Vital sign high-precision signal acquisition method based on millimeter wave radar

Abstract

The application discloses a vital sign high-precision signal acquisition method based on a millimeter wave radar and relates to the technical field of millimeter wave radar vital sign monitoring, which comprises the following steps: S1, completing radar hardware communication matching, performing adaptive parameter configuration on a transmitting power, a sampling frequency and a scanning angle, and making a radar detection range completely cover a human chest cavity monitoring area; S2, adopting a frequency-modulated continuous wave radar system to emit a detection electromagnetic wave, receiving a return signal generated by human chest cavity micro-motion, and outputting corresponding intermediate frequency sampling data; and S3, sequentially performing hardware end pre-filtering and upper computer multi-stage layered noise reduction processing on the intermediate frequency sampling data, and synchronously inhibiting signal interference caused by environmental clutter and human body motion. The application can inhibit clutter and body motion interference through multi-stage layered noise reduction of software and hardware, can extract vital sign components by combining variational mode decomposition and phase difference method, and can effectively improve signal separation precision and subtle vital sign fluctuation recognition capability.

Description

Technical Field

[0001] This invention relates to the field of millimeter-wave radar vital sign monitoring technology, specifically to a method for high-precision acquisition of vital sign signals based on millimeter-wave radar. Background Technology

[0002] With the rapid development of millimeter-wave frequency-modulated continuous wave radar and non-contact vital sign monitoring technology, wearable and unrestricted vital sign acquisition solutions are gradually being applied to home health monitoring and bedside continuous monitoring scenarios. Existing millimeter-wave radar vital sign acquisition solutions mostly adopt a fixed parameter acquisition architecture, uniformly setting the transmission power, sampling frequency, and scanning angle. The acquisition link is only equipped with a single hardware filtering module, relying on simple time-domain filtering to remove clutter. The acquisition signal decomposition method mostly uses fixed-order wavelet decomposition and simple Fourier transform to split the mixed echo. The output data only includes baseline heart rate and respiration values, lacking standardized time-series frequency domain data and corresponding signal labels. The acquired data is directly transmitted to the backend health assessment equipment.

[0003] However, existing acquisition schemes have several drawbacks: First, fixed radar parameters cannot be adaptively adjusted, making it impossible to match the chest cavity monitoring area of ​​different body types, easily leading to signal omissions and low signal-to-noise ratios. Second, single-layer hardware filtering and noise reduction are insufficient to suppress environmental clutter and significant human movement interference, resulting in severe aliasing of respiratory and heartbeat components in the mixed echoes. Third, fixed-order signal decomposition methods have poor separation accuracy, making it difficult to accurately extract subtle phase changes, leading to large errors in vital sign parameter extraction. Fourth, the lack of a normal threshold library and historical big data correction mechanism means that abnormal interference sampling points cannot be automatically removed, resulting in long-term accumulation of acquisition drift errors. Fifth, the output data format is singular, lacking hierarchical standardized time-series, frequency-domain indicators, and signal quality correlation markers, requiring additional secondary data analysis by the backend intelligent judgment system, resulting in poor coupling between upstream and downstream data links. To address these issues, we propose a high-precision vital sign signal acquisition method based on millimeter-wave radar. Summary of the Invention

[0004] To address the aforementioned technical issues, this paper provides a high-precision method for acquiring vital signs signals based on millimeter-wave radar. This technical solution solves the problems of low accuracy, poor anti-interference, and weak adaptability in radar vital sign acquisition.

[0005] To achieve the above objectives, the technical solution adopted by the present invention is as follows:

[0006] High-precision acquisition methods for vital signs signals based on millimeter-wave radar include: S1: Complete radar hardware communication matching, perform adaptive parameter configuration for transmission power, sampling frequency, and scanning angle, so that the radar detection range can completely cover the human chest cavity monitoring area; S2: Employs a frequency-modulated continuous wave radar system to transmit detection electromagnetic waves, receive echo signals generated by micro-movements in the human chest cavity, and output corresponding intermediate frequency sampling data; S3: Perform hardware-side pre-filtering and host computer multi-level layered noise reduction processing on the intermediate frequency sampling data in sequence to simultaneously suppress environmental noise and signal interference caused by human body movement; S4: The mixed signal is split by variational mode decomposition algorithm to separate independent respiratory and heartbeat signal components, and then the two core vital signs parameters of respiratory rate and heart rate fluctuation are extracted by phase difference method. S5: Call the normal vital sign threshold database and historical big data to complete the removal of abnormal data, correction and compensation of collection errors; S6: Output standardized vital sign data and associated identifiers for intelligent determination of human health status; the standardized vital sign data includes respiratory rate time sequence, heart rate fluctuation frequency domain, and statistical characteristics of vital sign stability; the associated identifiers include the signal quality level of each channel.

[0007] Preferably, the radar hardware communication matching includes: after the module is powered on and initialized, the host computer establishes a serial communication link through the UART serial port with a serial frame format of 115200bps baud rate, 8 data bits, 1 stop bit, and no parity bit. The adaptive parameter configuration includes adjustable transmission power based on monitoring distance, multiple selectable sampling frequencies based on vital sign signal characteristics, and adaptive setting of scanning angle based on the module's preset detection angle range.

[0008] Preferably, the frequency-modulated continuous wave radar system operates in the frequency band of 59GHz to 64GHz, with a center frequency of 61.5GHz and an equivalent omnidirectional radiated power of no more than 20mW. The intermediate frequency sampling data is a difference frequency signal sampling sequence obtained by mixing radar echoes, and the sampling accuracy is adaptively set according to the sampling frequency and the bandwidth of the characteristic signal. The radar's effective monitoring distance, optimal monitoring distance, heart rate detection accuracy, and data detection update frequency are all preset and adapted parameters.

[0009] Preferably, the hardware-side pre-filter includes: The intermediate frequency sampling data is first filtered by a low-pass filter to remove high-frequency aliasing components, and then by a band-pass filter to retain the effective frequency bands of the breathing and heartbeat signals; The lower passband limit of the bandpass filter is set according to the preset lowest frequency of the respiratory signal, and the upper passband limit is set according to the preset highest frequency of the heartbeat signal. The multi-level layered noise reduction process of the host computer includes power frequency interference suppression, baseline drift removal, and random noise smoothing.

[0010] Preferably, the variational mode decomposition algorithm is executed according to the following steps: Based on the frequency domain distribution characteristics of the vital signs signal, the optimal number of decomposition modes K is adaptively determined, and the mixed signal is decomposed into K mutually independent intrinsic mode functions; The respiratory signal corresponds to the low-frequency modal component, and the heartbeat signal corresponds to the mid-to-high-frequency modal component. The modal number K is adaptively matched and set according to the normal physiological distribution range of human respiration and heart rate.

[0011] Preferably, the phase difference method is performed according to the following steps: Phase information is extracted from the separated respiratory and heartbeat signal components, and differential operation is performed on adjacent sampling points according to a preset time window to obtain the phase change. The respiratory rate is obtained by statistically analyzing the periodicity of the phase change of the respiratory component, and the heart rate is obtained by statistically analyzing the periodicity of the phase change of the heartbeat component; the length of the time window is preset based on the periodic characteristics of the vital signs signal.

[0012] Preferably, the normal vital signs threshold library includes normal range thresholds for respiratory rate, normal range thresholds for heart rate fluctuation, and normal range thresholds for vital sign stability. The historical big data collection includes continuous historical vital sign records of individuals for a preset duration; The removal of abnormal collected data is performed according to the threshold library judgment rules, and sampling points that exceed the normal threshold range are marked as abnormal and removed. The acquisition error correction includes zero bias calibration and temperature drift compensation. The compensation logic is implemented based on an error prediction model constructed from historical big data, and the model can be iteratively updated periodically.

[0013] Preferably, the standardized vital sign data are composed of the following: The respiratory rate time series data is a respiratory rate sampling sequence arranged continuously in time series, which is collected using a preset sampling interval; The frequency domain data of heart rate fluctuations is a power spectrum distribution divided by frequency bands, which includes at least the power ratio of the low-frequency band to the high-frequency band of heart rate variability and the spectral entropy value. The statistical characteristics of vital sign stability include the standard deviation of vital sign data, coefficient of variation, stability entropy value, variance of body movement amplitude, and number of body movements.

[0014] Preferably, the association identifier includes four types of fields: Signal quality levels are divided into four levels from 0 to 3 based on data stability, with level 3 corresponding to the best signal quality and level 0 representing the worst signal quality. Data validity identifiers are used to determine whether the current data frame is valid and can be used in subsequent human health assessments. Human presence status markers are used to identify whether there are valid human monitoring targets within the monitoring area; The sampling timestamp is a marker of the data acquisition time with millisecond-level accuracy.

[0015] Preferably, the standardized vital sign data and associated identifiers are output to the downstream human health status intelligent judgment system through the serial port frame format of the matching protocol. Before output, data validity is filtered according to the signal quality level, and invalid data frames with a signal quality level of 0 are directly discarded and not output. The output frame format is fully compatible with the input interface protocol of the downstream system, ensuring a one-to-one correspondence between the data timing relationship and the associated identifier field.

[0016] Compared with the prior art, the beneficial effects of the present invention are as follows: This invention employs a radar hardware communication matching + multi-dimensional adaptive parameter configuration mechanism, which can dynamically adjust the transmission power, sampling frequency, and scanning angle to fully cover the human chest cavity region, solving the problems of poor adaptability and low signal-to-noise ratio in traditional fixed parameters, and significantly expanding the applicable monitoring scenarios. Through a hardware pre-filtering and upper-computer multi-level layered noise reduction combination scheme, it effectively suppresses power frequency clutter, baseline drift, random noise, and body motion interference. Combined with variational mode decomposition and phase difference method to extract vital sign components, compared with traditional single filtering and wavelet decomposition, the signal separation accuracy is higher, and the ability to identify subtle breathing and heart rate fluctuations is significantly improved. Relying on a normal vital sign threshold database and long-term historical big data, it completes anomaly removal, zero-bias calibration, and temperature drift compensation, and can automatically pass through... It filters out interference and abnormal sampling data, continuously corrects inherent errors in the acquisition system, and ensures stable accuracy of vital sign acquisition under long-term monitoring, avoiding error accumulation that could lead to distorted judgments. It uniformly outputs three types of statistical features: respiratory rate time series, heart rate fluctuation frequency domain, and vital sign stability, along with multi-level correlation identifiers. The data format is fully compatible with the downstream intelligent human health status judgment system interface, eliminating the need for additional secondary parsing and achieving seamless coupling of data flows between upstream and downstream devices, thus improving the overall operating efficiency of the monitoring system. The entire acquisition chain is fully automated, from radar adaptive configuration, echo noise reduction, component extraction to error correction and standardized output, all completed in one integrated process. No manual intervention is required for parameter adjustment and data preprocessing, lowering the barrier to entry for device use and adapting to long-term, uninterrupted home vital sign monitoring scenarios. Attached Figure Description

[0017] Figure 1 This is a schematic diagram of the method steps of the present invention. Detailed Implementation

[0018] The following description is intended to disclose the invention and enable those skilled in the art to implement it. The preferred embodiments described below are merely examples, and other obvious variations will occur to those skilled in the art.

[0019] Reference Figure 1 As shown, a high-precision vital sign signal acquisition method based on millimeter-wave radar includes: S1. Complete radar hardware communication matching, and perform adaptive parameter configuration for transmission power, sampling frequency and scanning angle to ensure that the radar detection range fully covers the human chest cavity monitoring area. S2. Employs a frequency-modulated continuous wave radar system to transmit detection electromagnetic waves, receive echo signals generated by micro-movements in the human chest cavity, and output corresponding intermediate frequency sampling data; S3. Perform hardware-side pre-filtering and host computer multi-level layered noise reduction processing on the intermediate frequency sampling data in sequence to simultaneously suppress environmental noise and signal interference caused by human body movement. S4. The mixed signal is split by variational mode decomposition algorithm to separate independent respiratory and heartbeat signal components, and then the two core vital signs parameters of respiratory rate and heart rate fluctuation are extracted by phase difference method. S5. Call the normal vital sign threshold database and historical big data to complete the removal of abnormal data, correction and compensation of collection errors; S6. Output standardized vital sign data and associated identifiers for intelligent determination of human health status; the standardized vital sign data includes respiratory rate time sequence, heart rate fluctuation frequency domain, and statistical characteristics of vital sign stability; the associated identifiers include the signal quality level of each channel.

[0020] The method described in this invention is not only applicable to human vital sign monitoring, but also adaptable to the collection of vital signs from common household pets such as cats and dogs. The echo signal generated by the micro-movement of the pet's chest cavity is consistent with that of the human body in terms of physical mechanism; both are reflection modulation of electromagnetic waves caused by micro-displacement of the body surface due to respiratory movements and heartbeats. The core signal processing link of this invention does not rely on specific physiological parameters of the human body; the monitoring target can be switched by configuring parameters and adapting thresholds. Specifically, in S1, the transmission power is adjusted to a low-power setting for close range and the monitoring distance is shortened; in S4, the prior range of the modal center frequency of respiration and heart rate is reset according to the pet species; and in S5, the normal vital sign threshold library is replaced with the normal physiological threshold range of the corresponding pet species. Through the above adaptation, this invention can be applied to scenarios such as home pet health monitoring and veterinary hospital auxiliary diagnosis, realizing non-contact continuous collection of pet respiration and heart rate, and reducing the stress response of pets caused by wearable devices.

[0021] The radar hardware communication matching includes: after the module is powered on and initialized, the host computer establishes a serial communication link through the UART serial port with a baud rate of 115200bps, 8 data bits, 1 stop bit, and no parity bit in the serial port frame format. The adaptive parameter configuration includes adjustable transmission power based on monitoring distance, multiple selectable sampling frequencies based on vital sign signal characteristics, and adaptive setting of scanning angle based on the module's preset detection angle range.

[0022] In this embodiment, the radar hardware communication matching adopts the standard UART serial port protocol. After the module is powered on, the internal phase-locked loop lock is completed within about 200ms. The host computer establishes a serial communication link in 115200bps 8N1 format. The module continuously outputs core data frames containing heart rate value, respiration value, human presence status, signal quality level and status code to the host computer at a preset period (typically 1 second). The host computer obtains the radar hardware working status by parsing the data frames. The radar module has a package size of 25mm × 23mm and uses a standard pin header interface. Its core signals include four pins: VCC (3.3V power input), GND (power ground), TX (module serial port transmit, outputs heart rate / respiration data), and RX (module serial port receive, receives configuration commands / reset). The module connects to a host computer via a UART serial port for data transmission. The module's power supply voltage is DC 3.3V, and its average operating current is 600mA. A power supply capacity greater than 1A is required to ensure stable operation. The module's operating temperature range is -20℃ to +85℃, its storage temperature range is -40℃ to +85℃, and its operating humidity range is 10% to 90% RH (non-condensing). The adaptive parameter configuration includes three aspects: the transmit power is adjusted according to the monitoring distance, with a low power level for short distances to reduce power consumption and a high power level for long distances to ensure the echo signal-to-noise ratio. The power level is achieved through the gain control register of the power amplifier inside the module. The sampling frequency is selectable in multiple levels according to the characteristics of the vital signs signal. The low frequency sampling level is used for respiratory monitoring and the high frequency sampling level is used for heart rate monitoring. The level switching is achieved through the clock divider configuration inside the module. The scanning angle is adaptively set according to the preset detection angle range of the module, covering ±60° in the horizontal direction and ±30° in the pitch direction. The angle range is fixed by the physical layout of the module's antenna array. The system achieves scanning angle adaptation by adaptively setting the effective sector range of signal processing within the fixed physical angle of the module.

[0023] The frequency-modulated continuous wave radar operates in the frequency band of 59GHz to 64GHz, with a center frequency of 61.5GHz and an equivalent omnidirectional radiated power of no more than 20mW. The intermediate frequency sampling data is a difference frequency signal sampling sequence obtained by mixing radar echoes, and the sampling accuracy is adaptively set according to the sampling frequency and the bandwidth of the characteristic signal. The radar's effective monitoring distance, optimal monitoring distance, heart rate detection accuracy, and data detection update frequency are all preset and adapted parameters.

[0024] The frequency modulated continuous wave radar system uses linear frequency modulated continuous wave to detect electromagnetic waves. The frequency of the transmitted signal changes linearly with time, the sweep bandwidth is 4 GHz, and the sweep period is 1 ms. The echo signal is formed by the reflection modulation of the transmitted signal by the micro-movement of the human chest cavity. The echo signal and the transmitted signal are mixed to output a difference frequency signal, and the difference frequency is positively correlated with the target distance. The intermediate frequency sampling data is the sampling sequence of the difference frequency signal, with a sampling frequency of 200kHz and a sampling precision of 16 bits. The module has a built-in vital sign separation algorithm. It captures the weak vibrations caused by heartbeat and breathing by emitting high-frequency electromagnetic waves and receiving echo signals generated by the slight movement of the human chest cavity. The effective monitoring distance is 0.4m to 1.5m, the optimal monitoring distance is 0.5m to 1.2m, the heart rate detection accuracy is ±2bpm, and the data detection update frequency is 1 time / second.

[0025] The hardware-side pre-filter includes: The intermediate frequency sampling data is first filtered by a low-pass filter to remove high-frequency aliasing components, and then by a band-pass filter to retain the effective frequency bands of the breathing and heartbeat signals; The lower passband limit of the bandpass filter is set according to the preset lowest frequency of the respiratory signal, and the upper passband limit is set according to the preset highest frequency of the heartbeat signal. The multi-level layered noise reduction process of the host computer includes power frequency interference suppression, baseline drift removal, and random noise smoothing.

[0026] In this embodiment, the hardware pre-filter adopts a two-stage filtering structure. The first stage is a low-pass filter, which is a 4th-order Butterworth filter with a cutoff frequency set according to the sampling theorem, used to filter out high-frequency aliasing components. The second stage is a band-pass filter, with the lower passband set according to the preset lowest frequency of the respiratory signal of 0.17Hz and the upper passband set according to the preset highest frequency of the heartbeat signal of 3Hz, used to retain the effective frequency bands of the respiratory and heartbeat signals.

[0027] The host computer performs multi-level layered noise reduction processing, sequentially executing three levels of noise reduction to simultaneously suppress environmental clutter and signal interference caused by human movement. The first level is power frequency interference suppression, using a 50Hz notch filter to filter out power line interference and simultaneously suppress electromagnetic clutter in the environment. The second level is baseline drift removal, using a high-pass filter or trend term elimination algorithm to remove low-frequency signal drift and suppress baseline shift caused by slow human movement or environmental temperature changes. The third level is random noise smoothing, using an adaptive Kalman filter or moving average algorithm to suppress random noise and smooth pulse-type interference caused by sudden human movement.

[0028] The variational mode decomposition algorithm is executed according to the following steps: Based on the frequency domain distribution characteristics of the vital signs signal, the optimal number of decomposition modes K is adaptively determined, and the mixed signal is decomposed into K mutually independent intrinsic mode functions; The respiratory signal corresponds to the low-frequency modal component, and the heartbeat signal corresponds to the mid-to-high-frequency modal component. The modal number K is adaptively matched and set according to the normal physiological distribution range of human respiration and heart rate.

[0029] Variational mode decomposition (VMD) algorithms decompose signals by constructing variational problems. The algorithm decomposes mixed signals into K independent eigenmode functions, each of which has a finite bandwidth and a specific center frequency. In this embodiment, the number of modes K is adaptively determined based on the frequency domain distribution characteristics of the vital signs signal; the respiratory signal corresponds to the low-frequency modal component, and the initial value of the center frequency is set according to the prior range of respiratory frequency of 0.17Hz to 0.5Hz (corresponding to 10~30 breaths / min); the heartbeat signal corresponds to the mid-to-high frequency modal component, and the initial value of the center frequency is set according to the prior range of heart rate frequency of 0.67Hz to 3Hz (corresponding to 40~180 beats / min); the number of modes K is adaptively matched and set according to the normal physiological distribution range of human respiration and heart rate. For example, when the signal frequency domain shows a clear bimodal distribution (the frequency peaks of respiration and heartbeat are significantly separated), K is 2. When there are harmonic or interference components, K is adaptively increased to 3 or 4 to separate additional components. After decomposition, power spectral density verification is performed on each modal component to ensure that the power proportion of the main frequency components of respiration and heartbeat exceeds the preset threshold and to avoid modal aliasing.

[0030] The phase difference method is performed according to the following steps: Phase information is extracted from the separated respiratory and heartbeat signal components, and differential operation is performed on adjacent sampling points according to a preset time window to obtain the phase change. The respiratory rate is obtained by statistically analyzing the periodicity of the phase change of the respiratory component, and the heart rate is obtained by statistically analyzing the periodicity of the phase change of the heartbeat component; the length of the time window is preset based on the periodic characteristics of the vital signs signal.

[0031] The phase difference method first performs Hilbert transform on each modal component to obtain the analytic signal, then calculates the instantaneous phase from the real and imaginary parts of the analytic signal, and performs unwinding processing on the instantaneous phase sequence to eliminate the 2π phase jump and obtain a continuous phase curve; Differential operations are performed on the unwound phase sequence according to a preset time window. The length of the time window is preset based on the periodic characteristics of the vital signs signal. The time window for the respiratory component is preset based on the normal period of the respiratory signal, and the time window for the heartbeat component is preset based on the normal period of the heartbeat signal. The differential operation obtains the phase change of adjacent sampling points. The respiratory frequency is obtained by statistically analyzing the period of the phase change of the respiratory component, and the heart rate is obtained by statistically analyzing the period of the phase change of the heartbeat component. The period statistics adopt peak detection or zero-crossing detection methods, and the number of periods per unit time is the frequency value. The instantaneous respiratory frequency and heart rate data obtained from solving multiple consecutive frames are subjected to time-series statistics and frequency domain analysis to further generate frequency domain data of heart rate fluctuations and statistical characteristics of vital signs stability, providing an effective data source for subsequent standardized data output.

[0032] The normal vital signs threshold library includes normal range thresholds for respiratory rate, normal range thresholds for heart rate fluctuations, and normal range thresholds for vital sign stability. The historical big data collection includes continuous historical vital sign records of individuals for a preset duration; The removal of abnormal collected data is performed according to the threshold library judgment rules, and sampling points that exceed the normal threshold range are marked as abnormal and removed. The acquisition error correction includes zero bias calibration and temperature drift compensation. The compensation logic is implemented based on an error prediction model constructed from historical big data, and the model can be iteratively updated periodically.

[0033] The normal vital signs threshold database is stored in the system memory in the form of a structured data table, including three categories: normal range thresholds for respiratory rate, normal range thresholds for heart rate fluctuation, and normal range thresholds for vital sign stability. Each threshold is preset with initial values ​​based on publicly available medical statistical data and is individually fine-tuned as individuals accumulate long-term data. Historical big data collection includes continuous historical vital sign records of individuals for a preset duration. During system initialization, at least 7 days of continuous data are collected to establish an initial baseline, which is then extended to 30 days after stabilization. Abnormal data collection is removed based on threshold library judgment rules. The judgment rules include four situations: single-point threshold exceeding, continuous multi-point threshold exceeding, short-term sudden change, and long-term drift. If any of the situations is met, it is marked as abnormal and removed. The acquisition error correction includes zero bias calibration and temperature drift compensation. Zero bias calibration is performed based on the output of the reference signal channel or the static measurement benchmark. Temperature drift compensation is based on the ambient temperature sensor data to establish a multiple linear regression model. The model parameters are obtained by training based on historical big data acquisition, and the model is updated periodically.

[0034] The standardized vital sign data are composed of the following: The respiratory rate time series data is a respiratory rate sampling sequence arranged continuously in time series, which is collected using a preset sampling interval; The frequency domain data of heart rate fluctuations is a power spectrum distribution divided by frequency bands, which includes at least the power ratio of the low-frequency band to the high-frequency band of heart rate variability and the spectral entropy value. The statistical characteristics of vital sign stability include the standard deviation of vital sign data, coefficient of variation, stability entropy value, variance of body movement amplitude, and number of body movements.

[0035] The preset sampling interval is 1 second, and the sequence length is set according to the monitoring duration. The heart rate fluctuation frequency domain data is a power spectrum distribution divided by frequency band. The power spectrum calculation can be performed using any of the Welch method, windowed FFT method, or Burg method. The Welch method is preferred to balance resolution and variance characteristics. The power spectrum includes the power ratio of the low-frequency band and the high-frequency band of heart rate variability and the spectral entropy value. The low-frequency band ranges from 0.04Hz to 0.15Hz, and the high-frequency band ranges from 0.15Hz to 0.4Hz. The frequency band division of the low-frequency band and the high-frequency band is set according to the medical frequency band division standard for heart rate variability.

[0036] The statistical characteristics of vital sign stability include five indicators: the standard deviation of vital sign data reflects the degree of data dispersion; the coefficient of variation reflects the relative fluctuation amplitude; the stability entropy reflects the signal complexity; the variance of body movement amplitude reflects the fluctuation of body movement intensity; and the number of body movements reflects the frequency of significant body movement events per unit time. These five indicators are calculated by statistically analyzing the time-domain and frequency-domain characteristics of vital sign data per unit time.

[0037] The association identifier includes four types of fields: Signal quality levels are divided into four levels from 0 to 3 based on data stability, with level 3 corresponding to the best signal quality and level 0 representing the worst signal quality. Data validity identifiers are used to determine whether the current data frame is valid and can be used in subsequent human health assessments. Human presence status markers are used to identify whether there are valid human monitoring targets within the monitoring area; The sampling timestamp is a marker of the data acquisition time with millisecond-level accuracy.

[0038] The association identifier includes four fields: The signal quality level is divided into four levels from 0 to 3 based on data stability. The evaluation is comprehensively determined by the built-in algorithm of the module. The evaluation dimensions include echo signal-to-noise ratio, signal variation coefficient and waveform statistical characteristics. Level 3 corresponds to the best signal quality, and level 0 represents the worst signal quality. The data validity identifier is used to determine whether the current data frame is valid and can participate in subsequent human health assessment. It is determined based on the frame checksum and frame integrity check. If the verification passes, it is marked as valid and usable; if the verification fails, it is marked as invalid. The presence status marker is used to identify whether there is a valid human monitoring target in the monitoring area. It is determined based on the echo energy threshold and the monitoring distance range. When a valid target is detected, it is marked as "person"; when no target is detected or the target leaves, it is marked as "no person". The sampling timestamp is a data acquisition time identifier with millisecond-level accuracy, generated by the module's internal clock, and synchronized with the host computer via the NTP or PTP protocol.

[0039] The standardized vital signs data and associated identifiers are output to the downstream human health status intelligent judgment system through the serial port frame format of the matching protocol. Before output, the data validity is filtered according to the signal quality level. Invalid data frames with a signal quality level of 0 are directly discarded and not output. The output frame format is fully compatible with the input interface protocol of the downstream system, ensuring a one-to-one correspondence between the data timing relationship and the associated identifier field.

[0040] Standardized vital sign data and associated identifiers are output through serial port frame format of the matching protocol. The output frame adopts a fixed-length frame structure, which includes frame header, data length, respiratory rate timing, heart rate fluctuation frequency domain, vital sign stability statistical characteristics, signal quality level, data validity identifier, human presence status identifier, sampling timestamp and checksum field. Before output, data validity is filtered based on signal quality level. Invalid data frames with a signal quality level of 0 are discarded and not output, and do not participate in downstream judgment. The output frame format is fully matched with the input interface protocol of the downstream intelligent human health status judgment system. The field order, data type, and byte alignment are all pre-defined to ensure that the data timing relationship corresponds one-to-one with the associated identifier field.

[0041] The foregoing has shown and described the basic principles, main features, and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited to the above embodiments. The embodiments and descriptions in the specification are merely principles of the invention. Various changes and modifications can be made to the invention without departing from its spirit and scope, and all such changes and modifications fall within the scope of the claimed invention. The scope of protection claimed by the appended claims and their equivalents is defined.

Claims

1. A method for high-precision acquisition of vital signs signals based on millimeter-wave radar, characterized in that, include: S1. Complete radar hardware communication matching, and perform adaptive parameter configuration for transmission power, sampling frequency and scanning angle to ensure that the radar detection range fully covers the human chest cavity monitoring area. S2. Employs a frequency-modulated continuous wave radar system to transmit detection electromagnetic waves, receive echo signals generated by micro-movements in the human chest cavity, and output corresponding intermediate frequency sampling data; S3. Perform hardware-side pre-filtering and host computer multi-level layered noise reduction processing on the intermediate frequency sampling data in sequence to simultaneously suppress environmental noise and signal interference caused by human body movement. S4. The mixed signal is split by variational mode decomposition algorithm to separate independent respiratory and heartbeat signal components, and then the two core vital signs parameters of respiratory rate and heart rate fluctuation are extracted by phase difference method. S5. Call the normal vital sign threshold database and historical big data to complete the removal of abnormal data, correction and compensation of collection errors; S6. Output standardized vital sign data and associated identifiers for intelligent determination of human health status; the standardized vital sign data includes respiratory rate time sequence, heart rate fluctuation frequency domain, and statistical characteristics of vital sign stability; the associated identifiers include the signal quality level of each channel.

2. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The radar hardware communication matching includes: after the module is powered on and initialized, the host computer establishes a serial communication link through the UART serial port with a baud rate of 115200bps, 8 data bits, 1 stop bit, and no parity bit in the serial port frame format. The adaptive parameter configuration includes adjustable transmission power based on monitoring distance, multiple selectable sampling frequencies based on vital sign signal characteristics, and adaptive setting of scanning angle based on the module's preset detection angle range.

3. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The frequency-modulated continuous wave radar operates in the frequency band of 59GHz to 64GHz, with a center frequency of 61.5GHz and an equivalent omnidirectional radiated power of no more than 20mW. The intermediate frequency sampling data is a difference frequency signal sampling sequence obtained by mixing radar echoes, and the sampling accuracy is adaptively set according to the sampling frequency and the bandwidth of the characteristic signal. The radar's effective monitoring distance, optimal monitoring distance, heart rate detection accuracy, and data detection update frequency are all preset and adapted parameters.

4. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The hardware-side pre-filter includes: The intermediate frequency sampling data is first filtered by a low-pass filter to remove high-frequency aliasing components, and then by a band-pass filter to retain the effective frequency bands of the breathing and heartbeat signals; The lower passband limit of the bandpass filter is set according to the preset lowest frequency of the respiratory signal, and the upper passband limit is set according to the preset highest frequency of the heartbeat signal. The multi-level layered noise reduction process of the host computer includes power frequency interference suppression, baseline drift removal, and random noise smoothing.

5. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The variational mode decomposition algorithm is executed according to the following steps: Based on the frequency domain distribution characteristics of the vital signs signal, the optimal number of decomposition modes K is adaptively determined, and the mixed signal is decomposed into K mutually independent intrinsic mode functions; The respiratory signal corresponds to the low-frequency modal component, and the heartbeat signal corresponds to the mid-to-high-frequency modal component. The modal number K is adaptively matched and set according to the normal physiological distribution range of human respiration and heart rate.

6. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The phase difference method is performed according to the following steps: Phase information is extracted from the separated respiratory and heartbeat signal components, and differential operation is performed on adjacent sampling points according to a preset time window to obtain the phase change. The respiratory rate is obtained by statistically analyzing the periodicity of the phase change of the respiratory component, and the heart rate is obtained by statistically analyzing the periodicity of the phase change of the heartbeat component; the length of the time window is preset based on the periodic characteristics of the vital signs signal.

7. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The normal vital signs threshold library includes normal range thresholds for respiratory rate, normal range thresholds for heart rate fluctuations, and normal range thresholds for vital sign stability. The historical big data collection includes continuous historical vital sign records of individuals for a preset duration; The removal of abnormal collected data is performed according to the threshold library judgment rules, and sampling points that exceed the normal threshold range are marked as abnormal and removed. The acquisition error correction includes zero bias calibration and temperature drift compensation. The compensation logic is implemented based on an error prediction model constructed from historical big data, and the model can be iteratively updated periodically.

8. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The standardized vital sign data are composed of the following: The respiratory rate time series data is a respiratory rate sampling sequence arranged continuously in time series, which is collected using a preset sampling interval; The frequency domain data of heart rate fluctuations is a power spectrum distribution divided by frequency bands, which includes at least the power ratio of the low-frequency band to the high-frequency band of heart rate variability and the spectral entropy value. The statistical characteristics of vital sign stability include the standard deviation of vital sign data, coefficient of variation, stability entropy value, variance of body movement amplitude, and number of body movements.

9. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The association identifier includes four types of fields: Signal quality levels are divided into four levels from 0 to 3 based on data stability, with level 3 corresponding to the best signal quality and level 0 representing the worst signal quality. Data validity identifiers are used to determine whether the current data frame is valid and can be used in subsequent human health assessments. Human presence status markers are used to identify whether there are valid human monitoring targets within the monitoring area; The sampling timestamp is a marker of the data acquisition time with millisecond-level accuracy.

10. The method for high-precision acquisition of vital signs signals based on millimeter-wave radar according to claim 1, characterized in that, The standardized vital signs data and associated identifiers are output to the downstream human health status intelligent judgment system through the serial port frame format of the matching protocol. Before output, the data validity is filtered according to the signal quality level. Invalid data frames with a signal quality level of 0 are directly discarded and not output. The output frame format is fully compatible with the input interface protocol of the downstream system, ensuring a one-to-one correspondence between the data timing relationship and the associated identifier field.

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