CN112162549A - Trolley control system and control method based on combination of electroencephalogram and myoelectricity - Google Patents
Trolley control system and control method based on combination of electroencephalogram and myoelectricity Download PDFInfo
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Abstract
A trolley control system based on combination of electroencephalogram and myoelectricity comprises electroencephalogram acquisition and processing equipment, myoelectricity acquisition and processing equipment and a trolley control device. The electroencephalogram acquisition and processing equipment comprises an electroencephalogram signal acquisition unit and an electroencephalogram signal processing unit; the electroencephalogram signal processing unit receives the electroencephalogram signals acquired by the electroencephalogram signal acquisition unit and processes the electroencephalogram signals to acquire a raw data value and an Attention value. The electromyographic acquisition processing equipment comprises an electromyographic signal acquisition unit and an electromyographic signal processing unit; the electromyographic signal acquisition unit receives the electromyographic signal acquired by the electromyographic signal acquisition unit and processes the electromyographic signal to obtain an electromyographic digital signal. The trolley control device comprises a signal receiving and processing unit and a control unit, wherein the signal receiving and processing unit respectively receives the rawdata value and the Attention value output by the electroencephalogram signal processing unit and compares the values with the threshold values, receives the electromyogram digital signal output by the electromyogram signal processing unit, calculates the electromyogram digital signal, compares the values with the threshold values, and then generates a control instruction to be output to the control unit.
Description
Technical Field
The invention relates to the field of man-machine interaction control realized through bioelectricity, in particular to a trolley control system and a trolley control method based on combination of electroencephalogram and myoelectricity.
Background
In recent years, with rapid development of related technologies such as brain-computer interface technology, electronic integration technology and the like, the application of bioelectric signals in a human-computer interaction control system is increasing. Among the bioelectricity signals, the electroencephalogram signal and the electromyogram signal are mainly applied, and the bioelectricity signals are mainly used for controlling devices such as a computer cursor, a keyboard, a remote controller, a trolley, a wheelchair and the like, so that target people (such as disabled people, old people and the like) can conveniently solve the difficulty of manually controlling the devices.
In the prior art, the trolley is usually controlled only through electroencephalogram signals, but the electroencephalogram signals have the characteristics of uncertainty, randomness and weakness, the types of recognizable signals are limited, the trolley has various different motion states, and the trolley is controlled only through the electroencephalogram signals, so that the control accuracy is low, and the practicability is low.
Disclosure of Invention
In order to make up for the defects of the prior art, the invention provides the trolley control system and the trolley control method based on the combination of electroencephalogram and electromyogram, wherein the trolley is controlled by the combination of electroencephalogram signals and electromyogram signals, so that the problem of low practicability caused by too few control modes of the electroencephalogram signals can be solved, and the accuracy in control can be improved.
A trolley control system based on combination of electroencephalogram and myoelectricity comprises electroencephalogram acquisition and processing equipment, myoelectricity acquisition and processing equipment and a trolley control device.
The electroencephalogram acquisition and processing equipment comprises an electroencephalogram signal acquisition unit and an electroencephalogram signal processing unit; the electroencephalogram signal processing unit receives the native electroencephalogram signals of the equipment user acquired by the electroencephalogram signal acquisition unit and processes the native electroencephalogram signals to obtain the raw data value and the Attention value of the electroencephalogram signals.
The electromyographic acquisition processing equipment comprises an electromyographic signal acquisition unit and an electromyographic signal processing unit; the electromyographic signal acquisition unit receives the native electromyographic signals of the equipment user acquired by the electromyographic signal acquisition unit, and the native electromyographic signals are processed to obtain electromyographic digital signals.
The trolley control device comprises a signal receiving and processing unit, a control unit, a first motor and a second motor, wherein the signal receiving and processing unit is used for respectively receiving a raw data value and an Attention value of an electroencephalogram signal output by the electroencephalogram signal processing unit and comparing the values with a threshold value, receiving an electromyogram digital signal output by the electromyogram signal processing unit, calculating the electromyogram digital signal and comparing the values with the threshold value, then generating a control command and outputting the control command to the control unit, and the control unit is used for respectively adjusting and driving the first motor and the second motor of wheels on two sides of the trolley according to the received control command.
Furthermore, the electroencephalogram signal processing unit is a TGAM module, and the raw electroencephalogram signal is amplified, filtered and A/D converted to output rawdata and Attention values.
Furthermore, the signal receiving and processing unit comprises a raw data threshold comparison module and an Attention threshold comparison module for processing the electroencephalogram signals; after the raw data threshold value comparison module receives the raw data value of the electroencephalogram signal output by the electroencephalogram signal processing unit, comparing the raw data value with a set trolley basic motion state threshold value, and generating an instruction for changing the trolley basic motion state if the conditions are met; after the Attention threshold value comparison module receives the Attention value of the electroencephalogram signal output by the electroencephalogram signal processing unit, the Attention value is compared with a set trolley speed gear threshold value range, and a trolley speed gear control instruction is generated if the condition is met.
Furthermore, the signal receiving and processing unit further comprises an electromyography calculation and threshold comparison module for processing the electromyography signals, and after the electromyography calculation and threshold comparison module receives the electromyography digital signals output by the electromyography signal processing unit, the standard deviation value of a group of electromyography signals is calculated by the following formula:
Furthermore, the signal receiving and processing unit is a raspberry pi 4B main board, and the raspberry pi 4B main board is connected with the electroencephalogram signal processing unit and the electromyogram signal processing unit through Bluetooth.
A trolley control method based on combination of electroencephalogram and myoelectricity comprises the following steps:
s10: respectively acquiring a rawdata value and an Attention value of an electroencephalogram signal of an equipment user, acquiring an electromyographic digital signal of the equipment user, and calculating the electromyographic digital signal to obtain a standard deviation sigma reflecting the dispersion degree of the electromyographic signal;
s20: respectively comparing and judging the threshold values of the raw data value, the Attention value and the standard deviation sigma of the electromyographic signal, and if the conditions are met, generating a control instruction for controlling the movement of the trolley.
Further, the step S20 further includes the following steps:
s21: comparing the rawdata value with a set basic motion state threshold value, and if the rawdata value exceeds the basic motion state threshold value, generating a control instruction for changing the basic motion state of the trolley;
s22: comparing the Attention value with a threshold range of a speed gear, and if the Attention value is in the threshold range of a certain speed gear, generating a control instruction for controlling the speed gear of the trolley;
s23: and comparing the standard deviation sigma of the myoelectric signal discrete degree with a set threshold range reflecting the occluding state of the masseter, and generating a control instruction for controlling the turning of the trolley if one side of the masseter is in the occluding state.
For a better understanding and practice, the invention is described in detail below with reference to the accompanying drawings.
Drawings
FIG. 1 is a schematic structural diagram of a trolley control system based on combination of electroencephalogram and electromyogram;
FIG. 2 is a diagram of the electromyographic digital signal output of the trolley control system according to the embodiment of the invention;
FIG. 3 is a flow chart of a trolley control method based on the combination of electroencephalogram and electromyogram;
FIG. 4 is a flow chart of a cart control method according to an embodiment of the present invention.
Detailed Description
Referring to fig. 1, which is a schematic structural diagram of a cart control system according to an embodiment of the present invention, the cart control system based on combination of electroencephalogram and electromyogram of the present invention includes an electroencephalogram acquisition and processing device 10, an electromyogram acquisition and processing device 20, and a cart control device 30, wherein the electroencephalogram acquisition and processing device 10 and the electromyogram acquisition and processing device 20 respectively acquire and process an electroencephalogram signal and an electromyogram signal, and then transmit the electroencephalogram signal and the electromyogram signal to the cart control device 30 to control movement of the cart.
Specifically, the electroencephalogram acquisition and processing device 10 comprises an electroencephalogram signal acquisition unit 12 and an electroencephalogram signal processing unit 14, and the electroencephalogram signal acquisition unit 12 is electrically connected with the electroencephalogram signal processing unit 14. The electroencephalogram signal acquisition unit 12 is a universal contact type acquisition device, the electroencephalogram signal acquisition unit 12 is worn on the head when the electroencephalogram signal acquisition unit is used, and the electroencephalogram signal acquisition unit 12 can acquire the primary electroencephalogram signals of the forehead of a user of the equipment. The electroencephalogram signal processing unit 14 is specifically a TGAM module in this embodiment, and receives the native electroencephalogram signal acquired by the electroencephalogram signal acquisition unit 12, and after amplification, filtering, and a/D conversion, obtains a raw data value and an Attention value of the electroencephalogram signal. The said raw data value is real-time potential value of the brain signal of the user, through experimental comparison, when the user intentionally makes double blink action, the raw data value will suddenly increase to over 800 in a very short time, and when the user does not intend or habitually double blink, the raw data value will not have potential variation with such a large amplitude. Therefore, if the detected value of rawdata is greater than 800, it is counted as 1 double blink. Therefore, the windata value can be used for detecting the blinking state of the equipment user, and further controlling the basic motion state of the trolley. The Attention value is a concentration value, which is specifically in the range of 0-100, and it can be obtained through many experiments, and under normal mental conditions, normal adults usually have an extracted Attention value of about 40, and rarely greater than 90. The Attention value can thus be used for controlling the speed gear of the vehicle.
The electromyographic signal acquisition and processing device 20 comprises an electromyographic signal acquisition unit 22 and an electromyographic signal processing unit 24, wherein the electromyographic signal acquisition unit 22 is electrically connected with the electromyographic signal processing unit 24. The electromyographic signal acquisition unit 22 is a universal contact type acquisition device, and when the electromyographic signal acquisition unit 22 is sleeved at the masseter muscle positions on two sides of the face, the primary electromyographic signals of the masseter muscles on two sides of the face of a user of the device can be acquired, and the left masseter muscle electric signal and the right masseter muscle electric signal are respectively output. The electromyographic signal processing unit 24 receives the left-side and right-side masseter electrical signals output by the electromyographic signal acquisition unit 22, and outputs electromyographic digital signals after primary amplification, band-pass filtering, secondary amplification and A/D conversion are carried out on the signals; the a/D conversion is performed by the Arduino module of the electromyographic signal processing unit 24, and the output result is as shown in fig. 2, where the left graph is the electromyographic signal of the left masseter muscle of the face, the right graph is the electromyographic signal of the right masseter muscle of the face, and at this time, the left masseter muscle is in a relaxed state, and the right masseter muscle is in an occluded state. Under normal conditions, when the facial crunches are in a relaxed state, the myoelectric signals are small in dispersion degree, and when the crunches are in an occluded state, the signal dispersion degree is obviously large. Therefore, the myoelectric signals of the masseter muscles on two sides have different discrete degrees and can be used for controlling the movement steering of the trolley.
The trolley control device 30 is arranged on a trolley and comprises a signal receiving and processing unit 32, a control unit 34, a first motor 36 and a second motor 38. The signal receiving and processing unit 32 is configured to set a threshold range of a trolley control instruction, respectively receive the electroencephalogram signal output by the electroencephalogram signal processing unit 14 and the electromyogram digital signal output by the electromyogram signal processing unit 24, calculate and compare the threshold to generate a control instruction, and output the control instruction to the control unit 34, where the control unit 34 respectively adjusts PWM (Pulse Width Modulation) duty ratios of the first motor 36 and the second motor 38 that drive wheels on two sides of the trolley according to the received control instruction, so as to control a movement state of the trolley. The control unit 34 is electrically connected to a first motor 36 and a second motor 38. In this embodiment, the signal receiving and processing unit 32 is a raspberry pi 4B main board, and is connected to both the electroencephalogram signal processing unit 14 and the electromyogram signal processing unit 24 via bluetooth.
Specifically, the signal receiving and processing unit 32 includes a rawdata threshold comparison module 321 and an Attention threshold comparison module 322 for processing the electroencephalogram signal, and an electromyography calculation and threshold comparison module 323 for processing the electromyography signal.
After receiving the raw data value of the electroencephalogram signal output by the electroencephalogram signal processing unit 14, the raw data threshold comparison module 321 generates an instruction for changing the basic motion state of the trolley by comparing the raw data value with the basic motion state threshold of the trolley. In order to improve the accuracy of motion recognition, only when the user of the equipment is detected to double blink 3 times within 3 seconds, a control instruction for controlling the basic motion state of the trolley to sequentially change once between forward movement, stop and backward movement is generated. For example, when the PMW duty ratio of the initial speed is 30% when the vehicle is moving forward or backward, and when the control command "stop" is received, the duty ratio is adjusted to 0, the motor stops operating, and the vehicle stops accordingly. The threshold value setting of the control instruction for changing the basic motion state of the trolley is as the following table 1:
table 1: threshold setting of basic motion state of trolley
| 3 raw data values in 3 seconds | Basic motion state of the trolley |
| >800 | Sequentially changing once between forward, stop and backward |
After the Attention threshold comparison module 322 receives the Attention value of the electroencephalogram signal output by the electroencephalogram signal processing unit 14, a corresponding trolley speed gear control instruction can be generated by comparing the Attention value with a trolley speed gear threshold range. The speed of the trolley can be divided into 3 gears, which are the threshold setting of the speed gear of the trolley and the corresponding PWM duty ratio as shown in Table 2.
Table 2: trolley speed gear threshold value setting and PWM duty ratio corresponding to same
After receiving the electromyographic digital signals output by the electromyographic signal processing unit 24, the electromyographic calculation and threshold comparison module 323 calculates a set of standard deviation values of the electromyographic signals by the following formula:
where N is the total number of sets, μ is the mean of the sets, χ i is the value of the ith element in the sets, and σ is the output, i.e., the standard deviation value. Because the effective frequency spectrum range of the electromyography acquisition equipment is 20Hz to 500Hz, namely 20 to 500 signals are acquired in 1 second, in the embodiment, 50 electromyography signals are acquired every 0.2 second, the signals acquired every 0.2 second are set as one group, then the 50 electromyography signal values in each group are processed by an electromyography visualization program of a raspberry pi 4B main board 32 through a standard deviation algorithm, and finally the obtained sigma is between 0 and 300. As shown in table 3, the masseter states are classified into three types.
Table 3: bite muscle standard deviation threshold setting
| Value of sigma | State of biting |
| 0≤σ≤B | In a relaxed state |
| B<σ≤D | Occluded state |
| D<σ≤300 | Interference state |
If the left (right) side masseter is detected to be in the occluded state, a control instruction that the trolley steers to the left (right) side is generated; and if the left (right) side masseter is detected to be in a relaxed state, generating a control instruction that the trolley turns left (right) to stop. The two-side biting-muscle status corresponds to 4 kinds of control commands, which are left _ on (start left turn), left _ off (stop left turn), right _ on (start right turn), and right _ off (stop right turn). The 4 control instructions are mutually exclusive, that is, only 1 instruction can be executed at the same time. When the trolley moves forward, the duty ratios of the tire motors on the two sides are always equal. When the vehicle is turning, the control unit 34 adjusts the duty ratio of the first motor 36 and the second motor 38 to be different between the tires on both sides of the vehicle, and at this time, a speed difference is generated when the tires on both sides are running, and the vehicle turns to the side with the slower tire speed. The present embodiment sets the duty ratio difference between the two sides to 10%, that is, if the vehicle is turning right when the vehicle is moving forward in the first gear, the duty ratio of the left motor is 40%, and the duty ratio of the right motor is 30%, thereby realizing the steering control of the vehicle.
More preferably, the individual bite muscle bite states are considered to be more diverse. After the device user performs the test, the bite muscle standard deviation threshold (B, D in table 3) can be adjusted according to the individual habit, for example, the value B is set to 50, and the value D is set to 130. In this embodiment, the standard deviation threshold value range suitable for the muscle biting habit of the user can be input through a graphical interface in the myoelectricity visualization program of the raspberry pi 4B mainboard, so that the accuracy and the applicability of the steering control of the trolley are improved.
Referring to fig. 3 and 4, the invention further provides a trolley control method based on the combination of electroencephalogram and electromyogram, which comprises the following steps:
s10: acquiring and processing an electroencephalogram signal (raw EEG signal) and an electromyogram signal (raw EMG signal) of a user of the device, respectively, wherein:
the method for processing the electroencephalogram signals comprises the following steps: and amplifying, filtering and A/D converting the original electroencephalogram signal by using a TGAM module to obtain a rawdata value and an Attention value of the electroencephalogram signal.
The electromyographic signal processing method comprises the following steps: amplifying and filtering the native electromyographic signals, and performing A/D conversion by using an Arduino module to obtain electromyographic digital signals; and then calculating to obtain a standard deviation sigma reflecting the dispersion degree of the electromyographic signals according to the obtained electromyographic digital signals.
S20: respectively comparing and judging the threshold values of the raw data value and the Attention value of the electroencephalogram signal and the standard deviation sigma of the electromyogram signal, and generating a control instruction for the movement of the trolley. In addition, the equipment user can also judge that the standard deviation sigma is the threshold range of the occlusion state according to the individual masseter habit setting.
S21: comparing the raw data value with a set basic motion state threshold value, and if the raw data value is detected to exceed the basic motion state threshold value for 3 times within 3 seconds, generating a control instruction for changing the basic motion state of the trolley, wherein the basic motion state of the trolley comprises the following steps: the trolley moves forwards, stops and moves backwards, and the basic motion state of the trolley is sequentially changed every time a control command is generated. In the present embodiment, the basic motion state threshold is set to the set state of table 1 described above.
S22: and comparing the Attention value with the threshold range of the speed gear to generate a control instruction for controlling the trolley to be switched to the corresponding speed gear, namely a speed gear control instruction. In the present embodiment, the speed range threshold range is set to the setting state of table 2 described above.
S23: and comparing the standard deviation sigma of the myoelectric signal discrete degree with a set threshold range reflecting the occluding state of the masseter to obtain a steering control instruction of the trolley when the masseter on one side is in the occluding state. In the present embodiment, the threshold range reflecting the biting muscle biting condition is set to the set state of table 3.
Therefore, compared with the prior art, the invention is beneficial to the advantages of high electromyographic signal identification degree, high signal-to-noise ratio, easy detection and the like, and the trolley is controlled together by the electroencephalogram signal and the electromyogram signal, so that the problem of low practicability caused by too few control modes of the electroencephalogram signal can be solved, and the accuracy in control can be improved. Meanwhile, the invention also considers that the individual masseter muscle occlusion state has larger difference, and the equipment user can independently adjust the related threshold value according to the self condition, thereby increasing the accuracy and the applicability of the trolley control.
The present invention is not limited to the above-described embodiments, and various modifications and variations of the present invention are intended to be included within the scope of the claims and the equivalent technology of the present invention if they do not depart from the spirit and scope of the present invention.
Claims (7)
1. The utility model provides a dolly control system based on brain electricity and flesh electricity combine which characterized in that: the device comprises electroencephalogram acquisition and processing equipment, myoelectricity acquisition and processing equipment and a trolley control device; wherein the content of the first and second substances,
the electroencephalogram acquisition and processing equipment comprises an electroencephalogram signal acquisition unit and an electroencephalogram signal processing unit; the electroencephalogram signal processing unit receives the native electroencephalogram signal of the equipment user acquired by the electroencephalogram signal acquisition unit, and processes the native electroencephalogram signal to obtain a raw data value and an Attention value of the electroencephalogram signal;
the electromyographic acquisition processing equipment comprises an electromyographic signal acquisition unit and an electromyographic signal processing unit; the electromyographic signal acquisition unit receives the native electromyographic signal of the equipment user acquired by the electromyographic signal acquisition unit, and processes the native electromyographic signal to obtain an electromyographic digital signal;
the trolley control device comprises a signal receiving and processing unit, a control unit, a first motor and a second motor, wherein the signal receiving and processing unit respectively receives a raw data value and an Attention value of an electroencephalogram signal output by the electroencephalogram signal processing unit, compares the raw data value and the Attention value with a threshold value, receives an electromyogram digital signal output by the electromyogram signal processing unit, calculates the electromyogram digital signal and compares the electromyogram digital signal with the threshold value, then generates a control command and outputs the control command to the control unit, and the control unit respectively adjusts and drives the first motor and the second motor of wheels on two sides of the trolley according to the received control command.
2. The trolley control system based on the combination of electroencephalogram and electromyogram of claim 1, which is characterized in that: the EEG signal processing unit is a TGAM module which amplifies, filters and A/D converts the native EEG signal and outputs a raw data value and an Attention value.
3. The trolley control system based on the combination of electroencephalogram and electromyogram of claim 2, which is characterized in that: the signal receiving and processing unit comprises a raw data threshold value comparison module and an Attention threshold value comparison module for processing the electroencephalogram signals; after receiving the raw data value of the electroencephalogram signal output by the electroencephalogram signal processing unit, the raw data threshold value comparison module generates an instruction for changing the basic motion state of the trolley by comparing the raw data value with a set trolley basic motion state threshold value range; after the Attention threshold value comparison module receives the Attention value of the electroencephalogram signal output by the electroencephalogram signal processing unit, a trolley speed gear control instruction is generated by comparing the Attention value with a set trolley speed gear threshold value range.
4. The trolley control system based on the combination of electroencephalogram and electromyogram of claim 3, wherein: the signal receiving and processing unit also comprises an electromyographic calculation sum for processing the electromyographic signalsThe electromyographic calculation and threshold comparison module receives the electromyographic digital signals output by the electromyographic signal processing unit and calculates a group of standard deviation values of the electromyographic signals according to the following formula:
wherein N is the total number of the set, mu is the average number of the set, chi i is the numerical value of the ith element in the set, and sigma is the output; and then comparing the standard deviation value sigma with a set masseter standard deviation valve value to generate a trolley steering control command.
5. The trolley control system based on the combination of electroencephalogram and electromyogram according to any one of claims 1 to 4, wherein: the signal receiving and processing unit is a raspberry pi 4B mainboard, and the raspberry pi 4B mainboard is connected with the electroencephalogram signal processing unit and the electromyogram signal processing unit in a Bluetooth mode.
6. A trolley control method based on combination of electroencephalogram and myoelectricity is characterized by comprising the following steps:
s10: respectively acquiring a rawdata value and an Attention value of an electroencephalogram signal of an equipment user, acquiring an electromyographic digital signal of the equipment user, and calculating to obtain a standard deviation sigma reflecting the dispersion degree of the electromyographic signal according to the acquired electromyographic digital signal;
s20: respectively comparing and judging the threshold values of the raw data value and the Attention value of the electroencephalogram signal and the standard deviation sigma of the electromyogram signal, and generating a control instruction for the movement of the trolley.
7. The trolley control method based on the combination of electroencephalogram and electromyogram of claim 6, wherein the step S20 further comprises the steps of:
s21: comparing the rawdata value with a set basic motion state threshold value, and if the rawdata value exceeds the basic motion state threshold value, generating a control instruction for changing the basic motion state of the trolley;
s22: comparing the Attention value with a speed gear threshold range to generate a control instruction for controlling the speed gear of the trolley;
s23: and comparing the standard deviation sigma of the myoelectric signal discrete degree with a set threshold range reflecting the occluding state of the masseter to obtain a control instruction for controlling the turning of the trolley when the masseter on one side is in the occluding state.
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Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112904856A (en) * | 2021-01-20 | 2021-06-04 | 广州大学 | Robot system and control method thereof |
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