CN109820373A - Adaptive adjustment method of sitting posture based on intelligent seat - Google Patents
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Abstract
本发明涉及一种基于智能座椅的坐姿自适应调整方法，属于智能健康设备领域，包括以下步骤：S1：利用布置在椅面的阵列式压力传感器采集人‑椅接触面之间的压力分布数据；S2：将压力分布数据作为不同坐姿的样本数据，再将样本数据处理后输入到卷积神经网络中训练构建坐姿识别模型；S3：将实时采集到的压力分布数据输入到坐姿识别模型中迭代运算，得出当前使用者对应的坐姿；S4：利用坐姿识别模型判断出的当前坐姿生成控制信息，控制坐姿调整模块调整坐姿，从而达到坐姿的自适应调整。本发明利用阵列压力传感器与卷积神经网络来对坐姿进行识别，使得坐姿的自适应调整在实时性、准确率和鲁棒性上有了表现良好。
The present invention relates to a method for self-adaptive adjustment of sitting posture based on an intelligent seat, belonging to the field of intelligent health equipment, comprising the following steps: S1: collecting pressure distribution data between a person-chair contact surface by using an array pressure sensor arranged on a seat surface ; S2: Take the pressure distribution data as the sample data of different sitting postures, and then process the sample data and input it into the convolutional neural network to train and construct the sitting posture recognition model; S3: Input the pressure distribution data collected in real time into the sitting posture recognition model to iterate Operation is performed to obtain the sitting posture corresponding to the current user; S4: use the current sitting posture determined by the sitting posture recognition model to generate control information, and control the sitting posture adjustment module to adjust the sitting posture, so as to achieve adaptive adjustment of the sitting posture. The invention uses the array pressure sensor and the convolutional neural network to identify the sitting posture, so that the self-adaptive adjustment of the sitting posture has good performance in real-time, accuracy and robustness.
Description
Technical field
The invention belongs to intelligent health equipment technical field, it is related to a kind of sitting posture based on the Intelligent seat adaptively side of adjustment
Method.
Background technique
With the development of society, the ratio that the time sat accounts in people's daily life and working environment is increasing.So
And posture of the most people in the most of the time of seat is all to belong to undesirable sitting posture.Prolonged incorrect sitting-pose is a system
One of the Etiological of column skeletal muscle disease.The previously used sitting posture based on machine vision differentiates for illumination and shooting angle
The dependence of degree is too strong, and other methods accuracy is not high, and can only all remind user, does not have adaptive adjustment, in reality
Effect is limited in the application environment on border.
Summary of the invention
In view of this, the purpose of the present invention is to provide a kind of sitting posture self-adapting regulation method based on Intelligent seat, benefit
The pressure distribution data between people-chair contact surface is obtained with array pressure sensor, passes through the analysis and fortune to pressure data
It calculates, accurately to the current sitting posture of human body, adjusting accordingly for sitting posture adjustment module makes user restore positive seat state.
In order to achieve the above objectives, the invention provides the following technical scheme:
Sitting posture self-adapting regulation method based on Intelligent seat, comprising the following steps:
S1: the pressure distribution data between people-chair contact surface is acquired using the array pressure sensor for being arranged in seat；
S2: using pressure distribution data as the sample data of different sitting postures, then the progress of collected sample data is corresponding
Processing after be input in convolutional neural networks be trained building sitting posture identification model；
S3: by the pressure distribution data between real-time collected people-chair contact surface be input in sitting posture identification model into
Row iteration operation, to obtain the corresponding sitting posture of currently used person；
S4: control information is generated using the current sitting posture that sitting posture identification model is judged, it is gentle by air pump accordingly to control
The sitting posture adjustment module of capsule composition adjusts sitting posture, to reach the adaptive adjustment of sitting posture.
Further, in step S1 the following steps are included:
S11: array pressure sensor is connect with data acquisition controller, data acquisition controller is connected to calculating
Machine；
S12: opening the power supply of computer and data acquisition controller, and parameter is arranged；
S13: computer sends acquisition instructions to data acquisition controller；
S14: data acquisition controller control pressure sensor acquisition data simultaneously carry out filtering and noise reduction, and according to communication protocol
Send data to computer；
S15: the host computer procedure of computer receives data, verification data and stores.
Further, pass through bluetooth or USB connection between the data acquisition controller and the computer.
Further, in step S2 the following steps are included:
S21: identifier is added to collected pressure distribution data, marks affiliated sitting posture classification；
S22: pressure distribution data is divided into training dataset and test data set, and by training dataset and tests number
It is converted into vector according to collection, is normalized；
S23: convolutional neural networks sitting posture identification model and initiation parameter are established；
S24: training dataset being input in the neural network model of foundation and is iterated training, improved model parameter；
S25: the identification of trained sitting posture is tested and is optimized using test data set；
S26: repeatedly the parameter (such as number of plies, neuronal quantity etc.) of adjustment neural network repeats step S22-S25, obtains
New discrimination model out.
Further, sitting posture classification described in step S21 includes positive seat, "Left"-deviationist, Right deviation, leans forward, hypsokinesis, leans on afterwards, left lift
Leg and right lift leg.
Further, in step S3 the following steps are included:
S31: the forward-propagating process of established convolutional neural networks model is embedded into host computer procedure；
S32: program installation kit is placed on using trained parameter as configuration file；
S33: when the program is run, program is first allowed to read trained parameter, then data acquisition controller is transmitted
Real-time sitting posture pressure distribution data be sent into convolutional neural networks and trained parameter and carry out operation together, judge currently to sit
Appearance classification.
Further, in step S4 the following steps are included:
S41: the sitting posture classification that step S33 is obtained is packaged into suitable format and is then transferred to data acquisition controller；
S42: the sitting posture classification information and generation that data acquisition controller receives and analytical Calculation machine transmits are corresponding
Control information；
S43: the control information that data acquisition controller installation generates is sent to I/O operation is carried out to sitting posture adjustment module
Control command, control air bag inflation, which is deflated, adjusts sitting posture；
S44: system constantly repeats step S41-S43, user is made to be maintained at correct sitting posture according to the frequency of 5 times/second.
Further, the sitting posture adjustment module is controlled by data acquisition controller, adjusts sitting posture by air bag and air pump.
The beneficial effects of the present invention are: in data acquisition, the present invention carries out sitting posture with by the method for machine vision
It identifies and the product reminded is compared, get rid of the influence of illumination and shooting angle to system, and remind guidance to use with other
Family changes the product of sitting posture compared to having the function of adaptively adjusting, not against user's consciousness, more practicability and effectiveness；In algorithm
In processing, the present invention identifies sitting posture with convolutional neural networks using array pressure sensor, so that sitting posture is adaptive
Should adjust has good improvement in real-time, accuracy rate and robustness.
Other advantages, target and feature of the invention will be illustrated in the following description to a certain extent, and
And to a certain extent, based on will be apparent to those skilled in the art to investigating hereafter, Huo Zheke
To be instructed from the practice of the present invention.Target of the invention and other advantages can be realized by following specification and
It obtains.
Detailed description of the invention
To make the objectives, technical solutions, and advantages of the present invention clearer, the present invention is made below in conjunction with attached drawing excellent
The detailed description of choosing, in which:
Fig. 1 is the structural schematic diagram of the sitting posture self-adapted adjustment system of the present invention based on Intelligent seat；
Fig. 2 is eight kinds of sitting posture type schematic diagrames described in the embodiment of the present invention；
Fig. 3 is the pressure-plotting of eight kinds of sitting posture types described in the embodiment of the present invention；
Fig. 4 is the air bag relative position figure that sitting posture described in the embodiment of the present invention adjusts module；
Fig. 5 is upper computer and lower computer workflow schematic diagram described in the embodiment of the present invention.
Appended drawing reference: array pressure sensor 101, data acquisition controller 102, computer 103, sitting posture adjust module
104, air bag 105, air bag 401, air bag 402, air bag 403, air bag 404, air bag 405, air bag 406, air bag 407, air bag 408,
Air pump 409.
Specific embodiment
Illustrate embodiments of the present invention below by way of specific specific example, those skilled in the art can be by this specification
Other advantages and efficacy of the present invention can be easily understood for disclosed content.The present invention can also pass through in addition different specific realities
The mode of applying is embodied or practiced, the various details in this specification can also based on different viewpoints and application, without departing from
Various modifications or alterations are carried out under spirit of the invention.It should be noted that diagram provided in following embodiment is only to show
Meaning mode illustrates basic conception of the invention, and in the absence of conflict, the feature in following embodiment and embodiment can phase
Mutually combination.
Wherein, the drawings are for illustrative purposes only and are merely schematic diagrams, rather than pictorial diagram, should not be understood as to this
The limitation of invention；Embodiment in order to better illustrate the present invention, the certain components of attached drawing have omission, zoom in or out, not
Represent the size of actual product；It will be understood by those skilled in the art that certain known features and its explanation may be omitted and be in attached drawing
It is understood that.
The same or similar label correspond to the same or similar components in the attached drawing of the embodiment of the present invention；It is retouched in of the invention
In stating, it is to be understood that if there is the orientation or positional relationship of the instructions such as term " on ", "lower", "left", "right", "front", "rear"
To be based on the orientation or positional relationship shown in the drawings, be merely for convenience of description of the present invention and simplification of the description, rather than indicate or
It implies that signified device or element must have a particular orientation, be constructed and operated in a specific orientation, therefore is described in attached drawing
The term of positional relationship only for illustration, is not considered as limiting the invention, for the ordinary skill of this field
For personnel, the concrete meaning of above-mentioned term can be understood as the case may be.
The present invention provides a kind of sitting posture self-adapting regulation method based on Intelligent seat, based on it is as shown in Figure 1 based on
The sitting posture self-adapted adjustment system of Intelligent seat acquires people-chair contact surface pressure by array-type flexible pressure sensor
Distributed data utilizes these using a large amount of sitting posture pressure distribution datas of acquisition as the sample data of building sitting posture recognizer
Sample data trains multiple using convolutional neural networks as the sitting posture identification model of basic boom.Then it was being trained using some
There is no used data to test and screen these models in journey, selects the model to behave oneself best as final mask simultaneously
And it is embedded into host computer procedure.Finally real-time collected sitting posture distributed data is input in fault diagnosis model and is sat
Appearance identification allows sitting posture adjustment module to be adjusted and reaches correct sitting posture according to the result identified.
Wherein, the specification of array-type flexible pressure sensor can be customized according to the needs of actual conditions.
Trained and sitting posture identification model includes classification to sitting posture data and to convolutional neural networks sitting posture identification model
Training.
When constructing and training fault diagnosis model, sitting posture pressure distribution data is acquired first, and to correspondence
Data mark sitting posture classification belonging to this plus identifier.The richness of sample data and the framework of model itself determine
The effect of identification model itself, therefore availability and expression effect in order to guarantee model, the data that we acquire will have as follows
It is required that:
The case where data volume is abundant, and data volume is abundanter, and model can be indicated and be used is more, and the expressive force of model is got over
By force, the accuracy rate of model is higher；
Data category wants multiplicity, since the method that I designs is used to a special group, so receiving
Each situation requires to consider (such as different sexes, different weight range etc.) when collecting sample data, and the diversity of data can have
Effect improves the stability of identification model expression effect.
When collecting data, need to search out multiple genders differences, weight allows it to be made in cloth in the experimenter of different range
It sets on the chair of array pressure sensor, with different people, every frame is collected and marked to situations such as different sitting postures, different position
Sitting posture pressure distribution data, using the mass data being collected into as sample data set.
And sample data set is divided into training dataset and test data set according to a certain percentage.
Due to some advantageous properties of convolutional neural networks, such as to displacement, scaling and other forms distortion invariance
The robustness of the identification of X-Y scheme, robustness the problems such as to the drift of position, this robustness is for sitting posture discrimination
It is critically important, so choosing CNN algorithm herein as basic boom constructs sitting posture recognizer.
Due to the distribution situation for being mainly characterized by interface pressure that sitting posture identification model considers, so in order to improve algorithm
Expression effect and training efficiency we need be normalized.Training dataset is input to established convolution mind
Through network sitting posture identification model, model is trained using the method for backpropagation.The different model of the multiple structures of training, so
Multiple models are tested using some data not used in the training process afterwards, choose the model conduct to behave oneself best
Sitting posture identification model used in system.In view of system needs in real time currently adaptively adjust user, use
Network structure cannot be excessively complicated, directly to carry out tradeoff and selection in time efficiency and model performance.
Obtained sitting posture model is applied in real-time sitting posture self-adapted adjustment system, realizes that the intelligence of human body sitting posture is adaptive
It should adjust.
Data interaction block diagram is as shown in figure 5, in the present embodiment, host computer, that is, computer 103, slave computer, that is, data acquire
Controller 102, in practical applications, according to the size custom arrays formula pressure sensor 101 of actual application environment, array
Formula pressure sensor 101 perceives the pressure distribution between people-chair contact surface, and array pressure sensor 101 and data acquisition are controlled
Device 102 processed connects, and 102 program of data acquisition controller passes through the host computer journey on Bluetooth communication or usb communication and computer 103
Sequence communication, computer 103 send instruction controlled data acquisition controller 102 and acquire pressure distribution data, data acquisition controller
102 acquire corresponding pressure distribution data by rank scanning, and are filtered denoising to collected data, then by data
It is transferred to the host computer on computer 103 according to protocol format, host computer procedure pre-processes data after receiving data,
Obtained data are input to trained sitting posture recognizer model, carry out identification judgement to current sitting posture.
Sitting posture is divided into classification as shown in Fig. 2, the seat 201 that is positive respectively, it is positive sit bend over 202, lean forward 203, afterwards by 204, it is left-leaning
205, Right deviation 206, left lift leg 207, right lift leg 208.
The visualization result of sitting posture distributed data is as shown in figure 3, the seat visualization 301 that is positive respectively, positive sit that bending over just to sit can
Depending on change 302, lean forward it is positive sit visualization 303, it is rear by it is positive sit visualization 304, it is left-leaning it is positive sit visualization 305, Right deviation is just sitting visualization
306, left lift leg is just sitting visualization 307, right lift leg is just sitting visualization 308.
Sitting posture identification specifically comprises the following steps:
1, sitting posture pressure distribution data is acquired to each tester and marked, form sample data set；
2, data set is divided into training dataset and test data set according to a certain percentage；
3, data set is converted into vector, and be normalized；
4 and the difference of network number of plies network structure construct multiple and different convolutional neural networks；
5, training dataset is inputted into these convolutional neural networks, the multiple sitting posture identification models of training；
6, data are acquired on new testee constitutes validation data set；
7, the model trained is tested using validation data set, selects the final mould of the best model of effect
Type；
8, final mask is embedded into 103 application program of computer, while corresponding trained model parameter is set；
9, data acquisition controller 102 acquires real-time sitting posture pressure distribution data and is transmitted to computer 103；
10, the data that computer transmits data acquisition controller 102 are sent into model and carry out operation, and result is fed back to number
According to acquisition controller 102.
Correcting sitting posture process includes the following steps:
A. the operation result that computer 103 is fed back is received, obtains current sitting posture classification；
B. the control information of sitting posture adjustment module 104 is generated according to current sitting posture classification；
C. the corresponding I/O operation control inflation of air bag 105 is executed according to control information to deflate, adjust current sitting posture；
D. continue to receive computer 103 as a result, repeating the above steps.
We combine Fig. 4 to illustrate specific sitting posture Adjusted Option, are done control as follows by air pump 409:
Sitting posture is just sat: all air bags are deflated；
Positive sit is bent over: air bag 402 is inflated, remaining deflation；
Sitting posture leans forward: air bag 407, air bag 408 are inflated；
Lean on after sitting posture: air bag 401, air bag 402, air bag 403, air bag 404 are inflated；
Sitting posture is left-leaning: air bag 404, air bag 406, air bag 408 are inflated；
Sitting posture Right deviation: air bag 403, air bag 405, air bag 407 are inflated；
Left lift leg: air bag 402, air bag 403, air bag 404, air bag 405 are inflated；
Right lift leg: air bag 402, air bag 403, air bag 404, air bag 406 are inflated.
By using above-mentioned technical proposal, accurate sitting posture identification and adaptive adjustment is may be implemented in the present invention.
Compared with the method based on machine vision, the present invention gets rid of the dependence to illumination and shooting angle, and algorithm
Complexity and operand reduce many, closer to actual application environment；With traditional method phase based on mechanics sensor
Than improving the accuracy rate of sitting posture identification and the Shandong of model by the dimension of enhancing input information and the complexity of Processing Algorithm
Stick.Compared with by reminding user to change method of the posture to adjust sitting posture, the present invention is utilized by the result of identification model
The air bag inflation arranged, which is deflated, reaches the function that sitting posture adaptively adjusts, and drops so that solution relies on user's consciousness
It is low.
Finally, it is stated that the above examples are only used to illustrate the technical scheme of the present invention and are not limiting, although referring to compared with
Good embodiment describes the invention in detail, those skilled in the art should understand that, it can be to skill of the invention
Art scheme is modified or replaced equivalently, and without departing from the objective and range of the technical program, should all be covered in the present invention
Scope of the claims in.
Claims (8)
1. a kind of sitting posture self-adapting regulation method based on Intelligent seat, it is characterised in that: the following steps are included:
S1: the pressure distribution data between people-chair contact surface is acquired using the array pressure sensor for being arranged in seat；
S2: using pressure distribution data as the sample data of different sitting postures, then collected sample data located accordingly
It is input to after reason in convolutional neural networks and is trained building sitting posture identification model；
S3: the pressure distribution data between real-time collected people-chair contact surface is input in sitting posture identification model and is changed
For operation, to obtain the corresponding sitting posture of currently used person；
S4: control information is generated using the current sitting posture that sitting posture identification model is judged, is controlled accordingly by air pump and air bag group
At sitting posture adjustment module adjust sitting posture, to reach the adaptive adjustment of sitting posture.
2. the sitting posture self-adapting regulation method according to claim 1 based on Intelligent seat, it is characterised in that: in step S1
The following steps are included:
S11: array pressure sensor is connect with data acquisition controller, data acquisition controller is connected to computer；
S12: opening the power supply of computer and data acquisition controller, and parameter is arranged；
S13: computer sends acquisition instructions to data acquisition controller；
S14: data acquisition controller control pressure sensor acquisition data simultaneously carry out filtering and noise reduction, and will count according to communication protocol
According to being sent to computer；
S15: the host computer procedure of computer receives data, verification data and stores.
3. the sitting posture self-adapting regulation method according to claim 2 based on Intelligent seat, it is characterised in that: the data
Pass through bluetooth or USB connection between acquisition controller and the computer.
4. the sitting posture self-adapting regulation method according to claim 1 based on Intelligent seat, it is characterised in that: in step S2
The following steps are included:
S21: identifier is added to collected pressure distribution data, marks affiliated sitting posture classification；
S22: being divided into training dataset and test data set for pressure distribution data, and by training dataset and test data set
It is converted into vector, is normalized；
S23: convolutional neural networks sitting posture identification model and initiation parameter are established；
S24: training dataset being input in the neural network model of foundation and is iterated training, improved model parameter；
S25: the identification of trained sitting posture is tested and is optimized using test data set；
S26: repeatedly the parameter (such as number of plies, neuronal quantity etc.) of adjustment neural network repeats step S22-S25, obtains new
Discrimination model.
5. the sitting posture self-adapting regulation method according to claim 4 based on Intelligent seat, it is characterised in that: step S21
Described in sitting posture classification include positive seat, "Left"-deviationist, Right deviation, lean forward, hypsokinesis, lean on afterwards, left lift leg and right lift leg.
6. the sitting posture self-adapting regulation method according to claim 1 based on Intelligent seat, it is characterised in that: in step S3
The following steps are included:
S31: the forward-propagating process of established convolutional neural networks model is embedded into host computer procedure；
S32: program installation kit is placed on using trained parameter as configuration file；
S33: when the program is run, program is first allowed to read trained parameter, then the reality that data acquisition controller is transmitted
When sitting posture pressure distribution data be sent into convolutional neural networks and trained parameter and carry out operation together, judge current sitting posture class
Not.
7. the sitting posture self-adapting regulation method according to claim 1 based on Intelligent seat, it is characterised in that: in step S4
The following steps are included:
S41: the sitting posture classification that step S33 is obtained is packaged into suitable format and is then transferred to data acquisition controller；
S42: the corresponding control of sitting posture classification information and generation that data acquisition controller receives and analytical Calculation machine transmits
Information；
S43: the control information that data acquisition controller installation generates sends control to I/O operation is carried out, to sitting posture adjustment module
Order, control air bag inflation, which is deflated, adjusts sitting posture；
S44: system constantly repeats step S41-S43, user is made to be maintained at correct sitting posture according to the frequency of 5 times/second.
8. the sitting posture self-adapting regulation method according to claim 1 based on Intelligent seat, it is characterised in that: the sitting posture
Adjustment module is controlled by data acquisition controller, adjusts sitting posture by air bag and air pump.
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Cited By (17)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN110897425A (en) * | 2019-11-18 | 2020-03-24 | 中国地质大学（武汉） | Child sitting posture detection cushion, device interacting with cushion and sitting posture monitoring method |
| CN111093485A (en) * | 2019-12-02 | 2020-05-01 | 焦旭 | Detection control method and device based on sensor array |
| CN111104905A (en) * | 2019-12-19 | 2020-05-05 | 北京奇艺世纪科技有限公司 | Sitting posture adjusting method and device, electronic equipment and storage medium |
| CN112190258A (en) * | 2020-09-30 | 2021-01-08 | 珠海格力电器股份有限公司 | Seat angle adjusting method and device, storage medium and electronic equipment |
| CN112773356A (en) * | 2020-11-16 | 2021-05-11 | 深圳数联天下智能科技有限公司 | Sitting posture detection method, system, terminal and storage medium |
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| CN113627236A (en) * | 2021-06-24 | 2021-11-09 | 广东技术师范大学 | Sitting posture identification method, device, equipment and storage medium |
| CN113647936A (en) * | 2021-08-03 | 2021-11-16 | 南京伟思医疗科技股份有限公司 | Sitting posture detection method and detection system for pelvic floor magnetic stimulation treatment equipment |
| CN114572068A (en) * | 2022-01-28 | 2022-06-03 | 中国第一汽车股份有限公司 | Electric seat adjusting method and device based on convolutional neural network and vehicle |
| CN114650928A (en) * | 2019-10-01 | 2022-06-21 | 福维亚泰克有限责任公司 | Smart Vehicle Seat |
| CN115082961A (en) * | 2022-06-24 | 2022-09-20 | 永艺家具股份有限公司 | A Sitting Posture Recognition Method Based on Input Data |
| CN115399597A (en) * | 2022-09-09 | 2022-11-29 | 永艺家具股份有限公司 | An adaptive sitting posture recognition method for smart seats |
| CN115568712A (en) * | 2022-10-11 | 2023-01-06 | 南京林业大学 | Method and device for monitoring correct sitting posture |
| AT525830A1 (en) * | 2022-02-14 | 2023-08-15 | Sarah Riedmann | Method for recognizing a living being, in particular a person |
| CN117838107A (en) * | 2024-03-07 | 2024-04-09 | 亿慧云智能科技（深圳）股份有限公司 | Healthy sitting posture monitoring method, device and equipment of intelligent cushion and storage medium |
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| CN118975794A (en) * | 2024-10-22 | 2024-11-19 | 深圳西昊智能家居股份有限公司 | A method and system for detecting office chair sitting posture health based on pressure distribution |
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