Non-contact sleep eye movement detection method and system based on biological radar
The biological radar system receives radar echo signals reflected by the human eye, performs autocorrelation processing and fast Fourier transform, realizing contactless detection of human eye movements, and verifies the correctness and feasibility of the detection results through synchronous detection with the electroophthalmic measurement platform, solving the problem that human eye movements cannot be detected in the prior art.
Patent Information
- Application Number
- CN202411744015.9
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2024-11-30
- Publication Date
- 2025-02-28
AI Technical Summary
Non-contact detection of human eye movement has not been achieved at this stage, and the accuracy and feasibility of its detection cannot be proved.
The biological radar system receives the radar echo signal reflected by the human eye, selects two time series on the target distance, performs autocorrelation processing and fast Fourier transformation, and obtains the power spectrum of the time series to verify the accuracy and feasibility of the detection results.
Non-contact detection of human eye movements is realized, and the accuracy and feasibility of the detection results are verified through synchronous detection with the electroophthalmic measurement platform.
Smart Images
Figure CN119523471A_ABST
Abstract
Description
Technical Field
[0001] The present invention relates to the technical field of bio-radar life detection, and in particular to a non-contact sleep eye movement detection method and system based on bio-radar. Background Art
[0002] Bioradar is a new concept radar technology with the human body as its main detection object. This technology uses the micro-power electromagnetic waves emitted by the radar as the detection medium. It can penetrate non-metallic obstructions or obstacles such as clothing, ruins, and walls, and non-contactly detect surface micro-movements caused by human life activities, thereby realizing the perception of human vital signs information. It has broad application potential in biomedicine, military struggle, national security, and emergency rescue fields.
[0003] Human eye movement is a complex reflex movement caused by the alternating contraction of the extraocular muscles and intraocular muscles. It is an involuntary, rhythmic, and directional movement controlled by nerves. It is not only one of the core physiological indicators of human sleep stages, but also highly related to people's cognition of things and psychological processing. In addition, it can also reflect eye injuries, oculomotor nerve paralysis, thyroid and other related eye diseases, and is used for human-computer interaction for the disabled. At this stage, bio-radar has basically achieved non-contact detection of human breathing and heartbeat, and has begun to explore non-contact detection of human pulse and blood pressure, but has not yet achieved non-contact detection of human eye movement and proved the correctness and feasibility of its detection. Summary of the invention
[0004] The present invention provides a bio-radar-based non-contact sleep eye movement detection method and system, which are used to solve the problem that non-contact detection of human eye movement has not yet been achieved, and can prove the correctness and feasibility of the detection. In one aspect, the present invention provides a non-contact sleep eye movement detection method based on bio-radar, comprising: Receive radar echo signals reflected by human eyes; Selecting two time series of target distance in the radar echo signal; Performing autocorrelation processing on the two time series to obtain a time series with enhanced eye movement periodicity characteristics; Solving the phase in the time series of the enhanced eye movement periodicity feature to obtain a real number time series; Performing a fast Fourier transform on the real number time series to obtain a power spectrum of the time series; Verify the correctness and feasibility of the power spectrum.
[0005] Preferably, the correctness and feasibility of the power spectrum is verified by comparing it with the power spectrum obtained by the electrooculogram measurement platform.
[0006] Preferably, after receiving the radar echo signal reflected by the human eye, baseline removal is performed on the two-way time series.
[0007] Preferably, after baseline removal is performed on the two-channel time series, low-pass filtering is performed.
[0008] Preferably, after solving the phase in the time series in which the eye movement periodicity feature is enhanced to obtain a real number time series, the real number time series is low-pass filtered.
[0009] Preferably, the radar echo signal is transmitted by a transmitter and received by a receiver.
[0010] Preferably, the transmitter comprises a signal source, a phase-locked loop and a power amplifier, the signal source is connected to the phase-locked loop, and the phase-locked loop is connected to the power amplifier.
[0011] Preferably, the receiver comprises a low noise amplifier, two filters, a mixer and an analog-to-digital converter, the low noise amplifier is connected to one of the filters, the mixer is connected between the two filters, and the other filter is connected to the analog-to-digital converter.
[0012] In another aspect, the present invention provides a non-contact sleep eye movement detection system based on bio-radar, comprising: A receiver for receiving radar echo signals reflected by human eyes; The computer is used to select two time series on the target distance in the radar echo signal, perform autocorrelation processing on the two time series to obtain a time series with enhanced eye movement periodicity characteristics, solve the phase in the time series with enhanced eye movement periodicity characteristics to obtain a real number time series, perform fast Fourier transform on the real number time series, and obtain a power spectrum of the time series.
[0013] Preferably, the system further comprises a transmitter, and the transmitter is used for transmitting a radar echo signal.
[0014] Beneficial Effects The present invention transmits a radar echo signal through a transmitter, receives the radar echo signal through a receiver, selects two time series on the target distance in the received radar echo signal through a computer, performs autocorrelation processing on the two time series to obtain a time series with enhanced eye movement periodicity characteristics, solves the phase in the time series with enhanced eye movement periodicity characteristics to obtain a real number time series, performs fast Fourier transform on the real number time series to obtain the power spectrum of the time series, thereby realizing non-contact detection of human eye movement.
[0015] At the same time, by using the electrooculogram measurement platform for synchronous detection, the non-contact detection results are compared with the synchronous detection results of the electrooculogram measurement platform to verify the correctness and feasibility of the non-contact detection results. BRIEF DESCRIPTION OF THE DRAWINGS
[0016] 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 description of 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.
[0017] Figure 1 A flow chart of a non-contact sleep eye movement detection method based on bio-radar provided in an embodiment of the present invention; Figure 2 A system schematic diagram of a non-contact sleep eye movement detection bio-radar based on a bio-radar provided in an embodiment of the present invention; Figure 3 A working principle diagram of an electrooculogram measurement platform for a non-contact sleep eye movement detection method based on bio-radar provided in an embodiment of the present invention; Figure 4 A diagram of a human eye movement measurement simulation experiment of a non-contact sleep eye movement detection method based on bio-radar provided in an embodiment of the present invention; Figure 5 A flow chart of a non-contact eye movement detection algorithm of a non-contact sleep eye movement detection method based on bio-radar provided in an embodiment of the present invention; Figure 6 A bio-radar echo information graph containing two-dimensional information of distance and time for a non-contact sleep eye movement detection method based on bio-radar provided in an embodiment of the present invention; Figure 7 A comparison chart of the detection results of the bioradar and the electrooculogram measurement platform when blinking in a non-contact sleep eye movement detection method based on bioradar provided in an embodiment of the present invention; Figure 8 A comparison chart of the detection results of the bioradar and the electrooculogram measurement platform when the eyes are closed (no eye movement), eyes are closed (slow eye movement), and eyes are closed (rapid eye movement) for a non-contact sleep eye movement detection method based on bioradar provided in an embodiment of the present invention. DETAILED DESCRIPTION
[0018] 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.
[0019] Example 1 Figure 1 A flow chart of a non-contact sleep eye movement detection method based on bioradar provided in an embodiment of the present invention. A non-contact sleep eye movement detection method based on bioradar provided in an embodiment of the present invention comprises the following steps: S1. Receive radar echo signal reflected by human eyes.
[0020] Exemplarily, the radar echo signal is transmitted by a transmitter and received by a receiver.
[0021] Exemplarily, the transmitter includes a signal source, a phase-locked loop and a power amplifier, the signal source is connected to the phase-locked loop, and the phase-locked loop is connected to the power amplifier.
[0022] Exemplarily, the receiver includes a low noise amplifier, two filters, a mixer and an analog-to-digital converter, the low noise amplifier is connected to one of the filters, the mixer is connected between the two filters, and the other filter is connected to the analog-to-digital converter.
[0023] S2. Select two time series of target distance in the radar echo signal.
[0024] Exemplarily, after receiving the radar echo signal reflected by the human eye, baseline removal is performed on the two-way time series.
[0025] Exemplarily, after baseline removal is performed on the two-way time series, low-pass filtering is performed.
[0026] S3. Perform autocorrelation processing on the two time series to obtain a time series with enhanced eye movement periodicity characteristics.
[0027] S4. Solve the phase in the time series with enhanced eye movement periodicity characteristics to obtain a real number time series.
[0028] Exemplarily, after solving the phase in the time series in which the eye movement periodicity feature is enhanced to obtain a real number time series, the real number time series is low-pass filtered.
[0029] S5. Perform fast Fourier transform on the real number time series to obtain a power spectrum of the time series.
[0030] S6. Verify the correctness and feasibility of the power spectrum.
[0031] Exemplarily, the correctness and feasibility of the power spectrum is verified by comparing it with the power spectrum obtained by an electrooculogram measurement platform.
[0032] The embodiment of the present invention also provides a non-contact sleep eye movement detection system based on bio-radar, such as Figure 2 As shown, including: A receiver for receiving radar echo signals reflected by human eyes; The computer is used to select two time series on the target distance in the radar echo signal, perform autocorrelation processing on the two time series to obtain a time series with enhanced eye movement periodicity characteristics, solve the phase in the time series with enhanced eye movement periodicity characteristics to obtain a real number time series, perform fast Fourier transform on the real number time series, and obtain a power spectrum of the time series.
[0033] Exemplarily, the system further includes a transmitter, which is used to transmit a radar echo signal.
[0034] Furthermore, if Figure 2 As shown in the figure, the non-contact sleep eye movement detection bio-radar system consists of three subsystems, namely the transmitter subsystem, the receiver subsystem, and the antenna subsystem. The transmitter subsystem mainly includes a signal source, a phase-locked loop, and a power amplifier. The signal source drives the phase-locked loop to generate an FMCW waveform signal, and then obtains the appropriate signal transmission power through the power amplifier, and radiates the signal directionally through the transmitting antenna; the receiver subsystem receives the human eye movement reflection echo through a link composed of a low-noise amplifier, a filter, and a mixer, down-converts it to an intermediate frequency, and converts the analog signal into a digital signal by an analog-to-digital converter, which is transmitted through a USB interface and stored in a computer for processing; Specifically, the biological radar system is mainly responsible for the generation, transmission, reception and acquisition of FMCW (Frequency Modulated Continuous Wave) signals. It operates at a frequency of 76GHz-81GHz, and can effectively take into account the parallel improvement of detection distance and detection sensitivity, ensuring the ability to detect human eye movements without contact.
[0035] Furthermore, if Figure 3As shown, the electrooculogram measurement platform consists of three parts: electromyographic electrodes, filter amplifiers, and analog-to-digital converters. The electrodes include four channels, A, B, C, and D, which are Ag / AgCI sintered disc electrodes with the advantages of low contact impedance, high signal-to-noise ratio, and good stability. They are used to detect electromyographic signals generated by eye movements. The filter amplifier filters and amplifies the electrooculogram signals measured by the four channels of electrodes to meet the requirements of subsequent conversion and processing. The analog-to-digital converter converts the analog EOG signals into digital form, transmits them via wireless Bluetooth, and stores them in a computer for processing. The platform uses a unipolar lead mode when working. Electrodes A and B are located just above the right eyebrow and at the edge of the eye corner, respectively, on the same vertical or horizontal line as the eyeball when looking straight ahead. Electrodes C and D are located at the left and right earlobes, respectively, and form a pair with electrodes A and B to collect electrooculogram signals in the horizontal and vertical directions. Specifically, the electro-oculogram measurement platform mainly verifies the correctness and feasibility of non-contact eye movement detection by bio-radar by measuring the EOG (electro-oculograph) signal that reflects the electromyographic activity of the human eye.
[0036] Furthermore, taking the eye movements related to sleep staging as an example, the simulation experimental method of measuring human eye movements is explained. According to the sleep staging standard of the American Academy of Sleep Medicine (AASM), a complete sleep cycle can be divided into the wakefulness period, N1 stage, N2 stage, N3 stage and REM stage, and the corresponding eye movement characteristics are continuous rapid eye movement\blinking movement, slow eye movement (0.2-0.6 Hz), slow eye movement, no eye movement and staged rapid eye movement (above 0.6Hz), including four main types of eye movement forms: blinking, no eye movement, slow eye movement and rapid eye movement. Figure 4 As shown, this embodiment uses a method of using a metronome to prompt volunteers to simulate these four eye movements, providing detection targets for non-contact detection and reference measurement of bio-radar, wherein blinking means that the volunteer blinks 10 times within 10 seconds under the prompt of the metronome - the frequency is about 1Hz, closing eyes (no eye movement) means that the volunteer closes his eyes for 10 seconds without the prompt of the metronome, closing eyes (slow eye movement) means that the volunteer moves his eyeballs 3 times within 10 seconds under the prompt of the metronome - the frequency is about 0.3Hz, and closing eyes (rapid eye movement) means that the volunteer moves his eyeballs 6 times within 10 seconds under the prompt of the metronome - the frequency is about 0.6Hz. During the experiment, the bio-radar conducts non-contact detection at about 0.1m in front of the volunteer's face, and the reference measurement platform synchronously detects the EOG signal. The volunteer keeps his head still and moves according to the prompts.
[0037] Furthermore, if Figure 5As shown in Figure 1, the non-contact eye movement detection algorithm mainly includes three parts: preprocessing, motion reconstruction and spectrum analysis. Preprocessing is to select the time series of the target distance from the two-dimensional bio-radar echo containing distance and time, and perform baseline removal and low-pass filtering to improve the signal-to-noise ratio of the time series. The distance selection can be expressed as:
[0038] In the formula Represents the bioradar echo containing two-dimensional information of distance and time, where Indicates distance, Indicates time, Indicates that the variance of the time series is calculated in the echo time dimension. It means taking the time series with the maximum variance distance, and finally obtaining the time series containing I and Q paths Motion reconstruction first performs autocorrelation on the I and Q time series to obtain a time series with enhanced eye movement periodicity features. , which can be expressed as:
[0039] In the formula Represents convolution. Then solve the phase of the I and Q time series to get a real time series , and low-pass filtering is performed to achieve motion reconstruction of eye movement information, where the phase solution can be expressed as:
[0040] In the formula represents arc tangent, and low-pass filtering uses a FIR digital filter with a cutoff frequency of 2 Hz. Spectral analysis uses FFT to calculate the power spectrum of the eye movement time series after motion reconstruction and compares it with the synchronously measured EOG signal.
[0041] Furthermore, if Figure 6 As shown in the figure, the bio-radar echo contains two-dimensional information of distance and time. The bright bar at 0.1m in the figure is the distance point where the target is located. The time series at the target distance can be obtained through the above distance selection method, which contains human eye movement information. However, due to the presence of a large amount of noise and clutter, it is not possible to present significant eye movement features and only the data of the I channel is shown in the figure.
[0042] Furthermore, if Figure 7As shown in the figure, the detection results of the bioradar non-contact and electrooculogram measurement platform during blinking are explained: ab) are the eye movement results of the bioradar non-contact detection, where (a) is the time series after motion reconstruction and (b) is the power spectrum; (cd) are the EOG results of the reference measurement platform synchronous detection, where (a) is the time series and (b) is the power spectrum. Comparing (a) and (c), it can be seen from the time domain that the detection results of the bioradar non-contact and the reference measurement platform both show obvious periodic blinking characteristics; further comparing (b) and (d), the power spectrum peaks of the two are 1 and 0.99Hz respectively, which is consistent with the simulation experiment, thus verifying the feasibility and correctness of the bioradar-based non-contact eye movement detection technology proposed in this invention.
[0043] Furthermore, if Figure 8 As shown, the detection results of the bio-radar non-contact and electrooculogram measurement platform when eyes are closed (no eye movement), eyes are closed (slow eye movement), and eyes are closed (rapid eye movement) are explained: (ab), (cd), and (ef) are the detection results when eyes are closed (no eye movement), eyes are closed (slow eye movement), and eyes are closed (rapid eye movement), respectively, and (a, c, e) and (b, d, f) are the eye movement results of the bio-radar non-contact detection and the EOG results of the electrooculogram measurement platform synchronous detection, respectively. Among them, when eyes are closed (slow eye movement) and eyes are closed (rapid eye movement), the spectral peaks of the bio-radar and the electrooculogram measurement platform are highly consistent, which further verifies the feasibility and correctness of the technology proposed in the present invention; when eyes are closed (no eye movement), the spectral peaks of the bio-radar and the electrooculogram measurement platform are different, because at this time there is no movement of the eyeball, resulting in the detection results of both being mainly noise, which is in line with expectations and does not affect the future application of the technology proposed in the present invention.
[0044] Although the preferred embodiments of the present invention have been described, those skilled in the art may make other changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present invention.
[0045] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.
Claims
1. A non-contact sleep eye movement detection method based on bio-radar, characterized in that: The following steps are involved: Receive radar echo signals reflected by human eyes; Selecting two time series of target distance in the radar echo signal; Performing autocorrelation processing on the two time series to obtain a time series with enhanced eye movement periodicity characteristics; Solving the phase in the time series of the enhanced eye movement periodicity feature to obtain a real number time series; Performing a fast Fourier transform on the real number time series to obtain a power spectrum of the time series; Verify the correctness and feasibility of the power spectrum.
2. The non-contact sleep eye movement detection method based on bio-radar according to claim 1, characterized in that: The correctness and feasibility of the power spectrum are verified by comparing it with the power spectrum obtained by the electrooculogram measurement platform.
3. The non-contact sleep eye movement detection method based on bio-radar according to claim 1, characterized in that: After receiving the radar echo signal reflected by the human eye, the baseline of the two time series is removed.
4. The non-contact sleep eye movement detection method based on bio-radar according to claim 3, characterized in that: After baseline removal, the two time series are subjected to low-pass filtering.
5. The non-contact sleep eye movement detection method based on bio-radar according to claim 1, characterized in that: The phase in the time series with enhanced eye movement periodicity characteristics is solved to obtain a real number time series, and then the real number time series is low-pass filtered.
6. The non-contact sleep eye movement detection method based on bio-radar according to claim 1, characterized in that: The radar echo signal is transmitted by a transmitter and received by a receiver.
7. The non-contact sleep eye movement detection method based on bio-radar according to claim 6, characterized in that: The transmitter comprises a signal source, a phase-locked loop and a power amplifier. The signal source is connected to the phase-locked loop, and the phase-locked loop is connected to the power amplifier.
8. The non-contact sleep eye movement detection method based on bio-radar according to claim 6, characterized in that: The receiver comprises a low noise amplifier, two filters, a mixer and an analog-to-digital converter. The low noise amplifier is connected to one of the filters, the mixer is connected between the two filters, and the other filter is connected to the analog-to-digital converter.
9. A non-contact sleep eye movement detection system based on bio-radar, characterized in that: The non-contact sleep eye movement detection method based on bio-radar according to any one of claims 1 to 8 is applied, and the system comprises: A receiver for receiving radar echo signals reflected by human eyes; The computer is used to select two time series on the target distance in the radar echo signal, perform autocorrelation processing on the two time series to obtain a time series with enhanced eye movement periodicity characteristics, solve the phase in the time series with enhanced eye movement periodicity characteristics to obtain a real number time series, perform fast Fourier transform on the real number time series, and obtain a power spectrum of the time series.
10. The non-contact sleep eye movement detection system based on bio-radar according to claim 9, characterized in that: The system further comprises a transmitter, which is used for transmitting a radar echo signal.