Interaction-used thought eye movement control method for intelligent equipment

Abstract

The invention relates to the technical field of intelligent control, and particularly discloses an idea eye movement control method for intelligent equipment interaction, which comprises the following steps: S1, capturing an idea signal generated by the brain of a user through brain-computer interface equipment; s2, converting the captured thought signal into a microwave signal with specific frequency and amplitude; s3, utilizing an electromagnetic wave resonance technology to enable the converted microwave signals to resonate with electromagnetic waves in the intelligent equipment, and performing preliminary control on the intelligent equipment, and S4, combining an eye movement tracking technology to capture eye movement information of a user; s5, combining the microwave signal with the eye movement information, analyzing the complete intention of the user, and generating a corresponding control instruction to control each function of the intelligent equipment; by capturing and analyzing the electroencephalogram signal of the user, the intelligent device can understand the intention of the user, the eye movement control has the advantages of being rapid, natural, intelligent and the like, and the eye movement control and the eye movement control are combined, so that the idea control does not need to recognize more difficult actions.

Description

Technical Field

[0001] The present invention belongs to the field of intelligent control technology, and in particular relates to a mind-eye movement control method for intelligent device interaction. Background Art

[0002] With the rapid development of science and technology, human-computer interaction technology is also constantly innovating. Although traditional interaction methods, such as keyboards, mice, and touch screens, meet the needs of users to a certain extent, they may not be natural and efficient enough in certain scenarios. Especially in situations that require high concentration or physical limitations, traditional interaction methods may become a burden for users.

[0003] For this purpose, a new technology of mind control has been developed. As a technology that directly interprets the intentions of the human brain, it achieves control without physical contact by capturing and analyzing EEG signals. However, there are currently very few categories of EEG signals with obvious characteristics that can be identified, which makes it difficult to decode EEG signals. If an activity in a specific area of ​​the brain cannot be accurately translated into a specific meaning, then mind control will not be able to issue effective commands to the outside world. Therefore, mind control can only achieve some primary movements at present, and it is difficult to complete complex movements. For this reason, a method of mind-eye control for intelligent device interaction is designed. Summary of the invention

[0004] The purpose of the present invention is to provide a method for controlling eye movements for interaction with intelligent devices to solve the problems raised in the above-mentioned background technology.

[0005] To achieve the above object, the present invention provides the following technical solutions:

[0006] A method for controlling eye movements for interaction with smart devices, comprising the following steps:

[0007] S1. Capture the thought signals generated by the user's brain through the brain-computer interface device;

[0008] S2. Convert the captured thought signal into a microwave signal with a specific frequency and amplitude;

[0009] S3. Using electromagnetic wave resonance technology, the converted microwave signal resonates with the electromagnetic wave inside the smart device to perform preliminary control on the smart device;

[0010] S4, combined with eye tracking technology to capture the user's eye movement information;

[0011] S5. Combine microwave signals with eye movement information to analyze the user's complete intention and generate corresponding control instructions to control various functions of smart devices.

[0012] Preferably, in said S1, the brain-computer interface device includes but is not limited to an electroencephalogram cap and an implantable brain-computer interface chip, which is used to capture thought signals generated by the user's brain.

[0013] Preferably, in said S3, the step of causing the microwave signal to resonate with the electromagnetic wave comprises:

[0014] S31, setting the resonance frequency of the electromagnetic wave inside the smart device to match the frequency of the converted microwave signal;

[0015] S32. When the microwave signal resonates with the electromagnetic wave, the smart device receives and analyzes the signal, and performs corresponding operations according to the analyzed signal.

[0016] Preferably, in S4, the eye tracking technology comprises the following steps:

[0017] S41. Use the Hough circle transform algorithm to detect the pupil center from the eye image obtained by the image sensor. The Hough circle transform algorithm is based on the edge information in the image and uses a voting mechanism to find the most likely circle. For pupil detection, we can simplify the algorithm to find the circle with the highest edge response. The algorithm is as follows:

[0018]

[0019] Among them, (x c ,y c ) is the coordinate of the center of the pupil, r is the radius of the pupil, E(x,y) is the edge response at the coordinate (x,y) in the image, θ is the angle between the line connecting any point on the circle and the center and the horizontal axis, r i is the value within the search radius;

[0020] S42. Estimate the user's sight direction based on the position of the pupil center and the eyeball model. The eyeball model is usually a simplified geometric model, such as a spherical model or an ellipsoidal model. In this embodiment, a simple linear model is used to approximate the relationship between the sight direction and the pupil center position. The algorithm is as follows:

[0021] theta = k x (x c -x0)+k y (y c -y0)+α0;

[0022] Among them, α is the angle between the line of sight and the horizontal axis, (x0, y0) is the coordinate of the center point of the eyeball model, and k x and k y are model parameters that describe the linear relationship between the pupil center position and the gaze direction, and α0 is a constant offset.

[0023] Preferably, in S5, generating corresponding control instructions specifically includes:

[0024] S51. Adjust the frequency of the microwave signal according to the line of sight so that it resonates with the electromagnetic waves inside the smart device. The algorithm is as follows:

[0025] f microwave =f base +Δf(β);

[0026] Among them, f microwave is the frequency of the generated microwave signal, f base is the base frequency, Δf(β) is the frequency offset adjusted according to the line of sight direction β, which can be a linear function, a quadratic function, or other forms of function, depending on the design and requirements of the smart device;

[0027] S52, analyzing the complete intention of the user and generating corresponding control instructions, using a weighted sum algorithm to fuse the microwave signal and the eye movement information, the algorithm is as follows:

[0028] C=w1·S microwave +w2·S eyemovement ;

[0029] Among them, C is the generated control instruction, S microwave is the characteristic value of the microwave signal, S eyemovement is the characteristic value of eye movement information, w1 and w2 are weight coefficients.

[0030] Preferably, in S5, the smart devices include but are not limited to virtual reality devices, augmented reality devices, wearable devices, and smart home devices.

[0031] Compared with the prior art, the present invention has the following beneficial effects:

[0032] (1) By capturing and analyzing the user's EEG signals, smart devices can understand the user's intentions and achieve control without physical contact. Eye movement control has the advantages of being fast, natural, and intelligent. The combination of the two makes it possible for mind control to eliminate the need to recognize many difficult movements, simplify the interaction process, increase the communication bandwidth between people and computers, and greatly reduce people's cognitive load.

[0033] (2) Compared with traditional control methods, the mind-eye control method does not require users to memorize complex operating instructions or gestures, which reduces the learning cost and enables users to get started using smart devices more quickly. Through the combination of mind and eye movements, users can complete operations without moving their bodies or hands, thereby improving operating efficiency. BRIEF DESCRIPTION OF THE DRAWINGS

[0034] Figure 1 The figure is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0035] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0036] Embodiment 1:

[0037] See also Figure 1 As shown, a method for controlling the interaction of smart devices with mind eye movement includes the following steps:

[0038] S1. Capture the thought signals generated by the user's brain through the brain-computer interface device;

[0039] S2. Convert the captured thought signal into a microwave signal with a specific frequency and amplitude;

[0040] S3. Using electromagnetic wave resonance technology, the converted microwave signal resonates with the electromagnetic wave inside the smart device to perform preliminary control on the smart device;

[0041] S4, combined with eye tracking technology to capture the user's eye movement information;

[0042] S5. Combine microwave signals with eye movement information to analyze the user's complete intention and generate corresponding control instructions to control various functions of smart devices.

[0043] In one embodiment of the present invention, in S1, the brain-computer interface device includes but is not limited to an electroencephalogram cap and an implantable brain-computer interface chip, which is used to capture the thought signal generated by the user's brain;

[0044] These devices are able to monitor the brain's electrical activity in real time, non-invasively or invasively, capturing EEG signals associated with specific thoughts or intentions.

[0045] In one embodiment of the present invention, in S3, the step of causing the microwave signal to resonate with the electromagnetic wave includes:

[0046] S31, setting the resonance frequency of the electromagnetic wave inside the smart device to match the frequency of the converted microwave signal;

[0047] S32. When the microwave signal resonates with the electromagnetic wave, the smart device receives and analyzes the signal, and performs corresponding operations according to the analyzed signal.

[0048] In one embodiment of the present invention, in S4, the eye tracking technology comprises the following steps:

[0049] S41. Use the Hough circle transform algorithm to detect the pupil center from the eye image obtained by the image sensor. The Hough circle transform algorithm is based on the edge information in the image and uses a voting mechanism to find the most likely circle. For pupil detection, the algorithm is simplified to finding a circle with the highest edge response. The algorithm is as follows:

[0050]

[0051] Among them, (x c ,y c ) is the coordinate of the center of the pupil, r is the radius of the pupil, E(x,y) is the edge response at the coordinate (x,y) in the image, θ is the angle between the line connecting any point on the circle and the center and the horizontal axis, r i is the value within the search radius;

[0052] S42. Estimate the user's sight direction based on the position of the pupil center and the eyeball model. The eyeball model is usually a simplified geometric model. Here, a simple linear model is used to approximate the relationship between the sight direction and the pupil center position. The algorithm is as follows:

[0053] theta = k x (x c -x0)+k y (y c -y0)+α0;

[0054] Among them, α is the angle between the line of sight and the horizontal axis, (x0, y0) is the coordinate of the center point of the eyeball model, and k x and k y are model parameters that describe the linear relationship between the pupil center position and the gaze direction, and α0 is a constant offset.

[0055] In one embodiment of the present invention, in S5, a corresponding control instruction is generated, which specifically includes:

[0056] S51. Adjust the frequency of the microwave signal according to the line of sight so that it resonates with the electromagnetic waves inside the smart device. The algorithm is as follows:

[0057] f microwave =f base +Δf(β);

[0058] Among them, f microwave is the frequency of the generated microwave signal, f base is the base frequency, Δf(β) is the frequency offset adjusted according to the line of sight direction β;

[0059] S52: Analyze the complete intention of the user and generate corresponding control instructions. The algorithm is as follows:

[0060] C=w1·S microwave +w2·S eyemovement ;

[0061] Among them, C is the generated control instruction, S microwave is the characteristic value of the microwave signal, S eyemovement is the characteristic value of eye movement information, w1 and w2 are weight coefficients.

[0062] In one embodiment of the present invention, in S5, smart devices include but are not limited to virtual reality devices, augmented reality devices, wearable devices, and smart home devices, all of which are equipped with cameras at the eyes for collecting eye movement status.

[0063] From the above, we can see that by capturing and analyzing the user's EEG signals and eye movement information, more accurate and efficient interactive control is achieved. This control method not only improves the naturalness and intuitiveness of the interaction, but also reduces the user's learning cost and difficulty of use.

[0064] The resonance of brain waves and electromagnetic waves can also be converted by adding quantum entanglement formula theory. Photons or electrons are used to "couple" one of the particles with the captured brain waves. This coupling can be physical (such as through electromagnetic fields) or information (such as through digital signal processing). The other particle is measured. Due to the existence of quantum entanglement, this measurement result will immediately affect the state of the particle coupled to the user's brain.

[0065] It can also be converted under normal logic.

[0066] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for controlling eye movements for interaction with intelligent devices, characterized in that: The following steps are involved: S1. Capture the thought signals generated by the user's brain through the brain-computer interface device; S2. Convert the captured thought signal into a microwave signal with a specific frequency and amplitude; S3. Using electromagnetic wave resonance technology, the converted microwave signal resonates with the electromagnetic wave inside the smart device to perform preliminary control on the smart device; S4, combined with eye tracking technology to capture the user's eye movement information; S5. Combine microwave signals with eye movement information to analyze the user's complete intention and generate corresponding control instructions to control various functions of smart devices.

2. The method for controlling the interaction of smart devices with mind eye movement according to claim 1, characterized in that: In S1, the brain-computer interface device includes but is not limited to an electroencephalogram cap and an implantable brain-computer interface chip, which is used to capture the thought signals generated by the user's brain.

3. The method for controlling the interaction of smart devices with mind eye movement according to claim 1, characterized in that: In said S3, the step of causing the microwave signal to resonate with the electromagnetic wave comprises: S31, setting the resonance frequency of the electromagnetic wave inside the smart device to match the frequency of the converted microwave signal; S32. When the microwave signal resonates with the electromagnetic wave, the smart device receives and analyzes the signal, and performs corresponding operations according to the analyzed signal.

4. The method for controlling the interaction of smart devices with mind eye movement according to claim 1, characterized in that: In said S4, the eye tracking technology comprises the following steps: S41. Use the Hough circle transform algorithm to detect the pupil center from the eye image obtained by the image sensor. The Hough circle transform algorithm is based on the edge information in the image and uses a voting mechanism to find the most likely circle. For pupil detection, we can simplify the algorithm to find the circle with the highest edge response. The algorithm is as follows: Among them, (x c ,y c ) is the coordinate of the center of the pupil, r is the radius of the pupil, E(x,y) is the edge response at the coordinate (x,y) in the image, θ is the angle between the line connecting any point on the circle and the center and the horizontal axis, r i is the value within the search radius; S42. Estimate the user's sight direction based on the position of the pupil center and the eyeball model. The eyeball model is usually a simplified geometric model, such as a spherical model or an ellipsoidal model. In this embodiment, a simple linear model is used to approximate the relationship between the sight direction and the pupil center position. The algorithm is as follows: theta=k x (x c -x0)+k y (y c -y0)+α0; Among them, α is the angle between the line of sight and the horizontal axis, (x0, y0) is the coordinate of the center point of the eyeball model, and k x and k y are model parameters that describe the linear relationship between the pupil center position and the gaze direction, and α0 is a constant offset.

5. The method for controlling the interaction of smart devices with mind eye movement according to claim 1, characterized in that: In S5, corresponding control instructions are generated, specifically including: S51. Adjust the frequency of the microwave signal according to the line of sight so that it resonates with the electromagnetic waves inside the smart device. The algorithm is as follows: f microwave =f base +Δf(β); Among them, f microwave is the frequency of the generated microwave signal, f base is the base frequency, Δf(β) is the frequency offset adjusted according to the line of sight direction β, which can be a linear function, a quadratic function, or other forms of function, depending on the design and requirements of the smart device; S52, analyzing the complete intention of the user and generating corresponding control instructions, using a weighted sum algorithm to fuse the microwave signal and the eye movement information, the algorithm is as follows: C=w1·S microwave +w2·S eyemovement ; Among them, C is the generated control instruction, S microwave is the characteristic value of the microwave signal, S eyemovement is the characteristic value of eye movement information, w1 and w2 are weight coefficients.

6. The method for controlling the interaction of smart devices with mind eye movement according to claim 1, characterized in that: In S5, the smart devices include but are not limited to virtual reality devices, augmented reality devices, wearable devices, and smart home devices.

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