters based on the target wave.
8. The apparatus of claim 7, wherein the determining unit comprises at least one of:
a first determining subunit, configured to determine the vital sign parameter based on a peak or a trough of the sine wave;
a second determining subunit, configured to determine the vital sign parameter based on a rising edge or a falling edge in the rectangular wave.
9. The device is characterized by comprising a processor, a communication interface, a memory and a communication bus, wherein the processor and the communication interface are used for realizing mutual communication by the memory through the communication bus;
a memory for storing a computer program;
a processor for implementing the method steps of any one of claims 1 to 5 when executing a program stored in the memory.
10. A computer-readable storage medium, on which a computer program is stored which, when being executed by a processor, carries out the method steps of any one of claims 1 to 5.
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| CN202110857360.3A CN113616039A (en) | 2021-07-28 | 2021-07-28 | Method and device for adjusting seat device, equipment and computer readable storage medium |
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| CN202110857360.3A CN113616039A (en) | 2021-07-28 | 2021-07-28 | Method and device for adjusting seat device, equipment and computer readable storage medium |
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