Heart rate determination method and system based on millimeter wave radar and neural network

Abstract

The invention discloses a heart rate determination method and system based on a millimeter wave radar and a neural network, and relates to the technical field of heart rate detection.The heart rate determination method comprises the steps that original radar echo data and original electrocardiosignals are synchronously collected, a plurality of initial instantaneous heart rates are generated according to target electrocardiosignals determined by the original electrocardiosignals through an initial sliding window, and the initial instantaneous heart rates are obtained; performing data rejection according to the high-frequency body movement time, outputting target radar echo data and target instantaneous heart rates, and constructing an initial radar complex data matrix corresponding to each target instantaneous heart rate based on the target radar echo data through an initial sliding window; and performing data equalization according to the initial radar complex data matrix to which the plurality of preset heart rate intervals belong, determining a training radar complex data matrix and a real instantaneous heart rate, determining a trained heart rate prediction network based on the training radar complex data matrix and the real instantaneous heart rate, and predicting the actually measured instantaneous heart rate of the actually measured radar echo data. The scheme is beneficial to improving the heart rate determination accuracy.

Description

Technical Field

[0001] The present invention relates to the technical field of heart rate detection, and in particular to a heart rate determination method and system based on millimeter wave radar and neural network. Background Art

[0002] Heart rate is an important part of human vital signal detection. Traditional detection methods mainly rely on wearable sensors or adhesive electrodes that directly contact the human body to achieve detection. In recent years, with the development of millimeter-wave radar, non-contact radar has unique advantages over other detection methods and has broad application prospects. Therefore, non-contact human vital signal detection has gradually become a research hotspot in some emerging application fields.

[0003] In the existing heart rate determination process, the human body's IQ data is first collected through millimeter wave radar, and the chest displacement signal is extracted from the IQ data. The heartbeat signal is separated using methods such as filtering, VMD, and DWT, and then the heart rate is calculated based on the heartbeat signal. However, since the human heartbeat movement is very weak and there is interference from human movements such as breathing, there is a problem of insufficient feature extraction of radar data, resulting in low accuracy in heart rate determination. Summary of the invention

[0004] The present invention provides a heart rate determination method and system based on millimeter wave radar and neural network, which solves the technical problem that the existing heart rate determination scheme is prone to insufficient radar data feature extraction, resulting in low heart rate determination accuracy.

[0005] A first aspect of the present invention provides a method for determining heart rate based on millimeter wave radar and neural network, comprising:

[0006] Acquire synchronously collected original radar echo data and original ECG signals;

[0007] Preprocessing the original ECG signal to generate a target ECG signal, and using an initial sliding window to perform R peak positioning on the target ECG signal to determine multiple initial instantaneous heart rates;

[0008] According to the high-frequency body movement time determined by the amplitude of the original radar echo data, the original radar echo data and each of the initial instantaneous heart rates are subjected to data elimination, and the target radar echo data and the target instantaneous heart rate are output;

[0009] The target radar echo data is intercepted through the initial sliding window to construct an initial radar complex data matrix corresponding to the instantaneous heart rate of each target;

[0010] If data imbalance is determined according to the initial radar complex data matrices to which the plurality of preset heart rate intervals belong, data equalization is performed based on the target radar echo data and the initial sliding window to determine a training radar complex data matrix and a corresponding real instantaneous heart rate;

[0011] Determine a training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and determine a trained heart rate prediction network using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training;

[0012] The measured radar echo data is obtained, and the measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data is input into the trained heart rate prediction network to output the measured instantaneous heart rate.

[0013] Optionally, if data imbalance is determined according to the initial radar complex data matrices to which the plurality of preset heart rate intervals belong, data equalization is performed based on the target radar echo data and the initial sliding window to determine the training radar complex data matrix and the corresponding real instantaneous heart rate, including:

[0014] If the data is determined to be unbalanced according to the initial radar complex data matrix to which the plurality of preset heart rate intervals belong, a marginal missing heart rate interval is determined;

[0015] When the edge missing heart rate interval is a low heart rate interval, data windowing is performed on the target radar echo data through a low heart rate sliding window and linear interpolation is performed to determine a plurality of low heart rate radar complex data matrices;

[0016] Determine multiple initial low instantaneous heart rates of the target electrocardiogram signal based on the low heart rate sliding window, use the window length ratio of the low heart rate sliding window to the initial sliding window, and perform multiplication operation with each of the initial low instantaneous heart rates respectively to generate the target low instantaneous heart rate corresponding to each of the low heart rate radar complex data matrices;

[0017] When the edge missing heart rate interval is a high heart rate interval, a high heart rate sliding window is used to slice and downsample the target radar echo data to determine a plurality of high heart rate radar complex data matrices;

[0018] Constructing multiple initial high instantaneous heart rates of the target ECG signal according to the high heart rate sliding window, and multiplying each of the initial high instantaneous heart rates by a window length ratio of the high heart rate sliding window to the initial sliding window, corresponding to each of the target high instantaneous heart rates associated with the high heart rate radar complex data matrix;

[0019] If data balance is determined according to each of the preset heart rate intervals based on the initial radar complex data matrix, the low heart rate radar complex data matrix and the high heart rate radar complex data matrix, a training radar complex data matrix is ​​formed by sparse stratified sampling, and the corresponding true instantaneous heart rate is determined.

[0020] Optionally, determining a training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training to determine a trained heart rate prediction network includes:

[0021] After first-order filtering of the training radar complex data matrix, static clutter is filtered out by a moving target indication algorithm to determine a filtered radar complex data matrix;

[0022] Calculating phase changes in the filtered radar complex data matrix and converting each phase change into a displacement to construct a chest displacement matrix;

[0023] Using an IIR bandpass filter to filter the chest displacement matrix to generate a heartbeat feature matrix, and performing matrix normalization on the heartbeat feature matrix to determine a spatiotemporal map of the heartbeat feature matrix;

[0024] Performing image enhancement on the heartbeat feature matrix spatiotemporal map to generate a training enhanced heartbeat feature matrix spatiotemporal map;

[0025] Inputting the training enhanced heartbeat feature matrix spatiotemporal map into the heart rate prediction network to be trained, and outputting the predicted instantaneous heart rate;

[0026] The heart rate prediction network to be trained is iteratively optimized based on the loss function value calculated based on the predicted instantaneous heart rate and the true instantaneous heart rate to determine a trained heart rate prediction network.

[0027] Optionally, the obtaining of measured radar echo data, inputting a measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data into a trained heart rate prediction network, and outputting the measured instantaneous heart rate, comprises:

[0028] When measured radar echo data is collected, a measured sliding window associated with the measured radar echo data is used to perform data segmentation to construct a measured radar complex data matrix;

[0029] Determine a spatiotemporal map of a measured enhanced heartbeat feature matrix based on the measured radar complex data matrix;

[0030] The heart rate is predicted by the trained heart rate prediction network on the measured enhanced heartbeat feature matrix spatiotemporal map, and the target instantaneous heart rate is output.

[0031] Optionally, the processing of the heart rate prediction network includes:

[0032] Perform channel cutting on the input enhanced heartbeat feature matrix spatiotemporal map to determine multi-channel input features;

[0033] Perform preliminary channel interaction according to the multi-channel input features, and output channel interaction features;

[0034] Performing convolution extraction on the channel interaction features to generate low-level features;

[0035] Based on the low-level features, continuous residual learning and residual attention processing are performed to construct residual enhancement features;

[0036] Adopting the residual enhancement feature to perform adaptive feature pooling to determine the global feature;

[0037] The global features are sequentially subjected to linear transformation, ReLU nonlinear mapping and Dropout regularization, and then linearly transformed to output the instantaneous heart rate.

[0038] Optionally, the adopting the residual enhancement feature to perform adaptive feature pooling to determine the global feature includes:

[0039] Using the residual enhancement feature to perform global average pooling to determine the average pooling feature;

[0040] Performing global maximum pooling according to the residual enhancement feature, and outputting the maximum pooling feature;

[0041] Concatenating the average pooling feature with the maximum pooling feature to construct a pooling concatenation feature;

[0042] Performing a softmax operation after linear transformation based on the pooled splicing features to generate an average pooling weight and a maximum pooling weight;

[0043] The average pooling feature and the average pooling weight, the maximum pooling feature and the maximum pooling weight are weighted respectively and then concatenated to output a global feature.

[0044] A second aspect of the present invention provides a heart rate determination system based on millimeter wave radar and neural network, comprising:

[0045] A data acquisition module, used to acquire synchronously collected original radar echo data and original ECG signals;

[0046] An ECG signal processing module, used for preprocessing the original ECG signal to generate a target ECG signal, and using an initial sliding window to perform R peak positioning on the target ECG signal to determine multiple initial instantaneous heart rates;

[0047] A data elimination module, used for eliminating the original radar echo data and each of the initial instantaneous heart rates according to the high-frequency body movement time determined by the amplitude of the original radar echo data, and outputting the target radar echo data and the target instantaneous heart rate;

[0048] A data matrix construction module, used to intercept the target radar echo data through the initial sliding window, and construct an initial radar complex data matrix corresponding to the instantaneous heart rate of each target;

[0049] a training data determination module, configured to, if data imbalance is determined according to the initial radar complex data matrices to which the plurality of preset heart rate intervals belong, perform data equalization based on the target radar echo data and the initial sliding window, and determine the training radar complex data matrix and the corresponding real instantaneous heart rate;

[0050] A model training module, used to determine a training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and determine a trained heart rate prediction network using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training;

[0051] The heart rate measurement module is used to obtain the measured radar echo data, input the measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data into the trained heart rate prediction network, and output the measured instantaneous heart rate.

[0052] A third aspect of the present invention provides a computer device, comprising a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the heart rate determination method based on millimeter wave radar and neural network as described in any one of the above items.

[0053] A fourth aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, which, when executed, implements the heart rate determination method based on millimeter-wave radar and neural network as described in any one of the above items.

[0054] A fifth aspect of the present invention provides a computer program product, comprising a computer program / instruction, which, when executed by a processor, implements a heart rate determination method based on millimeter wave radar and a neural network as described in any one of the above items.

[0055] It can be seen from the above technical solutions that the present invention has the following advantages:

[0056] The above scheme of the present invention provides a heart rate determination method based on millimeter wave radar and neural network, comprising: obtaining synchronously collected original radar echo data and original electrocardiogram signals; preprocessing the original electrocardiogram signals to generate target electrocardiogram signals, using an initial sliding window to perform R peak positioning on the target electrocardiogram signals to determine multiple initial instantaneous heart rates; according to the high-frequency body movement time determined by the amplitude of the original radar echo data, data removal is performed on the original radar echo data and each initial instantaneous heart rate, and the target radar echo data and the target instantaneous heart rate are output; data interception is performed on the target radar echo data through the initial sliding window, and the initial radar echo data corresponding to each target instantaneous heart rate is constructed. A complex data matrix is ​​obtained; if the data imbalance is determined according to the initial radar complex data matrix belonging to multiple preset heart rate intervals, data equalization is performed based on the target radar echo data and the initial sliding window to determine the training radar complex data matrix and the corresponding real instantaneous heart rate; the training enhanced heartbeat feature matrix spatiotemporal map is determined based on the training radar complex data matrix, and the trained heart rate prediction network is determined by using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training; the measured radar echo data is obtained, and the measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data is input into the trained heart rate prediction network, and the measured instantaneous heart rate is output. In the above scheme, data preprocessing and data equalization are performed based on radar echo data and electrocardiogram signals, and the data of each spatial distance of the radar is fully utilized to construct training data, and the heart rate prediction network is trained through the training data, and the heart rate prediction network fully extracts the heartbeat features contained in the radar echo data collected by the millimeter wave radar, which is beneficial to improving the accuracy of heart rate determination. BRIEF DESCRIPTION OF THE DRAWINGS

[0057] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative labor.

[0058] Figure 1 A flowchart of a method for determining heart rate based on millimeter wave radar and neural network provided by an embodiment of the present invention;

[0059] Figure 2 A schematic diagram of a data collection process according to an embodiment of the present invention;

[0060] Figure 3 A comparison diagram of various signals including body motion data provided by an embodiment of the present invention;

[0061] Figure 4 A schematic diagram of a spatiotemporal map of a chest displacement matrix provided by an embodiment of the present invention;

[0062] Figure 5 A schematic diagram of a heartbeat feature matrix spatiotemporal map provided by an embodiment of the present invention;

[0063] Figure 6 An image enhancement effect diagram of the spatiotemporal map of the heartbeat feature matrix provided by an embodiment of the present invention;

[0064] Figure 7 A curve diagram showing the change in loss and accuracy during the training process of a heart rate prediction network provided by an embodiment of the present invention;

[0065] Figure 8 The network framework of the heart rate prediction network provided by the embodiment of the present invention Figure 1 ;

[0066] Fig. 9 The network framework of the heart rate prediction network provided by the embodiment of the present invention Figure 2 ;

[0067] Fig.10 A schematic diagram of the structure of an adaptive pooling layer provided in an embodiment of the present invention;

[0068] Fig.11 A structural block diagram of a heart rate determination system based on millimeter wave radar and neural network provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0069] The embodiments of the present invention provide a heart rate determination method and system based on millimeter wave radar and neural network, which are used to solve the technical problem that the existing heart rate determination scheme is prone to insufficient radar data feature extraction, resulting in low heart rate determination accuracy.

[0070] In order to make the purpose, features and advantages of the present invention more obvious and easy to understand, the technical solutions in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the embodiments described below are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0071] See also Figure 1 , Figure 1 A flowchart of the steps of a heart rate determination method based on millimeter wave radar and neural network provided in an embodiment of the present invention.

[0072] The present invention provides a method for determining heart rate based on millimeter wave radar and neural network, comprising:

[0073] Step 101: Acquire synchronously collected original radar echo data and original electrocardiogram signals.

[0074] It should be noted that if Figure 2 As shown, in this embodiment, a millimeter-wave radar device is used to collect data from a resting subject lying quietly in bed to obtain original radar echo data (radar IQ data). At the same time, an ECG acquisition device with reliable accuracy is used to collect the original electrocardiogram (ECG signal) of the resting subject. The main consideration is that the original radar echo data is mixed with signals such as the heartbeat and breathing of the resting human body, and the synchronously collected original electrocardiogram can be used as a benchmark to provide an accurate heartbeat signal reference.

[0075] Step 102: pre-process the original ECG signal to generate a target ECG signal, and use an initial sliding window to locate the R peak of the target ECG signal to determine multiple initial instantaneous heart rates.

[0076] It should be noted that the PT method is used to calculate the corresponding initial instantaneous heart rate for the original ECG signal. In the specific implementation, the original ECG signal is first preprocessed by low-pass filtering, band-pass filtering, differential operation and square operation in sequence to determine the target ECG signal. Then, a preset initial sliding window is used to perform sliding window detection on the target ECG signal and perform R peak positioning. The instantaneous heart rate is calculated according to the R peak position in each initial sliding window and recorded as the initial instantaneous heart rate. The initial sliding window can be set to a window length of 12s and a step length of 1s.

[0077] Step 103: according to the high-frequency body movement time determined by the amplitude of the original radar echo data, the original radar echo data and each initial instantaneous heart rate are removed, and the target radar echo data and the target instantaneous heart rate are output.

[0078] It should be noted that, since the original radar echo data may contain poorly collected data, it is necessary to perform a preliminary screening. In this embodiment, the high-frequency body motion factors caused by intense body motion and environmental influences are used as the screening basis, such as Figure 3 As shown, the body movement degree of the subject is judged according to the amplitude of the original radar echo data, and an amplitude threshold is set to determine the high-frequency body movement time that causes data abnormality, and the original radar echo data is eliminated according to the high-frequency body movement time. Synchronously, the corresponding initial instantaneous heart rate is also eliminated, thereby determining the target radar echo data and the target instantaneous heart rate.

[0079] It is understood that radar echo data (IQ data) includes an in-phase component (I) and a quadrature component (Q). In IQ data, the amplitude is usually used to represent the strength of the signal, which can be measured by To calculate the amplitude.

[0080] Step 104: intercept the target radar echo data through the initial sliding window to construct an initial radar complex data matrix corresponding to the instantaneous heart rate of each target.

[0081] It should be noted that in order to determine the instantaneous heart rate of each target as the target radar echo data corresponding to the heart rate label, the data is intercepted by sliding the initial sliding window on the target radar echo data, and the target radar echo data of each initial sliding window is organized into a matrix form and recorded as the initial radar complex data matrix.

[0082] Step 105: If data imbalance is determined based on the initial radar complex data matrices belonging to multiple preset heart rate intervals, data equalization is performed based on the target radar echo data and the initial sliding window to determine the training radar complex data matrix and the corresponding real instantaneous heart rate.

[0083] Step 105 includes the following sub-steps:

[0084] S11, if the data is determined to be unbalanced according to the initial radar complex data matrix to which the plurality of preset heart rate intervals belong, then determining an edge missing heart rate interval;

[0085] S12, when the edge missing heart rate interval is a low heart rate interval, data windowing is performed on the target radar echo data through a low heart rate sliding window and linear interpolation is performed to determine multiple low heart rate radar complex data matrices;

[0086] S13, determining multiple initial low instantaneous heart rates of the target ECG signal based on the low heart rate sliding window, using the window length ratio of the low heart rate sliding window to the initial sliding window, and performing multiplication operations with each initial low instantaneous heart rate, respectively, to generate target low instantaneous heart rates corresponding to each low heart rate radar complex data matrix;

[0087] S14, when the edge missing heart rate interval is a high heart rate interval, using a high heart rate sliding window to slice and downsample the target radar echo data to determine multiple high heart rate radar complex data matrices;

[0088] S15, constructing multiple initial high instantaneous heart rates of the target ECG signal according to the high heart rate sliding window, multiplying the window length ratio of the high heart rate sliding window to the initial sliding window by each initial high instantaneous heart rate, corresponding to the target high instantaneous heart rate associated with each high heart rate radar complex data matrix;

[0089] S16. If data balance is determined according to each preset heart rate interval based on the initial radar complex data matrix, the low heart rate radar complex data matrix and the high heart rate radar complex data matrix, a training radar complex data matrix is ​​formed through sparse stratified sampling, and the corresponding true instantaneous heart rate is determined.

[0090] It should be noted that each initial radar complex data matrix corresponds to a target instantaneous heart rate as a heart rate label. According to the heart rate label, the initial radar complex data matrix belonging to each interval is counted according to multiple preset heart rate (HR) intervals to determine whether the data is unbalanced;

[0091] If the data is balanced, sparse stratified sampling is performed based on each initial radar complex data matrix to form a training radar complex data matrix, and the target instantaneous heart rate corresponding to the training radar complex data matrix is ​​determined as the true instantaneous heart rate, so that the sample distribution is uniform;

[0092] If the data is unbalanced, by observing the number of radar complex data matrices in each preset heart rate interval, it can be found that there are intervals with missing data. Since the radar complex data matrices in the medium heart rate interval in the preset heart rate interval must be relatively large, the main thing is to determine the edge missing heart rate interval to expand the edge data and complete data balancing. The edge missing heart rate interval includes the low heart rate interval and the high heart rate interval relative to the medium heart rate interval;

[0093] When the edge missing heart rate interval is a low heart rate interval, the target radar echo data is windowed through a preset low heart rate sliding window, wherein the window length of the low heart rate sliding window is smaller than the window length of the initial sliding window, and the target radar echo data of each low heart rate sliding window is stretched and enlarged to the window length of the initial sliding window through linear interpolation to generate multiple low heart rate radar complex data matrices, and the target electrocardiogram signal is R-peak located through the low heart rate sliding window to calculate multiple initial low instantaneous heart rates, and the window length ratio determined by performing a ratio operation between the low heart rate sliding window and the initial sliding window is used as the stretching ratio of the target radar echo data in the low heart rate sliding window, and each initial low instantaneous heart rate is scaled according to the window length ratio, so as to obtain multiple target low instantaneous heart rates as heart rate labels corresponding to each low heart rate radar complex data matrix;

[0094] When the edge missing heart rate interval is a high heart rate interval, data slicing is performed on the target radar echo data through a preset high heart rate sliding window, wherein the window length of the high heart rate sliding window is greater than the window length of the initial sliding window, and the target radar echo data of each high heart rate sliding window is compressed and narrowed to the window length of the initial sliding window by downsampling to generate multiple high heart rate radar complex data matrices, and the target electrocardiogram signal is R-peak located through the high heart rate sliding window to calculate multiple initial high instantaneous heart rates, and the window length ratio determined by the ratio operation between the high heart rate sliding window and the initial sliding window is used as the compression ratio of the target radar echo data in the high heart rate sliding window, and each initial high instantaneous heart rate is scaled according to the window length ratio to determine multiple target high instantaneous heart rates as heart rate labels corresponding to each high heart rate radar complex data matrix;

[0095] After completing the above data equalization processing, classification and statistics are performed again based on the initial radar complex data matrix, the low heart rate radar complex data matrix and the high heart rate radar complex data matrix according to each preset heart rate interval, until the statistical results show that the radar complex data matrices of each preset heart rate interval are basically balanced. In order to make the sample distribution more uniform, sparse stratified sampling is performed on the initial radar complex data matrix, the low heart rate radar complex data matrix and the high heart rate radar complex data matrix to form a training radar complex data matrix, and the corresponding true instantaneous heart rate is determined.

[0096] Step 106: determine the training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and determine the trained heart rate prediction network using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training.

[0097] Step 106 includes the following sub-steps:

[0098] After first-order filtering of the training radar complex data matrix, static clutter is filtered out by a moving target indication algorithm to determine the filtered radar complex data matrix;

[0099] Calculate the phase changes in the filtered radar complex data matrix and convert each phase change into a displacement to construct a chest displacement matrix;

[0100] An IIR bandpass filter is used to filter the chest displacement matrix to generate a heartbeat feature matrix, and the heartbeat feature matrix is ​​normalized to determine the space-time spectrum of the heartbeat feature matrix;

[0101] Perform image enhancement on the heartbeat feature matrix spatiotemporal map to generate a training enhanced heartbeat feature matrix spatiotemporal map;

[0102] Input the training enhanced heartbeat feature matrix spatiotemporal map into the heart rate prediction network to be trained, and output the predicted instantaneous heart rate;

[0103] The heart rate prediction network to be trained is iteratively optimized based on the loss function value calculated based on the predicted instantaneous heart rate and the true instantaneous heart rate to determine the trained heart rate prediction network.

[0104] It should be noted that after completing the data selection to determine the training radar complex data matrix and the real instantaneous heart rate, further processing is performed based on the training radar complex data matrix to obtain the heartbeat data. First, each training radar complex data matrix is ​​first-order filtered to obtain a smoothed radar complex data matrix. The smoothed radar complex data matrix is ​​filtered out of static clutter according to the moving target indication (MTI) algorithm to determine the filtered radar complex data matrix. Then, the phase change between each matrix element in the filtered radar complex data matrix is ​​calculated, and the phase change is converted into displacement, thereby constructing a chest displacement matrix. The spatiotemporal spectrum of the chest displacement matrix is ​​shown in Figure 4 As shown, the chest displacement matrix is ​​then processed through a 0.8-2Hz IIR bandpass filter to generate a heartbeat feature matrix, and then the matrix is ​​normalized to obtain the following Figure 5 Finally, the obtained heartbeat feature matrix time-space map is subjected to image enhancement to obtain the trained enhanced heartbeat feature matrix time-space map. Image enhancement includes histogram equalization and dark enhancement. The enhancement effect is as follows: Figure 6 As shown;

[0105] After the heart rate prediction network to be trained is built, the training enhanced heartbeat feature matrix spatiotemporal map is input into the heart rate prediction network to be trained, and the corresponding predicted instantaneous heart rate is output. The preset loss function is used to calculate the loss function value based on the predicted instantaneous heart rate and the true instantaneous heart rate. The change curves of the training loss (train_loss) and training accuracy (train_acc) during the training process are shown in the following figure. Figure 7 As shown, if the loss function value has not converged, a suitable optimizer is selected to iteratively optimize the heart rate prediction network to be trained with the goal of minimizing the loss function value until the loss function value converges, and the trained heart rate prediction network is determined. During the training process, the validation set can also be used to determine the validation loss (val_loss) and validation accuracy (val_acc) to evaluate the model performance and prevent overfitting; in one implementation, the loss function can use the mean squared error loss function (MSE Loss): , where is the sample size, is the real instantaneous heart rate, To predict instantaneous heart rate.

[0106] In a specific implementation, this embodiment provides a heart rate prediction network that introduces an attention mechanism and is designed with multi-channel residuals. The network architecture diagram is as follows: Figure 8 and Fig. 9 As shown in the figure, it includes the channel interaction layer (ChannelInteraction), feature initialization layer (Initial processe), residual attention layer (ResLayer), adaptive pooling layer (AdaptivePooling), and prediction output layer (Classifier); the processing process of the heart rate prediction network includes:

[0107] S21, performing channel cutting on the input enhanced heartbeat feature matrix spatiotemporal map to determine multi-channel input features.

[0108] It should be noted that the enhanced heartbeat feature matrix spatiotemporal map x processed by the heart rate prediction network is cut into 31 channels to determine the multi-channel input features, which can be regarded as a grayscale image with a dimension of (1, 31, 300, 20), where "1" represents that it has only grayscale value, "31" corresponds to cutting into 31 channel inputs, the width "300" corresponds to the time dimension, and the height "20" corresponds to the space dimension; it can be understood that this embodiment has been verified through a large number of experiments that segmentation into 31 channels can achieve the best prediction effect while occupying relatively few computing resources.

[0109] S22. Perform preliminary channel interaction according to the multi-channel input features, and output channel interaction features.

[0110] It should be noted that the channel interaction layer is used to perform preliminary channel interaction on the multi-channel input features, where the channel interaction layer includes a 3×3 convolution unit, a BatchNorm2d unit and a ReLU activation function. After the multi-channel input features are convolved by the convolution unit, the BatchNorm2d unit is used to perform two-dimensional batch normalization processing, and ReLU nonlinear mapping is performed based on the ReLU activation function, thereby completing preliminary channel interaction to enrich the channel features and outputting the channel interaction features.

[0111] S23. Perform convolution extraction on channel interaction features to generate low-level features.

[0112] It should be noted that the channel interaction features are convolved and extracted through the feature initialization layer, which includes convolution units (convolution kernel size is 7×7, stride is 2 and padding is 3), BatchNorm2d units, ReLU activation functions and 3×3 global maximum pooling MaxPool units (stride is 2). After the channel interaction features are extracted by the convolution unit, the feature map size is reduced by half ( / 2), and two-dimensional batch normalization is performed through the BatchNorm2d unit. After ReLU nonlinear processing is performed through the ReLU activation function, maximum pooling is performed based on the global maximum pooling unit to obtain low-level features. At this time, the feature map size is further reduced by half, reducing the amount of calculation for subsequent processing.

[0113] S24. Perform continuous residual learning and residual attention processing based on low-level features to construct residual enhanced features.

[0114] It should be noted that the low-level features are input into multiple cascaded residual attention layers for continuous residual learning and residual attention processing. Each residual attention layer includes a residual block (ResNet Block) and a channel attention unit (Channel Attention). In the processing of each residual attention layer, after residual learning is performed on the input residual input features through each residual attention layer, the channel attention unit is used to input the channel weights, and the channel weights are multiplied by the residual input features to obtain the corresponding residual output features, thereby enhancing important features and outputting residual enhanced features.

[0115] S25. Adopt residual enhancement features to perform adaptive feature pooling and determine global features.

[0116] Optionally, sub-step S25 includes:

[0117] Use the residual enhancement feature to perform global average pooling and determine the average pooling feature;

[0118] Perform global maximum pooling based on the residual enhancement features and output the maximum pooling features;

[0119] Concatenate the average pooling feature and the maximum pooling feature to construct the pooling concatenation feature;

[0120] After performing linear transformation based on the pooled concatenated features, a softmax operation is performed to generate average pooling weights and maximum pooling weights;

[0121] The average pooling features and average pooling weights, the maximum pooling features and maximum pooling weights are weighted and concatenated to output the global features.

[0122] It should be noted that the adaptive feature pooling is performed on the residual enhancement features based on the adaptive pooling layer. In one implementation, the structure of the adaptive pooling layer is as follows: Fig.10 As shown, it includes a global maximum pooling unit (Maxpool), a global average pooling unit (Avgpool) and a weight learning unit (weight). The weight learning unit includes a fully connected subunit and a softmax activation function. In the specific implementation, the global maximum pooling unit performs global maximum pooling on the residual enhancement feature to generate the maximum pooling feature, and the global average pooling unit performs global average pooling on the residual enhancement feature to output the average pooling feature. The average pooling feature and the maximum pooling feature are concatenated (Concatenate) to construct a pooling concatenation feature. After the pooling concatenation feature is linearly transformed by the fully connected subunit, the softmax activation function is used to perform the softmax operation, and the average pooling weight and the maximum pooling weight are output. They are weighted and combined with the average pooling feature and the maximum pooling feature respectively, so as to generate a compact feature containing global information, namely, a global feature.

[0123] S26. After linear transformation, ReLU nonlinear mapping and Dropout regularization are performed on the global features in sequence, a linear change is performed to output the instantaneous heart rate.

[0124] It should be noted that the heart rate prediction is performed based on the global features through the prediction output layer, wherein the prediction output layer includes a fully connected unit (FC) and a ReLU activation function; after the global features are linearly transformed by the fully connected unit, ReLU nonlinear mapping is performed based on the ReLU activation function, and after Dropout regularization, the fully connected unit is linearly changed again to complete the prediction task and output the estimated instantaneous heart rate.

[0125] Step 107: obtain the measured radar echo data, input the measured enhanced heartbeat feature matrix spatiotemporal map constructed based on the measured radar echo data into the trained heart rate prediction network, and output the measured instantaneous heart rate.

[0126] Step 107 includes the following sub-steps:

[0127] When the measured radar echo data is collected, the measured sliding window associated with the measured radar echo data is used to segment the data and construct the measured radar complex data matrix;

[0128] Determine the spatiotemporal map of the measured enhanced heartbeat feature matrix based on the measured radar complex data matrix;

[0129] The trained heart rate prediction network is used to predict the heart rate of the measured enhanced heartbeat feature matrix spatiotemporal map and output the target instantaneous heart rate.

[0130] It should be noted that after determining the trained heart rate prediction network, when the measured radar echo data of the heart rate to be determined is collected, the measured radar echo data is slidably segmented according to the preset measured sliding window to construct a measured radar complex data matrix, and the step of determining the training enhanced heartbeat feature matrix spatiotemporal spectrum in step 106 is followed to determine the measured enhanced heartbeat feature matrix spatiotemporal spectrum corresponding to the measured radar complex data matrix, and input it into the trained heart rate prediction network for heart rate evaluation, thereby outputting the target instantaneous heart rate.

[0131] In an embodiment of the present invention, data preprocessing and data equalization are performed based on radar echo data and electrocardiogram signals, and the data of each spatial distance of the radar are fully utilized to construct training data. The heart rate prediction network is trained with the training data, and the heart rate prediction network is used to fully extract the heartbeat features contained in the radar echo data collected by the millimeter wave radar, which is beneficial to improving the accuracy of heart rate determination.

[0132] See also Fig.11 , Fig.11A structural block diagram of a heart rate determination system based on millimeter wave radar and neural network provided in an embodiment of the present invention.

[0133] The present invention provides a heart rate determination system based on millimeter wave radar and neural network, comprising:

[0134] The data acquisition module 1101 is used to acquire the synchronously collected original radar echo data and original ECG signal;

[0135] The ECG signal processing module 1102 is used to pre-process the original ECG signal to generate a target ECG signal, and use an initial sliding window to perform R peak positioning on the target ECG signal to determine multiple initial instantaneous heart rates;

[0136] The data removal module 1103 is used to remove the original radar echo data and each initial instantaneous heart rate according to the high-frequency body movement time determined by the amplitude of the original radar echo data, and output the target radar echo data and the target instantaneous heart rate;

[0137] The data matrix construction module 1104 is used to intercept the target radar echo data through the initial sliding window to construct the initial radar complex data matrix corresponding to the instantaneous heart rate of each target;

[0138] The training data determination module 1105 is used to perform data equalization based on the target radar echo data and the initial sliding window to determine the training radar complex data matrix and the corresponding real instantaneous heart rate if the data is determined to be unbalanced according to the initial radar complex data matrix belonging to the plurality of preset heart rate intervals;

[0139] The model training module 1106 is used to determine the training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and determine the trained heart rate prediction network using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training;

[0140] The heart rate measurement module 1107 is used to obtain the measured radar echo data, input the measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data into the trained heart rate prediction network, and output the measured instantaneous heart rate.

[0141] Optionally, the training data determination module 1105 is specifically used for:

[0142] If the data is determined to be unbalanced according to the initial radar complex data matrix to which the plurality of preset heart rate intervals belong, a marginal missing heart rate interval is determined;

[0143] When the edge missing heart rate interval is a low heart rate interval, the target radar echo data is windowed and linearly interpolated through a low heart rate sliding window to determine multiple low heart rate radar complex data matrices;

[0144] Determine multiple initial low instantaneous heart rates of the target ECG signal based on the low heart rate sliding window, use the window length ratio of the low heart rate sliding window to the initial sliding window, and perform multiplication operation with each initial low instantaneous heart rate to generate the target low instantaneous heart rate corresponding to each low heart rate radar complex data matrix;

[0145] When the edge missing heart rate interval is a high heart rate interval, a high heart rate sliding window is used to slice and downsample the target radar echo data to determine multiple high heart rate radar complex data matrices;

[0146] Constructing multiple initial high instantaneous heart rates of the target ECG signal according to the high heart rate sliding window, multiplying each initial high instantaneous heart rate by the window length ratio of the high heart rate sliding window to the initial sliding window, corresponding to the target high instantaneous heart rate associated with each high heart rate radar complex data matrix;

[0147] If data balance is determined according to each preset heart rate interval based on the initial radar complex data matrix, the low heart rate radar complex data matrix and the high heart rate radar complex data matrix, a training radar complex data matrix is ​​formed by sparse stratified sampling, and the corresponding true instantaneous heart rate is determined.

[0148] Optionally, the model training module 1106 is specifically used for:

[0149] After first-order filtering of the training radar complex data matrix, static clutter is filtered out by a moving target indication algorithm to determine the filtered radar complex data matrix;

[0150] Calculate the phase changes in the filtered radar complex data matrix and convert each phase change into a displacement to construct a chest displacement matrix;

[0151] An IIR bandpass filter is used to filter the chest displacement matrix to generate a heartbeat feature matrix, and the heartbeat feature matrix is ​​normalized to determine the space-time spectrum of the heartbeat feature matrix;

[0152] Perform image enhancement on the heartbeat feature matrix spatiotemporal map to generate a training enhanced heartbeat feature matrix spatiotemporal map;

[0153] Input the training enhanced heartbeat feature matrix spatiotemporal map into the heart rate prediction network to be trained, and output the predicted instantaneous heart rate;

[0154] The heart rate prediction network to be trained is iteratively optimized based on the loss function value calculated based on the predicted instantaneous heart rate and the true instantaneous heart rate to determine the trained heart rate prediction network.

[0155] Optionally, the heart rate measurement module 1107 is specifically used for:

[0156] When the measured radar echo data is collected, the measured sliding window associated with the measured radar echo data is used to segment the data and construct the measured radar complex data matrix;

[0157] Determine the spatiotemporal map of the measured enhanced heartbeat feature matrix based on the measured radar complex data matrix;

[0158] The trained heart rate prediction network is used to predict the heart rate of the measured enhanced heartbeat feature matrix spatiotemporal map and output the target instantaneous heart rate.

[0159] Optionally, the processing of the heart rate prediction network includes:

[0160] Perform channel cutting on the input enhanced heartbeat feature matrix spatiotemporal map to determine multi-channel input features;

[0161] Perform preliminary channel interaction based on multi-channel input features and output channel interaction features;

[0162] Perform convolution extraction on channel interaction features to generate low-level features;

[0163] Continuous residual learning and residual attention processing are performed based on low-level features to construct residual enhancement features;

[0164] Adopt residual enhancement features to perform adaptive feature pooling and determine global features;

[0165] The global features are subjected to linear transformation, ReLU nonlinear mapping and Dropout regularization in sequence, and then linearly transformed to output the instantaneous heart rate.

[0166] Optionally, adaptive feature pooling is performed using residual enhancement features to determine global features, including:

[0167] Use the residual enhancement feature to perform global average pooling and determine the average pooling feature;

[0168] Perform global maximum pooling based on the residual enhancement features and output the maximum pooling features;

[0169] Concatenate the average pooling feature and the maximum pooling feature to construct the pooling concatenation feature;

[0170] After performing linear transformation based on the pooled concatenated features, a softmax operation is performed to generate average pooling weights and maximum pooling weights;

[0171] The average pooling features and average pooling weights, the maximum pooling features and maximum pooling weights are weighted and concatenated to output the global features.

[0172] An embodiment of the present invention also provides a computer device, including a memory and a processor, wherein a computer program is stored in the memory; when the computer program is executed by the processor, the processor executes the steps of the heart rate determination method based on millimeter wave radar and neural network as in any of the above embodiments.

[0173] An embodiment of the present invention also provides a computer-readable storage medium having a computer program / instruction stored thereon. When the computer program / instruction is executed by a processor, the steps of the heart rate determination method based on millimeter wave radar and neural network as in any of the above embodiments are implemented.

[0174] An embodiment of the present invention also provides a computer program product, including a computer program / instruction, which, when executed by a processor, implements the steps of the heart rate determination method based on millimeter wave radar and neural network as in any of the above embodiments.

[0175] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working process of the above-described system and module can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0176] In the several embodiments provided in the present application, it should be understood that the disclosed systems and methods can be implemented in other ways. For example, the system embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be an indirect coupling or communication connection through some interfaces, devices or units, which can be electrical, mechanical or other forms.

[0177] The units described as separate components may or may not be physically separated, and the components shown as units may or may not be physical units, that is, they may be located in one place or distributed on multiple network units. Some or all of the units may be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0178] In addition, each functional unit in each embodiment of the present invention may be integrated into one processing unit, or each unit may exist physically separately, or two or more units may be integrated into one unit. The above-mentioned integrated unit may be implemented in the form of hardware or in the form of software functional units.

[0179] If the integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on this understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of the technical solution can be embodied in the form of a software product. The computer software product is stored in a storage medium, including several instructions for a computer device (which can be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in each embodiment of the present invention. The aforementioned storage medium includes: U disk, mobile hard disk, read-only memory (ROM, Read-Only Memory), random access memory (RAM, Random Access Memory), disk or optical disk, etc. Various media that can store program codes.

[0180] As described above, the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that the technical solutions described in the aforementioned embodiments may still be modified, or some of the technical features thereof may be replaced by equivalents. However, these modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A heart rate determination method based on millimeter wave radar and neural network, characterized in that: include: Acquire synchronously collected original radar echo data and original ECG signals; Preprocessing the original ECG signal to generate a target ECG signal, and using an initial sliding window to perform R peak positioning on the target ECG signal to determine multiple initial instantaneous heart rates; According to the high-frequency body movement time determined by the amplitude of the original radar echo data, the original radar echo data and each of the initial instantaneous heart rates are subjected to data elimination, and the target radar echo data and the target instantaneous heart rate are output; The target radar echo data is intercepted through the initial sliding window to construct an initial radar complex data matrix corresponding to the instantaneous heart rate of each target; If data imbalance is determined according to the initial radar complex data matrices to which the plurality of preset heart rate intervals belong, data equalization is performed based on the target radar echo data and the initial sliding window to determine a training radar complex data matrix and a corresponding real instantaneous heart rate; Determine a training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and determine a trained heart rate prediction network using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training; The measured radar echo data is obtained, and the measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data is input into the trained heart rate prediction network to output the measured instantaneous heart rate.

2. The heart rate determination method based on millimeter wave radar and neural network according to claim 1, characterized in that: If the data imbalance is determined according to the initial radar complex data matrices to which the plurality of preset heart rate intervals belong, data equalization is performed based on the target radar echo data and the initial sliding window to determine the training radar complex data matrix and the corresponding real instantaneous heart rate, including: If the data is determined to be unbalanced according to the initial radar complex data matrix to which the plurality of preset heart rate intervals belong, a marginal missing heart rate interval is determined; When the edge missing heart rate interval is a low heart rate interval, data windowing is performed on the target radar echo data through a low heart rate sliding window and linear interpolation is performed to determine a plurality of low heart rate radar complex data matrices; Determine multiple initial low instantaneous heart rates of the target electrocardiogram signal based on the low heart rate sliding window, use the window length ratio of the low heart rate sliding window to the initial sliding window, and perform multiplication operation with each of the initial low instantaneous heart rates respectively to generate the target low instantaneous heart rate corresponding to each of the low heart rate radar complex data matrices; When the edge missing heart rate interval is a high heart rate interval, a high heart rate sliding window is used to slice and downsample the target radar echo data to determine a plurality of high heart rate radar complex data matrices; Constructing multiple initial high instantaneous heart rates of the target ECG signal according to the high heart rate sliding window, and multiplying each of the initial high instantaneous heart rates by a window length ratio of the high heart rate sliding window to the initial sliding window, corresponding to each of the target high instantaneous heart rates associated with the high heart rate radar complex data matrix; If data balance is determined according to each of the preset heart rate intervals based on the initial radar complex data matrix, the low heart rate radar complex data matrix and the high heart rate radar complex data matrix, a training radar complex data matrix is ​​formed by sparse stratified sampling, and the corresponding true instantaneous heart rate is determined.

3. The heart rate determination method based on millimeter wave radar and neural network according to claim 1, characterized in that: The method of determining a training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and determining a trained heart rate prediction network using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training, includes: After first-order filtering of the training radar complex data matrix, static clutter is filtered out by a moving target indication algorithm to determine a filtered radar complex data matrix; Calculating phase changes in the filtered radar complex data matrix and converting each phase change into a displacement to construct a chest displacement matrix; Using an IIR bandpass filter to filter the chest displacement matrix to generate a heartbeat feature matrix, and performing matrix normalization on the heartbeat feature matrix to determine a spatiotemporal map of the heartbeat feature matrix; Performing image enhancement on the heartbeat feature matrix spatiotemporal map to generate a training enhanced heartbeat feature matrix spatiotemporal map; Inputting the training enhanced heartbeat feature matrix spatiotemporal map into the heart rate prediction network to be trained, and outputting the predicted instantaneous heart rate; The heart rate prediction network to be trained is iteratively optimized based on the loss function value calculated based on the predicted instantaneous heart rate and the true instantaneous heart rate to determine a trained heart rate prediction network.

4. The heart rate determination method based on millimeter wave radar and neural network according to claim 1, characterized in that: The step of obtaining the measured radar echo data, inputting the measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data into the trained heart rate prediction network, and outputting the measured instantaneous heart rate includes: When measured radar echo data is collected, a measured sliding window associated with the measured radar echo data is used to perform data segmentation to construct a measured radar complex data matrix; Determine a spatiotemporal map of a measured enhanced heartbeat feature matrix based on the measured radar complex data matrix; The heart rate is predicted by the trained heart rate prediction network on the measured enhanced heartbeat feature matrix spatiotemporal map, and the target instantaneous heart rate is output.

5. The heart rate determination method based on millimeter wave radar and neural network according to claim 1, characterized in that: The processing of the heart rate prediction network includes: Perform channel cutting on the input enhanced heartbeat feature matrix spatiotemporal map to determine multi-channel input features; Perform preliminary channel interaction according to the multi-channel input features, and output channel interaction features; Performing convolution extraction on the channel interaction features to generate low-level features; Based on the low-level features, continuous residual learning and residual attention processing are performed to construct residual enhancement features; Adopting the residual enhancement feature to perform adaptive feature pooling to determine the global feature; The global features are sequentially subjected to linear transformation, ReLU nonlinear mapping and Dropout regularization, and then linearly transformed to output the instantaneous heart rate.

6. The heart rate determination method based on millimeter wave radar and neural network according to claim 5, characterized in that: The step of using the residual enhancement feature to perform adaptive feature pooling to determine the global feature includes: Using the residual enhancement feature to perform global average pooling to determine the average pooling feature; Performing global maximum pooling according to the residual enhancement feature, and outputting the maximum pooling feature; Concatenating the average pooling feature with the maximum pooling feature to construct a pooling concatenation feature; Performing a softmax operation after linear transformation based on the pooled splicing features to generate an average pooling weight and a maximum pooling weight; The average pooling feature and the average pooling weight, the maximum pooling feature and the maximum pooling weight are weighted respectively and then concatenated to output a global feature.

7. A heart rate determination system based on millimeter wave radar and neural network, characterized in that: include: A data acquisition module, used to acquire synchronously collected original radar echo data and original ECG signals; An ECG signal processing module, used for preprocessing the original ECG signal to generate a target ECG signal, and using an initial sliding window to perform R peak positioning on the target ECG signal to determine multiple initial instantaneous heart rates; A data elimination module, used for eliminating the original radar echo data and each of the initial instantaneous heart rates according to the high-frequency body movement time determined by the amplitude of the original radar echo data, and outputting the target radar echo data and the target instantaneous heart rate; A data matrix construction module, used to intercept the target radar echo data through the initial sliding window, and construct an initial radar complex data matrix corresponding to the instantaneous heart rate of each target; a training data determination module, configured to, if data imbalance is determined according to the initial radar complex data matrices to which the plurality of preset heart rate intervals belong, perform data equalization based on the target radar echo data and the initial sliding window, and determine the training radar complex data matrix and the corresponding real instantaneous heart rate; A model training module, used to determine a training enhanced heartbeat feature matrix spatiotemporal map based on the training radar complex data matrix, and determine a trained heart rate prediction network using the training enhanced heartbeat feature matrix spatiotemporal map and the real instantaneous heart rate training; The heart rate measurement module is used to obtain the measured radar echo data, input the measured enhanced heartbeat feature matrix spatiotemporal map constructed according to the measured radar echo data into the trained heart rate prediction network, and output the measured instantaneous heart rate.

8. A computer device, characterized in that: It includes a memory and a processor, wherein a computer program is stored in the memory, and when the computer program is executed by the processor, the processor executes the steps of the heart rate determination method based on millimeter wave radar and neural network as described in any one of claims 1 to 6.

9. A computer-readable storage medium having a computer program / instruction stored thereon, characterized in that: When the computer program / instructions are executed by the processor, the steps of the heart rate determination method based on millimeter wave radar and neural network as described in any one of claims 1 to 6 are implemented.

10. A computer program product comprising a computer program / instructions, characterized in that When the computer program / instructions are executed by the processor, the steps of the heart rate determination method based on millimeter wave radar and neural network as described in any one of claims 1 to 6 are implemented.

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