Attention modular detection system based on VR

Abstract

The invention belongs to the technical field of VR glasses, and particularly relates to a VR-based attention modular detection system, which comprises VR glasses, an eye myoelectricity module, a voice assistant module, an audio processing unit, a heart rate module, a central main control chip and a connection relation thereof. The eye myoelectricity module, the voice assistant module, the audio processing unit, the heart rate module and the like are arranged to collect bioelectricity signals, sound signals and heart rate information of a human body, the interest and participation degree of a testee are stimulated in cooperation with the VR glasses, it is avoided that the testee feels nervous or anxious to affect the testing result, electroencephalogram data and eye movement data are added, and the testing accuracy is improved. And a more comprehensive and more accurate evaluation result is provided.

Description

Technical Field

[0001] The present invention relates to the field of VR glasses technology, and in particular to a VR-based modular attention detection system. Background Art

[0002] At present, attention assessment systems usually use VR glasses as a platform, combined with built-in induction and relaxation modules, electronic questionnaire modules and data processing modules to detect the user's attention level, and remind users to stay focused through visual and auditory feedback.

[0003] Hospitals and professional institutions mainly use the traditional questionnaire assessment method, which is to design questionnaires of different dimensions and be completed by the subjects (usually children).

[0004] Therefore, the prior art mainly has the following major disadvantages:

[0005] The assessment results are susceptible to interference: the subjects may feel nervous or anxious when facing the questionnaire, resulting in inaccurate assessment results.

[0006] Incomplete test coverage: The invention is intended for the assessment of children's ADHD, autism and other problems, and does not have testing standards for the elderly. If an elderly patient suffers from Alzheimer's disease, this standard cannot be used for testing.

[0007] Lack of specificity: Since the evaluation results are not detailed enough, they cannot provide effective guidance for subsequent targeted treatments.

[0008] The main reason for these shortcomings is the singleness and limitations of the traditional questionnaire evaluation method. It cannot fully stimulate the interest and participation of the subjects; at the same time, it lacks monitoring and analysis of other physiological indicators, such as EEG data and eye movement data, and thus cannot provide more comprehensive and accurate evaluation results. Summary of the invention

[0009] The purpose of the present invention is to solve the technical problems existing in the background technology. To this end, a VR-based modular attention detection system is provided.

[0010] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0011] A modular attention detection system based on VR, including VR glasses, an eye electromyography module, a voice assistant module, an audio processing unit, a heart rate module and a central main control chip;

[0012] The VR glasses are equipped with an eye electromyography module, a voice assistant module, an audio processing unit, and a heart rate module;

[0013] The eye electromyography module, the audio processing unit, and the heart rate module are all electrically connected to the central main control chip, and the eye electromyography module, the audio processing unit, and the heart rate module are all electrically connected to the voice assistant module;

[0014] The human body's bioelectric signals, sound signals and heart rate information are collected through the eye electromyography module, voice assistant module, audio processing unit and heart rate module, and converted into digital signals, which are processed by the central main control chip and displayed on the display screen.

[0015] The following is a technical solution further defined by the present invention, wherein the central main control chip is electrically connected to the power management module, the Bluetooth module, the wifi module and the API interface module.

[0016] The following is a technical solution further defined by the present invention, wherein the power management module includes a solar panel.

[0017] The following is a technical solution further limited by the present invention: the eye muscle electromyography module is provided with a bandpass filter amplifier circuit for amplifying and bandpass filtering the eye muscle electromyography signal.

[0018] The following is a technical solution further defined in the present invention, wherein the bandpass filter amplifier circuit comprises a first-stage amplifier circuit, a second-stage amplifier circuit, a high-pass filter component and a low-pass filter component;

[0019] The first-stage amplifying circuit includes an amplifier U1, the second-stage amplifying circuit includes an amplifier U2, the high-pass filter component includes a resistor R2 and a capacitor C2, and the low-pass filter component includes a resistor R3 and a capacitor C3;

[0020] Pin 1 and pin 4 of the amplifier U1 are connected to input signal 1 and input signal 2, a resistor R1 is connected between pin 2 and pin 3 of the amplifier U1, a resistor R2 is connected between pin 7 of the amplifier U1 and pin 3 of the amplifier U2, a capacitor C2 is connected between pin 2 and pin 3 of the amplifier U2, a resistor R3 is connected between pin 1 and pin 5 of the amplifier U2, and a capacitor C3 is connected between pin 5 and pin 6 of the amplifier U2.

[0021] The following is a technical solution further defined in the present invention: Pin 5 and Pin 6 of the amplifier U1 are both grounded.

[0022] The following is a technical solution further defined in the present invention: the pin 8 of the amplifier U1 is connected to a power supply voltage, and the line between the pin 8 of the amplifier U1 and the power supply voltage is grounded via a capacitor C1.

[0023] The following is a technical solution further defined in the present invention, in which the pin 4 of the amplifier U2 is grounded.

[0024] The following is a technical solution further defined in the present invention: the pin 8 of the amplifier U2 is connected to a power supply voltage, and the line between the pin 8 of the amplifier U2 and the power supply voltage is grounded through a capacitor C12.

[0025] Compared with the prior art, the present invention has the following technical effects:

[0026] The present invention collects the human body's bioelectric signals, sound signals and heart rate information by setting up an eye electromyography module, a voice assistant module, an audio processing unit, a heart rate module, etc., and then cooperates with VR glasses to stimulate the subject's interest and participation, avoid the subject's tension or anxiety affecting the test results, increase the EEG data and eye movement data, and provide more comprehensive and accurate evaluation results.

[0027] The present invention is further described below in conjunction with the accompanying drawings and embodiments. BRIEF DESCRIPTION OF THE DRAWINGS

[0028] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0029] Figure 1 is a system connection relationship diagram of the present invention;

[0030] Figure 2 is a connection diagram between the eye myoelectric module and the VR glasses in the present invention;

[0031] Figure 3 It is a connection relationship diagram of the first stage amplifier circuit in the present invention;

[0032] Figure 4 It is a connection diagram of the second-stage amplifier circuit in the present invention. DETAILED DESCRIPTION

[0033] In order to make the above-mentioned objects, features and advantages of the present invention more obvious and easy to understand, the specific embodiments of the present invention are described in detail below in conjunction with the accompanying drawings. In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention can be implemented in many other ways different from those described herein, and those skilled in the art can make similar improvements without violating the connotation of the present invention, so the present invention is not limited by the specific embodiments disclosed below.

[0034] like Figure 1 and 2As shown, this embodiment provides a modular attention detection system based on VR, which mainly collects human bioelectric signals, sound signals and heart rate information through eye electromyography module, voice assistant module, audio processing unit, heart rate module, etc., and then converts these information into digital signals, which are processed by the central main control chip (MCU) and displayed on the display screen. Among them, the eye electromyography module, voice assistant module, audio processing unit, and heart rate module are arranged on VR glasses to stimulate the interest and participation of the subjects.

[0035] The central control chip is electrically connected to the power management module, Bluetooth module, wifi module and API interface module. Therefore, the system can also transmit this information to the mobile device through the Bluetooth module for further analysis or storage. The power management module includes a solar panel, which absorbs solar energy to power the system.

[0036] 1. Working principle of eye muscle electromyography module

[0037] 1. Signal reception: The eye muscle electromechanical module is attached to the outer corner of the human eye through electrodes to receive bioelectric signals generated by eye muscle activity.

[0038] 2. Signal amplification: Since bioelectric signals are usually very weak, they need to be amplified by an amplifier circuit for subsequent processing.

[0039] 3. Filtering and denoising: The amplified signal may contain some noise and needs to be filtered and denoised through a filter to improve the signal-to-noise ratio.

[0040] 4. Analog-to-digital conversion: The filtered and denoised signal is still an analog signal and needs to be converted into a digital signal through an A / D converter so that it can be processed by the central main control chip.

[0041] 5. Data processing and display: After receiving the digital signal, the central control chip performs further processing and analysis, extracts characteristic values, performs signal classification, etc., compares the processed data with the peak and valley values ​​of the standardized data, and then displays the processing results on the display screen.

[0042] 6. Data transmission: Through the Bluetooth module, the central main control chip can transmit the processed data to the mobile device.

[0043] The eye muscle electromyography module is provided with a bandpass filter amplifier circuit for amplifying and bandpass filtering the eye muscle electromyography signal. Specifically:

[0044] like Figure 3 and 4As shown, the bandpass filter amplifier circuit adopts a two-stage amplification structure to ensure accurate amplification of weak eye muscle electrical signals, and specifically includes a first-stage amplifier circuit, a second-stage amplifier circuit, a high-pass filter component, and a low-pass filter component.

[0045] The first-stage amplifier circuit includes amplifier U1. Amplifier U1 uses AD8236, and its pins 1-8 are -IN, RG1, RG2, +IN, -Vs, REF, Vout, and +Vs. AD8236 is an operational amplifier specially used for biosignal amplification. It has the characteristics of low noise, low power consumption, and high gain, and can amplify the eye muscle electrical signal by 100 times.

[0046] 1. Power supply connection: Connect the positive power supply (+Vs terminal, i.e. pin 8) of AD8236 to the required +5V power supply voltage and the negative power supply (-Vs terminal, i.e. pin 5) to ground.

[0047] 2. Gain setting: Connect resistor R1 between RG1 (i.e. pin 2) and RG2 (i.e. pin 3), resistor R1 = 500Ω, to set the gain to 100 times.

[0048] 3. Input signal connection: Connect input signal 1 (Right 1) and input signal 2 (Left 1) to -IN (pin 1) and +IN (pin 4) respectively.

[0049] 4. Output connection: The output signal is led out from the Vout terminal (i.e. pin 7) and supplied to the second stage amplifier circuit.

[0050] 5. Reference voltage (REF terminal, i.e., pin 6) setting: used to set the output reference voltage. In this embodiment, the reference voltage is 0.

[0051] The second-stage amplification circuit includes amplifier U2. Amplifier U2 uses AD8646, and its pins 1-8 are OUTA, -INA, +INA, VDD- / GND, +INB, -INB, OUTB, and VDD+ respectively.

[0052] A high-pass filter component is used to remove low-frequency noise, and its cut-off frequency is designed to be 10Hz; a low-pass filter component is used to remove high-frequency noise, and its cut-off frequency is designed to be 150Hz, thereby achieving a band-pass filtering effect.

[0053] The high-pass filter component includes a resistor R2 and a capacitor C2, and the low-pass filter component includes a resistor R3 and a capacitor C3.

[0054] 1. Connection between AD8646 and high-pass filter components: The output of AD8236 is connected to resistor R2, the other end of resistor R2 is connected to one end of capacitor C2, the other end of capacitor C2 is connected to the -INA end of AD8646 (i.e. pin 2), and the connection point between resistor R2 and capacitor C2 is connected to the +INA end of AD8646 (i.e. pin 3).

[0055] 2. Connection between AD8646 and low-pass filter components: The OUTA terminal (i.e. pin 1) output of AD8646 is connected to resistor R3, the other end of resistor R3 is connected to one end of capacitor C3, the other end of capacitor C3 is connected to the -INB terminal (i.e. pin 6) of AD8646, and the connection point between resistor R3 and capacitor C3 is connected to the +INB terminal (i.e. pin 5) of AD8646.

[0056] 3. AD8646 power supply connection: Connect the positive power supply (VDD+ terminal, i.e. pin 8) of AD8646 to the required +5V power supply voltage and the negative power supply (VDD- / GND terminal, i.e. pin 4) to ground.

[0057] In summary, after the input signal is amplified 100 times by AD8236, high-pass filtering is achieved through resistor R2 = 16kΩ and capacitor C2 = 1μF; the output signal is buffered by AD8646, and low-pass filtering is achieved through resistor R3 = 1kΩ and capacitor C3 = 1μF, and the final signal is output from OUTB (i.e. pin 7) after buffering by AD8646.

[0058] 2. Voice Assistant Module

[0059] 1. Sound collection:

[0060] The voice assistant module uses a high-sensitivity microphone to collect the user's voice signal and convert it into an electrical signal.

[0061] 2. Preprocessing:

[0062] The audio processing unit performs filtering, denoising and other operations on the collected electrical signals to improve the accuracy of subsequent processing.

[0063] The analog signal is converted into a digital signal by an A / D converter for digital signal processing.

[0064] 3. Feature extraction:

[0065] Digital signal processing algorithms are used to extract features from speech signals, such as Mel frequency spectrum analysis (MFCC), linear prediction spectrum analysis (LPCC), etc. These features can reflect key information in speech signals, such as phonemes, intonation, etc.

[0066] 4. Model matching and identification:

[0067] The extracted features are matched with the pre-trained speech recognition model to identify what the user said.

[0068] Speech recognition models are usually trained and optimized using algorithms such as deep neural networks (DNNs) and hidden Markov models (HMMs).

[0069] 5. Dialogue Management:

[0070] Manage the conversation process between users and AI voice assistants, including conversation state maintenance, context management, conversation strategies, etc.

[0071] Through the dialogue management module, the AI ​​voice assistant can remember the user’s previous conversations and provide intelligent responses based on the context.

[0072] 6. Intent Recognition and Entity Recognition:

[0073] Identify user intents and entities, such as querying test results, asking for improvement suggestions, playing music, etc. Intent recognition is usually trained and optimized using machine learning algorithms to improve recognition accuracy.

[0074] 7. Speech synthesis and feedback principles

[0075] Text-to-speech:

[0076] Convert semantic information understood by computers into natural language responses and convert text into speech through speech synthesis technology.

[0077] Speech synthesis technology usually uses methods such as waveform splicing and parameter synthesis to generate natural and fluent speech output.

[0078] 3. Interface and communication with other modules:

[0079] 1. The AI ​​voice assistant communicates and exchanges data with other modules through interfaces.

[0080] Through the API interface, it connects with bio-signal monitoring modules such as eye and muscle electromyography module and heart rate module and transmits data.

[0081] 2. Collaborative Workflow:

[0082] When users control other modules through voice commands, the AI ​​voice assistant converts the commands into corresponding control signals and sends them to the target modules.

[0083] 3. After receiving the control signal, the target module performs the corresponding operation and feeds the result back to the AI ​​voice assistant for speech synthesis and output.

[0084] 4. Heart rate and other application modules

[0085] Using electrocardiogram (ECG) measurement technology

[0086] 1. Electrode patch and conductivity:

[0087] The heart rate module uses electrode patches to contact the skin to form a conductive path.

[0088] The electrode patches sense the tiny electrical signals produced when your heart beats.

[0089] 2. Electrical signal acquisition and amplification:

[0090] When your heart beats, the voltage around your heart changes.

[0091] The electrode patches collect these electrical signals and amplify them through amplifier circuits.

[0092] 3. Filtering and denoising:

[0093] The amplified electrical signal may contain noise and interference.

[0094] Therefore, it is necessary to filter and denoise the signal to improve the signal-to-noise ratio.

[0095] 4. Heart rate calculation and display:

[0096] The filtered and de-noised electrical signal is converted into a digital signal.

[0097] The digital signal is analyzed by an algorithm to calculate the heart rate value and display it on the screen.

[0098] The above is only a preferred embodiment of the present invention, and does not limit the present invention in any form. Any technician familiar with the art can make many possible changes and modifications to the technical solution of the present invention by using the above disclosed methods and technical contents without departing from the scope of the technical solution of the present invention, or modify it into an equivalent embodiment of equivalent changes. Therefore, all equivalent changes made according to the shape, structure and principle of the present invention without departing from the content of the technical solution of the present invention should be included in the protection scope of the present invention.

Claims

1. A modular attention detection system based on VR, characterized in that: Including VR glasses, eye electromyography module, voice assistant module, audio processing unit, heart rate module and central control chip; The VR glasses are equipped with an eye electromyography module, a voice assistant module, an audio processing unit, and a heart rate module; The eye electromyography module, the audio processing unit, and the heart rate module are all electrically connected to the central main control chip, and the eye electromyography module, the audio processing unit, and the heart rate module are all electrically connected to the voice assistant module; The human body's bioelectric signals, sound signals and heart rate information are collected through the eye electromyography module, voice assistant module, audio processing unit and heart rate module, and converted into digital signals, which are processed by the central main control chip and displayed on the display screen.

2. A modular attention detection system based on VR as claimed in claim 1, characterized in that: The central main control chip is electrically connected to the power management module, the bluetooth module, the wifi module and the API interface module.

3. A modular attention detection system based on VR as claimed in claim 1, characterized in that: The power management module includes a solar panel.

4. A modular attention detection system based on VR as claimed in claim 1, characterized in that: The eye muscle electromyography module is provided with a bandpass filter amplifier circuit for amplifying and bandpass filtering the eye muscle electromyography signal.

5. A modular attention detection system based on VR as claimed in claim 4, characterized in that: The bandpass filter amplifier circuit comprises a first-stage amplifier circuit, a second-stage amplifier circuit, a high-pass filter component and a low-pass filter component; The first-stage amplifying circuit includes an amplifier U1, the second-stage amplifying circuit includes an amplifier U2, the high-pass filter component includes a resistor R2 and a capacitor C2, and the low-pass filter component includes a resistor R3 and a capacitor C3; Pin 1 and pin 4 of the amplifier U1 are connected to input signal 1 and input signal 2, a resistor R1 is connected between pin 2 and pin 3 of the amplifier U1, a resistor R2 is connected between pin 7 of the amplifier U1 and pin 3 of the amplifier U2, a capacitor C2 is connected between pin 2 and pin 3 of the amplifier U2, a resistor R3 is connected between pin 1 and pin 5 of the amplifier U2, and a capacitor C3 is connected between pin 5 and pin 6 of the amplifier U2.

6. A modular attention detection system based on VR as claimed in claim 5, characterized in that: Pins 5 and 6 of the amplifier U1 are both grounded.

7. A modular attention detection system based on VR as claimed in claim 5, characterized in that: The pin 8 of the amplifier U1 is connected to a power supply voltage, and the line between the pin 8 of the amplifier U1 and the power supply voltage is grounded via a capacitor C1.

8. A modular attention detection system based on VR as claimed in claim 5, characterized in that: Pin 4 of the amplifier U2 is grounded.

9. A modular attention detection system based on VR as claimed in claim 5, characterized in that: Pin 8 of the amplifier U2 is connected to a power supply voltage, and the line between pin 8 of the amplifier U2 and the power supply voltage is grounded via a capacitor C12.

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