CN112057250B - Intelligent eye-watching wheelchair and control method thereof - Google Patents
Intelligent eye-watching wheelchair and control method thereof Download PDFInfo
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- CN112057250B CN112057250B CN202010995740.9A CN202010995740A CN112057250B CN 112057250 B CN112057250 B CN 112057250B CN 202010995740 A CN202010995740 A CN 202010995740A CN 112057250 B CN112057250 B CN 112057250B
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
- A61G5/00—Chairs or personal conveyances specially adapted for patients or disabled persons, e.g. wheelchairs
- A61G5/04—Chairs or personal conveyances specially adapted for patients or disabled persons, e.g. wheelchairs motor-driven
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
- A61G5/00—Chairs or personal conveyances specially adapted for patients or disabled persons, e.g. wheelchairs
- A61G5/10—Parts, details or accessories
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61G—TRANSPORT, PERSONAL CONVEYANCES, OR ACCOMMODATION SPECIALLY ADAPTED FOR PATIENTS OR DISABLED PERSONS; OPERATING TABLES OR CHAIRS; CHAIRS FOR DENTISTRY; FUNERAL DEVICES
- A61G2203/00—General characteristics of devices
- A61G2203/10—General characteristics of devices characterised by specific control means, e.g. for adjustment or steering
- A61G2203/18—General characteristics of devices characterised by specific control means, e.g. for adjustment or steering by patient's head, eyes, facial muscles or voice
Abstract
The invention discloses an intelligent eye-ward wheelchair and a control method thereof. The system comprises a wheelchair mechanism, a signal acquisition device, a signal processing device and a wheelchair motion control device. The signal acquisition device comprises a bipolar lead electrode patch assembly and a signal acquisition module. The signal processing device comprises a feature extraction module and a pattern recognition module. The characteristic extraction module is used for extracting the waveform characteristics of the electro-oculogram signals. The pattern recognition module is used for acquiring various feature information of the waveform features, and then comparing the various feature information with various preset recognition parameters in a preset recognition pattern to determine the motion mode corresponding to the waveform features. The wheelchair motion control device is used for driving the wheelchair body to move according to the motion mode determined by the mode recognition module. The intelligent wheelchair can be accurately controlled through the eye electrical signals, the real-time performance is good, the extraction is more reliable, the parameter adjustment is simple and convenient, the applicability is strong, the operation is simple, and the adjustment is convenient.
Description
Technical Field
The invention relates to a wheelchair control system in the technical field of wheelchair control, in particular to an eye-powered intelligent wheelchair and a control method of the eye-powered intelligent wheelchair.
Background
Disabled people are socially in a vulnerable group, accounting for a large portion of our population. The physically handicapped people are numerous, which causes serious difficulties for their survival and development. Although a series of policy and policy guidelines are introduced in the society security of the disabled, the concept of the disabled by modern people is gradually popularized, and humanitarian care is gradually taken. But it is difficult to give them sufficient help in a specific life. Unfortunately, people with physical disabilities are limited to a narrow place and cannot freely enjoy the fun of outdoor activities. The aging problem of the population in China is highlighted, children cannot always look after the old, and the old with limb inconvenience cannot freely enjoy the wonderful outdoor activities.
At present, most of wheelchair control modes are controlled through four limbs of a human body, a manual wheelchair is more dependent on the strength of arms, and an electric wheelchair needs to operate a control rod by hands to move the wheelchair. However, for some people with handicapped hands and feet or people with limb defects, the wheelchair cannot be operated well by hands or feet, which causes great inconvenience to their movement. There is a need to solve these problems by new technical means to improve their quality of life.
Disclosure of Invention
The invention provides an intelligent eye-power wheelchair and a control method thereof, and aims to solve the technical problem that the existing wheelchair cannot meet the use requirements of people with inconvenience in hands and feet or people with limb defects.
The invention is realized by adopting the following technical scheme: an eye-ward intelligent wheelchair, comprising:
a wheelchair mechanism comprising a wheelchair body and a motor; the motor is arranged on the wheelchair body and is used for driving the wheelchair body to move;
the signal acquisition device comprises a bipolar lead electrode patch assembly and a signal acquisition module; the bipolar lead electrode patch assembly comprises an electrode patch I, an electrode patch II, an electrode patch III, an electrode patch IV, an electrode patch V, an electrode patch VI and an electrode patch VII; the electrode patch I is used for being attached to an area between two eyes of the face of a human body, is used as a reference electrode and is used for detecting an electric signal I carried by the face of the human body; the second electrode patch and the third electrode patch are respectively attached to the upper side and the lower side of one of eyes of the face of the human body and used for measuring a second electric signal generated by the movement of the corresponding eyeball in the vertical direction; the electrode patch IV and the electrode patch V are respectively attached to the left side and the right side of two eyes of the face of the human body and are used for measuring an electric signal III generated when the corresponding eyeball moves in the horizontal direction; the electrode patch six and the electrode patch seven are respectively attached to the upper side and the lower side of the other eye of the human face and used for measuring an electric signal four generated by the movement of the corresponding eyeball in the vertical direction; the signal acquisition module is used for amplifying, gain adjusting and bias adjusting the first electric signal, the second electric signal, the third electric signal and the fourth electric signal to acquire corresponding eye electric signals and serve as acquired data;
the signal processing device comprises a feature extraction module and a pattern recognition module; the characteristic extraction module is used for filtering, sampling and compressing the acquired data, repeatedly sampling waveforms and extracting the waveform characteristics of the electro-oculogram signals; the pattern recognition module is used for firstly acquiring various feature information of the waveform features and then comparing the various feature information with various preset recognition parameters in a preset recognition pattern so as to determine a motion mode corresponding to the waveform features; the movement modes comprise forward movement, backward movement, stop, left rotation, right rotation, acceleration, deceleration and uniform speed; and
and the wheelchair motion control device is used for driving the wheelchair body to move according to the motion mode determined by the mode identification module through the motor.
According to the invention, each electrode patch in the bipolar lead electrode patch component of the signal acquisition device is attached to the forehead, the upper side, the lower side, the left side and the right side of the eyeball of the face of a human body, a user generates different electric signals through the change action of glasses, and the electric signals are amplified, gain-adjusted and offset-adjusted to form eye electric signals input into the signal processing device. The characteristic extraction module of the signal processing device carries out filtering, sampling compression and waveform repeated sampling processing on the electro-oculogram signals to obtain waveform characteristics, and the mode recognition module can determine the motion mode represented by the electro-oculogram signals generated by users according to various characteristic information of the waveform characteristics, so that the wheelchair motion control device can control the intelligent wheelchair according to the motion mode to enable the wheelchair to move according to the motion mode desired by the users. Therefore, a user can control the intelligent wheelchair to perform various movements only by changing eyes, such as blinking, moving eyeballs and the like, the non-limb control operation of the wheelchair is realized, the technical problem that the existing wheelchair cannot meet the requirement of people with inconvenient hands and feet or people with limb defects to use is solved, and the technical effects of convenience in use and convenience in use of various limb mobility-handicapped people are achieved.
As a further improvement of the above solution, in the recognition mode, the mode recognition module judges whether a maximum value in the waveform features is within a preset range one; when the maximum value is located in the first preset range, the mode identification module judges whether the minimum value in the waveform characteristics is located in a second preset range; when the minimum value is within the second preset range, the mode identification module determines that the motion mode is right turning; when the maximum value is not in the first preset range, the mode identification module judges whether the maximum value is in a third preset range; when the maximum value is within the third preset range, the mode identification module judges whether the minimum value is within a fourth preset range; when the minimum value is within the preset range four, the mode identification module determines that the motion mode is left turn; when the minimum value is not within the preset range four, the mode identification module judges whether the minimum value is within a preset range five or not; when the minimum value is within the preset range five, the mode recognition module counts a count in a group of numbers, wherein the count meets the condition that the waveform feature is within a preset range six, judges whether the count is equal to a preset number one, if so, the motion mode is determined to be parking, otherwise, the count is judged to be equal to a preset number two, if so, the motion mode is determined to be forward, otherwise, the count is judged to be equal to a preset number three, if so, the motion mode is determined to be backward, and if not, the waveform feature is obtained again; and when the minimum value is not in the second preset range, or the maximum value is not in the third preset range, or the minimum value is not in the fifth preset range, the mode identification module acquires the waveform characteristics again.
Further, the lower limit of the first preset range is greater than the upper limit of the second preset range, the lower limit of the third preset range is greater than or equal to the upper limit of the first preset range, the upper limit of the fourth preset range is less than the lower limit of the second preset range, the fifth preset range is the same as the second preset range, and the sixth preset range is the same as the third preset range.
Still further, the first preset range is [2.2,2.3 ], the second preset range and the fifth preset range are both [2,2.15 ], the third preset range and the sixth preset range are both [2.3,2.4 ], and the fourth preset range is [1.8,1.95 ").
As a further improvement of the above scheme, the signal acquisition module comprises two signal conditioning units, a two-way acquisition board and a data acquisition card; the signal conditioning unit comprises an instrument amplifier and an operational amplifier which are used for amplifying the first electric signal, the second electric signal and the third electric signal, and is also used for inhibiting the potential of the electrode half-cell; the dual-path acquisition board is used for matching with the two signal conditioning units to realize dual-path input, and each path of input is used for realizing gain adjustment and bias adjustment; and the data acquisition card is used for transmitting the adjusted eye electrical signals to the signal processing device.
As a further improvement of the scheme, the signal processing device is established based on a LabView environment; the characteristic extraction module comprises a DAQ assistant, a filter, a sampling compression unit, a waveform resampling unit and a waveform display unit; the DAQ assistant is used for setting a specific serial port of the acquired data, the type of the acquired data is voltage, and the acquisition range is-5V; the filter is used for filtering the acquired data to remove the voltage which does not meet the acquisition range and filtering the noise with cutoff frequency greater than a preset frequency; the sampling compression unit is used for continuously sampling and compressing the acquired data, the sampling rate is 1000 times/second, and the compression factor is 50; the waveform resampling unit is used for performing waveform resampling on the acquired data, and the repetition degree is set to be one half; the waveform display unit is used for displaying the waveform of the electro-oculogram signal in the collected data.
Further, the pattern recognition module provides a visualized pattern recognition parameter adjustment interface, and the pattern recognition parameter adjustment interface has the following input modes:
(1) The eye emmetropia screen records the signal amplitude when the blink frequency is 1,2 and 3 times after the waveform of the eye electric signal is stable;
(2) The eye emmetropia screen is used for respectively looking left and then returning to the emmetropia direction after the waveform of the eye electric signal is stable, and respectively recording the waveform and the amplitude when looking right and then returning to the emmetropia direction;
(3) And filling parameters in the corresponding adjusting interface.
Still further, the wheelchair motion control device comprises a direct current servo driver and a photoelectric encoder; the upper computer where the wheelchair motion control device is located generates a corresponding command signal to the direct current servo driver according to the motion mode determined by the mode identification module, and the direct current servo driver controls the motor to correspondingly rotate according to the motion mode designated by the command signal, so that the wheelchair body moves according to the motion mode; the photoelectric encoder is used for measuring the rotating speed of the motor and returning the rotating speed to the upper computer;
the command signals corresponding to different eye electric signals are respectively as follows:
(a) Blinking twice by two eyes to drive the wheelchair body to move forwards;
(b) Blinking three times by two eyes to drive the wheelchair body to retreat;
(c) The wheelchair body is driven to rotate left after being swept to the left;
(d) The wheelchair is swept to the right, and the wheelchair body is driven to rotate to the right;
(e) Blinking once through two eyes to drive the wheelchair body to stop;
(f) The left eye blinks once and the right eye does not blink, so that the wheelchair body is driven to move in an accelerated manner;
(g) Enabling the right eye to blink once and the left eye not to blink, and driving the wheelchair body to do deceleration movement;
(h) Both eyes blink and are asynchronous, and the wheelchair body is driven to move at a uniform speed.
Furthermore, the bipolar lead electrode surface mount component is an Ag/AgCI electrode component, the signal conditioning unit is an AD8232 chip, the double-circuit acquisition board is an AD620 double-circuit acquisition board, and the data acquisition card is an NI PCIe-6321 data acquisition card; the direct current servo driver is an MLD3605-D driver, and the motor is an MY1016Z direct current motor; the number of the direct current servo drivers, the number of the motors and the number of the wheels of the intelligent wheelchair are two and are in one-to-one correspondence, and each direct current servo driver is used for driving the corresponding wheel to move through the corresponding motor.
The invention also provides an eye-powered intelligent wheelchair control method, which is applied to any eye-powered intelligent wheelchair, and is characterized by comprising the following steps:
firstly, detecting an electric signal I carried by the face of the human body, measuring an electric signal II and an electric signal IV generated by moving the corresponding eyeballs in the vertical direction and an electric signal III generated by moving the corresponding eyeballs in the horizontal direction, and then carrying out amplification, gain adjustment and offset adjustment on the electric signal I, the electric signal II, the electric signal III and the electric signal IV to obtain corresponding eye electric signals to serve as collected data;
filtering, sampling compression and waveform repeated sampling are carried out on the acquired data, and the waveform characteristics of the electro-oculogram signals are extracted;
firstly, acquiring various feature information of the waveform features, and then comparing the various feature information with various preset identification parameters in a preset identification mode to determine a motion mode corresponding to the waveform features;
and driving the wheelchair body to move according to the movement mode determined by the mode recognition module.
Compared with the existing wheelchair control system, the electro-oculogram intelligent wheelchair and the control method thereof have the following beneficial effects:
1. this eye electricity intelligence wheelchair, its through each electrode paster laminating in the bipolar electrode paster subassembly that leads of signal acquisition device about forehead, eyeball and the left and right sides of human face, the user of service produces different signals of telecommunication through the change action of glasses, and the signal of telecommunication is for inputing the eye signal of telecommunication among the signal processing device after enlarging, gain control and bias adjustment. The characteristic extraction module of the signal processing device carries out filtering, sampling compression and waveform repeated sampling processing on the electro-oculogram signals to obtain waveform characteristics, and the mode recognition module can determine the motion mode represented by the electro-oculogram signals generated by users according to various characteristic information of the waveform characteristics, so that the wheelchair motion control device can control the intelligent wheelchair according to the motion mode to enable the wheelchair to move according to the motion mode desired by the users. Therefore, a user can control the intelligent wheelchair to perform various movements only by changing eyes, such as blinking, moving eyeballs and the like, and realizes non-limb control operation of the wheelchair.
2. This eye electric intelligence wheelchair, its mode identification module is according to the maximum and minimum in the wave form characteristic and each preset scope carries out the comparison, the preset scope at these two extreme values place is judged the motion mode and is the left turn or the right turn, utilize the count that satisfies the preset scope in a set of number with judge the motion mode and be advancing, retreat, park which in, to left turn and right turn order like this, only satisfy respectively set for minimum and maximum can, to advancing, retreat and park the order, still satisfy respectively the number of times of the maximum of setting for when satisfying maximum and minimum scope, just can send the order. So, can control intelligent wheelchair accurately through the eye signal of telecommunication, the real-time of control is better to can set up the scope of predetermineeing according to the individual difference, it is pointed to have, can satisfy different personnel's demands.
3. This eye electricity intelligence wheelchair, it is more reliable to the extraction of eye's signal of telecommunication: the method has the advantages that the electro-oculogram signals are collected in a double-lead connection mode, the signal amplitude is larger, and the extracted electro-oculogram signals are subjected to double filtering processing by hardware and LabView software, so that the extracted electro-oculogram signals are more reliable.
4. This eye electric intelligence wheelchair, it is simple and convenient more to mode identification parameter adjustment: and a graphical operation interface is provided in a LabView environment, and the parameters of the mode identification module are easier to adjust.
5. This eye electric intelligence wheelchair, its application is stronger: the wheelchair can help patients with limb movement dysfunction or disabled with deficient limbs to realize non-limb control operation of the wheelchair, and can be used in occasions inconvenient to operate by hands or high-risk working areas.
6. This eye electricity intelligence wheelchair, its easy operation: provides a friendly man-machine operation interface, can observe the electro-oculogram signal oscillogram in real time and prevent misoperation.
7. This eye electricity intelligence wheelchair, its adjustment is convenient: because the control program written based on LabView software can change the control mode and the like without changing hardware equipment, the method has the characteristic of flexibility.
8. The beneficial effects of the control method of the electro-oculogram intelligent wheelchair are the same as those of the electro-oculogram intelligent wheelchair, and are not repeated herein.
Drawings
Fig. 1 is a block flow diagram of an electro-oculogram intelligent wheelchair according to embodiment 1 of the present invention.
Fig. 2 is a block diagram of an electro-oculogram intelligent wheelchair according to embodiment 1 of the present invention.
Fig. 3 is a position diagram of a bipolar lead electrode patch assembly of the electro-ocularly intelligent wheelchair of fig. 1.
Fig. 4 is a module wiring diagram of a signal acquisition device of the electro-oculogram intelligent wheelchair in fig. 1.
Fig. 5 is a simulated waveform diagram of the electro-ocularly intelligent wheelchair of fig. 1 produced by a user blinking once.
Fig. 6 is a simulated waveform diagram generated by the electro-oculogram smart wheelchair of fig. 1 when a user blinks twice in succession.
Fig. 7 is a simulated waveform diagram generated by the electro-oculogram smart wheelchair of fig. 1 when a user blinks three times in succession.
Fig. 8 is a simulated waveform diagram generated when the electro-ocularly intelligent wheelchair in fig. 1 looks to the right by the user.
Fig. 9 is a simulated waveform diagram generated when the electro-oculogram intelligent wheelchair in fig. 1 is used by a user looking to the left.
Fig. 10 is a block diagram of a wheelchair motion control apparatus of the electro-ocularly intelligent wheelchair of fig. 1.
Fig. 11 is a flowchart of a mode determination procedure of a mode identification module of the electro-ocular intelligent wheelchair according to embodiment 2 of the present invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
Referring to fig. 1 and 2, the embodiment provides an intelligent wheelchair with an eye-powered function. The wheelchair is an electro-oculogram intelligent wheelchair based on LabView, and an electro-oculogram signal is collected in a bipolar lead mode. The wheelchair comprises a wheelchair mechanism, a signal acquisition device, a signal processing device and a wheelchair motion control device, and the communication modes among the devices can adopt the existing communication modes, such as Bluetooth, wiFi, serial ports and the like.
The wheelchair mechanism comprises a wheelchair body and a motor. The motor is installed on the wheelchair body and is used for driving the wheelchair body to move. The wheelchair mechanism may be an existing electrically powered wheelchair that is electrically powered to enable movement in a variety of terrain, although in some embodiments the wheelchair mechanism may be other wheelchairs that are capable of movement under external power support.
Referring to fig. 3 and 4, the signal acquisition device includes a bipolar lead electrode patch assembly and a signal acquisition module. The bipolar lead electrode patch assembly comprises a first electrode patch, a second electrode patch, a third electrode patch, a fourth electrode patch, a fifth electrode patch, a sixth electrode patch and a seventh electrode patch. The first electrode patch is used for being attached to the area between two eyes of the face of the human body, namely above the nose, and is used as a reference electrode and used for detecting a first electric signal carried by the face of the human body. The second electrode patch and the third electrode patch are respectively attached to the upper side and the lower side of one of eyes of the face of the human body and used for measuring a second electric signal generated when the corresponding eyeball moves in the vertical direction. The electrode patch four and the electrode patch five are respectively attached to the left side and the right side of two eyes of the face of the human body and are used for measuring an electric signal three generated when the corresponding eyeball moves in the horizontal direction. And the electrode patch six and the electrode patch seven are respectively attached to the upper side and the lower side of the other eye of the human face and used for measuring an electric signal four generated by the movement of the corresponding eyeball in the vertical direction. The signal acquisition module is used for amplifying, gain adjusting and bias adjusting the first electric signal, the second electric signal, the third electric signal and the fourth electric signal, acquiring corresponding eye electric signals and taking the eye electric signals as acquired data.
In this embodiment, the electrode patches a, B, C, D, E, F, G are the electrode patches one, two, three, four, five, six, and seven, respectively. The electrode patch A is attached to the center of the forehead and used as a reference electrode for collecting the electric signal carried by the human body. Electrode patches B and C are attached above and below the right eye for measuring potential changes caused by vertical movement of the eye. Electrode patches D and E are attached to the left and right of the eye for measuring potential changes caused by horizontal movement of the eyeball. Electrode patches F and G are attached above and below the left eye to measure the potential change caused by the vertical movement of the eyeball. The bipolar lead electrode patch assembly is an Ag/AgCI electrode assembly, namely an Ag/AgCI electrode is adopted as an electrode of the electrode patch.
When the electrode patch is pasted, the specific pasting method comprises the following steps:
1) Removing the electrodes from the transparent sheet;
2) Cleaning the region where the electrode cable is to be adhered with clear water;
3) The electrode was attached to the test site and gently pressed flat.
The signal acquisition module comprises two signal conditioning units, a double-path acquisition board and a data acquisition card. The signal conditioning unit comprises an instrument amplifier and an operational amplifier which are used for amplifying the first electric signal, the second electric signal and the third electric signal, and is also used for inhibiting the potential of the electrode half-cell. The double-path acquisition board is used for being matched with the two signal conditioning units to realize double-path input, each path of input realizes gain adjustment and bias adjustment, and the electro-oculogram signals can be adjusted to be in proper size in real time. The data acquisition card is used for transmitting the adjusted eye electrical signals to the signal processing device, namely to a computer where the signal processing device is located. In the aspect of model selection, the signal conditioning unit is an AD8232 chip, the double-circuit acquisition board is an AD620 double-circuit acquisition board, and the data acquisition card is an NI PCIe-6321 data acquisition card.
Referring to fig. 5-9, the signal processing apparatus includes a feature extraction module and a pattern recognition module. The characteristic extraction module is used for filtering, sampling and compressing the acquired data, repeatedly sampling the waveform and extracting the waveform characteristic of the electro-oculogram signal. The pattern recognition module is used for firstly acquiring various feature information of the waveform features and then comparing the various feature information with various preset recognition parameters in a preset recognition pattern to determine the motion mode corresponding to the waveform features. Wherein, the signal processing device is established based on LabView environment. The feature extraction module may include a DAQ helper, a filter, a sample compression unit, a waveform resampling unit, and a waveform display unit. The DAQ assistant is used for setting a specific serial port for collecting data, the type of the collected data is voltage, the collection range is-5V, and excessive voltage is filtered. The filter is used for filtering the acquired data to remove the voltage which does not meet the acquisition range, filtering out the noise with cut-off frequency greater than a preset frequency and reserving effective signals. As the frequency of the eye electric signal is mostly between 0Hz and 38Hz, a 3 rd order Butterworth low-pass filter with the cut-off frequency of 40Hz is selected to filter out the noise higher than 40 Hz. The sampling compression unit is used for continuously sampling and compressing the acquired data, the sampling rate is 1000 times/second, the compression factor is 50, namely the acquired data 50 are grouped, and the average value of the acquired data is taken, so that a relatively stable waveform can be obtained. The waveform resampling unit is used for performing waveform resampling on the acquired data, and the repetition degree is set to be one half. The waveform resampling is used for carrying out certain repeated acquisition on data, and data omission is prevented, because in the data transmission process, useful signals can not be contained right when a data segment is intercepted every time, the data is required to be resampled, and the repetition degree is set to be one half. The waveform display unit is used for displaying the waveform of the electro-oculogram signal in the collected data.
In this embodiment, after a group of collected data is sent to the pattern recognition module, it is determined whether the ranges of the maximum value and the minimum value satisfy the set values, and only the minimum value and the maximum value set for the left turn command and the right turn command are satisfied, and the commands for the forward, the backward and the stop commands satisfy the ranges of the maximum value and the minimum value and also satisfy the number of times of the maximum value set for each command, and then the commands can be issued. The threshold value is set to a size that varies from person to person, and even for the same person, the size varies slightly depending on the position at which the electrode is attached. But the adjustment is also very convenient, and the adjustment can be carried out in time only by observing the oscillogram caused by various eye actions.
The mode recognition module provides a visual mode recognition parameter adjustment interface, and the mode recognition parameter adjustment interface has the following input modes:
(1) The eyes are looking straight on the screen, after the waveform of the eye electrical signal is stable, the blinking times are 1,2 and 3 in sequence, and the signal amplitude is recorded;
(2) The eye emmetropia screen is used for respectively looking left and then returning to the emmetropia direction after the waveform of the eye electrical signal is stable, and respectively recording the waveform and the amplitude when looking right and then returning to the emmetropia direction;
(3) And filling parameters in the corresponding adjusting interface.
Referring to fig. 10, the wheelchair motion control device is configured to drive the wheelchair body to move according to the motion mode determined by the mode identification module. In this embodiment, the motion mode includes forward, backward, stop, left turn, right turn, acceleration, deceleration, and uniform speed. The wheelchair motion control device comprises a direct current servo driver and a photoelectric encoder. The upper computer where the wheelchair motion control device is located generates corresponding command signals to the direct current servo driver according to the motion mode determined by the mode recognition module, and the direct current servo driver controls the motor to correspondingly rotate according to the motion mode designated by the command signals, so that the wheelchair body moves according to the motion mode. The photoelectric encoder is used for measuring the rotating speed of the motor and returning the rotating speed to the upper computer. Wherein, the DC servo driver is an MLD3605-D driver, and the motor is an MY1016Z DC motor. The number of the direct current servo drivers, the number of the motors and the number of the wheels of the intelligent wheelchair are two and are in one-to-one correspondence, and each direct current servo driver is used for driving the corresponding wheel to move through the corresponding motor.
The command signals corresponding to different eye electrical signals are respectively as follows:
(a) Blinking twice by two eyes to drive the wheelchair body to move forwards;
(b) Blinking three times by two eyes to drive the wheelchair body to retreat;
(c) The wheelchair is swept leftwards, and the wheelchair body is driven to turn leftwards;
(d) The wheelchair is swept to the right, and the wheelchair body is driven to rotate to the right;
(e) Blinking once through two eyes to drive the wheelchair body to stop;
(f) The left eye blinks once and the right eye does not blink, so that the wheelchair body is driven to move in an accelerated manner;
(g) Enabling the right eye to blink once and the left eye not to blink, and driving the wheelchair body to do deceleration movement;
(h) Both eyes blink and are asynchronous, and the wheelchair body is driven to move at a uniform speed.
To sum up, compare in current wheelchair control system, the intelligent wheelchair of eye electricity of this embodiment has following beneficial effect:
1. this eye electricity intelligence wheelchair, its through each electrode paster laminating in the bipolar electrode paster subassembly that leads of signal acquisition device about forehead, eyeball and the left and right sides on human face, the user of service produces different signals of telecommunication through the change action of glasses, the signal of telecommunication is for inputing the eye electricity signal among the signal processing device after enlarging, gain adjustment and bias adjustment. The characteristic extraction module of the signal processing device carries out filtering, sampling compression and waveform repeated sampling processing on the electro-oculogram signals to obtain waveform characteristics, and the mode recognition module can determine the motion mode represented by the electro-oculogram signals generated by users according to various characteristic information of the waveform characteristics, so that the wheelchair motion control device can control the intelligent wheelchair according to the motion mode to enable the wheelchair to move according to the motion mode desired by the users. Therefore, a user can control the intelligent wheelchair to perform various movements only by changing eyes, such as blinking, moving eyeballs and the like, and realizes non-limb control operation of the wheelchair.
2. This eye electric intelligence wheelchair, its mode identification module is according to the maximum and minimum in the wave form characteristic and each preset scope carries out the comparison, the preset scope at these two extreme values place is judged the motion mode and is the left turn or the right turn, utilize the count that satisfies the preset scope in a set of number with judge the motion mode and be advancing, retreat, park which in, to left turn and right turn order like this, only satisfy respectively set for minimum and maximum can, to advancing, retreat and park the order, still satisfy respectively the number of times of the maximum of setting for when satisfying maximum and minimum scope, just can send the order. So, can control intelligent wheelchair accurately through the eye signal of telecommunication, the real-time of control is better to can set up the scope of predetermineeing according to the individual difference, it is corresponding to have, can satisfy different personnel's demands.
3. This eye electricity intelligence wheelchair, it is more reliable to the extraction of eye's signal of telecommunication: the method has the advantages that the electro-oculogram signals are collected in a double-lead connection mode, the signal amplitude is larger, and the extracted electro-oculogram signals are subjected to double filtering processing by hardware and LabView software, so that the extracted electro-oculogram signals are more reliable.
4. This eye electric intelligence wheelchair, it is simple and convenient more to mode identification parameter adjustment: and a graphical operation interface is provided in a LabView environment, and the parameters of the pattern recognition module are easier to adjust.
5. This eye electric intelligence wheelchair, its application is stronger: the wheelchair can help patients with limb movement dysfunction or disabled with limbs defective to realize non-limb control operation of the wheelchair, and can be used in occasions inconvenient to operate by hands or high-risk working areas.
6. This eye electricity intelligence wheelchair, its easy operation: provides a friendly man-machine operation interface, can observe the electro-oculogram signal oscillogram in real time and prevent misoperation.
7. This eye electricity intelligence wheelchair, its adjustment is convenient: because the control program compiled based on LabView software can change the control mode and the like without changing hardware equipment, the method has the characteristic of flexibility.
Example 2
Referring to fig. 11, the present embodiment provides an eye-powered intelligent wheelchair, and the system refines the specific identification process of the mode identification module based on embodiment 1. The pattern recognition module judges whether the maximum value in the waveform characteristics is within a first preset range. When the maximum value is within the first preset range, the mode identification module judges whether the minimum value in the waveform characteristics is within a second preset range. And when the minimum value is within the second preset range, the mode identification module determines that the motion mode is right turning. And when the maximum value is not in the first preset range, the mode identification module judges whether the maximum value is in a third preset range. And when the maximum value is within the third preset range, the mode identification module judges whether the minimum value is within a fourth preset range. And when the minimum value is within the fourth preset range, the mode identification module determines that the motion mode is left turning. And when the minimum value is not in the preset range four, the mode identification module judges whether the minimum value is in a preset range five. When the minimum value is within a preset range five, the mode identification module counts a group of numbers, wherein the number meets the condition that the waveform characteristics are within a preset range six, and judges whether the count is equal to a preset number one, if so, the motion mode is determined to be parking, otherwise, the count is judged to be equal to a preset number two, if so, the motion mode is determined to be forward, otherwise, the count is judged to be equal to a preset number three, if so, the motion mode is determined to be backward, and if not, the waveform characteristics are obtained again. And when the minimum value is not in the second preset range or the maximum value is not in the third preset range or the minimum value is not in the fifth preset range, the mode identification module acquires the waveform characteristics again.
In this embodiment, the lower limit of the first preset range is greater than the upper limit of the second preset range, the lower limit of the third preset range is greater than or equal to the upper limit of the first preset range, the upper limit of the fourth preset range is less than the lower limit of the second preset range, the fifth preset range is the same as the second preset range, and the sixth preset range is the same as the third preset range. Wherein the first preset range is [2.2,2.3 ], the second preset range and the fifth preset range are both [2,2.15 ], the third preset range and the sixth preset range are both [2.3,2.4 ], and the fourth preset range is [1.8,1.95 ].
Example 3
The embodiment provides an eye-powered intelligent wheelchair, and the system is further added with the following steps on the basis of the embodiment 1. The pattern recognition module judges whether the maximum value in the waveform characteristics is within a first preset range. When the maximum value is within the first preset range, the mode identification module judges whether the minimum value in the waveform characteristics is within a second preset range. And when the minimum value is within the second preset range, the mode identification module determines that the motion mode is right turning. And when the maximum value is not in the first preset range, the mode identification module judges whether the maximum value is in a third preset range. And when the maximum value is within the third preset range, the mode identification module judges whether the minimum value is within a fourth preset range. And when the minimum value is within the preset range four, the mode identification module determines that the motion mode is left turn. And when the minimum value is not within the preset range four, the mode identification module judges whether the minimum value is within a preset range five. When the minimum value is within a preset range five, the mode identification module counts a group of numbers, the count meeting the condition that the waveform features are within a preset range six is judged, whether the count is equal to a preset number one is judged, if yes, the motion mode is determined to be parking, otherwise, whether the count is equal to a preset number two is judged, if yes, the motion mode is determined to be forward, otherwise, whether the count is equal to a preset number three is judged, if yes, the motion mode is determined to be backward, otherwise, whether the count is equal to a preset number four is judged, if yes, the motion mode is determined to be acceleration, if not, whether the count is equal to a preset number five is judged, if yes, the motion mode is determined to be uniform, if not, the count is equal to a preset number six, the motion mode is determined to be deceleration, and if not, the waveform features are obtained again. And when the minimum value is not in the second preset range or the maximum value is not in the third preset range or the minimum value is not in the fifth preset range, the mode identification module acquires the waveform characteristics again.
Example 4
The implementation provides an eye-powered intelligent wheelchair control method which is applied to the eye-powered intelligent wheelchair in the embodiment 1 or 2 and comprises the following steps.
Firstly, detecting an electric signal I carried by the face of the human body, measuring an electric signal II, an electric signal IV and an electric signal III generated by the movement of the corresponding eyeball in the vertical direction and the horizontal direction, and then amplifying, gain adjusting and bias adjusting the electric signal I, the electric signal II, the electric signal III and the electric signal IV to obtain corresponding eye electric signals to serve as collected data. The step is mainly realized by the signal acquisition device in the embodiment 1, the electro-oculogram signals are acquired by the bipolar lead electrode patch component, and the electro-oculogram signals are processed by the signal acquisition module.
And secondly, filtering, sampling and compressing the acquired data, repeatedly sampling the waveform, and extracting the waveform characteristics of the electro-oculogram signal. This step is mainly implemented by the feature extraction module of the signal processing apparatus in embodiment 1, and can extract the required waveform feature, and the waveform feature can reflect the information that the user needs to express.
And thirdly, acquiring various feature information of the waveform feature, and comparing the various feature information with various preset identification parameters in a preset identification mode to determine the motion mode corresponding to the waveform feature. The step is mainly realized by a mode recognition module of the signal processing device in the embodiment 1, and the main function of the step is to recognize the characteristic information in the electro-oculogram signal and determine that the intelligent wheelchair required by a user acts.
And fourthly, driving the wheelchair body to move according to the movement mode determined by the mode recognition module. This step is mainly realized by the wheelchair motion control device in embodiment 1, and the purpose is to make the intelligent wheelchair perform actions according to the determined motion mode in the previous step, such as forward movement, backward movement, parking, left turn, right turn, acceleration, uniform speed, deceleration, and the like.
Example 5
The embodiment provides an eye-powered intelligent wheelchair control method, which adds the following steps on the basis of the embodiment 4.
Judging whether the maximum value in the waveform characteristics is within a first preset range or not;
when the maximum value is within a first preset range, judging whether the minimum value in the waveform characteristics is within a second preset range;
when the minimum value is within a second preset range, determining that the motion mode is right turning;
when the maximum value is not in the first preset range, judging whether the maximum value is in a third preset range;
when the maximum value is within a third preset range, judging whether the minimum value is within a fourth preset range;
when the minimum value is within the fourth preset range, determining that the motion mode is left turning;
when the minimum value is not in the preset range four, judging whether the minimum value is in a preset range five or not;
when the minimum value is within a preset range five, counting the number of a group of numbers which meets the condition that the waveform characteristics are within a preset range six, judging whether the number is equal to a preset number one, if so, determining that the motion mode is parking, otherwise, judging whether the number is equal to a preset number two, if so, determining that the motion mode is forward, otherwise, judging whether the number is equal to a preset number three, if so, determining that the motion mode is backward, otherwise, acquiring the waveform characteristics again;
and when the minimum value is not in the second preset range, or the maximum value is not in the third preset range, or the minimum value is not in the fifth preset range, re-acquiring the waveform characteristics.
In this embodiment, the lower limit of the first preset range is greater than the upper limit of the second preset range, the lower limit of the third preset range is greater than or equal to the upper limit of the first preset range, the upper limit of the fourth preset range is less than the lower limit of the second preset range, the fifth preset range is the same as the second preset range, and the sixth preset range is the same as the third preset range. Wherein the first preset range is [2.2,2.3 ], the second preset range and the fifth preset range are both [2,2.15 ], the third preset range and the sixth preset range are both [2.3,2.4 ], and the fourth preset range is [1.8,1.95 ].
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (8)
1. An eye-ward intelligent wheelchair, comprising:
a wheelchair mechanism comprising a wheelchair body and a motor; the motor is arranged on the wheelchair body and is used for driving the wheelchair body to move;
the signal acquisition device comprises a bipolar lead electrode patch assembly and a signal acquisition module; the bipolar lead electrode patch assembly comprises an electrode patch I, an electrode patch II, an electrode patch III, an electrode patch IV, an electrode patch V, an electrode patch VI and an electrode patch VII; the electrode patch I is used for being attached to an area between two eyes of the face of a human body, is used as a reference electrode and is used for detecting an electric signal I carried by the face of the human body; the second electrode patch and the third electrode patch are respectively attached to the upper side and the lower side of one of eyes of the face of the human body and used for measuring a second electric signal generated by the movement of the corresponding eyeball in the vertical direction; the electrode patch IV and the electrode patch V are respectively attached to the left side and the right side of two eyes of the face of the human body and are used for measuring an electric signal III generated when the corresponding eyeball moves in the horizontal direction; the electrode patch six and the electrode patch seven are respectively attached to the upper side and the lower side of the other eye of the human face and used for measuring an electric signal four generated when the corresponding eyeball moves in the vertical direction; the signal acquisition module is used for amplifying, gain adjusting and bias adjusting the first electric signal, the second electric signal, the third electric signal and the fourth electric signal to acquire corresponding eye electric signals and use the eye electric signals as acquired data;
the signal processing device comprises a feature extraction module and a pattern recognition module; the characteristic extraction module is used for filtering, sampling and compressing the acquired data, repeatedly sampling waveforms and extracting the waveform characteristics of the electro-oculogram signals; the pattern recognition module is used for acquiring various feature information of the waveform features, and then comparing the various feature information with various preset recognition parameters in a preset recognition pattern to determine a motion mode corresponding to the waveform features; the motion modes comprise forward motion, backward motion, stop motion, left rotation, right rotation, acceleration, deceleration and uniform speed; and
the wheelchair motion control device is used for driving the wheelchair body to move according to the motion mode determined by the mode identification module through the motor;
in the identification mode, the mode identification module judges whether the maximum value in the waveform characteristics is positioned in a first preset range or not; when the maximum value is located in the first preset range, the mode identification module judges whether the minimum value in the waveform characteristics is located in a second preset range; when the minimum value is within the second preset range, the mode identification module determines that the motion mode is right turning; when the maximum value is not in the first preset range, the mode identification module judges whether the maximum value is in a third preset range; when the maximum value is within the third preset range, the mode identification module judges whether the minimum value is within a fourth preset range; when the minimum value is within the preset range four, the mode identification module determines that the motion mode is left turning; when the minimum value is not within the preset range four, the mode identification module judges whether the minimum value is within a preset range five or not; when the minimum value is within a preset range five, the mode identification module counts a count which satisfies that the waveform feature is within a preset range six in a group of numbers, judges whether the count is equal to a preset number one, determines that the motion mode is parking if the count is equal to a preset number two, determines that the motion mode is forward if the count is equal to a preset number two, determines whether the count is equal to a preset number three if the count is not equal to a preset number three, determines that the motion mode is backward if the count is equal to a preset number three, and otherwise, acquires the waveform feature again; when the minimum value is not in the second preset range, or the maximum value is not in the third preset range, or the minimum value is not in the fifth preset range, the mode identification module acquires the waveform characteristics again;
the lower limit of the first preset range is larger than the upper limit of the second preset range, the lower limit of the third preset range is larger than or equal to the upper limit of the first preset range, the upper limit of the fourth preset range is smaller than the lower limit of the second preset range, the fifth preset range is the same as the second preset range, and the sixth preset range is the same as the third preset range.
2. The electro-ocularly intelligent wheelchair of claim 1, wherein the preset range one is [2.2, 2.3), the preset range two and the preset range five are both [2, 2.15), the preset range three and the preset range six are both [2.3,2.4 ], and the preset range four is [1.8, 1.95).
3. The electro-ocular smart wheelchair of claim 1 wherein the signal acquisition module comprises two signal conditioning units, a dual path acquisition board, and a data acquisition card; the signal conditioning unit comprises an instrument amplifier and an operational amplifier which are used for amplifying the first electric signal, the second electric signal and the third electric signal, and is also used for inhibiting the electric potential of the electrode half cell; the dual-path acquisition board is used for matching with the two signal conditioning units to realize dual-path input, and each path of input is used for realizing gain adjustment and bias adjustment; and the data acquisition card is used for transmitting the adjusted eye electrical signals to the signal processing device.
4. The electro-ocular smart wheelchair of claim 1 wherein the signal processing means is built on a LabView environment; the characteristic extraction module comprises a DAQ assistant, a filter, a sampling compression unit, a waveform resampling unit and a waveform display unit; the DAQ assistant is used for setting a specific serial port of the acquired data, the type of the acquired data is voltage, and the acquisition range is-5 to-5V; the filter is used for filtering the acquired data to remove the voltage which does not meet the acquisition range and filtering the noise with cutoff frequency greater than a preset frequency; the sampling compression unit is used for continuously sampling and compressing the acquired data, the sampling rate is 1000 times/second, and the compression factor is 50; the waveform resampling unit is used for performing waveform resampling on the acquired data, and the repetition degree is set to be one half; the waveform display unit is used for displaying the waveform of the electro-oculogram signal in the collected data.
5. The electro-ocular smart wheelchair of claim 4 wherein the pattern recognition module provides a visualized pattern recognition parameter adjustment interface having the following inputs:
(1) The eye emmetropia screen records the signal amplitude when the blinking times are 1,2 and 3 times in sequence after the waveform of the eye electrical signal is stable;
(2) The eye emmetropia screen is used for respectively looking to the left and then returning to the emmetropia direction after the waveform of the eye electric signal is stable, looking to the right and then returning to the emmetropia direction, and respectively recording the waveform and the amplitude;
(3) And filling parameters in the corresponding adjusting interface.
6. The electro-ocularly intelligent wheelchair of claim 3, wherein the wheelchair motion control means comprises a dc servo drive and a photoelectric encoder; the upper computer where the wheelchair motion control device is located generates a corresponding command signal to the direct current servo driver according to the motion mode determined by the mode identification module, and the direct current servo driver controls the motor to correspondingly rotate according to the motion mode designated by the command signal, so that the wheelchair body moves according to the motion mode; the photoelectric encoder is used for measuring the rotating speed of the motor and returning the rotating speed to the upper computer;
the command signals corresponding to different eye electrical signals are respectively as follows:
(a) Blinking twice by two eyes to drive the wheelchair body to move forwards;
(b) Blinking three times by two eyes to drive the wheelchair body to retreat;
(c) The wheelchair body is driven to rotate left after being swept to the left;
(d) The wheelchair is swept to the right, and the wheelchair body is driven to rotate to the right;
(e) Twinkling is carried out by two eyes once, and the wheelchair body is driven to stop;
(f) The left eye blinks once and the right eye does not blink, so that the wheelchair body is driven to move in an accelerated manner;
(g) Enabling the right eye to blink once and the left eye not to blink, and driving the wheelchair body to do deceleration movement;
(h) The eyes blink and are asynchronous, and the wheelchair body is driven to move at a uniform speed.
7. The electro-ocularly intelligent wheelchair of claim 6, wherein the bipolar lead electrode patch assembly is an Ag/AgCI electrode assembly, the signal conditioning unit is an AD8232 chip, the dual-path acquisition board is an AD620 dual-circuit acquisition board, and the data acquisition card is an NI PCIe-6321 data acquisition card; the direct current servo driver is an MLD3605-D driver, and the motor is an MY1016Z direct current motor; the number of the direct current servo drivers, the number of the motors and the number of the wheels of the intelligent wheelchair are two and are in one-to-one correspondence, and each direct current servo driver is used for driving the corresponding wheel to move through the corresponding motor.
8. An eye-powered intelligent wheelchair control method applied to the eye-powered intelligent wheelchair as claimed in any one of claims 1 to 7, characterized by comprising the following steps:
firstly, detecting an electric signal I carried by the face of the human body, measuring an electric signal II and an electric signal IV generated by moving the corresponding eyeballs in the vertical direction and an electric signal III generated by moving the corresponding eyeballs in the horizontal direction, and then carrying out amplification, gain adjustment and offset adjustment on the electric signal I, the electric signal II, the electric signal III and the electric signal IV to obtain corresponding eye electric signals to serve as collected data;
filtering, sampling compression and waveform repeated sampling are carried out on the acquired data, and the waveform characteristics of the electro-oculogram signals are extracted;
firstly, acquiring various feature information of the waveform feature, and then comparing the various feature information with various preset identification parameters in a preset identification mode to determine a motion mode corresponding to the waveform feature;
and driving the wheelchair body to move according to the movement mode determined by the mode identification module.
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Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100127121A (en) * | 2009-05-25 | 2010-12-03 | 한양대학교 산학협력단 | Electric wheel chair using eog signal |
| CN102129307A (en) * | 2011-03-10 | 2011-07-20 | 电子科技大学 | Electro-oculogram signal-based computer input control method |
Family Cites Families (10)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| GB0220265D0 (en) * | 2002-09-02 | 2002-10-09 | Univ Ulster | Signal processing strategy to control external devices by means of human electro-oculagraphic potentials |
| CN101598973B (en) * | 2009-06-26 | 2011-01-05 | 安徽大学 | Human-computer interaction system based on electro-ocular signal |
| US8348841B2 (en) * | 2010-04-09 | 2013-01-08 | The Board Of Trustees Of The University Of Arkansas | Wireless nanotechnology based system for diagnosis of neurological and physiological disorders |
| CN102968072A (en) * | 2012-11-09 | 2013-03-13 | 上海大学 | Electro-oculogram control system and method based on correction/training |
| CN104323773B (en) * | 2014-10-31 | 2016-11-16 | 东北大学 | A kind of ERG signal acquiring and processing system based on EOG and method |
| CN105904459A (en) * | 2016-05-19 | 2016-08-31 | 南京邮电大学 | Robot control system based on eye electrical signal recognition and design method of system |
| CN106775023B (en) * | 2017-01-09 | 2023-07-18 | 成都信息工程大学 | Eye electric signal acquisition method and Bluetooth mouse system based on eye electric signal control |
| CN107007407B (en) * | 2017-04-12 | 2018-09-18 | 华南理工大学 | Wheelchair control system based on eye electricity |
| CN107260420B (en) * | 2017-07-03 | 2018-11-23 | 南京邮电大学 | Intelligent wheel chair human-computer interactive control system and method based on eye motion recognition |
| CN108733217A (en) * | 2018-07-24 | 2018-11-02 | 高维度（深圳）生物信息智能应用有限公司 | Electro-ocular signal processing circuit and man-machine interactive system based on eye electricity |
-
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Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20100127121A (en) * | 2009-05-25 | 2010-12-03 | 한양대학교 산학협력단 | Electric wheel chair using eog signal |
| CN102129307A (en) * | 2011-03-10 | 2011-07-20 | 电子科技大学 | Electro-oculogram signal-based computer input control method |
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