A head physiological monitoring method for determining in-flight syncope and hypoxia

Abstract

This invention relates to a head physiological monitoring method for determining in-flight hypoxia and syncope, comprising: acquiring real-time physiological parameter data and motion data of the pilot's head in a small civil aircraft during flight; the physiological parameter data includes values ​​and PI values; obtaining the pilot's blood oxygen decay ratio based on the values ​​and a pre-determined pilot baseline value; obtaining a prediction result of whether the pilot is hypoxic based on the motion data and a pre-trained blood oxygen prediction model; determining whether the pilot is suspected of hypoxia based on the blood oxygen decay ratio and values; if suspected of hypoxia, determining whether the pilot has experienced in-flight hypoxia based on the hypoxia prediction result; if in-flight hypoxia has occurred, determining the pilot's state based on the PI decay coefficient obtained based on the PI value and a pre-determined PI baseline value; if the pilot's state is suspected of syncope, determining whether the pilot has experienced syncope based on the real-time acquired motion data, and if syncope is detected, automatically leveling the aircraft. This achieves real-time monitoring of hypoxia and syncope in civil aviation pilots.

Description

Technical Field

[0001] This invention relates to the fields of aviation medicine, civil aviation piloting, artificial intelligence and human-computer interaction, and in particular to a method for monitoring head physiology to determine in-flight syncope and hypoxia. Background Technology

[0002] Currently, the small civil aircraft industry is experiencing rapid development, with its scale continuously expanding and application scenarios deepening, leading to a rapid increase in the number of pilots and operators. As small civil aircraft become more widespread across various sectors, flight safety issues are becoming increasingly prominent. The stability of a pilot's physiological state is directly related to flight safety; therefore, the need for monitoring the physiological state of small civil aircraft pilots is becoming increasingly urgent.

[0003] In modern small civil aircraft flights, pilots must endure continuous cognitive and physiological loads, facing multiple physiological challenges in complex flight environments. Small civil aircraft are lightweight and relatively unstable; when encountering strong turbulence, sudden attitude adjustments, emergency descents, or evasive maneuvers, they experience significant inertial overload, leading to significant fluctuations in cerebral blood flow perfusion (reducing it by 30%-50%). Small civil aircraft mostly fly in the 1000-10000 foot airspace, with some models reaching 25000 feet (approximately 7600 meters). Atmospheric pressure decreases significantly with altitude, and their cockpits are often simply pressurized or unpressurized. If the pilot experiences cockpit seal failure, improper use of the oxygen mask, or oxygen supply equipment malfunction, pulse oximetry saturation will rapidly drop from the normal value (95%-100%) to a critical level (<85%). The flight environment presents a dual pressure: the pressure of reduced blood flow to the pilot's brain due to inertial overload and insufficient oxygen supply caused by high-altitude hypoxia, as well as the pressure of triggering acute ischemia and hypoxia in brain tissue. Physiological studies have shown that when regional cerebral oxygen saturation drops below 50%, it will cause abnormal nerve impulse conduction, manifesting as confusion, narrowed visual field ("gray visual field"), and eventually developing into syncope. It should be clarified that there is a significant synergistic effect between hypoxia and overload; hypoxia lowers the pilot's syncope threshold and accelerates the onset of syncope. The two are not independent events but have a clear causal relationship.

[0004] The pathology of hypoxia and syncope follows a progressive physiological mechanism; they are not independent events but rather involve a clear causal chain and time dependence. The specific mechanism can be subdivided into three stages: Phase 1: Silent Hypoxia (0-30 seconds): In the early stages of hypoxia during flight in small civil aircraft (pulse oxygen saturation 85%-90%), there are no obvious subjective symptoms. The pilot may only feel mild fatigue or inattention, but because the brain's compensatory mechanisms for hypoxia (such as increased heart rate and deeper breathing) mask the abnormalities, it is difficult to recognize them. At this time, without intervention, hypoxia will rapidly worsen; Phase Two: Amplified Hypoxia Under Overload (30-60 seconds): When a small civil aircraft encounters strong airflow or inertial overload caused by sudden maneuvers, the pilot's cerebral blood flow is further reduced, creating a "double blow" with hypoxia. Under hypoxic conditions, the pilot's fainting threshold will be significantly lowered, and maneuvers that could originally be tolerated (such as routine evasive maneuvers) will directly trigger fainting precursors under hypoxic conditions; Stage Three: Syncope (>60 seconds): When brain oxygenation remains below a critical level, the activity of inhibitory neurons in the cerebral cortex increases, and loss of consciousness (syncope) occurs within seconds. At this point, the pilot of a small civil aircraft has lost control, and the accident rate will increase significantly. For the operator of a small unmanned aerial vehicle, similar physiological abnormalities can also lead to errors in control commands and cause flight accidents.

[0005] Therefore, early detection of hypoxia is an effective window for preventing fainting. While fainting is traditionally considered a "sudden event," it is actually an inevitable result of the combined effects of hypoxia and G-forces. If an early warning is issued when hypoxia first appears in Phase 1, the pilot can promptly adjust flight attitude, increase oxygen flow, or request ground support, thereby preventing progression to Phase 2 and ensuring flight safety.

[0006] The fatal flaw of existing methods for monitoring hypoxia and syncope is their fragmented design, which severely ignores the progressive logic between hypoxia and syncope, leading to prevention failure. Specifically: (1) Existing syncope monitoring systems (such as heart rate variability analysis) only trigger alarms after syncope occurs (stage three above), for example, by detecting arrhythmias via ECG or sensing body imbalance via accelerometer. However, at this time, the pilot of a small civil aircraft has already lost consciousness and cannot take any intervention measures, rendering the warning meaningless; (2) Existing hypoxia prevention systems (such as traditional hypoxia training) rely on the driver's subjective perception (such as "feeling shortness of breath"), but physiological studies have shown that symptoms are mild in the early stages of hypoxia (pulse oxygen saturation of 85%-90%), leading to a high rate of driver misjudgment. More importantly, the effectiveness of existing training (such as simulated hypoxia training) decays extremely quickly, with an effectiveness rate decreasing by 50% within 6 months and almost reaching zero after 1 year. The system cannot monitor physiological indicators in real time and can only provide post-event assessment, making it difficult to achieve early prevention. (3) The fundamental problem with existing methods is that they treat "hypoxia" and "syncope" as independent events rather than a continuous process. For example, when the cabin pressure of a small civil aircraft drops suddenly, causing the pilot's pulse oxygen saturation to drop from 92% to 86%, if only "hypoxia" is recorded without associating it with the risk of syncope, the pilot may misjudge it as "normal fatigue." Conversely, if only the signs of syncope are focused on, the critical intervention opportunity will be missed, and the flight accident cannot be prevented from happening at the source. Summary of the Invention

[0007] Based on the above analysis, the present invention aims to provide a method for monitoring the physiological state of the head to determine hypoxia and syncope in the air, in order to solve the technical problem that existing methods lack reliable physiological monitoring and early warning means for real-time monitoring of pilot hypoxia and syncope in actual flight environments.

[0008] This invention provides a method for monitoring head physiology to determine hypoxia and syncope in mid-air, comprising the following steps: During flight, real-time physiological parameter data and motion data of the pilot's head are acquired; wherein, the physiological parameter data includes... Value and PI value; Based on real-time acquisition The value and the predetermined driver's The baseline value is used to obtain the driver's blood oxygen decay ratio; based on the motion data and the pre-trained blood oxygen prediction model, a prediction result is obtained as to whether the driver is hypoxic. Based on the blood oxygen attenuation ratio and the real-time acquired... The system determines whether the pilot is suspected of hypoxia; if suspected hypoxia, it determines whether the pilot has experienced in-flight hypoxia based on the predicted hypoxia result; if in-flight hypoxia occurs, it determines the pilot's condition based on the PI value and the PI attenuation coefficient obtained from the pre-determined PI baseline value of the pilot; if the pilot's condition is suspected of fainting, it determines whether the pilot has fainted based on the real-time acquired motion data, and if fainting occurs, the aircraft automatically levels off.

[0009] Furthermore, the motion data includes head motion data, pitch angle, yaw angle, and roll angle; The head movement data includes time windows. Number of nods Nodding frequency Angle of looking down and the percentage of people looking down Among them, time window Nodding frequency within and the percentage of people looking down based on , Calculated.

[0010] Furthermore, the standard deviation of the pitch angle is calculated based on the pitch angle, yaw angle, and roll angle, respectively. Yaw angle standard deviation and roll angle standard deviation ; Will , , , and After preprocessing, the data is input into a pre-trained blood oxygen prediction model to obtain a prediction result of whether the driver is hypoxic.

[0011] Furthermore, if or If so, it is determined that the driver is suspected of having hypoxia; among them, This represents the percentage of blood oxygen depletion. The threshold for the proportion of blood oxygen decay. The data monitored in real time every 1 second. The average value for Threshold.

[0012] Furthermore, if it is determined that the driver is suspected of hypoxia, and the prediction result of hypoxia is hypoxia, then it is determined that the driver has experienced in-flight hypoxia. Based on PI attenuation coefficient To determine the driver's condition.

[0013] Furthermore, if If so, the driver's condition is judged to be suspected of fainting; like If so, the driver's condition is determined to be extremely low infusion. like If so, the driver's condition is determined to be low perfusion; like If so, the driver's status is determined to be normal (PI). in, , and These are the threshold values ​​for the first, second, and third PI attenuation coefficients, respectively. ; If the driver's condition is suspected to be syncope, the determination of whether the driver is syncope is based on the pitch angle in the real-time acquired motion data.

[0014] Furthermore, if the absolute value of the pitch angle in the real-time acquired motion data is greater than the preset pitch angle threshold, then the driver is confirmed to have fainted.

[0015] Furthermore, the preset pitch angle threshold is 20°.

[0016] Furthermore, the blood oxygen prediction model includes an input layer, an LSTM temporal feature extraction layer, and a fully connected classification decision layer; the LSTM layer is used to extract temporal features; the fully connected layer makes classification decisions based on the temporal features. The pre-trained blood oxygen prediction model is obtained through the following process: Obtain physiological parameters and motion data of the heads of multiple drivers within a certain historical period to obtain corresponding... , , , and After preprocessing, the samples are used as training samples, and together with the corresponding samples labeled as hypoxic, they form a training sample set. The loss function is the binary cross-entropy loss between the prediction of hypoxia and the true label of the sample; the Adam optimizer is used for iterative training. If continuous The accuracy of the validation set in epochs is less than Or the F1 score on the validation set is greater than or equal to And the recall rate is greater than or equal to If the training stops, the well-trained blood oxygen prediction model is obtained. in, , and These are the accuracy threshold, F1 threshold, and recall threshold, respectively.

[0017] Furthermore, before the pilot takes flight, the pilot's head is captured. The average PI value over the minutes is used as the driver's PI baseline value. ; Get the driver's head minutes The average value of the driver's benchmark value .

[0018] Compared with the prior art, the present invention can achieve at least one of the following beneficial effects: 1. This invention combines physiological parameters and head movement data of small civil aircraft pilots for collaborative judgment. It uses dual verification of blood oxygen saturation reduction ratio (physiological parameters) and movement characteristics (standard deviations of head nodding frequency, head-down ratio, pitch angle, yaw angle, and roll angle) to determine whether the pilot is experiencing in-flight hypoxia; because the cerebral oxygen saturation of different individual pilots varies. The differences are significant, and the decrease in oxygen levels varies greatly under hypoxic conditions. Therefore, relying on a single indicator can lead to a large number of misjudgments. Furthermore, the driver's head movement characteristics will also change significantly after hypoxia. By combining multi-dimensional data on the driver's head movement characteristics for verification, the accuracy of judging whether the driver is hypoxic is greatly improved and the false positive rate is reduced. 2. The personalized baseline values ​​and dynamic thresholds of this invention are adapted to individual pilot differences. Before flight, the individual pilot's... Benchmark values ​​and PI (Perfusion Index) benchmark values; to avoid judgment bias caused by individual differences and improve the specificity of monitoring; 3. This invention utilizes a time window during the process of determining hypoxia by analyzing the driver's head movement characteristics. Nodding frequency within and the percentage of people looking down Standard deviation of pitch angle Yaw angle standard deviation and roll angle standard deviation Five indicators, all of which change significantly under hypoxic conditions, can be combined using machine learning methods to automatically obtain a binary classification result of whether hypoxia has occurred. The entire process requires no manual intervention. Furthermore, since the blood oxygen prediction model has been trained on a large amount of data, the classification result is accurate. This classification method can identify different head movements caused by hypoxia, and has a wider recognition range than a single indicator. 4. The method in this invention provides real-time monitoring down to the second level, controlling the delay from data acquisition to status assessment to the second level, thus allowing time for subsequent intervention in case of pilot fainting. If pilot fainting is confirmed, automatic leveling is triggered without manual intervention, preventing loss of aircraft control due to pilot incapacitation and reducing the risk of accidents.

[0019] In this invention, the above-described technical solutions can be combined with each other to achieve more preferred combinations. Other features and advantages of this invention will be set forth in the following description, and some advantages may become apparent from the description or be learned by practicing the invention. The objects and other advantages of this invention can be realized and obtained from what is particularly pointed out in the description and drawings. Attached Figure Description

[0020] The accompanying drawings are for illustrative purposes only and are not intended to limit the invention. Throughout the drawings, the same reference numerals denote the same parts.

[0021] Figure 1 This is a flowchart of a head physiological monitoring method for determining hypoxia and syncope in an embodiment of the present invention; Figure 2This is a schematic diagram of the blood oxygen prediction model structure and training in an embodiment of the present invention. Detailed Implementation

[0022] Preferred embodiments of the present invention will now be described in detail with reference to the accompanying drawings, which form part of this application and are used together with the embodiments of the present invention to illustrate the principles of the present invention, but are not intended to limit the scope of the present invention.

[0023] A specific embodiment of the present invention discloses a method for monitoring head physiology to determine hypoxia and syncope in mid-air, such as... Figure 1 As shown, it includes the following steps: Step S1: During flight, acquire real-time physiological parameter data and motion data of the pilot's head in the small civil aircraft; wherein, the physiological parameter data includes... Value and PI value; Step S2, based on the real-time acquired data... The value and the predetermined driver's The baseline value is used to obtain the driver's blood oxygen decay ratio; based on the motion data and the pre-trained blood oxygen prediction model, a prediction result is obtained as to whether the driver is hypoxic. Step S3: Based on the blood oxygen attenuation ratio and the real-time acquired... The system determines whether the pilot is suspected of hypoxia; if suspected hypoxia, it determines whether the pilot has experienced in-flight hypoxia based on the predicted hypoxia result; if in-flight hypoxia occurs, it determines the pilot's condition based on the PI value and the PI attenuation coefficient obtained from the pre-determined PI baseline value of the pilot; if the pilot's condition is suspected of fainting, it determines whether the pilot has fainted based on the real-time acquired motion data, and if fainting occurs, the aircraft automatically levels off.

[0024] The small civil aircraft in this invention are lightweight, relatively unstable, and mostly fly in the airspace of 1,000-10,000 feet, with some models reaching 25,000 feet (about 7,600 meters). The cockpit is mostly a simple pressurized or unpressurized civil aircraft.

[0025] This invention starts with the helmet that pilots of small civil aircraft must wear during flight. It embeds a PPG sensor (Photoplethysmography Sensor) and a motion sensor inside the helmet. The PPG sensor is placed on the forehead and the motion sensor is placed on the top of the head, both of which are covered by the helmet liner.

[0026] Prior to step S1, the blood oxygen saturation of each individual driver is predetermined. Benchmark value and PI benchmark value.

[0027] Acquire the pilot's head before the flight. The average PI value over the minutes is used as the driver's PI baseline value. ; Get the driver's head minutes The average value of the driver's benchmark value .

[0028] For example, Take 5 minutes, and modify it according to specific needs in actual application.

[0029] This invention sets up a system where, after the driver wears a helmet, the PPG sensor automatically records the PI value over five minutes, calculates the average, and uses this average as the PI baseline value, denoted as [reference value]. .

[0030] Using a PPG sensor to obtain PI value and blood oxygen saturation value.

[0031] For ease of description in this invention, Recorded as After the driver puts on the helmet, the PPG sensor automatically records the driver's activity for five minutes. Value, calculate the mean, and use it as The baseline value is denoted as .

[0032] A PPG sensor is a device used to measure pulse waves. Its name translates to "photoplethysmography," and it offers advantages such as being non-invasive, easy to operate, and providing stable measurement results. It works by detecting the periodic changes in blood volume with the heartbeat using photoelectric methods, thereby recording the pulse wave signal. The PPG sensor can simultaneously obtain cerebral oxygen saturation. value, The measurement measures changes in cerebral blood oxygenation in the frontal cortex, reflecting the balance of oxygen supply to the brain and demonstrating changes in the driver's cognitive function.

[0033] The perfusion index (PI) value is obtained using a PPG sensor. The PI value is a real-time output hemodynamic parameter that reflects the "quality" of blood flow in peripheral microvessels. The larger the value, the better the local arterial dilation and perfusion; the smaller the value, the more likely it is vasoconstriction or insufficient perfusion. PI is a dimensionless ratio (%) and there is no absolute normal value. It needs to be observed dynamically or combined with other perfusion indicators for comprehensive judgment.

[0034] The median PI value for healthy adults is approximately 1.4 to 3.9, while the median PI value for patients in shock is approximately 0.5 to 1.3.

[0035] Step S1, specifically.

[0036] During the pilot's flight, physiological parameters and motion data of the pilot's head are acquired in real time.

[0037] The physiological parameter data includes Value and PI value; The motion data includes head motion data, pitch angle, yaw angle, and roll angle; The head movement data includes time windows. Number of nods Nodding frequency Angle of looking down and the percentage of people looking down Among them, time window Nodding frequency within and the percentage of people looking down based on , Calculated.

[0038] Motion sensors are used to obtain data on the driver's head movements.

[0039] The motion sensor includes a three-axis gyroscope and a three-axis accelerometer, used to measure the rotation angle of the human body along three axes, including pitch angle. Yaw angle Roll angle The three axes are the sagittal axis, coronal axis, and vertical axis along the human body.

[0040] Motion sensors capture the driver's head movements, and based on these movements, the helmet's built-in data processing chip calculates the head nodding frequency and the percentage of head tilting.

[0041] Time window The number of nods within is recorded as ,based on The nodding frequency was obtained and denoted as . Previous studies have shown that when pilots experience hypoxia or extreme fatigue in the air (which is usually accompanied by cerebral hypoxia), The frequency of nodding will increase significantly, as shown below: Formula (1) For example, the time window The value is 5s; The method of judgment is the pitch angle recorded by the motion sensor. And then back Each nod is recorded as one instance.

[0042] In hypoxic conditions, in addition to nodding, drivers may also look down for extended periods. To describe this phenomenon, the percentage of drivers looking down was calculated. Within a time window of t, the time during which the head tilt angle is greater than 15° is recorded as... pitch angle The length of time, The calculation is as follows: Formula (2) The purpose of step S1 is to acquire physiological parameter data and motion data of the head in real time during the pilot's flight, so as to provide basic data for subsequent judgment of the pilot's hypoxia and syncope.

[0043] Step S2 includes steps S21-S23.

[0044] Step S21: Construct a blood oxygen prediction model to obtain a pre-trained blood oxygen prediction model.

[0045] like Figure 2 As shown, the blood oxygen prediction model includes an input layer, an LSTM temporal feature extraction layer, and a fully connected classification decision layer; the LSTM temporal feature extraction layer is used to extract temporal features; the fully connected classification decision layer makes classification decisions based on the temporal features. The pre-trained blood oxygen prediction model is obtained through the following process: Obtain physiological parameters and motion data of the heads of multiple drivers within a certain historical period to obtain corresponding... , , , and After preprocessing, the samples are used as training samples, and together with the corresponding samples labeled as hypoxic, they form a training sample set. The loss function is the binary cross-entropy loss between the prediction of hypoxia and the true label of the sample; the Adam optimizer is used for iterative training. If continuous The accuracy of the validation set in epochs is less than Or the F1 score on the validation set is greater than or equal to And the recall rate is greater than or equal to If the training stops, the well-trained blood oxygen prediction model is obtained. in, , and These are the accuracy threshold, F1 threshold, and recall threshold, respectively.

[0046] The blood oxygen prediction model balances the capture of temporal features with the simplicity of classification decision-making tasks.

[0047] Physiological parameters and motion data of the heads of multiple pilots within a certain historical period were acquired, with training samples derived from historical flight data of multiple pilots; the sample data were preprocessed as follows: The pilot's historical flight data is organized into a three-dimensional input format: [number of samples, time step, number of features], where the number of samples is the number of samples in a training batch; the number of features = 5 (corresponding to...). , , , and (5 indicators) The time step needs to be determined based on the actual data collection frequency (e.g., collect data once every 10 seconds, take 30 consecutive time points as a sample, and the time step = 30).

[0048] Sample label coding: Convert the binary label of "hypoxia / non-hypoxia" into numerical labels of 0 (non-hypoxia) and 1 (hypoxia).

[0049] (1) Input layer, used to receive input time-series samples.

[0050] A time series sample The shape is (seq_len, 5); seq-len is the time step, for example, a 10-second time window; Five input features: , , , and ; The input batch is used during training and has a shape of (batch_size, seq_len, 5). The value of batch_size is determined by considering hardware conditions and dataset size. For example, in this invention, the value is 64, and it is dynamically adjusted based on training efficiency, convergence stability, and hardware load.

[0051] The time series partitioning method was adopted, and the training sample set was divided into training set, validation set and test set in a ratio of 7:2:1. The first 70% of the time series was used as training set, the middle 20% as validation set and the last 10% as test set, so as to preserve the time series correlation.

[0052] (2) LSTM temporal feature extraction layer, which processes the input temporal sample sequence time by time to capture the long-term dependency relationship and temporal dynamic pattern between temporal sample features; Output: Shaped (seq_len, h), including the encoded information for each time step, where h is the hidden state dimension of the LSTM temporal feature extraction layer, i.e., the dimension of the feature vector output at each time step.

[0053] (3) Fully connected classification decision layer, including fully connected layer, Dropout layer and output layer, is used to map the temporal features extracted by LSTM temporal feature extraction layer to the final classification decision.

[0054] Fully connected layers: There are one or more of them, with the activation function being ReLU, used to further combine high-level features. The number of neurons, m, is adjustable, for example, 32 or 64.

[0055] Dropout layer: Drops out a portion of neurons at any time during training to prevent overfitting. The ratio (r) is the probability of keeping neurons; for example, r is 0.6.

[0056] Output layer: 1 neuron, used for binary classification decision tasks; activation function Sigmoid, compresses the output to the range (0, 1), representing the predicted probability of whether the pilot experienced hypoxia during flight. .

[0057] (4) Training and optimization Loss function: Binary cross-entropy, which measures the probability of prediction. With real labels The difference between (0 or 1); The optimizer Adam is used to update all parameters of the LSTM temporal feature extraction layer and the fully connected classification decision layer in the model based on the loss gradient.

[0058] The binary cross-entropy (BCE) loss is used to output the probability value of a single neuron, where 0 ≤ 0. ≤1, The predicted probability of hypoxia is perfectly suited to the binary cross-entropy loss. The core idea of ​​the loss function is to measure the probability distribution predicted by the model. With real labels The differences are as follows: Formula (3) This loss function is stable for gradient calculation in binary classification tasks and converges quickly.

[0059] For example, regarding the training termination condition, The value is 10. Values , The value is 0.95. The value is 0.93.

[0060] Stop when any of the following conditions are met: (1) The accuracy of the validation set for 10 consecutive epochs is less than ; (2) The F1 value of the validation set is ≥0.95 and the recall rate is ≥0.93.

[0061] This step yields a pre-trained blood oxygen prediction model.

[0062] Step S22, based on the real-time acquired data... The value and the predetermined driver's The baseline value is used to determine the percentage of blood oxygen depletion in the driver.

[0063] The data will be monitored in real time every 1 second. The average of the values ​​is denoted as The percentage of blood oxygen saturation is calculated as follows: Formula (4) in, This represents the percentage of blood oxygen depletion. for Benchmark value.

[0064] Step S23: Based on the motion data and the pre-trained blood oxygen prediction model, obtain the prediction result of whether the driver is hypoxic.

[0065] During flight, under hypoxic conditions, the pilot's consciousness will become dulled, and the standard deviation of the head attitude angle will decrease significantly. The standard deviation of the attitude angle of the motion sensor will be calculated separately.

[0066] The standard deviation of the pitch angle is calculated based on the pitch angle, yaw angle, and roll angle, respectively. Yaw angle standard deviation and roll angle standard deviation ; Will , , , and After preprocessing, the data is input into a pre-trained blood oxygen prediction model to obtain a prediction result of whether the driver is hypoxic.

[0067] Get in real time , , , and ,Will , , , and After timestamp alignment, the pre-trained blood oxygen prediction model is input, and the prediction result of whether the driver is hypoxic is output.

[0068] The purpose of step S2 is to calculate the blood oxygen decay ratio based on real-time acquired physiological parameters, and to obtain the prediction result of whether the pilot is hypoxic during flight by constructing a pre-trained blood oxygen prediction model and combining it with motion data.

[0069] Step S3 includes steps S31-S34.

[0070] Step S31: Based on the blood oxygen attenuation ratio , and the real-time acquisition of the The value is used to determine whether the driver is suspected of having hypoxia; like or If so, it is determined that the driver is suspected of having hypoxia; among them, This represents the percentage of blood oxygen depletion. The threshold for the proportion of blood oxygen decay. For every The real-time monitoring of the second The average value for Threshold.

[0071] For example, It is 15%. It is 55%. The value is 1 second, but in practical applications, it can be modified according to specific needs.

[0072] when 15% or In 55% of cases, it is believed that the pilot has experienced suspected hypoxia. At this time, the result information of "suspected in-flight hypoxia" is sent to the airborne data processing terminal. The airborne data processing terminal sends the result to the ground control tower or command center through the data link between the aircraft and the ground. Ground command personnel continue to verify and intervene in a timely manner based on the real-time situation.

[0073] Step S32: If hypoxia is suspected, then combine the hypoxia prediction results output by the blood oxygen prediction model to determine whether the pilot has experienced in-flight hypoxia. If it is determined that the pilot is suspected of hypoxia, and the prediction result of hypoxia is hypoxia, then it is determined that the pilot is experiencing in-flight hypoxia. Step S33: If it is determined that the pilot has experienced in-flight hypoxia, the PI attenuation coefficient is calculated based on the PI value and the pre-determined PI baseline value of the pilot to determine the pilot's condition.

[0074] Based on PI attenuation coefficient To determine the driver's condition.

[0075] The PI value reflects the level of blood perfusion to the head. When syncope occurs in mid-air, the PI value drops significantly.

[0076] The PI value is collected every 1 second in the air and recorded as follows. ,based on Based on the PI reference value, the PI attenuation coefficient is calculated as follows: Formula (5) in, This is the PI attenuation coefficient.

[0077] Determine the driver's status as follows: like If so, the driver's condition is judged to be suspected of fainting; like If so, the driver's condition is determined to be extremely low infusion. like If so, the driver's condition is determined to be low perfusion; like If so, the driver's status is determined to be normal (PI). in, , and These are the threshold values ​​for the first, second, and third PI attenuation coefficients, respectively. ; For example, The values ​​are 0.6, 0.4, and 0.2.

[0078] Real-time attenuation coefficient The value is used to make a judgment, and the following mapping judgment formula is used as shown below: Formula (5) in, Based on attenuation coefficient The state judgment function is based on The value of is output as a predefined blood perfusion level.

[0079] The formula can classify the blood perfusion level of the pilot's head in the air into four categories (including: normal PI, low perfusion, extremely low perfusion, and suspected syncope), and ground control personnel continue to verify and intervene in a timely manner according to the situation.

[0080] Step S34: If the pilot's state is suspected of fainting, then determine whether the pilot is fainting based on the real-time acquired motion data. If fainting is detected, the aircraft will automatically level off.

[0081] If the result indicates suspected syncope, further verification of the result is required.

[0082] If the driver's condition is suspected to be syncope, the determination of whether the driver is syncope is based on the pitch angle in the real-time acquired motion data.

[0083] If the absolute value of the pitch angle in the real-time acquired motion data is greater than the preset pitch angle threshold, then the driver is confirmed to have fainted.

[0084] For example, the preset pitch angle threshold is 20°.

[0085] If at this time, the absolute value of the pitch angle obtained by the motion sensor If the system detects that the pilot has fainted and lost consciousness, and the head is naturally drooping, it sends a "confirmed fainting" message to the onboard data processing terminal. The aircraft then enters automatic leveling mode and waits for the pilot to regain consciousness before relinquishing control. If at this time, the motion sensor measures If the pilot faints, an "unconfirmed fainting" message is sent to the onboard data processing terminal, which then sends it to the ground control center. Ground control personnel closely monitor the pilot's condition and decide on subsequent intervention measures.

[0086] In summary, the head physiological monitoring method for determining hypoxia and syncope according to an embodiment of the present invention has the following beneficial effects: 1. This invention combines physiological parameters and head movement data of small civil aircraft pilots for collaborative judgment. It uses dual verification of blood oxygen saturation reduction ratio (physiological parameters) and movement characteristics (standard deviations of head nodding frequency, head-down ratio, pitch angle, yaw angle, and roll angle) to determine whether the pilot is experiencing in-flight hypoxia; because the cerebral oxygen saturation of different individual pilots varies. The differences are significant, and the decrease in oxygen levels varies greatly under hypoxic conditions. Therefore, relying on a single indicator can lead to a large number of misjudgments. Furthermore, the driver's head movement characteristics will also change significantly after hypoxia. By combining multi-dimensional data on the driver's head movement characteristics for verification, the accuracy of judging whether the driver is hypoxic is greatly improved and the false positive rate is reduced. 2. The personalized baseline values ​​and dynamic thresholds of this invention are adapted to individual pilot differences. Before flight, the individual pilot's... Benchmark values ​​and PI benchmark values; to avoid judgment bias caused by individual differences and improve the targeting of monitoring; 3. This invention utilizes a time window during the process of determining hypoxia by analyzing the driver's head movement characteristics. Nodding frequency within and the percentage of people looking down Standard deviation of pitch angle Yaw angle standard deviation and roll angle standard deviation Five indicators, all of which change significantly under hypoxic conditions, can be combined using machine learning methods to automatically obtain a binary classification result of whether hypoxia has occurred. The entire process requires no manual intervention. Furthermore, since the blood oxygen prediction model has been trained on a large amount of data, the classification result is accurate. This classification method can identify different head movements caused by hypoxia, and has a wider recognition range than a single indicator. 4. The method in this invention provides real-time monitoring down to the second level, controlling the delay from data acquisition to status assessment to the second level, thus allowing time for subsequent intervention in case of pilot fainting. If pilot fainting is confirmed, automatic leveling is triggered without manual intervention, preventing loss of aircraft control due to pilot incapacitation and reducing the risk of accidents.

[0087] Those skilled in the art will understand that all or part of the processes of the methods described in the above embodiments can be implemented by a computer program instructing related hardware, and the program can be stored in a computer-readable storage medium. The computer-readable storage medium may be a disk, optical disk, read-only memory, or random access memory, etc.

[0088] The above description is only a preferred embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any changes or substitutions that can be easily conceived by those skilled in the art within the scope of the technology disclosed in the present invention should be included within the scope of protection of the present invention.

Claims

1. A method for monitoring head physiology to determine hypoxia and syncope in mid-air, characterized in that, include: During flight, real-time physiological parameter data and motion data of the pilot's head are acquired; wherein, the physiological parameter data includes... Value and PI value; Based on real-time acquisition The value and the predetermined driver's The baseline value is used to obtain the driver's blood oxygen decay ratio; based on the motion data and the pre-trained blood oxygen prediction model, a prediction result is obtained as to whether the driver is hypoxic. Based on the blood oxygen attenuation ratio and the real-time acquired... The system determines whether the pilot is suspected of hypoxia; if suspected hypoxia, it determines whether the pilot has experienced in-flight hypoxia based on the predicted hypoxia result; if in-flight hypoxia occurs, it determines the pilot's condition based on the PI value and the PI attenuation coefficient obtained from the pre-determined PI baseline value of the pilot; if the pilot's condition is suspected of fainting, it determines whether the pilot has fainted based on the real-time acquired motion data, and if fainting occurs, the aircraft automatically levels off.

2. The method for head physiological monitoring in determining hypoxia and syncope according to claim 1, characterized in that, The motion data includes head motion data, pitch angle, yaw angle, and roll angle; The head movement data includes time windows. Number of nods Nodding frequency Angle of looking down and the percentage of people looking down Among them, time window Nodding frequency within and the percentage of people looking down based on , Calculated.

3. The method for head physiological monitoring in determining hypoxia and syncope according to claim 2, characterized in that, The standard deviation of the pitch angle is calculated based on the pitch angle, yaw angle, and roll angle, respectively. Yaw angle standard deviation and roll angle standard deviation ; Will , , , and After preprocessing, the data is input into a pre-trained blood oxygen prediction model to obtain a prediction result of whether the driver is hypoxic.

4. The method for head physiological monitoring in determining hypoxia and syncope according to claim 1, characterized in that, like or If so, it is determined that the driver is suspected of having hypoxia; among them, This represents the percentage of blood oxygen depletion. The threshold for the proportion of blood oxygen decay. For every The real-time monitoring of the second The average value for Threshold.

5. The method for head physiological monitoring in determining hypoxia and syncope according to claim 4, characterized in that, If it is determined that the pilot is suspected of hypoxia, and the prediction result of hypoxia is hypoxia, then it is determined that the pilot is experiencing in-flight hypoxia. Based on PI attenuation coefficient To determine the driver's condition.

6. The method for head physiological monitoring in determining hypoxia and syncope according to claim 5, characterized in that, like If so, the driver's condition is judged to be suspected of fainting; like If so, the driver's condition is determined to be extremely low infusion. like If so, the driver's condition is determined to be low perfusion; like If so, the driver's status is determined to be normal (PI). in, , and These are the threshold values ​​for the first, second, and third PI attenuation coefficients, respectively. ; If the driver's condition is suspected to be syncope, the determination of whether the driver is syncope is based on the pitch angle in the real-time acquired motion data.

7. The method for head physiological monitoring in determining hypoxia and syncope according to claim 7, characterized in that, If the absolute value of the pitch angle in the real-time acquired motion data is greater than the preset pitch angle threshold, then the driver is confirmed to have fainted.

8. The method for head physiological monitoring in determining hypoxia and syncope according to claim 7, characterized in that, The preset pitch angle threshold is 20°.

9. The method for head physiological monitoring to determine hypoxia and syncope according to claim 1, characterized in that, The blood oxygen prediction model includes an input layer, an LSTM temporal feature extraction layer, and a fully connected classification decision layer; the LSTM layer is used to extract temporal features; the fully connected layer makes classification decisions based on the temporal features. The pre-trained blood oxygen prediction model is obtained through the following process: Obtain physiological parameters and motion data of the heads of multiple drivers within a certain historical period to obtain corresponding... , , , and After preprocessing, the samples are used as training samples, and together with the corresponding samples labeled as hypoxic, they form a training sample set. The loss function is the binary cross-entropy loss between the prediction of hypoxia and the true label of the sample; the Adam optimizer is used for iterative training. If continuous The accuracy of the validation set in epochs is less than Or the F1 score on the validation set is greater than or equal to And the recall rate is greater than or equal to If the training stops, the well-trained blood oxygen prediction model is obtained. in, , and These are the accuracy threshold, F1 threshold, and recall threshold, respectively.

10. The method for head physiological monitoring in determining hypoxia and syncope according to any one of claims 1-9, characterized in that, Acquire the pilot's head before the flight. The average PI value over the minutes is used as the driver's PI baseline value. ; Get the driver's head minutes The average value of the driver's benchmark value .

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