Motion sickness recognition device and system for monitoring specific brain region and storage medium
By monitoring physiological signals in specific brain regions, including the visual cortex of the occipital lobe, the motor and sensory cortex of the central midline area, and the forehead pole and prefrontal cortex of the frontal pole, the problem of inaccurate identification in the prior art is solved, and the accurate identification and monitoring of motion sickness is achieved.
Patent Information
- Application Number
- CN202510104571.8
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-01-23
- Publication Date
- 2025-02-28
AI Technical Summary
The existing methods of using functional near-infrared spectroscopy (fNIRS) to identify motion sickness are focused on monitoring the physiological status of the prefrontal lobe. There are defects such as unclear physiological indicators and data characteristics vary from person to person, so it is impossible to accurately and efficiently identify motion sickness.
It provides a motion sickness recognition device and system that monitors specific brain areas. Through the signal acquisition module, it obtains physiological signals of the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline area, as well as the prefrontal cortex of the frontal pole and the prefrontal cortex of the frontal pole area, performs signal processing, denoising processing and feature calculations, and calculates blood oxygen characteristic values to identify motion sickness.
Accurate identification of motion sickness status is achieved, providing a data basis for subsequent motion sickness intervention, and reducing the negative impact of motion sickness when riding in transportation.
Smart Images
Figure CN119523498A_ABST
Abstract
Description
Technical Field
[0001] The present application relates to the technical fields of brain-computer interaction and motion sickness recognition, and in particular to a motion sickness recognition device and system and a storage medium for monitoring specific brain regions. Background Art
[0002] With the popularization of the Internet, smart phones, and the fifth generation of mobile communication technology, people often choose to use their mobile phones to enrich their riding time or use laptops to work when riding various means of transportation. However, some passengers may experience motion sickness when using the above devices while the vehicle is moving. As a result, the riding experience and work performance are affected, and the body and mind are in a sub-healthy state.
[0003] Motion sickness, also known as carsickness, seasickness, and airsickness, refers to a disease caused by a loss of balance when riding various means of transportation. It is generally believed that when the movement felt by the senses such as vision, muscles, and joints does not match the movement felt by the vestibular system, it causes dizziness, nausea, vomiting, loss of appetite and other symptoms. In severe cases, it may manifest as a feeling of the house and objects spinning, inability to stand, and even fainting. It is estimated that about one-third of the population in China exhibits symptoms of motion sickness when riding various means of transportation. Motion sickness affects people's physical and mental health and reduces work performance. Therefore, it is necessary to find ways to monitor and identify motion sickness, which plays a very important role in improving travel quality and optimizing work performance.
[0004] Functional near-infrared spectroscopy (fNIRS) is a non-invasive brain imaging technology that can obtain information on blood oxygen concentration in the cerebral cortex and indirectly reflect the brain's neural activity. Compared with other brain imaging technologies, it has the characteristics of high temporal resolution, strong mobility, high safety, and low cost. In view of the requirement for real-time monitoring while riding in public transportation, fNIRS has unparalleled advantages over other brain imaging technologies.
[0005] Existing methods for identifying motion sickness using fNIRS focus on monitoring the physiological state of the prefrontal lobe. However, they have defects such as unclear physiological indicators and data characteristics that vary from person to person, making it impossible to accurately and efficiently identify motion sickness. In order to improve the recognition efficiency and accuracy, it is necessary to propose a motion sickness recognition method that monitors specific brain areas from a physiological perspective based on the pathogenesis of motion sickness and the functional characteristics of different brain areas. Summary of the invention
[0006] In order to solve the above problems, the present application provides a motion sickness identification device and system and a storage medium for monitoring specific brain areas to effectively identify the motion sickness state and provide data basis for subsequent motion sickness intervention, thereby reducing the negative impact of motion sickness when riding in public transportation.
[0007] In order to achieve the above purpose, the technical solution adopted in this application is as follows: In a first aspect, the present application provides a motion sickness recognition device for monitoring a specific brain region, the device comprising: A signal acquisition module is configured to acquire physiological signals of a specific brain region of the target object; wherein the physiological signals of the specific brain region include the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline region, and the prefrontal cortex of the frontal polar region and the prefrontal cortex of the frontal lobe; A signal processing module, the signal processing module is connected to the signal acquisition module, and the signal processing module is configured to perform format conversion on the physiological signal to obtain a blood oxygen signal; A signal denoising module, the signal denoising module is connected to the signal processing module, and the signal denoising module is configured to perform denoising processing on the blood oxygen signal to obtain a pure blood oxygen signal; A feature calculation module, the feature calculation module is connected to the signal denoising module, and the feature calculation module is configured to calculate a blood oxygen characteristic value based on the pure blood oxygen signal; wherein the blood oxygen characteristic value includes a peak value and a change rate of oxygenated hemoglobin, a moving average within a 10s window time, and a difference between oxygenated hemoglobin and deoxygenated hemoglobin; An identification module, wherein the identification module is connected to the feature calculation module, and the identification module is configured to compare the blood oxygen feature value with the set threshold value based on a set threshold value to identify motion sickness.
[0008] Furthermore, the physiological signal of the specific brain area of the target object is a light intensity signal collected by the target object wearing a near-infrared brain imaging system device in a real vehicle.
[0009] Furthermore, the signal processing module is further configured to: When using 735nm and 850nm near-infrared light sources, four light intensity signals are obtained, which are the transmitted light intensity at 735nm wavelength. , Transmitted light intensity at 850nm wavelength , incident light intensity at 735nm wavelength And the incident light intensity at 850nm wavelength ; The absorbance change is: ; In the formula, is the absorbance change at a wavelength of 735 nm, is the absorbance change at a wavelength of 850nm; According to the modified Bigram's law, the differential path length factor is introduced , construct the following equation: ; In the formula, and are the molar absorption coefficients of oxygenated hemoglobin at wavelengths of 735 nm and 850 nm, respectively; and are the molar absorption coefficients of deoxyhemoglobin at wavelengths of 735 nm and 850 nm, respectively; is the change in oxygenated hemoglobin concentration, is the change in deoxyhemoglobin concentration; is the wavelength of near-infrared light; Based on the equation, the change in oxygenated hemoglobin concentration is calculated Changes in deoxyhemoglobin concentration , combined with the initial concentration baseline, the absolute concentrations of oxyhemoglobin and deoxyhemoglobin are obtained.
[0010] Furthermore, the signal denoising module is further configured as follows: Using a first-order linear fit to de-drift the blood oxygen signal, to obtain a first signal; Decomposing the first signal into different principal components, each principal component represents a signal change pattern; obtaining a second signal by eliminating slow-changing components with frequencies lower than a set frequency and components inconsistent with the frequency band of neural activity; The second signal is band-pass filtered to filter out irrelevant signals; wherein the frequency of the band-pass filter is set to 0.01-0.15 Hz, and the irrelevant signals include heart rate signals, breathing signals, and ambient light interference signals.
[0011] Furthermore, the feature calculation module is further configured as follows: The peak value and rate of change of oxygenated hemoglobin are calculated by the following method: the local maximum value in the signal within 4 seconds is identified as the peak value, that is, at time n p When the peak value is reached, ; , m is a constant, HbO[n p ] is n p The oxygenated hemoglobin concentration at the moment, HbO[n] is the oxygenated hemoglobin concentration at the moment n, and n is the time point mark number; the HbO change rate is calculated using the backward difference method; The moving average within the 10s window time is calculated by the following method: When the sampling rate is 12.5Hz, the window size is N=125, ;in, is the moving average of the oxygenated hemoglobin concentration within the 10s window time, is the oxygenated hemoglobin concentration at time k, Mark the time point of the starting moment of the 10s window time with a serial number. Mark the time points with serial numbers; The difference between oxyhemoglobin and deoxyhemoglobin was calculated using the following formula: ; Where D[n] is the difference between the concentrations of oxyhemoglobin and deoxyhemoglobin, HbO[n] is the concentration of oxyhemoglobin, and HbR[n] is the concentration of deoxyhemoglobin.
[0012] Furthermore, the identification module is further configured to determine the set threshold by the following method: Determining a set threshold based on a blood oxygen characteristic value at at least one target moment; wherein the target moment is the moment when the target subject issues an instruction to the recognition module when he / she believes that motion sickness has occurred; When there is only one blood oxygen characteristic value at a target moment, the blood oxygen characteristic value at the target moment is used as the set threshold; When there are at least two target blood oxygen characteristic values, the minimum value among the peak value and change rate of oxygenated hemoglobin, the moving average value within the 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin is selected as the set threshold.
[0013] In a second aspect, the present application provides a motion sickness recognition system for monitoring a specific brain region, the system comprising: a memory for storing a computer program; and a processor for executing the computer program to implement the following method: Acquiring physiological signals of specific brain regions of the target object; wherein the physiological signals of the specific brain regions include the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline region, and the prefrontal cortex of the frontal polar region and the prefrontal cortex of the frontal lobe; Converting the physiological signal into a blood oxygen signal; Performing denoising on the blood oxygen signal to obtain a pure blood oxygen signal; Based on the pure blood oxygen signal, a blood oxygen characteristic value is calculated; wherein the blood oxygen characteristic value includes the peak value and change rate of oxygenated hemoglobin, the moving average value within a 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin; Based on a set threshold, the blood oxygen characteristic value is compared with the set threshold to identify motion sickness.
[0014] Furthermore, the physiological signal is format converted to obtain a blood oxygen signal, including: When using 735nm and 850nm near-infrared light sources, four light intensity signals are obtained, which are the transmitted light intensity at 735nm wavelength. , Transmitted light intensity at 850nm wavelength , incident light intensity at 735nm wavelength And the incident light intensity at 850nm wavelength ; The absorbance change is: ; In the formula, is the absorbance change at a wavelength of 735 nm, is the absorbance change at a wavelength of 850nm; According to the modified Bigram's law, the differential path length factor is introduced , construct the following equation: ; In the formula, and are the molar absorption coefficients of oxygenated hemoglobin at wavelengths of 735 nm and 850 nm, respectively; and are the molar absorption coefficients of deoxyhemoglobin at wavelengths of 735 nm and 850 nm, respectively; is the change in oxygenated hemoglobin concentration, is the change in deoxyhemoglobin concentration; is the wavelength of near-infrared light; Based on the equation, the change in oxygenated hemoglobin concentration is calculated Changes in deoxyhemoglobin concentration , combined with the initial concentration baseline, the absolute concentrations of oxyhemoglobin and deoxyhemoglobin are obtained.
[0015] Furthermore, the blood oxygen signal is subjected to denoising to obtain a pure blood oxygen signal, including: Using a first-order linear fit to de-drift the blood oxygen signal, to obtain a first signal; Decomposing the first signal into different principal components, each principal component represents a signal change pattern; obtaining a second signal by eliminating slow-changing components with frequencies lower than a set frequency and components inconsistent with the frequency band of neural activity; The second signal is band-pass filtered to filter out irrelevant signals; wherein the frequency of the band-pass filter is set to 0.01-0.15 Hz, and the irrelevant signals include heart rate signals, breathing signals, and ambient light interference signals.
[0016] In a third aspect, the present application provides a non-transitory computer-readable storage medium storing instructions. When the instructions are executed by a processor, the following steps are performed: Acquiring physiological signals of specific brain regions of the target object; wherein the physiological signals of the specific brain regions include the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline region, and the prefrontal cortex of the frontal polar region and the prefrontal cortex of the frontal lobe; Converting the physiological signal into a blood oxygen signal; Performing denoising on the blood oxygen signal to obtain a pure blood oxygen signal; Based on the pure blood oxygen signal, a blood oxygen characteristic value is calculated; wherein the blood oxygen characteristic value includes the peak value and change rate of oxygenated hemoglobin, the moving average value within a 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin; Based on a set threshold, the blood oxygen characteristic value is compared with the set threshold to identify motion sickness.
[0017] This application has at least the following beneficial effects: This application proposes to collect characteristic and meaningful physiological signals from the specific brain area of the target object; convert the format of the physiological signals to obtain blood oxygen signals; perform drift removal, TDDR correction, and IIR bandpass filtering on the blood oxygen signals to obtain pure blood oxygen signals; and perform time domain analysis on the pure blood oxygen signals to obtain blood oxygen characteristic values. This enables simple and efficient monitoring of the occurrence of motion sickness. BRIEF DESCRIPTION OF THE DRAWINGS
[0018] Figure 1 A structural diagram of a motion sickness identification device for monitoring a specific brain area is proposed for an embodiment of the present application.
[0019] Figure 2 A schematic diagram of a specific brain region provided in an embodiment of the present application; wherein (a) represents the Brodmann partition of the lateral cerebral hemisphere, (b) represents the Brodmann partition of the medial cerebral hemisphere, the boxes in the figure represent the unit partitions delineated within the partitions, and the numbers in the boxes represent the numbers of the unit partitions, i.e., 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27, 28, 29, 30, 31, 32 , 33, 34, 35, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46 and 47 represent different unit partitions, among which unit partitions 1, 2 and 3 are adjacent, so they are displayed in a box. Unit partitions 1, 2 and 3 form the sensory cortex of the central midline area; unit partition 4 is the motor cortex of the central midline area; unit partitions 9, 10 and 11 form the prefrontal cortex of the frontal lobe; unit partition 10 is the prefrontal polar cortex of the frontal polar area; unit partition 17 is the visual cortex of the occipital lobe.
[0020] Figure 3A structural diagram of a near-infrared brain imaging system device provided in an embodiment of the present application; the circles in the figure refer to target points in the device, and the combination of letters and / or numbers in the circles are used to indicate the coordinates of the target points, namely, NZ, F9, FT9, T9, A1, TP9, P9, P10, TP10, T10, A2, IZ, FpZ, Fp1, AF7, F7, FT7, T7, TP7, P7, PO7, O1, OZ, O2, PO8, P8, TP8, T8, FT8, F8 , AF8, Fp2, AF3, AFZ, AF4, F5, F3, F1, Fz, F2, F4, F6, F8, FT7, FC5, FC3, FC1, FCZ, FC2, FC4, FC6, C5, C3, C1, Cz, C2, C4, C6, TP7, CP5, CP3, CP1, CPZ, CP4, CP6, P7, P5, P3, P1, Pz, P4, P6, PO3, POZ and PO4 represent the coordinates of different target points, respectively.
[0021] Figure 4 A flowchart of a method for identifying motion sickness by monitoring a specific brain region provided in an embodiment of the present application. DETAILED DESCRIPTION
[0022] The following describes the embodiments of the present application through specific examples, and those skilled in the art can easily understand other advantages and effects of the present application from the contents disclosed in this specification. The present application can also be implemented or applied through other different specific embodiments, and the details in this specification can also be modified or changed in various ways based on different viewpoints and applications without departing from the spirit of the present application. It should be noted that the following embodiments and features in the embodiments can be combined with each other without conflict.
[0023] The specific implementation of the present application is further described in detail below in conjunction with the drawings and examples.
[0024] Glossary: HbO: oxygenated hemoglobin.
[0025] HbR: deoxyhemoglobin.
[0026] HbT: total hemoglobin.
[0027] Motion sickness is a physiological reaction caused by information conflict between the vestibular system, visual system and proprioceptive system. When the brain receives inconsistent movement and posture information, it can cause symptoms such as dizziness, nausea, and vomiting, accompanied by emotions such as tension, anxiety, and fear.
[0028] In order to accurately identify motion sickness, the present application embodiment provides a motion sickness identification device that monitors specific brain regions. Figure 1, is a structural diagram of a motion sickness recognition device for monitoring a specific brain region provided in an embodiment of the present application. The device includes the following components connected in sequence: a signal acquisition module 100, a signal processing module 200, a signal denoising module 300, a feature calculation module 400, and a recognition module 500.
[0029] The functions of each component and its specific definition are described in detail below.
[0030] The signal acquisition module 100 is configured to acquire physiological signals of a specific brain region of the target object.
[0031] In this embodiment, please refer to Figure 2 , is a schematic diagram of a specific brain region provided in an embodiment of the present application. The specific brain region is the Brodmann partition of the medial surface of the cerebral hemisphere. The specific brain region includes: the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline area, the prefrontal cortex of the frontal polar area, and the prefrontal cortex of the frontal lobe.
[0032] The occipital lobe is located at the back of the brain and is the main area for visual information processing. The visual cortex is the core component of the occipital lobe, which receives signals from the lateral geniculate body through the optic radiation, that is, receives and interprets visual signals from the retina, and the processed information is transmitted to other brain areas for higher-level cognitive functions such as spatial navigation and motor coordination.
[0033] When the visual cortex of the occipital lobe perceives the body as still and the vestibular cortex perceives the body as moving, or when the visual cortex of the occipital lobe perceives the body as moving and the vestibular cortex perceives the body as still, the contradictory information causes the sensory integration center composed of the vestibular nucleus and cerebellum to produce a confused reaction, thereby causing motion sickness; due to the compensation effect, the visual cortex will become overexcited, which will manifest itself in blood oxygen as an increase in HbO concentration.
[0034] The central midline area mainly includes the precentral gyrus and postcentral gyrus of the cerebral cortex. The precentral gyrus is the main motor cortex, located in front of the central sulcus, responsible for the voluntary movement control of various parts of the body; the postcentral gyrus is the main sensory cortex, located behind the central sulcus, processing sensory information from the whole body, such as touch, temperature and proprioception; the motor cortex sends movement commands through descending neural pathways to regulate muscle contraction and relaxation, and the sensory cortex receives information from proprioceptive receptors, such as muscle spindles and joint receptors, and feedbacks the current position and movement status of various parts of the body. The two together constitute the proprioceptive system.
[0035] When riding in public transportation, the proprioceptive system perceives the body as still, but the vestibular perception is that the body is moving, which will intensify the information conflict in the sensory integration center and aggravate the symptoms of motion sickness; due to the compensatory effect, the motor cortex and sensory cortex will become overexcited, which will manifest as an increase in HbO concentration in the blood oxygen.
[0036] The prefrontal cortex is located in the frontal polar region and is the most anterior part of the prefrontal cortex. The prefrontal cortex of the frontal lobe is located in the front of the frontal lobe of the brain and is the center of high-level cognitive functions. It is responsible for functions such as cognition and emotional regulation. The prefrontal polar cortex and the prefrontal cortex are responsible for integrating information from different sensory pathways, including vision, hearing, vestibular and proprioception, etc. It helps individuals predict future events and environmental changes, so as to make appropriate responses; the prefrontal polar cortex and the prefrontal cortex are connected to the limbic system and participate in the regulation of emotional processing and stress response, which affects the individual's emotional response to uncomfortable or conflicting situations.
[0037] The prefrontal polar cortex and the prefrontal cortex are involved in understanding and processing information conflicts generated by vision, vestibular and proprioception, and resolve conflicts through cognitive strategies. Motion sickness also produces emotions such as tension, anxiety, and fear. The prefrontal polar cortex and the prefrontal cortex are involved in regulating emotions. If the regulation fails, the human body may have excessive anxiety and stress reactions to the symptoms of motion sickness, further aggravating the discomfort. As the brain area is activated and performs functions, the prefrontal polar cortex and the prefrontal cortex will consume oxygen, which will be reflected in the blood oxygen level as an increase in HbO concentration and a decrease in HbR concentration, and an increase in the overall HbT concentration.
[0038] When motion sickness occurs, some areas of the cerebral cortex are activated due to compensation phenomena, resulting in higher blood oxygen consumption. Monitoring only this part of the brain area can not only reduce the number of probes and receivers, save costs and reduce the weight of the equipment, but also reduce the number of channels to be processed and speed up the extraction of blood oxygen characteristic values.
[0039] In some embodiments, the signal acquisition module 100 is a near-infrared brain imaging system device, such as Figure 3 The figure shows a structure diagram of a near-infrared brain imaging system device provided in an embodiment of the present application. In the figure, multiple target points are marked with circles, and the symbols in the circles refer to the coordinates of the target points. Figure 3 The near-infrared brain imaging system equipment will arrange the probes at the coordinates: O1, O2, F3, F4, Fp1, Fp2, Cz according to the 10-20 international standard lead system, and the receivers will be arranged at the coordinates: P3, P4, Pz, C3, C4, Fz, AF3, AF4, and the receivers will collect light intensity information.
[0040] Only the data of the channel with a distance of 3cm is collected, and the channels and monitored areas are as follows: Fp1 (probe) - AF3 (receiver): left prefrontal cortex, i.e. left frontal pole area; Fp2 - AF4: right prefrontal cortex, i.e. right frontal pole area; F3 -C3: left frontal lobe to motor cortex area; F4 - C4: right frontal lobe to motor cortex area; F3 - AF3: left frontal cortex; F4 - AF4: right frontal cortex; Cz - C3: central area to left motor cortex; Cz - C4: central area to right motor cortex; Cz - Fz: central to central frontal area; Cz - Pz: central to central parietal area; O1 - P3: left occipital lobe to parietal lobe area; O2 - P4: right occipital lobe to parietal lobe area; a total of 12 channels are formed to realize blood oxygen monitoring of specific brain areas.
[0041] Therefore, when collecting physiological signals of specific brain areas of the target subject, the target subject can be made to wear a near-infrared brain imaging system device in a real vehicle to collect light intensity information as physiological signals.
[0042] The signal processing module 200 is configured to perform format conversion on the physiological signal to obtain a blood oxygen signal.
[0043] In some embodiments, the signal processing module 200 obtains the light intensity signal and calculates the absorbance change; applies the modified Bigram's law to construct the equation group and solves HbO and HbR. The specific process of obtaining the blood oxygen signal is as follows: When using 735nm and 850nm near-infrared light sources, four light intensity signals can be obtained, namely, the transmitted light intensity at 735nm wavelength , Transmitted light intensity at 850nm wavelength , incident light intensity at 735nm wavelength And the incident light intensity at 850nm wavelength ; The absorbance change is: ; In the formula, is the absorbance change at a wavelength of 735 nm, is the absorbance change at a wavelength of 850 nm.
[0044] According to the modified Bigram's law, the differential path length factor is introduced , the following equation can be constructed: ; In the formula, and are the molar absorption coefficients of oxygenated hemoglobin at wavelengths of 735 nm and 850 nm, respectively; and are the molar absorption coefficients of deoxyhemoglobin at wavelengths of 735 nm and 850 nm, respectively; is the change in oxygenated hemoglobin concentration, is the change in deoxyhemoglobin concentration; is the wavelength of near-infrared light.
[0045] The distance between the light source and the detector is l = 3cm, and the molar absorption coefficient of oxygenated hemoglobin at wavelength λ , the molar absorption coefficient of deoxyhemoglobin at wavelength λ According to the table, where λ=735nm or 850nm, the equation can be solved to find the change in oxygenated hemoglobin concentration. Changes in deoxyhemoglobin concentration , combined with the initial concentration baseline, the absolute concentrations of HbO and HbR can be obtained, and the absolute concentrations of HbO and HbR are used as the characterization of the blood oxygen signal.
[0046] The signal denoising module 300 is configured to perform denoising on the blood oxygen signal to obtain a pure blood oxygen signal.
[0047] In some embodiments, the signal denoising module 300 is configured to perform drift removal, target principal component analysis-based data-driven real-time denoising method (TDDR), infinite impulse response filter (IIR) and bandpass filtering on the blood oxygen signal. The specific process is as follows: (1) Drift removal: Due to slight changes in the probe position, slow wave components of blood flow, temperature and humidity changes, the blood oxygen signal will rise or fall slowly. The drift can be removed using first-order linear fitting.
[0048] (2) Data-driven real-time denoising method based on target principal component analysis: Since the target object is located on a moving vehicle, there will be large head movements. This method decomposes the blood oxygen signal into different principal components, each of which represents a signal change pattern. By eliminating lower-frequency slowly changing components and components that are inconsistent with the frequency band of neural activity, a pure blood oxygen signal that is highly correlated with neural activity is obtained.
[0049] (3) Infinite impulse response filter and bandpass filter: The first two steps cannot remove all irrelevant signals. By releasing signals in a specific frequency range, namely bandpass filtering, the signal can be further purified. Irrelevant signals include heart rate signals (0.8-2.0Hz), breathing signals (0.2-0.3Hz), and ambient light interference (50Hz or 60Hz). To exclude irrelevant signals, the frequency of the bandpass filter can be set to 0.01-0.15Hz. The infinite impulse response filter is a tool for implementing bandpass filtering. Compared with other methods, it uses less computing resources to achieve similar filtering effects.
[0050] The feature calculation module 400 is configured to calculate blood oxygen feature values based on pure blood oxygen signals; wherein the blood oxygen feature values include the peak value and change rate of oxygenated hemoglobin, the moving average within a 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin.
[0051] In some embodiments, the feature calculation module 400 is configured to perform time domain analysis to calculate the peak value, change rate, moving average value within a 10s window time, and difference with HbR of oxygenated hemoglobin. The specific process is as follows: 1) Peak value and rate of change of oxygenated hemoglobin: Identify the local maximum value in the signal within 4 seconds as the peak value, that is, at time n p When the peak value is reached, , ,m=50,HbO[n p ] is n p HbO[n] is the oxygenated hemoglobin concentration at time n, and n is the time point mark number, which is used to prevent the data at an earlier time from affecting the judgment at the current time. The backward difference method is used to calculate the change rate of oxygenated hemoglobin. , used to avoid sudden changes in the rate of change caused by signal fluctuations.
[0052] 2) Moving average within a 10s window: When the sampling rate is 12.5Hz, the window size is N=125. ;in, is the moving average of the oxygenated hemoglobin concentration within the 10s window time, is the oxygenated hemoglobin concentration at time k, Mark the time point of the starting moment of the 10s window time with a serial number. Mark the time points with serial numbers. Near infrared detection has a lag, and the 10s time window can provide sufficient response time and ensure the real-time nature of the data.
[0053] 3) The difference between oxygenated hemoglobin and deoxygenated hemoglobin: , D[n] is the difference between the concentrations of oxyhemoglobin and deoxyhemoglobin, HbO[n] is the concentration of oxyhemoglobin, and HbR[n] is the concentration of deoxyhemoglobin. According to the neurovascular coupling process, the activation of brain regions is accompanied by the increase of oxyhemoglobin and the decrease of deoxyhemoglobin, and the difference between them can reflect the degree of activation.
[0054] The identification module 500 is configured to compare the blood oxygen characteristic value with a set threshold value based on the set threshold value to identify motion sickness.
[0055] In some embodiments, a personalized threshold setting method is provided; since different people have different sensitivities to motion sickness, the blood oxygen characteristic values also vary. When the target subject believes that motion sickness has occurred, he or she is allowed to activate the recording function of the device. The system will record the blood oxygen characteristic value at this time and use it as a personalized threshold; if the target subject activates multiple times, the peak value, rate of change, moving average value within a 10s window time, and minimum value of the difference with HbR of HbO are selected as the threshold to improve recognition sensitivity.
[0056] The present application also provides a motion sickness recognition system for monitoring a specific brain region, the system comprising: Memory for storing computer programs; A processor is used to execute the computer program to implement the following method: Obtain physiological signals from specific brain regions of the target subject; Converting the physiological signal into a blood oxygen signal; Performing denoising on the blood oxygen signal to obtain a pure blood oxygen signal; Based on the pure blood oxygen signal, a blood oxygen characteristic value is calculated; wherein the blood oxygen characteristic value includes the peak value and change rate of oxygenated hemoglobin, the moving average value within a 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin; Based on a set threshold, the blood oxygen characteristic value is compared with the set threshold to identify motion sickness.
[0057] In some embodiments, converting the physiological signal into a format to obtain a blood oxygen signal includes: When using 735nm and 850nm near-infrared light sources, four light intensity signals are obtained, which are the transmitted light intensity at 735nm wavelength. , Transmitted light intensity at 850nm wavelength , incident light intensity at 735nm wavelength And the incident light intensity at 850nm wavelength ; The absorbance change is: ; In the formula, is the absorbance change at a wavelength of 735 nm, is the absorbance change at a wavelength of 850nm; According to the modified Bigram's law, the differential path length factor is introduced , the following equation can be constructed: ; In the formula, and are the molar absorption coefficients of oxygenated hemoglobin at wavelengths of 735 nm and 850 nm, respectively; and are the molar absorption coefficients of deoxyhemoglobin at wavelengths of 735 nm and 850 nm, respectively; is the change in oxygenated hemoglobin concentration, is the change in deoxyhemoglobin concentration; is the wavelength of near-infrared light; Based on the equation, the change in oxygenated hemoglobin concentration is calculated Changes in deoxyhemoglobin concentration , combined with the initial concentration baseline, the absolute concentrations of oxyhemoglobin and deoxyhemoglobin are obtained.
[0058] In some embodiments, performing denoising on the blood oxygen signal to obtain a pure blood oxygen signal includes: Using a first-order linear fit to de-drift the blood oxygen signal, to obtain a first signal; Decomposing the first signal into different principal components, each principal component represents a signal change pattern; obtaining a second signal by eliminating slow-changing components with frequencies lower than a set frequency and components inconsistent with the frequency band of neural activity; The second signal is band-pass filtered to filter out irrelevant signals; wherein the frequency of the band-pass filter is set to 0.01-0.15 Hz, and the irrelevant signals include heart rate signals, breathing signals, and ambient light interference signals.
[0059] In some embodiments, the peak value and the rate of change of oxygenated hemoglobin are calculated by the following method: the local maximum value in the signal within 4 seconds is identified as the peak value, that is, at time n p When the peak value is reached, , , m is a constant, HbO[n p ] is n p The oxygenated hemoglobin concentration at the moment, HbO[n] is the oxygenated hemoglobin concentration at the moment n, and n is the time point mark number; the HbO change rate is calculated using the backward difference method; The moving average within the 10s window time is calculated by the following method: When the sampling rate is 12.5Hz, the window size is N=125, ;in, is the moving average of the oxygenated hemoglobin concentration within the 10s window time, is the oxygenated hemoglobin concentration at time k, Mark the time point of the starting moment of the 10s window time with a serial number. Mark the time points with serial numbers; The difference between oxyhemoglobin and deoxyhemoglobin was calculated using the following formula: ; Where D[n] is the difference between the concentrations of oxyhemoglobin and deoxyhemoglobin, HbO[n] is the concentration of oxyhemoglobin, and HbR[n] is the concentration of deoxyhemoglobin.
[0060] In some embodiments, the set threshold is determined by the following method: Determining a set threshold based on a blood oxygen characteristic value at at least one target moment; wherein the target moment is the moment when the target subject issues an instruction to the recognition module when he / she believes that motion sickness has occurred; When there is only one blood oxygen characteristic value at a target moment, the blood oxygen characteristic value at the target moment is used as the set threshold; When there are at least two target blood oxygen characteristic values, the minimum value among the peak value and change rate of oxygenated hemoglobin, the moving average value within the 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin is selected as the set threshold.
[0061] The present application also provides a non-transitory computer-readable storage medium storing instructions. When the instructions are executed by a processor, Figure 4 As shown, perform the following steps: S1. Obtain physiological signals from specific brain regions of the target subject; S2, converting the format of the physiological signal to obtain a blood oxygen signal; S3, performing denoising processing on the blood oxygen signal to obtain a pure blood oxygen signal; S4. Calculate blood oxygen characteristic values based on the pure blood oxygen signal; wherein the blood oxygen characteristic values include the peak value and change rate of oxygenated hemoglobin, the moving average value within a 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin; S5. Based on the set threshold, the blood oxygen characteristic value is compared with the set threshold to identify motion sickness.
[0062] It should be noted that the motion sickness recognition system for monitoring a specific brain region and the non-transitory computer-readable storage medium storing instructions proposed in the embodiments of the present application belong to the same technical concept as the previous motion sickness recognition device for monitoring a specific brain region, have the same technical principles and can achieve the same beneficial effects, which will not be repeated here.
[0063] The above implementation modes are only used to illustrate the present application, and are not intended to limit the present application. Ordinary technicians in the relevant technical field may make various changes and modifications without departing from the spirit and scope of the present application. Therefore, all equivalent technical solutions also belong to the scope of the present application, and the scope of patent protection of the present application shall be limited by the claims.
Claims
1. A motion sickness recognition device for monitoring a specific brain region, characterized in that: The device comprises: A signal acquisition module is configured to acquire physiological signals of a specific brain region of the target object; wherein the physiological signals of the specific brain region include the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline region, and the prefrontal cortex of the frontal polar region and the prefrontal cortex of the frontal lobe; A signal processing module, the signal processing module is connected to the signal acquisition module, and the signal processing module is configured to perform format conversion on the physiological signal to obtain a blood oxygen signal; A signal denoising module, the signal denoising module is connected to the signal processing module, and the signal denoising module is configured to perform denoising processing on the blood oxygen signal to obtain a pure blood oxygen signal; A feature calculation module, the feature calculation module is connected to the signal denoising module, and the feature calculation module is configured to calculate a blood oxygen characteristic value based on the pure blood oxygen signal; wherein the blood oxygen characteristic value includes a peak value and a change rate of oxygenated hemoglobin, a moving average within a 10s window time, and a difference between oxygenated hemoglobin and deoxygenated hemoglobin; An identification module, wherein the identification module is connected to the feature calculation module, and the identification module is configured to compare the blood oxygen feature value with the set threshold value based on a set threshold value to identify motion sickness.
2. The motion sickness recognition device for monitoring a specific brain region according to claim 1, characterized in that: The physiological signal of the specific brain area of the target object is a light intensity signal collected by the target object in a real vehicle by wearing a near-infrared brain imaging system device.
3. The motion sickness recognition device for monitoring a specific brain region according to claim 2, characterized in that: The signal processing module is further configured to: When using 735nm and 850nm near-infrared light sources, four light intensity signals are obtained, which are the transmitted light intensity at 735nm wavelength. , Transmitted light intensity at 850nm wavelength , incident light intensity at 735nm wavelength And the incident light intensity at 850nm wavelength ; The absorbance change is: ； In the formula, is the absorbance change at a wavelength of 735 nm, is the absorbance change at a wavelength of 850nm; According to the modified Bigram's law, the differential path length factor is introduced , construct the following equation: ； In the formula, and are the molar absorption coefficients of oxygenated hemoglobin at wavelengths of 735 nm and 850 nm, respectively; and are the molar absorption coefficients of deoxyhemoglobin at wavelengths of 735 nm and 850 nm, respectively; is the change in oxygenated hemoglobin concentration, is the change in deoxyhemoglobin concentration; is the wavelength of near-infrared light; Based on the equation, the change in oxygenated hemoglobin concentration is calculated Changes in deoxyhemoglobin concentration , combined with the initial concentration baseline, the absolute concentrations of oxyhemoglobin and deoxyhemoglobin are obtained.
4. The motion sickness recognition device for monitoring a specific brain region according to claim 3, characterized in that: The signal denoising module is further configured as follows: Using a first-order linear fit to de-drift the blood oxygen signal, to obtain a first signal; Decomposing the first signal into different principal components, each principal component represents a signal change pattern; obtaining a second signal by eliminating slow-changing components with frequencies lower than a set frequency and components inconsistent with the frequency band of neural activity; The second signal is band-pass filtered to filter out irrelevant signals; wherein the frequency of the band-pass filter is set to 0.01-0.15 Hz, and the irrelevant signals include heart rate signals, breathing signals, and ambient light interference signals.
5. The motion sickness recognition device for monitoring a specific brain region according to claim 1, characterized in that: The feature calculation module is further configured as follows: The peak value and rate of change of oxygenated hemoglobin are calculated by the following method: the local maximum value in the signal within 4 seconds is identified as the peak value, that is, at time n p When the peak value is reached, , , m is a constant, HbO[n p ] is n p The oxygenated hemoglobin concentration at the moment, HbO[n] is the oxygenated hemoglobin concentration at the moment n, and n is the time point mark number; the HbO change rate is calculated using the backward difference method; The moving average within the 10s window time is calculated by the following method: When the sampling rate is 12.5Hz, the window size is N=125, ;in, is the moving average of the oxygenated hemoglobin concentration within the 10s window time, is the oxygenated hemoglobin concentration at time k, Mark the time point of the starting moment of the 10s window time with a serial number. Mark the time points with serial numbers; The difference between the concentrations of oxyhemoglobin and deoxyhemoglobin was calculated using the following formula: ； Where D[n] is the difference between the concentrations of oxyhemoglobin and deoxyhemoglobin, HbO[n] is the concentration of oxyhemoglobin, and HbR[n] is the concentration of deoxyhemoglobin.
6. The motion sickness recognition device for monitoring a specific brain region according to claim 1, characterized in that: The identification module is further configured to determine the set threshold by the following method: Determining a set threshold based on a blood oxygen characteristic value at at least one target moment; wherein the target moment is the moment when the target subject issues an instruction to the recognition module when he / she believes that motion sickness has occurred; When there is only one blood oxygen characteristic value at a target moment, the blood oxygen characteristic value at the target moment is used as the set threshold; When there are at least two target blood oxygen characteristic values, the minimum value among the peak value and change rate of oxygenated hemoglobin, the moving average value within the 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin is selected as the set threshold.
7. A motion sickness recognition system for monitoring specific brain regions, characterized in that: The system comprises: Memory for storing computer programs; A processor is used to execute the computer program to implement the following method: Acquiring physiological signals of specific brain regions of the target object; wherein the physiological signals of the specific brain regions include the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline region, and the prefrontal cortex of the frontal polar region and the prefrontal cortex of the frontal lobe; Converting the physiological signal into a blood oxygen signal; Performing denoising on the blood oxygen signal to obtain a pure blood oxygen signal; Based on the pure blood oxygen signal, a blood oxygen characteristic value is calculated; wherein the blood oxygen characteristic value includes the peak value and change rate of oxygenated hemoglobin, the moving average value within a 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin; Based on a set threshold, the blood oxygen characteristic value is compared with the set threshold to identify motion sickness.
8. The motion sickness recognition system for monitoring a specific brain region according to claim 7, characterized in that: Converting the physiological signal into a format to obtain a blood oxygen signal includes: When using 735nm and 850nm near-infrared light sources, four light intensity signals are obtained, which are the transmitted light intensity at 735nm wavelength. , Transmitted light intensity at 850nm wavelength , incident light intensity at 735nm wavelength And the incident light intensity at 850nm wavelength ; The absorbance change is: ； In the formula, is the absorbance change at a wavelength of 735 nm, is the absorbance change at a wavelength of 850nm; According to the modified Bigram's law, the differential path length factor is introduced , construct the following equation: ； In the formula, and are the molar absorption coefficients of oxygenated hemoglobin at wavelengths of 735 nm and 850 nm, respectively; and are the molar absorption coefficients of deoxyhemoglobin at wavelengths of 735 nm and 850 nm, respectively; is the change in oxygenated hemoglobin concentration, is the change in deoxyhemoglobin concentration; is the wavelength of near-infrared light; Based on the equation, the change in oxygenated hemoglobin concentration is calculated Changes in deoxyhemoglobin concentration , combined with the initial concentration baseline, the absolute concentrations of oxyhemoglobin and deoxyhemoglobin are obtained.
9. The motion sickness recognition system for monitoring a specific brain region according to claim 8, characterized in that: The blood oxygen signal is subjected to denoising to obtain a pure blood oxygen signal, including: Using a first-order linear fit to de-drift the blood oxygen signal, to obtain a first signal; Decomposing the first signal into different principal components, each principal component represents a signal change pattern; obtaining a second signal by eliminating slow-changing components with frequencies lower than a set frequency and components inconsistent with the frequency band of neural activity; The second signal is band-pass filtered to filter out irrelevant signals; wherein the frequency of the band-pass filter is set to 0.01-0.15 Hz, and the irrelevant signals include heart rate signals, breathing signals, and ambient light interference signals.
10. A non-transitory computer-readable storage medium storing instructions, characterized in that: When the instruction is executed by the processor, the following steps are performed: Acquiring physiological signals of specific brain regions of the target object; wherein the physiological signals of the specific brain regions include the visual cortex of the occipital lobe, the motor cortex and sensory cortex of the central midline region, and the prefrontal cortex of the frontal polar region and the prefrontal cortex of the frontal lobe; Converting the physiological signal into a blood oxygen signal; Performing denoising on the blood oxygen signal to obtain a pure blood oxygen signal; Based on the pure blood oxygen signal, a blood oxygen characteristic value is calculated; wherein the blood oxygen characteristic value includes the peak value and change rate of oxygenated hemoglobin, the moving average value within a 10s window time, and the difference between oxygenated hemoglobin and deoxygenated hemoglobin; Based on a set threshold, the blood oxygen characteristic value is compared with the set threshold to identify motion sickness.