CN109017602A - A kind of adaptive console and its control method based on human body attitude identification - Google Patents
A kind of adaptive console and its control method based on human body attitude identification Download PDFInfo
- Publication number
- CN109017602A CN109017602A CN201810769785.7A CN201810769785A CN109017602A CN 109017602 A CN109017602 A CN 109017602A CN 201810769785 A CN201810769785 A CN 201810769785A CN 109017602 A CN109017602 A CN 109017602A
- Authority
- CN
- China
- Prior art keywords
- display screen
- motor
- distance
- max
- human body
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Granted
Links
- 230000003044 adaptive effect Effects 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims abstract description 22
- 238000005070 sampling Methods 0.000 claims description 29
- 238000013528 artificial neural network Methods 0.000 claims description 18
- 230000005484 gravity Effects 0.000 claims description 6
- 238000012937 correction Methods 0.000 claims description 2
- NJPPVKZQTLUDBO-UHFFFAOYSA-N novaluron Chemical compound C1=C(Cl)C(OC(F)(F)C(OC(F)(F)F)F)=CC=C1NC(=O)NC(=O)C1=C(F)C=CC=C1F NJPPVKZQTLUDBO-UHFFFAOYSA-N 0.000 abstract 1
- 230000036544 posture Effects 0.000 description 15
- 230000008569 process Effects 0.000 description 9
- 238000012549 training Methods 0.000 description 8
- 238000013461 design Methods 0.000 description 4
- 230000001276 controlling effect Effects 0.000 description 3
- 238000010606 normalization Methods 0.000 description 3
- 230000009471 action Effects 0.000 description 1
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000011161 development Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000013178 mathematical model Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003062 neural network model Methods 0.000 description 1
- 238000007781 pre-processing Methods 0.000 description 1
- 230000001105 regulatory effect Effects 0.000 description 1
- 230000004044 response Effects 0.000 description 1
- 230000011664 signaling Effects 0.000 description 1
Classifications
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R11/00—Arrangements for holding or mounting articles, not otherwise provided for
- B60R11/02—Arrangements for holding or mounting articles, not otherwise provided for for radio sets, television sets, telephones, or the like; Arrangement of controls thereof
- B60R11/0229—Arrangements for holding or mounting articles, not otherwise provided for for radio sets, television sets, telephones, or the like; Arrangement of controls thereof for displays, e.g. cathodic tubes
- B60R11/0235—Arrangements for holding or mounting articles, not otherwise provided for for radio sets, television sets, telephones, or the like; Arrangement of controls thereof for displays, e.g. cathodic tubes of flat type, e.g. LCD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
- G06V40/00—Recognition of biometric, human-related or animal-related patterns in image or video data
- G06V40/20—Movements or behaviour, e.g. gesture recognition
- G06V40/23—Recognition of whole body movements, e.g. for sport training
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R11/00—Arrangements for holding or mounting articles, not otherwise provided for
- B60R2011/0001—Arrangements for holding or mounting articles, not otherwise provided for characterised by position
- B60R2011/0003—Arrangements for holding or mounting articles, not otherwise provided for characterised by position inside the vehicle
- B60R2011/0007—Mid-console
-
- B—PERFORMING OPERATIONS; TRANSPORTING
- B60—VEHICLES IN GENERAL
- B60R—VEHICLES, VEHICLE FITTINGS, OR VEHICLE PARTS, NOT OTHERWISE PROVIDED FOR
- B60R11/00—Arrangements for holding or mounting articles, not otherwise provided for
- B60R2011/0042—Arrangements for holding or mounting articles, not otherwise provided for characterised by mounting means
- B60R2011/008—Adjustable or movable supports
- B60R2011/0092—Adjustable or movable supports with motorization
-
- Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
- Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
- Y02P—CLIMATE CHANGE MITIGATION TECHNOLOGIES IN THE PRODUCTION OR PROCESSING OF GOODS
- Y02P90/00—Enabling technologies with a potential contribution to greenhouse gas [GHG] emissions mitigation
- Y02P90/02—Total factory control, e.g. smart factories, flexible manufacturing systems [FMS] or integrated manufacturing systems [IMS]
Landscapes
- Engineering & Computer Science (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Health & Medical Sciences (AREA)
- Health & Medical Sciences (AREA)
- General Physics & Mathematics (AREA)
- Computing Systems (AREA)
- Mathematical Physics (AREA)
- Data Mining & Analysis (AREA)
- Evolutionary Computation (AREA)
- Biophysics (AREA)
- Molecular Biology (AREA)
- Biomedical Technology (AREA)
- General Engineering & Computer Science (AREA)
- Artificial Intelligence (AREA)
- Computational Linguistics (AREA)
- Software Systems (AREA)
- Life Sciences & Earth Sciences (AREA)
- Mechanical Engineering (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Psychiatry (AREA)
- Social Psychology (AREA)
- Human Computer Interaction (AREA)
- Multimedia (AREA)
- Feedback Control In General (AREA)
- Fittings On The Vehicle Exterior For Carrying Loads, And Devices For Holding Or Mounting Articles (AREA)
Abstract
The invention discloses a kind of adaptive consoles based on human body attitude identification, comprising: support is provided with shifting sledge；Moving slide board moves horizontally on it for cooperating with the shifting sledge；Pedestal is fixed on the moving slide board；First motor is fixed on the base, and power output end connects the first joint, for driving the first joint to rotate in the horizontal plane；Second motor, power output end connect first rotating arm one end；Second joint rotatably connects the first rotating arm other end and second rotating arm one end simultaneously；Third joint is fixedly connected on the other end of second rotating arm；Third motor, power output end connect driving gear；Display screen, the back side are connected with driven gear by connecting strut, and the driving gear mutual cooperation rotates in the horizontal plane.The position of console can be adjusted.The present invention also provides a kind of control methods of adaptive console based on human body attitude identification.
Description
Technical Field
The invention relates to a center console, in particular to a self-adaptive center console based on human body posture recognition and a control method thereof.
Background
The center console is usually a work station located between a driver and passengers, and most of control buttons of the automobile except for driving are concentrated on the center console, and meanwhile, function keys of comfort and entertainment devices such as an air conditioner, a sound system and the like are also arranged on the center console, so that the center console has a vital role on the automobile. During driving, a driver needs to make a way with the center console at any time, and the design and arrangement of the center console affect the comfort of each vehicle and the use feeling of the driver.
With the development of automobile technology, the influence of member comfort on automobile design is increasingly profound. After unmanned driving is achieved, four limbs of a driver are released from existing restraint, and postures of passengers on the vehicle are diversified. It is currently desirable to make riders more comfortable by performing adaptive adjustments of the center console based on rider attitude and body pressure distribution data.
Disclosure of Invention
The invention designs and develops a self-adaptive center console based on human body posture recognition, which can adjust the position of the center console, change the angle of a display screen and improve the driving comfort.
The invention also designs and develops a control method of the self-adaptive center console based on human body posture recognition, which can adjust the actual position and the display screen angle of the center console according to the posture scale of the driver.
The invention also aims to control the actual position of the center console through the BP neural network, improve the precision of the position adjustment of the center console and enable the driver to drive more comfortably.
The technical scheme provided by the invention is as follows:
an adaptive center console based on human body posture recognition, comprising:
the support is provided with a movable slide rail;
the movable sliding plate is matched with the movable sliding rail and moves horizontally on the movable sliding rail;
a base fixed on the movable sliding plate;
the first motor is fixed on the base, and the power output end of the first motor is connected with the first joint and is used for driving the first joint to rotate in a horizontal plane;
the power output end of the second motor penetrates through the first joint, is connected with one end of the first rotating arm and is used for driving the first rotating arm to rotate in a vertical plane;
the second joint is simultaneously and rotatably connected with the other end of the first rotating arm and one end of the second rotating arm;
the third joint is fixedly connected to the other end of the second rotating arm;
the power output end of the third motor penetrates through the third joint, and the power output end of the third motor is connected with the driving gear;
the back of the display screen is connected with a driven gear through a connecting support rod, and the display screen can be matched with the driving gear to rotate in the horizontal plane.
Preferably, the second joint further comprises:
the power output end of the first connecting motor is connected with the other end of the first rotating arm and is used for driving the first rotating arm to rotate;
the second connecting motor is used for driving the second rotating arm to rotate.
Preferably, a plurality of control buttons are provided on the second rotating arm.
Preferably, one end of the base is provided with a jack.
Preferably, an arc-shaped connecting plate is arranged on one side of the chassis.
Preferably, the method further comprises the following steps:
the camera is fixed on the column A of the cab roof;
a pressure sensor provided at the bottom of the rider seat;
a controller electrically connected to the camera, the pressure sensor, the first motor, the second motor, the third motor, and the movable slide rail, and controlling the first motor, the second motor, the third motor, and the movable slide rail.
Preferably, the controller connects and controls the first connection motor and the second connection motor.
A control method of an adaptive center console based on human body posture recognition is characterized in that the adaptive center console based on human body posture recognition is used, and comprises the following steps:
acquiring a distance Y between eyes and a display screen of a center console, a distance J between shoulders and the display screen of the center console, a distance K between a hip and the display screen of the center console, a pressure G above a seat and a camera rotating speed V by using a sensor according to a sampling period;
step two, normalizing the parameters and establishing an input layer vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5}, wherein ,x1Is the eye distance coefficient, x2Is the coefficient of shoulder distance, x3Is the hip distance coefficient, x4Is the pressure coefficient, x5Is a velocity coefficient;
step three, the input layer is mapped to a middle layer, and a vector y of the middle layer is equal to { y ═ y1,y2,...,ylAnd l is the number of intermediate layer nodes, wherein the number l of the intermediate layer nodes meets the following conditions:wherein m is the number of nodes of an input layer, l is the number of nodes of an intermediate layer, and n is the number of nodes of an output layer;
step four, obtaining an output layer vector o ═ o1,o2,o3,o4}, wherein ,o1For adjusting the horizontal rotation angle of the display screen, o2For adjusting the vertical angle of the display screen, o3For the display screen to move a distance coefficient in the horizontal plane, o4Moving a display screen within a vertical display screen by a distance coefficient;
and step five, controlling the horizontal corner, the vertical corner, the horizontal movement distance and the vertical movement distance of the display screen to enable:
θ(i+1)＝o1 iθmax；
γ(i+1)＝o2 iγmax；
L(i+1)＝o3 iLmax；
S(i+1)＝o4 iSmax；
wherein ,is the output layer parameter of the ith sampling period, thetamaxIs the maximum horizontal rotation angle, gamma, of the display screenmaxIs the maximum vertical angle of the display screen, LmaxMaximum horizontal movement distance of the display screen, SmaxIs the maximum vertical movement distance of the display screen.
Preferably, the horizontal rotation angle θ of the display screen1Vertical angle of rotation gamma1Horizontal moving distance L15 and vertical movement distance S1Satisfies the following conditions:
θ1＝0.3θmax；
γ1＝0.6γmax；
L1＝0.4Lmax；
S1＝0.5Smax；
preferably, the camera rotation speed V satisfies:
wherein P is the gravity center position of the rider, P0Is a standard value of the gravity center position, G is the pressure above the seat, e is the natural logarithmic base number, h1Is the distance h between the camera and the saddle2As a vehicle seatDistance from chassis, ViA standard rotational speed set for the camera.
The invention has the following beneficial effects: the position data of the driver is collected through the camera, the position of the center console is dynamically adjusted according to the real-time image and the action posture of the driver, and the adjustment of the center console is not limited to small-amplitude adjustment of the original position and is changed into large-range all-field multi-angle all-dimensional adjustment. The BP neural network controls the actual position of the center console, so that the position adjustment precision of the center console is improved, the driver can drive more comfortably, and the safety is higher.
Drawings
Fig. 1 is a schematic structural diagram of an adaptive center console based on human body posture recognition according to the present invention.
FIG. 2 is a front view of an adaptive console based on human body posture recognition according to the present invention.
Fig. 3 is a flowchart of a control method of the adaptive center console based on human body posture recognition according to the present invention.
Detailed Description
The present invention is further described in detail below with reference to the attached drawings so that those skilled in the art can implement the invention by referring to the description text.
As shown in fig. 1-3, the present invention provides an adaptive console based on human body gesture recognition, comprising: the display device comprises a display screen 100, a first motor 210, a first rotating arm 230, a second rotating arm 310, a third motor 410 and a movable sliding rail 430.
The support is fixed on the bottom surface of the inner part of the vehicle body and is positioned at the front part of the cab. The support is provided with a movable slide rail 430 which can be matched with a movable sliding plate arranged on the support, and a driving motor is arranged inside the movable slide rail 430 and can drive the movable slide rail 430 to drive the movable sliding plate to move horizontally. The base 420 is fixed to the movable slide plate and can move together with the movable slide plate. An arc-shaped connecting plate is arranged on one side of the base 420, and a jack is arranged at one end of the base.
The first motor 410 is fixed on the base, and a power output end of the first motor 410 is connected with the first joint 330, and can drive the first joint 330 to rotate in a horizontal plane. The power output end of the second motor 320 passes through the first joint from left to right, is connected with one end of the first rotating arm 310, and can drive the first rotating arm 310 to rotate in a vertical plane. A plurality of control buttons are provided on the first rotating arm 310.
A cavity is formed inside the second joint 240, a first connecting motor is arranged inside the second joint 240, and the power output end of the first connecting motor is connected with the other end of the first rotating arm 310 and is used for driving the first rotating arm 310 to rotate; a second connection motor is further disposed inside the second joint 240, and a power output end of the second connection motor is connected to one end of the second rotation arm 230, so that the second connection motor can drive the second rotation arm 230 to rotate, and therefore the second joint can be simultaneously rotatably connected to the other end of the first rotation arm 310 and one end of the second rotation arm 230.
The other end of the second rotating arm 230 is fixedly connected with a third joint 220, the power output end of the third motor 210 passes through the third shutdown machine 220 from left to right, and the power output end of the third motor 210 is further connected with a driving gear. Display screen 100 sets up at well accuse bench top, is fixed with connecting rod at the back of display screen 100, and connecting rod one end is connected with driven gear, can cooperate with the driving gear, rotates in the horizontal plane.
In the cab, a camera is further arranged and mounted on the column A of the cab top cover, the camera can rotate in the horizontal direction and the vertical direction, the driver can be shot, and the distance between each part of the driver can be measured. The pressure sensor is arranged at the bottom of the seat, can measure the pressure above the seat and senses whether a rider is on the seat. The controller is connected with the camera, the pressure camera sensor, the first motor 410, the second motor 320, the third motor 210, the first connecting motor, the second connecting motor and the movable sliding rail 430, so that the position of the center console can be adjusted.
The invention also provides a control method of the self-adaptive center console based on human body posture recognition, which controls the actual position of the center console through the BP neural network and improves the control precision of the position of the center console.
Meanwhile, in the control process, based on parameters such as pressure above the seat in the control process, the empirical formula for obtaining the rotation speed of the camera meets the following requirements:
wherein, P is the gravity center position of the rider in mm and P0Is a standard value of the gravity center position in mm, G is the pressure above the seat in N, e is the natural logarithm base number, lambda is the correction coefficient, the range is 0-10, h1Is the distance between the camera and the saddle, and the unit is mm, h2Is the distance between the saddle and the chassis, and has the unit of mm and ViThe standard rotational speed set for the camera is given in deg/s.
Step S210, establishing a BP neural network model,
the BP network system structure adopted by the invention is composed of three layers, wherein the first layer is an input layer, m nodes are provided in total, m detection signals representing the working state of the equipment are correspondingly provided, and the signal parameters are given by a data preprocessing module. The second layer is a hidden layer, and comprises l nodes which are determined by the training process of the network in a self-adaptive mode. The third layer is an output layer, which comprises n nodes and is determined by the response actually required to be output by the system.
The mathematical model of the network is:
inputting a vector: x ═ x1,x2,...,xm)T；
Intermediate layer vector: y ═ y1,y2,...,yl)T；
Outputting a vector: o ═ O1,o2,...,on)T；
In the present invention, the number m of nodes in the input layer is 5, the number n of nodes in the output layer is 4, and the number l of nodes in the hidden layer is estimated by the following formula:
according to the sampling period, acquiring the distance Y between the eyes and a display screen of a center console, the distance J between the shoulders and the display screen of the center console, the distance K between the hips and the display screen of the center console, the pressure G above the seat and the rotating speed V of a camera by using a sensor;
the input signal has 5 parameters expressed as: x is the number of1Is the eye distance coefficient, x2Is the coefficient of shoulder distance, x3Is the hip distance coefficient, x4Is the pressure coefficient, x5Is a velocity coefficient;
the data acquired by the sensors belong to different physical quantities, and the dimensions of the data are different. Therefore, the data needs to be normalized to a number between 0-1 before it is input into the artificial neural network.
Specifically, the distance Y between the eyes of the rider and the display screen of the center console is measured through a camera, and after normalization is carried out, an eye distance coefficient x is obtained1：
wherein ,YmaxMaximum eye distance, YminIs the minimum eye distance;
similarly, the distance J between the shoulders of the driver and the display screen of the center console is measured through the camera, and after normalization is carried out, the shoulder distance coefficient x is obtained2：
wherein ,JmaxMaximum shoulder distance, JminIs the minimum shoulder distance;
similarly, the distance K between the hip of the driver and the display screen of the center console is measured through the camera, and the hip distance coefficient x is obtained after normalization is carried out3：
wherein ,KmaxMaximum hip distance, KminIs the minimum hip distance;
similarly, the pressure G of the rider on the seat is measured by a pressure sensor and normalized to obtain a pressure coefficient x4：
wherein ,GmaxAt maximum pressure, GminIs the minimum pressure;
similarly, the rotation speed V of the camera is measured by a speed sensor, and normalized to obtain a speed coefficient x5：
wherein ,VmaxIs the maximum rotational speed, V, of the cameraminIs the minimum rotational speed of the camera.
The 4 parameters of the output layer are respectively expressed as: o1For adjusting the horizontal rotation angle of the display screen, o2To showVertical angle adjustment coefficient of display screen, o3For horizontal movement distance coefficient of display screen, o4Is the display screen vertical movement distance coefficient.
Display screen horizontal rotation angle adjusting coefficient o1Expressed as the ratio of the horizontal rotation angle of the display screen in the next sampling period to the maximum horizontal rotation angle of the display screen set in the current sampling period, namely the horizontal rotation angle of the display screen collected in the ith sampling period is thetaiOutputting the horizontal rotation angle adjusting coefficient of the display screen of the ith sampling period through a BP neural networkThen, the horizontal rotation angle theta of the display screen in the (i + 1) th sampling period is controlledi+1So that it satisfies:
wherein ,θmaxThe maximum horizontal rotation angle of the display screen.
Vertical rotation angle adjusting coefficient o of display screen2Is expressed as the ratio of the vertical rotation angle of the display screen in the next sampling period to the maximum vertical rotation angle of the display screen set in the current sampling period, namely the vertical rotation angle of the display screen collected in the ith sampling period is gammaiOutputting the vertical rotation angle adjusting coefficient of the display screen in the ith sampling period through a BP neural networkThen, the vertical rotation angle gamma of the display screen in the (i + 1) th sampling period is controlledi+1So that it satisfies:
γi+1＝o2 iγmax，
wherein ,γmaxThe maximum vertical rotation angle of the display screen.
Horizontal movement distance coefficient o of display screen3Indicated as the display screen in the next sample periodThe ratio of the horizontal moving distance to the maximum horizontal moving distance set in the current sampling period, namely the horizontal moving distance of the display screen collected in the ith sampling period is LiOutputting the display screen horizontal movement distance adjusting coefficient of the ith sampling period through a BP neural networkThen, the horizontal movement distance L of the display screen in the (i + 1) th sampling period is controlledi+1So that it satisfies:
wherein ,LmaxThe maximum horizontal movement distance of the display screen.
Vertical moving distance coefficient o of display screen4Expressed as the ratio of the vertical moving distance of the display screen in the next sampling period to the maximum vertical moving distance set in the current sampling period, i.e. the vertical moving distance of the display screen collected in the ith sampling period is SiOutputting the vertical movement distance adjusting coefficient of the display screen in the ith sampling period through a BP neural networkThen, the horizontal movement distance S of the display screen in the (i + 1) th sampling period is controlledi+1So that it satisfies:
wherein ,SmaxIs the maximum vertical movement distance of the display screen.
Step S220, carrying out BP neural network training
Obtaining training samples according to historical experience data, and giving a connection weight W between an input node i and a hidden layer node jijHidden layer node j and output layerConnection weight W between nodes kjkThreshold value theta of hidden layer node jjThreshold value theta of output layer node kk、Wij、Wjk、θj、θkAre all random numbers between-1 and 1.
During the training process, continuously correcting Wij、WjkUntil the system error is less than or equal to the expected error, the training process of the neural network is completed.
As shown in table 1, a set of training samples is given, along with the values of the nodes in the training process.
TABLE 1 training Process node values
S230, collecting a central console operation signal, inputting the central console operation signal into a neural network to obtain an output signal, and controlling the central console;
the trained artificial neural network is solidified in the controller chip, so that the hardware circuit has the functions of prediction and intelligent decision making, and intelligent hardware is formed. After the intelligent hardware is powered on and started, the distance Y between the eyes and the display screen of the center console, the distance J between the shoulders and the display screen of the center console, the distance K between the hip and the display screen of the center console, the pressure G above the seat and the rotating speed V of the camera are detected at the same time, the parameters are normalized, and the initial input vector of the BP neural network is obtainedObtaining an initial output vector through operation of a BP neural network
Step S240, obtaining an initial output vectorAfter that, i.e.The speed can be regulated, the distance Y between the eyes and the display screen of the center console, the distance J between the shoulders and the display screen of the center console, the distance K between the hip and the display screen of the center console, the pressure G above the seat and the rotating speed V of the camera in the ith sampling period are obtained through the sensors, and the input vector in the ith sampling period is obtained through formattingObtaining the output vector of the ith sampling period through the operation of a BP neural networkThen the horizontal corner, the vertical corner, the horizontal movement distance and the vertical movement distance of the display screen are controlled and adjusted, so that the horizontal corner, the vertical corner, the horizontal movement distance and the vertical movement distance of the display screen in the (i + 1) th sampling period are respectively as follows:
γi+1＝o2 iγmax
L(i+1)＝o3 iLmax；
S(i+1)＝o4 iSmax
at the beginning of the process, the process is carried out,
θ1＝0.3θmax；
γ1＝0.6γmax；
L1＝0.4Lmax；
S1＝0.5Smax；
wherein ,are respectively provided withIs the output parameter in the ith sampling period, thetamaxIs the maximum horizontal rotation angle, gamma, of the display screenmaxIs the maximum vertical corner L of the display screenmaxFor maximum horizontal movement distance, S, of the display screenmaxIs the maximum vertical movement distance of the display screen.
Through the arrangement, the control method of the self-adaptive center console based on human body posture recognition controls the actual position of the center console through a BP neural network algorithm, improves the precision of position adjustment of the center console, and enables a driver to drive more comfortably and the safety is higher.
While embodiments of the invention have been described above, it is not limited to the applications set forth in the description and the embodiments, which are fully applicable in various fields of endeavor to which the invention pertains, and further modifications may readily be made by those skilled in the art, it being understood that the invention is not limited to the details shown and described herein without departing from the general concept defined by the appended claims and their equivalents.
Claims (10)
1. An adaptive center console based on human body posture recognition is characterized by comprising:
the support is provided with a movable slide rail;
the movable sliding plate is matched with the movable sliding rail and moves horizontally on the movable sliding rail;
a base fixed on the movable sliding plate;
the first motor is fixed on the base, and the power output end of the first motor is connected with the first joint and is used for driving the first joint to rotate in a horizontal plane;
the power output end of the second motor penetrates through the first joint, is connected with one end of the first rotating arm and is used for driving the first rotating arm to rotate in a vertical plane;
the second joint is simultaneously and rotatably connected with the other end of the first rotating arm and one end of the second rotating arm;
the third joint is fixedly connected to the other end of the second rotating arm;
the power output end of the third motor penetrates through the third joint, and the power output end of the third motor is connected with the driving gear;
the back of the display screen is connected with a driven gear through a connecting support rod, and the display screen can be matched with the driving gear to rotate in the horizontal plane.
2. The adaptive console based on human body posture recognition of claim 1, wherein the second joint further comprises:
the power output end of the first connecting motor is connected with the other end of the first rotating arm and is used for driving the first rotating arm to rotate;
the second connecting motor is used for driving the second rotating arm to rotate.
3. The adaptive console based on human body posture recognition of claim 2, wherein a plurality of control buttons are disposed on the second rotating arm.
4. The adaptive console based on human body posture recognition of claim 3, wherein one end of the base is provided with a jack.
5. The adaptive console based on human body posture recognition of claim 4, wherein an arc-shaped connecting plate is arranged on one side of the base.
6. The adaptive console based on human body posture recognition of claim 5, further comprising:
the camera is fixed on the column A of the cab roof;
a pressure sensor provided at the bottom of the rider seat;
a controller electrically connected to the camera, the pressure sensor, the first motor, the second motor, the third motor, and the movable slide rail, and controlling the first motor, the second motor, the third motor, and the movable slide rail.
7. The adaptive console based on human body posture recognition of claim 6, wherein the controller connects and controls the first connecting motor and the second connecting motor.
8. A control method of an adaptive console based on human body posture recognition, which is characterized by using the adaptive console based on human body posture recognition according to any one of claims 1 to 7, and comprises the following steps:
acquiring a distance Y between eyes and a display screen of a center console, a distance J between shoulders and the display screen of the center console, a distance K between a hip and the display screen of the center console, a pressure G above a seat and a camera rotating speed V by using a sensor according to a sampling period;
step two, normalizing the parameters and establishing an input layer vector x ═ x of the three-layer BP neural network1,x2,x3,x4,x5}, wherein ,x1Is the eye distance coefficient, x2Is the coefficient of shoulder distance, x3Is the hip distance coefficient, x4Is the pressure coefficient, x5Is a velocity coefficient;
step three, the input layer is mapped to a middle layer, and a vector y of the middle layer is equal to { y ═ y1,y2,...,ylAnd l is the number of intermediate layer nodes, and the number l of the intermediate layer nodes meets the following requirements:wherein m is the number of nodes of an input layer, l is the number of nodes of an intermediate layer, and n is the number of nodes of an output layer;
step four, obtaining an output layer vector o ═ o1,o2,o3,o4}, wherein ,o1For adjusting the horizontal rotation angle of the display screen, o2For adjusting the vertical angle of the display screen, o3For the display screen to move a distance coefficient in the horizontal plane, o4Moving a display screen within a vertical display screen by a distance coefficient;
and step five, controlling the horizontal corner, the vertical corner, the horizontal movement distance and the vertical movement distance of the display screen to enable:
θ(i+1)＝o1 iθmax；
γ(i+1)＝o2 iγmax；
L(i+1)＝o3 iLmax；
S(i+1)＝o4 iSmax；
wherein ,is the output layer parameter of the ith sampling period, thetamaxIs the maximum horizontal rotation angle, gamma, of the display screenmaxIs the maximum vertical angle of the display screen, LmaxMaximum horizontal movement distance of the display screen, SmaxIs the maximum vertical movement distance of the display screen.
9. The method for controlling an adaptive console based on human body posture recognition of claim 8, wherein the horizontal rotation angle θ of the display screen at the initial state1Vertical angle of rotation gamma1Horizontal moving distance L15 and vertical movement distance S1Satisfies the following conditions:
θ1＝0.3θmax；
γ1＝0.6γmax；
L1＝0.4Lmax；
S1＝0.5Smax。
10. the control method of the adaptive console based on human body posture recognition according to claim 9, wherein the camera rotation speed V satisfies:
wherein P is the gravity center position of the rider, P0Is a standard value of the center of gravity, G is the pressure above the seat, e is the natural logarithmic base number, lambda is the correction coefficient, h1Is the distance h between the camera and the saddle2Is the distance between the saddle and the chassis, ViA standard rotational speed set for the camera.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810769785.7A CN109017602B (en) | 2018-07-13 | 2018-07-13 | Self-adaptive center console based on human body gesture recognition and control method thereof |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201810769785.7A CN109017602B (en) | 2018-07-13 | 2018-07-13 | Self-adaptive center console based on human body gesture recognition and control method thereof |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN109017602A true CN109017602A (en) | 2018-12-18 |
| CN109017602B CN109017602B (en) | 2023-08-18 |
Family
ID=64642116
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201810769785.7A Active CN109017602B (en) | 2018-07-13 | 2018-07-13 | Self-adaptive center console based on human body gesture recognition and control method thereof |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN109017602B (en) |
Cited By (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109795341A (en) * | 2019-01-22 | 2019-05-24 | 南京越博动力***股份有限公司 | A kind of energy-efficient control method of pure electric automobile equipped with CVT stepless transmission |
| CN110254362A (en) * | 2019-06-18 | 2019-09-20 | 浙江吉利控股集团有限公司 | Large-size screen monitors assembly is controlled in a kind of adjustable automobiles |
| CN111717124A (en) * | 2020-06-05 | 2020-09-29 | 北京超星未来科技有限公司 | Angle adjusting device of adjustable laser radar angle |
| CN112298059A (en) * | 2020-10-26 | 2021-02-02 | 武汉华星光电技术有限公司 | Vehicle-mounted display screen adjusting device and vehicle |
| CN114679529A (en) * | 2022-04-07 | 2022-06-28 | 深圳市华屹医疗科技有限公司 | Optical processing device for non-contact human body posture acquisition |
| WO2024002303A1 (en) * | 2021-11-01 | 2024-01-04 | 华人运通(江苏)技术有限公司 | Robotic arm control method and apparatus for vehicle-mounted screen, device, and vehicle |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5434783A (en) * | 1993-01-06 | 1995-07-18 | Nissan Motor Co., Ltd. | Active control system |
| US20050080565A1 (en) * | 2003-10-14 | 2005-04-14 | Olney Ross D. | Driver adaptive collision warning system |
| EP3015313A1 (en) * | 2014-11-03 | 2016-05-04 | Volvo Car Corporation | A display assembly for an interior panel in a vehicle |
| CN105538325A (en) * | 2015-12-30 | 2016-05-04 | 哈尔滨理工大学 | Decoupling control method of single leg joint of hydraulic four-leg robot |
| CN105783896A (en) * | 2016-05-17 | 2016-07-20 | 吉林大学 | Interactive type unmanned aerial vehicle magnetic compass calibration device and method |
| CN106314303A (en) * | 2016-08-31 | 2017-01-11 | 法乐第(北京)网络科技有限公司 | Center control screen assembly and vehicle |
| CN106938764A (en) * | 2017-04-28 | 2017-07-11 | 浙江硕和机器人科技有限公司 | A kind of guide plate reciprocating conveying mechanism |
| CN106990410A (en) * | 2017-03-08 | 2017-07-28 | 中国汽车技术研究中心 | A kind of vehicle external dimensions device for fast detecting and method |
| CN107176098A (en) * | 2017-07-10 | 2017-09-19 | 辽宁工业大学 | A kind of poor automatic monitoring warning device in blind area of lubrication groove and control method |
| CN107344528A (en) * | 2016-05-06 | 2017-11-14 | 杨新宇 | Car-mounted device, motor vehicles and the method for installing car-mounted device |
| CN107579699A (en) * | 2017-09-12 | 2018-01-12 | 安徽亦知企业管理有限公司 | A kind of solar energy photovoltaic generator and its adjusting method based on BP neural network regulation angle |
| CN207056860U (en) * | 2017-04-17 | 2018-03-02 | 姚汉奇 | A kind of path optimization atomizing spraying system of view-based access control model identification technology |
| CN208376662U (en) * | 2018-07-13 | 2019-01-15 | 吉林大学 | A kind of adaptive console based on body state detection |
-
2018
- 2018-07-13 CN CN201810769785.7A patent/CN109017602B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US5434783A (en) * | 1993-01-06 | 1995-07-18 | Nissan Motor Co., Ltd. | Active control system |
| US20050080565A1 (en) * | 2003-10-14 | 2005-04-14 | Olney Ross D. | Driver adaptive collision warning system |
| EP3015313A1 (en) * | 2014-11-03 | 2016-05-04 | Volvo Car Corporation | A display assembly for an interior panel in a vehicle |
| CN105538325A (en) * | 2015-12-30 | 2016-05-04 | 哈尔滨理工大学 | Decoupling control method of single leg joint of hydraulic four-leg robot |
| CN107344528A (en) * | 2016-05-06 | 2017-11-14 | 杨新宇 | Car-mounted device, motor vehicles and the method for installing car-mounted device |
| CN105783896A (en) * | 2016-05-17 | 2016-07-20 | 吉林大学 | Interactive type unmanned aerial vehicle magnetic compass calibration device and method |
| CN106314303A (en) * | 2016-08-31 | 2017-01-11 | 法乐第(北京)网络科技有限公司 | Center control screen assembly and vehicle |
| CN106990410A (en) * | 2017-03-08 | 2017-07-28 | 中国汽车技术研究中心 | A kind of vehicle external dimensions device for fast detecting and method |
| CN207056860U (en) * | 2017-04-17 | 2018-03-02 | 姚汉奇 | A kind of path optimization atomizing spraying system of view-based access control model identification technology |
| CN106938764A (en) * | 2017-04-28 | 2017-07-11 | 浙江硕和机器人科技有限公司 | A kind of guide plate reciprocating conveying mechanism |
| CN107176098A (en) * | 2017-07-10 | 2017-09-19 | 辽宁工业大学 | A kind of poor automatic monitoring warning device in blind area of lubrication groove and control method |
| CN107579699A (en) * | 2017-09-12 | 2018-01-12 | 安徽亦知企业管理有限公司 | A kind of solar energy photovoltaic generator and its adjusting method based on BP neural network regulation angle |
| CN208376662U (en) * | 2018-07-13 | 2019-01-15 | 吉林大学 | A kind of adaptive console based on body state detection |
Non-Patent Citations (2)
| Title |
|---|
| 邱志成;许燕飞;: "基于自适应RBF模糊神经网络的旋转柔性铰接梁的振动控制", no. 07, pages 97 - 103 * |
| 陈俊豪，任金东，刘涛，华猛，刘洪浩, vol. 39, no. 5, pages 593 - 605 * |
Cited By (9)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN109795341A (en) * | 2019-01-22 | 2019-05-24 | 南京越博动力***股份有限公司 | A kind of energy-efficient control method of pure electric automobile equipped with CVT stepless transmission |
| CN110254362A (en) * | 2019-06-18 | 2019-09-20 | 浙江吉利控股集团有限公司 | Large-size screen monitors assembly is controlled in a kind of adjustable automobiles |
| CN111717124A (en) * | 2020-06-05 | 2020-09-29 | 北京超星未来科技有限公司 | Angle adjusting device of adjustable laser radar angle |
| CN111717124B (en) * | 2020-06-05 | 2021-11-23 | 北京超星未来科技有限公司 | Angle adjusting device of adjustable laser radar angle |
| CN112298059A (en) * | 2020-10-26 | 2021-02-02 | 武汉华星光电技术有限公司 | Vehicle-mounted display screen adjusting device and vehicle |
| US11995229B2 (en) | 2020-10-26 | 2024-05-28 | Wuhan China Star Optoelectronics Technology Co., Ltd. | Vehicle-mounted display adjustment device and vehicle |
| WO2024002303A1 (en) * | 2021-11-01 | 2024-01-04 | 华人运通(江苏)技术有限公司 | Robotic arm control method and apparatus for vehicle-mounted screen, device, and vehicle |
| CN114679529A (en) * | 2022-04-07 | 2022-06-28 | 深圳市华屹医疗科技有限公司 | Optical processing device for non-contact human body posture acquisition |
| CN114679529B (en) * | 2022-04-07 | 2024-06-04 | 深圳市华屹医疗科技有限公司 | Optical processing device for non-contact human body posture acquisition |
Also Published As
| Publication number | Publication date |
|---|---|
| CN109017602B (en) | 2023-08-18 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN109017602B (en) | Self-adaptive center console based on human body gesture recognition and control method thereof | |
| CN108657029B (en) | Intelligent automobile driver seat adjusting system and method based on limb length prediction | |
| CN106994915B (en) | Driving seat self-adaptive adjusting device based on body characteristics of driver | |
| CN110843614B (en) | Self-adaptive adjusting method and system for automobile seat | |
| DE69810095T2 (en) | EXTERNAL SKELETON PLATFORM FOR CONTROLLING MULTI-DIRECTION AVATER KINETICS IN A VIRTUAL ENVIRONMENT | |
| JP6899905B2 (en) | Devices and methods for automatically adjusting the driver's sitting posture | |
| US20210154430A1 (en) | Systems and methods for predicting and preventing motion sickness | |
| CN109263654B (en) | Self-adaptive speed regulation system and speed regulation method for automobile speed passing belt | |
| CN111267681B (en) | Shared automobile intelligent seat adjusting method based on ergonomics | |
| CN110843613B (en) | Seat automatic adjusting method based on human factors engineering | |
| JP6645543B1 (en) | Operation control adjustment device and operation control adjustment method | |
| CN108944581B (en) | Alternate seat arrangement | |
| JPH11198075A (en) | Behavior support system | |
| KR20200069585A (en) | Appratus and method for intelligent vehicle convenience control | |
| CN107458482A (en) | A kind of Location of Mass Center of Automobiles adjusting apparatus and its control method | |
| DE102019133544A1 (en) | Support device | |
| CN111524412B (en) | System and method for realizing real motion sensing of forklift simulation driving | |
| CN111717147A (en) | Automatic adjusting system and method for vehicle seat | |
| CN108515936B (en) | Self-adaptive safety airbag based on posture of rider and control method thereof | |
| CN107560777A (en) | Three-dimensional force feedback handle restoring force control structure and method based on power/torque sensor | |
| CN206534797U (en) | A kind of multi-functional regulation wheelchair | |
| CN107536311A (en) | Intelligent multifunctional seat system | |
| JP2020075570A (en) | Vehicle control device | |
| CN117087504A (en) | Method, system, equipment and medium for adjusting and controlling vehicle seat | |
| CN208376662U (en) | A kind of adaptive console based on body state detection |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |