A driver health monitoring and early warning method and system

Abstract

The application discloses a driver health monitoring and early warning method and system, and belongs to the technical field of intelligent automobile cockpits. The method comprises the following steps: calling camera data in DMS in the intelligent cockpit, positioning a driver's chest cavity, and obtaining three-dimensional coordinates of the driver's chest in the cockpit space; combining the three-dimensional coordinates of the chest cavity part, adjusting a millimeter wave radar integrated in a steering wheel to send radar signals; receiving echo signals of the radar signals interfered by the chest cavity part; extracting breathing signals and heartbeat signals in real time based on the echo signals, and obtaining vital sign signals; extracting features of the vital sign signals, obtaining vital sign parameters; analyzing the vital sign parameters; and sending an abnormal processing request when the vital sign parameters exceed a threshold. The method analyzes the heart and lung data of the driver, and judges the generation of crisis abnormal conditions such as sudden diseases of the heart and lung, for example, inhibitory breathing and heartbeat pause, in advance.

Description

Technical Field

[0001] This invention belongs to the field of automotive intelligent cockpit technology, and specifically relates to a method and system for driver health monitoring and early warning. Background Technology

[0002] As modern automobiles increasingly feature intelligent cockpits and automotive chips rapidly develop computing power, conventional monitoring of driver fatigue, distraction, and dangerous driving is no longer sufficient to meet people's needs. Current technologies also lack effective monitoring of drivers' critical vital signs and cannot assess drivers' physical condition in real time. Therefore, they cannot avoid the risks of sudden respiratory arrest and heart disease that could pose safety hazards to drivers and other road users. Summary of the Invention

[0003] This invention aims to solve the problem of monitoring key vital signs of drivers and provides a method and system for driver health monitoring and early warning. This method analyzes the driver's cardiopulmonary data and can predict the occurrence of sudden cardiopulmonary diseases such as inhibitory breathing and cardiac arrest.

[0004] To solve the above-mentioned technical problems, the technical solution adopted by the present invention is as follows:

[0005] In a first aspect, the present invention provides a method for driver health monitoring and early warning, comprising:

[0006] By calling the camera data in the DMS of the smart cockpit, the driver's chest cavity is located, and the three-dimensional coordinates of the driver's chest in the cockpit space are obtained.

[0007] By combining the three-dimensional coordinates of the chest cavity, the millimeter-wave radar integrated in the steering wheel is adjusted to transmit radar signals; the echo signals of the radar signals after interference from the undulating cavity motion of the chest cavity are received.

[0008] Based on the echo signal, respiratory and heartbeat signals are extracted in real time to obtain vital signs signals;

[0009] Feature extraction is performed on vital sign signals to obtain vital sign parameters;

[0010] Analyze vital signs parameters; when the vital signs parameters exceed the threshold, send an exception handling request.

[0011] As a further improvement of the present invention, the step of calling camera data from the DMS in the smart cockpit to locate the driver's chest cavity and obtain the three-dimensional coordinates of the driver's chest portion in the cockpit space includes:

[0012] By utilizing the camera function in the smart cockpit, the vertical position of the driver's chest is determined. Combined with the specific adjustment of the driver's seat, the planar position of the driver is determined, thus obtaining the three-dimensional coordinates of the driver's chest in the cockpit space.

[0013] As a further improvement to the present invention, the step of adjusting the radar signal transmitted by the millimeter-wave radar integrated in the steering wheel, based on the three-dimensional coordinates of the chest cavity, includes:

[0014] By combining the three-dimensional coordinates of the driver's chest in the cockpit space, the gimbal base and transmission power of the millimeter-wave radar integrated in the steering wheel are adjusted to adjust the direction and signal strength of the radar signal detection, so that the cross-section of the radar's effective detection area covers the driver's chest cavity, thus enabling adaptive adjustment of the radar position.

[0015] After adaptive adjustment, it sends radar signals to the driver's chest area.

[0016] As a further improvement of the present invention, the receiving of the echo signal of the radar signal after interference from the motion of the partial undulation cavity of the thoracic cavity includes:

[0017] After sending a radar signal to the driver's chest, it receives the echo signal of the radar signal after it has been interfered with by the movement of the chest cavity.

[0018] The echo quality of the radar signal after interference from the chest cavity is verified. If the quality meets the requirements, the quality of the detection signal after radar positioning adjustment is judged, and an echo signal with satisfactory detection signal quality is obtained.

[0019] As a further improvement of the present invention, the real-time extraction of respiratory and heartbeat signals based on the echo signal to obtain vital signs signals includes:

[0020] The echo signal is subjected to noise reduction processing;

[0021] The echo signal after noise reduction is optimized by vital sign signal processing, which includes noise elimination, motion compensation strategy to eliminate random body motion, heartbeat signal enhancement algorithm to enhance heartbeat signal, respiratory harmonic reduction algorithm to reduce respiratory harmonics, and real-time vital sign signal extraction algorithm to separate respiratory heartbeat and obtain vital sign signal.

[0022] As a further improvement of the present invention, the step of extracting features from vital sign signals to obtain vital sign parameters is to calculate vital sign signals by using a real-time vital sign signal extraction algorithm to obtain the vital sign signals from the optimized vital sign signals.

[0023] The real-time vital signs signal extraction algorithm is based on the PKISS-EWT real-time vital signs signal extraction algorithm, and includes:

[0024] Input vital signs signals;

[0025] Prior knowledge calculations are performed on the vital signs signals, and known physiological and medical knowledge is used to preprocess the signals.

[0026] Using the normalized Fourier transform, the original time-domain signal is converted into a frequency-domain signal, yielding the spectral peaks;

[0027] An improved spectrum segmentation method is used to divide the spectrum into different intervals, each interval corresponding to a specific frequency component;

[0028] Wavelet transform is applied to each frequency band of the signal to output extracted vital signs information, including respiratory rate and heart rate.

[0029] As a further improvement of the present invention, the step of analyzing vital sign parameters; when the vital sign parameters exceed a threshold, sending an exception handling request includes:

[0030] Scenario 1: Fatigue driving. At this time, the driver's respiratory rate and heart rate exceed the fatigue driving threshold, and a warning is issued to the driver.

[0031] Scenario 2: Dangerous driving. In this case, the driver's breathing rate and heart rate exceed the dangerous driving threshold, automatically dialing an emergency number and the autonomous driving system intervenes.

[0032] Secondly, the present invention provides a driver health monitoring and early warning system, comprising:

[0033] The acquisition module is used to acquire the user's command to start the simulation scenario and to display the simulation scenario type generated based on the scenario model preset by the vehicle system.

[0034] The scene generation module is used to construct simulated scenarios and set roles based on the simulated scenario type and role selected by the user, and generate virtual dialogue scenarios;

[0035] The voice synthesis module is used in virtual dialogue scenarios to analyze the user's voice features and emotional state based on the user's language and expression content, and dynamically adjust the construction of the simulation scenario and the setting of the role; according to the voice features and emotional state, it selects an appropriate voice synthesis role from the vehicle voice database to represent the voice of the other party.

[0036] The speech and logic generation module is used to generate the speech and logic of the other party based on the user's language and expression content using a large language model;

[0037] The scenario simulation module is used to provide immediate scenario simulation feedback from the other party based on the words and logic of the synthesized and generated roles, combined with real-time analysis of the user's voice characteristics and emotional state.

[0038] Thirdly, the present invention provides an electronic device including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement the driver health monitoring and early warning method.

[0039] Fourthly, the present invention provides a computer-readable storage medium storing a computer program, which, when executed by a processor, implements the driver health monitoring and early warning method.

[0040] Fifthly, the present invention provides a computer program product, the computer program product including computer instructions, characterized in that the computer instructions instruct a computer to execute the driver health monitoring and early warning method.

[0041] The advantages of this invention over the prior art are as follows:

[0042] This invention integrates an optical camera with the driver's seat position information in the cockpit for comprehensive three-dimensional positioning. This allows the effective detection location of millimeter-wave radar to be accurately radiated to the driver's chest cavity. By integrating the millimeter-wave radar into the steering wheel, the driver's driving activities are not excessively affected. Without compromising the aesthetics of the cockpit, the driver's health can be monitored non-contactly at a low cost. The driver's cardiopulmonary data is analyzed to predict the occurrence of sudden cardiopulmonary diseases such as inhibitory breathing and cardiac arrest, so as to provide early warning and trigger the intelligent driving system to take appropriate measures. Attached Figure Description

[0043] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the following description is provided with accompanying drawings of the relevant technical solutions in the embodiments of the present invention or the prior art. It should be understood that the accompanying drawings described below are only for the purpose of clearly illustrating some embodiments of the technical solutions of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0044] Figure 1 This is a flowchart of the driver health monitoring and early warning method in the embodiments of this application;

[0045] Figure 2 This is a flowchart of the continuous wave radar vital sign signal optimization method in the embodiments of this application;

[0046] Figure 3 This is a comparison diagram of the time-domain effects of the heartbeat enhancement algorithm in the embodiments of this application;

[0047] Figure 4 This is a comparison chart of the frequency domain effects of the heartbeat enhancement algorithm in the embodiments of this application;

[0048] Figure 5 This is a comparison of the signal spectra before and after notch filtering in the embodiments of this application;

[0049] Figure 6 This is a flowchart of the real-time vital signs signal extraction algorithm in the embodiments of this application;

[0050] Figure 7 This embodiment compares the extracted heartbeat signal with the standard heartbeat signal from the sensor.

[0051] Figure 8 This embodiment compares the extracted respiratory signal with the standard respiratory signal from the sensor.

[0052] Figure 9 This invention provides a driver health monitoring and early warning system;

[0053] Figure 10 This is a schematic diagram of an electronic device provided by the present invention. Detailed Implementation

[0054] The embodiments of the present invention are described in detail below. Examples of these embodiments are shown in the accompanying drawings, wherein the same or similar reference numerals denote the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present invention, and should not be construed as limiting the present invention. The step numbers in the following embodiments are set only for ease of explanation, and there is no limitation on the order between the steps. The execution order of each step in the embodiments can be adaptively adjusted according to the understanding of those skilled in the art.

[0055] In the description of this invention, unless otherwise explicitly defined, terms such as "set up," "install," and "connect" should be interpreted broadly, and those skilled in the art can reasonably determine the specific meaning of the above terms in this invention in conjunction with the specific content of the technical solution.

[0056] This system is used to simulate and analyze emotional processing and expression training in complex interpersonal scenarios such as disputes, interviews, and debates. Combining technologies such as Natural Language Processing (NLP), sentiment analysis, and speech recognition, the system aims to help users improve their expressive and emotional management abilities in real-world interpersonal conflicts, interviews, and debates through in-car scenario simulations.

[0057] This invention provides a method for driver health monitoring and early warning, such as... Figure 1 As shown, it includes:

[0058] S101 calls the camera data in the DMS of the smart cockpit to locate the driver's chest cavity and obtain the three-dimensional coordinates of the driver's chest in the cockpit space;

[0059] S102, combined with the three-dimensional coordinates of the chest cavity, adjusts the millimeter-wave radar integrated in the steering wheel to transmit radar signals; receives the echo signal of the radar signal after interference from the undulating cavity motion of the chest cavity;

[0060] S103, Based on the echo signal, the respiratory signal and heartbeat signal are extracted in real time to obtain vital signs signal;

[0061] S104, extract features from vital sign signals to obtain vital sign parameters;

[0062] S105, Analyze the vital signs parameters; when the vital signs parameters exceed the threshold, send an exception handling request.

[0063] This invention utilizes camera data acquisition and chest cavity positioning to create a Driver Monitoring System (DMS) in the smart cockpit. A DMS system is typically integrated inside the vehicle to monitor the driver's condition, such as fatigue or distraction. It usually includes sensors such as cameras to capture images of the driver's face and upper body.

[0064] Furthermore, chest cavity localization involves locating the driver's chest cavity using computer vision techniques (such as deep learning algorithms) based on images captured by a camera. This typically involves steps such as image preprocessing, feature extraction, and bounding box detection to determine the chest cavity's position within the image. Three-dimensional coordinate calculation combines the camera's position, angle, and field of view with the chest cavity's location in the image, using stereo vision or depth estimation techniques to calculate the chest cavity's three-dimensional coordinates within the cockpit space.

[0065] Furthermore, millimeter-wave radar signal adjustment and reception: Millimeter-wave radar, with its strong penetration and good anti-interference capabilities, is suitable for monitoring the driver's vital signs. It can detect the position and speed of objects by sending and receiving radar signals. Based on the three-dimensional coordinates of the chest cavity, the transmission direction of the millimeter-wave radar is adjusted so that it can accurately illuminate the driver's chest cavity. After the radar signal illuminates the chest cavity, it will be interfered with by the chest cavity's rise and fall (such as breathing and heartbeat), forming echo signals. These echo signals are received and recorded by the radar.

[0066] As an example, the process of extracting and analyzing vital signs signals involves using signal processing techniques (such as filtering and Fourier transform) to process the echo signals and extract respiratory and heartbeat signals. Feature extraction, such as respiratory rate and heart rate, is then performed on the extracted vital signs signals to obtain vital sign parameters.

[0067] Furthermore, anomaly analysis compares vital sign parameters with preset thresholds to determine if any abnormalities exist. Examples include excessively fast or slow breathing, or excessively high or low heart rate. When vital sign parameters exceed the threshold, the system sends an anomaly handling request. This may include issuing an alarm, displaying a warning message to the driver, or even automatically taking measures (such as slowing down or stopping) to ensure the safety of the driver and vehicle.

[0068] This invention combines a driver monitoring system (DMS) and millimeter-wave radar to monitor driver vital signs in real time and detect abnormalities promptly. This monitoring method eliminates the need for direct contact with the driver, avoiding the discomfort and interference that can occur with traditional methods. Utilizing advanced computer vision and signal processing technologies, high-precision vital sign monitoring and anomaly detection are achieved. By monitoring and analyzing the driver's vital signs in real time, measures can be taken before health problems arise, improving driving safety. The entire monitoring process is automated by the intelligent system, requiring no human intervention, thus enhancing the system's intelligence level. Therefore, using a DMS and millimeter-wave radar in a smart cockpit for driver vital sign monitoring offers advantages such as real-time monitoring, non-contact operation, high precision, safety, and intelligence. This monitoring method helps to detect driver health problems promptly, improves driving safety, and provides a new technological path for the development of intelligent driving.

[0069] The present invention will now be described in further detail with reference to the accompanying drawings and specific embodiments.

[0070] This invention provides a driver cardiopulmonary health monitoring algorithm that integrates cameras and millimeter-wave radar. By calling the camera function in the cockpit and combining it with the position of the driver's seat, the wireless signal of the millimeter-wave radar is accurately located to the driver's chest cavity. By detecting the rise and fall of the human chest cavity, respiratory and heartbeat signals are extracted, and vital signs parameters such as respiratory rate, heart rate, and heart rate variability are extracted. After analyzing the data, reminders are issued and emergency measures are taken.

[0071] The main steps of driver health monitoring and early warning methods include:

[0072] Chest cavity positioning: The camera function in the DMS (fatigue monitoring system) of the intelligent cockpit is called to determine the vertical position of the driver's chest cavity. Combined with the specific adjustment of the driver's seat, the driver's planar position is determined. The two methods are combined to output the three-dimensional coordinates of the driver's chest cavity in the cockpit space, which guides the detection direction of the millimeter-wave radar in the next step.

[0073] Radar adjustment: By combining the three-dimensional coordinates of the chest cavity, the direction and signal strength of the radar signal detection are adjusted by adjusting the gimbal base and transmission power of the millimeter-wave radar integrated in the steering wheel.

[0074] Signal verification: Verify the echo quality of the radar signal after interference from the chest cavity movement, and determine the quality of the detection signal after radar positioning adjustment, specifically including clutter noise intensity, random body movement and arm obstruction.

[0075] Vital sign extraction: Real-time extraction of respiratory and heartbeat signals is achieved through a series of related algorithms, including: elimination of random body movements, noise cancellation, enhancement of heartbeat signals, reduction of respiratory harmonics, and separation of respiratory and heartbeat signals.

[0076] Analysis and prediction: Feature extraction is performed on the real-time vital signs signals, including respiratory rate, heart rate, changes in respiratory rhythm, and heart rate variability. The extracted vital signs parameters are analyzed to predict possible abnormalities so that timely measures can be taken.

[0077] Abnormal alarm: Based on the analysis of vital signs parameters, if the occurrence of serious conditions such as respiratory depression or cardiac arrest is predicted, an alarm will be triggered, emergency contacts and emergency numbers will be contacted, and the intelligent driving system will be activated, such as turning on hazard lights, slowing down longitudinally, and pulling over to the side of the road at a speed.

[0078] When the radar base receives the combined positioning coordinates from the camera and the driver's seat, it controls the gimbal to adjust its position and the power of the transmitting antenna, so that the cross-section of the radar's effective detection area covers the driver's chest cavity as much as possible, thus completing the adaptive adjustment of the radar position.

[0079] As a specific embodiment, the real-time extraction method for vital signs signals includes three parts: data preprocessing, signal optimization, and signal extraction, specifically including:

[0080] 1) Noise reduction algorithm.

[0081] 2) such as Figure 2 As shown, the radar signal emitted by the continuous wave radar system undergoes filter selection and signal denoising, followed by filter order adjustment and time-shift compensation processing to obtain a respiratory rate estimate and a filtered signal. The filtered signal is then processed using a heartbeat signal enhancement algorithm to obtain an enhanced signal. This enhanced signal, along with the respiratory rate estimate, is then processed using a respiratory harmonic reduction algorithm to obtain an optimized signal. Finally, a real-time vital sign signal extraction algorithm is used to obtain an optimized vital sign signal. The specific steps are as follows:

[0082] like Figure 2 As shown, the process of real-time extraction of vital signs signals by a continuous wave radar system includes steps such as radar signal acquisition, filter selection and time shift compensation, signal noise reduction, respiratory rate estimation, filtered signal enhancement, respiratory harmonic reduction, and heartbeat signal enhancement.

[0083] Radar signal: Radar systems emit electromagnetic waves, which are reflected back after encountering a target (such as a human body). The received reflected signal contains information such as the target's distance, speed, and angle.

[0084] Filter order: In signal processing, filters are used to remove unwanted frequency components and retain useful signals. The order of a filter determines the complexity of its filtering performance; the higher the order, the narrower the transition band, but the greater the phase delay.

[0085] Filter selection and time-shift compensation strategies: Choosing the right filter is crucial for signal quality. Time-shift compensation strategies are used to correct phase errors caused by signal propagation delays or system delays.

[0086] Signal denoising: Radar signals are subject to various noise interferences during transmission and reception. Denoising steps use various algorithms (such as Wiener filtering, wavelet transform, etc.) to reduce noise and improve the signal-to-noise ratio.

[0087] Respiratory rate estimation: The respiratory rate can be estimated using the filtered signal. This is typically achieved by analyzing the periodic variations of the signal, such as using Fourier transform or autocorrelation analysis.

[0088] Filtered signal enhancement: The enhancement step aims to increase the amplitude of specific components in the signal, such as by adjusting the gain or using a specific filter to highlight breathing or heartbeat signals.

[0089] Respiratory harmonic reduction: Respiratory signals may contain multiple harmonic components. Reducing these harmonics can reduce the complexity of the signal, making the main respiratory frequency components more prominent.

[0090] Heartbeat signal enhancement: Heartbeat signals can be masked by other physiological or non-physiological noise. Enhancing the heartbeat signal can help monitor heart rate and heart rate variability more accurately.

[0091] Real-time extraction algorithm for vital signs signals: An algorithm for real-time extraction of vital signs signals has been developed. This algorithm can continuously monitor and analyze respiratory and heartbeat signals, providing real-time data for medical monitoring and health assessment.

[0092] The flowchart and effect diagram of the vital signs signal optimization algorithm are shown below. Figures 2 to 5 As shown, the key to the entire process lies in the precise processing and analysis of radar signals to extract accurate vital sign information. This method has broad application prospects in fields such as remote health monitoring, emergency rescue, and sleep research.

[0093] Among them, in the vital signs signal optimization algorithm: the heartbeat signal enhancement algorithm adopts a heartbeat signal enhancement algorithm based on cubic spline interpolation function; the respiratory harmonic reduction algorithm adopts a respiratory harmonic reduction algorithm based on adaptive BLMS notch filter.

[0094] The definition formula for the interpolation points of the cubic spline interpolation function is as follows:

[0095]

[0096] These conditions are commonly used to detect peaks or troughs in a signal, which is crucial in many applications such as electrocardiogram (ECG) signal analysis, speech processing, or any application that requires identifying significant changes in a signal.

[0097] The first condition: This condition is checked in time. t i signal value at S x ( t i Whether it is greater than the value at its previous and next time points, and greater than the average value at its 32 previous and next time points. This means t i It is a local maximum because it is larger than the values ​​of its immediate neighbors and surrounding small regions.

[0098] The second condition: This condition is checked in time. t i The signal value at (S) x (t j Whether it is less than the value at its previous and next time points, and less than the average value at 32 time points before and after it. This means t i It is a local minimum because it is smaller than the values ​​of its immediate neighbors and surrounding small regions.

[0099] In both conditions, a window of 32 time points is used to calculate local averages, which helps smooth the signal and identify more significant extrema. This method can reduce the influence of noise and improve detection accuracy.

[0100] The filter weight coefficient update process of the BLMS algorithm can be expressed as:

[0101]

[0102] An update equation for the Recursive Least Squares (RLS) algorithm, used for online estimation of model parameters. RLS is an adaptive filtering technique widely used in signal processing and system identification.

[0103] The transfer function of the residual output of the dual-frequency adaptive notch filter relative to the input signal can be derived as follows:

[0104]

[0105] The transfer function of a second-order digital filter is commonly used in signal processing and control systems. The transfer function describes the relationship between the system's output and input. This transfer function describes a second-order digital filter. By adjusting these parameters, filters with specific frequency characteristics can be designed.

[0106] A real-time vital sign signal extraction algorithm based on PKISS-EWT. For example... Figure 6 As shown, it includes the following steps:

[0107] The vital sign signal extraction process based on the PKISS-EWT (Pulse Keying based on Improved Signal Subspace for Empirical Wavelet Transform) algorithm includes inputting vital sign signals, calculating prior knowledge, optimizing signal transformation, spectral peak analysis, segmenting intervals, improved spectral segmentation, wavelet transform, and finally outputting the extracted results. The following is a principle analysis and explanation of this process:

[0108] Input vital signs signals: This is the starting point of the entire process, which involves acquiring vital signs signals from sensors or data acquisition devices, such as electrocardiogram (ECG) signals, pulse oxygen saturation (SpO2) signals, or respiratory movement signals.

[0109] Prior knowledge computation: In this step, the algorithm uses known physiological and medical knowledge to preprocess the signal. This may include operations such as noise removal, filtering, and normalization to improve the accuracy of subsequent processing.

[0110] Optimize signal transformation: This step involves transforming the original signal into a form more suitable for analysis. For example, a Fourier transform might be used to convert a time-domain signal into a frequency-domain signal, making it easier to identify and analyze periodic components in the signal.

[0111] Spectral peak analysis: In the frequency domain, the algorithm identifies spectral peaks in a signal, representing the dominant frequency components. For vital signs signals, these peaks are typically associated with the frequencies of heartbeat and respiration.

[0112] Interval segmentation: Based on the spectral peaks, the algorithm divides the spectrum into different intervals, each corresponding to a specific frequency component. This helps the subsequent wavelet transform to process each frequency component more accurately.

[0113] Improved spectrum segmentation: This step is a further optimization of spectrum segmentation, which may involve finer frequency band division to ensure that wavelet transform can more accurately capture subtle changes in the signal.

[0114] Wavelet Transform: The Empirical Wavelet Transform (EWT) is applied to each frequency band of the signal. EWT is an adaptive time-frequency analysis method that can select the most suitable wavelet basis based on the local characteristics of the signal, thereby more effectively separating and extracting different components in the signal.

[0115] Output Extraction Results: Outputs extracted vital sign information, such as estimated respiratory rate and heart rate. This information can be used for real-time monitoring, health assessment, or further medical analysis.

[0116] The core of the entire process lies in leveraging the adaptability and flexibility of EWT to process nonlinear and non-stationary vital sign signals, thereby achieving accurate extraction of key information from the signals. This method has significant application value in the fields of medical monitoring and health assessment.

[0117] Among them, the empirical wavelet function and empirical scaling function for constructing wavelets can be expressed as:

[0118]

[0119] The flowchart and effect diagram of the real-time extraction algorithm for vital signs signals are shown below. Figures 6 to 8 As shown.

[0120] Based on the analysis of vital signs, scenario 1: Fatigue driving. In this case, the driver's respiratory rate and heart rate are abnormal, possibly due to prolonged driving or physical discomfort. The system will warn the driver to reduce speed or stop at the nearest stop to allow them to adjust their condition. Scenario 2: Dangerous driving. In this case, the driver is in an emergency and may experience sudden respiratory depression or cardiac arrest. The system will automatically dial an emergency number, intervene in the autonomous driving system, and assist the driver to pull over or automatically navigate to the nearest clinic.

[0121] The second objective of this invention is to provide a driver health monitoring and early warning system. Figure 9 The flowchart for this system includes:

[0122] The coordinate positioning module 100 is used to call the camera data in the DMS of the smart cockpit to locate the driver's chest cavity and obtain the three-dimensional coordinates of the driver's chest in the cockpit space.

[0123] The transmitting and receiving module 200 is used to adjust the radar signal transmitted by the millimeter-wave radar integrated in the steering wheel by combining the three-dimensional coordinates of the chest cavity; and to receive the echo signal of the radar signal after interference from the undulating cavity motion of the chest cavity.

[0124] The real-time extraction module 300 is used to extract respiratory and heartbeat signals in real time based on the echo signal to obtain vital signs signals.

[0125] The feature extraction module 400 is used to extract features from vital sign signals to obtain vital sign parameters;

[0126] The analysis and processing module 500 is used to analyze vital sign parameters; when the vital sign parameters exceed the threshold, an anomaly handling request is sent.

[0127] The system is based on the aforementioned driver health monitoring and early warning methods.

[0128] like Figure 10 As shown, a third objective of this invention is to provide an electronic device, including a memory 701, a processor 702, and a computer program stored in the memory 701 and executable on the processor. When the processor executes the computer program, it implements the driver health monitoring and early warning method. The device also includes a communication interface 703 and a bus 704.

[0129] A fourth objective of this invention is to provide a computer-readable storage medium storing a computer program that, when executed by a processor, implements the driver health monitoring and early warning method.

[0130] A fifth objective of this invention is to provide a computer program product comprising computer instructions, wherein the computer instructions instruct a computer to execute the driver health monitoring and early warning method.

[0131] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0132] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0133] This invention may take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, this invention may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, readable storage media, optical storage, etc.) containing computer-usable program code.

[0134] This invention is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the invention. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, generate instructions for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0135] Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without inventive effort should fall within the scope of protection of the present invention.

[0136] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and not to limit it. Although the present invention has been described in detail with reference to the above embodiments, those skilled in the art should understand that modifications or equivalent substitutions can still be made to the specific implementation of the present invention. Any modifications or equivalent substitutions that do not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. A method for monitoring and early warning of driver health, characterized in that, include: By calling the camera data in the DMS of the smart cockpit, the driver's chest cavity is located, and the three-dimensional coordinates of the driver's chest in the cockpit space are obtained. By combining the three-dimensional coordinates of the chest cavity, the millimeter-wave radar integrated in the steering wheel is adjusted to transmit radar signals; the echo signals of the radar signals after interference from the undulating cavity motion of the chest cavity are received. Based on the echo signal, respiratory and heartbeat signals are extracted in real time to obtain vital signs signals; Feature extraction is performed on vital sign signals to obtain vital sign parameters; Analyze vital signs parameters; when the vital signs parameters exceed the threshold, send an exception handling request; The process of accessing camera data from the DMS in the smart cockpit to locate the driver's chest cavity and obtain the three-dimensional coordinates of the driver's chest portion in the cockpit space includes: By utilizing the camera function in the smart cockpit, the vertical position of the driver's chest is determined. Combined with the specific adjustment of the driver's seat, the planar position of the driver is determined, and the three-dimensional coordinates of the driver's chest in the cockpit space are obtained. The method of adjusting the radar signal transmitted by the millimeter-wave radar integrated in the steering wheel, based on the three-dimensional coordinates of the chest cavity, includes: By combining the three-dimensional coordinates of the driver's chest in the cockpit space, the gimbal base and transmission power of the millimeter-wave radar integrated in the steering wheel are adjusted to adjust the direction and signal strength of the radar signal detection, so that the cross-section of the radar's effective detection area covers the driver's chest cavity, thus enabling adaptive adjustment of the radar position. After adaptive adjustment, it sends radar signals to the driver's chest area.

2. The driver health monitoring and early warning method according to claim 1, characterized in that, The echo signal received after receiving the radar signal through the motion interference of the pleural cavity includes: After sending a radar signal to the driver's chest, it receives the echo signal of the radar signal after it has been interfered with by the movement of the chest cavity. The echo quality of the radar signal after interference from the chest cavity is verified. If the quality meets the requirements, the quality of the detection signal after radar positioning adjustment is judged, and an echo signal with satisfactory detection signal quality is obtained.

3. The driver health monitoring and early warning method according to claim 1, characterized in that, The real-time extraction of respiratory and heartbeat signals based on the echo signal to obtain vital signs signals includes: The echo signal is subjected to noise reduction processing; The echo signal after noise reduction is optimized by vital sign signal processing, which includes noise elimination, motion compensation strategy to eliminate random body motion, heartbeat signal enhancement algorithm to enhance heartbeat signal, respiratory harmonic reduction algorithm to reduce respiratory harmonics, and real-time vital sign signal extraction algorithm to separate respiratory heartbeat and obtain vital sign signal.

4. The driver health monitoring and early warning method according to claim 1, characterized in that, The process of extracting features from vital sign signals to obtain vital sign parameters involves calculating the vital sign signals by using a real-time vital sign signal extraction algorithm to optimize the vital sign signals. The real-time vital signs signal extraction algorithm is based on the PKISS-EWT real-time vital signs signal extraction algorithm, and includes: Input vital signs signals; Prior knowledge calculations are performed on the vital signs signals, and known physiological and medical knowledge is used to preprocess the signals. Using the normalized Fourier transform, the original time-domain signal is converted into a frequency-domain signal, yielding the spectral peaks; An improved spectrum segmentation method is used to divide the spectrum into different intervals, each interval corresponding to a specific frequency component; Wavelet transform is applied to each frequency band of the signal to output extracted vital signs information, including respiratory rate and heart rate.

5. The driver health monitoring and early warning method according to claim 1, characterized in that, The analysis of vital sign parameters; when the vital sign parameters exceed a threshold, an exception handling request is sent, including: Scenario 1: Fatigue driving. At this time, the driver's respiratory rate and heart rate exceed the fatigue driving threshold, and a warning is issued to the driver. Scenario 2: Dangerous driving. In this case, the driver's breathing rate and heart rate exceed the dangerous driving threshold, automatically dialing an emergency number and the autonomous driving system intervenes.

6. A driver health monitoring and early warning system, implementing the driver health monitoring and early warning method according to any one of claims 1-5, characterized in that, include: The coordinate positioning module is used to call the camera data in the DMS in the smart cockpit to locate the driver's chest cavity and obtain the three-dimensional coordinates of the driver's chest in the cockpit space; The transmitting and receiving module is used to adjust the radar signal transmitted by the millimeter-wave radar integrated in the steering wheel by combining the three-dimensional coordinates of the chest cavity; and to receive the echo signal of the radar signal after interference from the undulating cavity motion of the chest cavity. The real-time extraction module is used to extract respiratory and heartbeat signals in real time based on the echo signal to obtain vital signs signals. The feature extraction module is used to extract features from vital sign signals to obtain vital sign parameters; The analysis and processing module is used to analyze vital sign parameters; when the vital sign parameters exceed the threshold, an exception handling request is sent.

7. An electronic device, characterized in that, The method includes a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor, when executing the computer program, implements the driver health monitoring and early warning method according to any one of claims 1-5.

8. A computer-readable storage medium, characterized in that, The computer-readable storage medium stores a computer program that, when executed by a processor, implements the driver health monitoring and early warning method according to any one of claims 1-5.

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