Athlete competition state evaluation method and system based on millimeter wave radar

Abstract

The application discloses a kind of athlete competitive state evaluation method and system based on millimeter wave radar, the method steps include, launch millimeter wave detection wave signal, and receive the detection wave signal reaches the part to be measured, and the echo signal formed with chest displacement signal is carried;The detection wave signal and echo signal are mixed and low-pass filtered, and the intermediate frequency signal is obtained;The intermediate frequency signal is filtered and signal separation, and the breathing signal and heartbeat signal are obtained;According to the breathing signal and heartbeat signal, competitive state evaluation is carried out;The system includes detection single and signal processing unit;Signal processing unit includes frequency mixer, filter and data processor;The application does not affect the case where athlete competitive state effectively plays, realizes the non-contact detection of athlete competitive state and realizes the evaluation of athlete competitive state.

Description

Technical Field

[0001] This invention relates to the field of athlete performance evaluation technology, and more specifically to a method and system for athlete performance evaluation based on millimeter-wave radar. Background Technology

[0002] Currently, sleep is a crucial way for elite athletes to achieve rapid physical recovery and eliminate fatigue during training or competition, effectively promoting the recovery of physical functions and optimal competitive performance. Studying athletes' sleep duration and quality provides a theoretical basis and quantitative guidance for coaches' training and on-field personnel arrangements during training or competition. It also allows for a better understanding of the relationship between athletes' overall sleep quality and competitive performance, enabling targeted adjustments to improve sleep quality for each athlete based on sleep monitoring results. Therefore, monitoring athletes' sleep processes and quality, and analyzing the intrinsic link between sleep and physical recovery, is of great significance for improving athletes' competitive performance. By monitoring athletes' sleep physiological indicators and the rate of physical recovery, we can assess the training load and intensity for coaches, providing theoretical guidance for coaching arrangements, making training plans more rational, and optimizing tactical development.

[0003] However, non-contact physiological information monitoring is gaining increasing popularity, such as cardiac impulse signal acquisition based on principles like fiber optics or piezoelectricity. Compared to other monitoring methods like electrocardiograms and photoelectric pulse waves, non-contact physiological information monitoring offers advantages such as being non-invasive, imperceptible, and convenient. However, it still requires indirect contact with the person being monitored, failing to achieve truly non-contact monitoring through the air, and is therefore not a truly non-contact monitoring method. Existing athlete performance assessment systems based on wearable monitoring sensors suffer from drawbacks such as high monitoring load, affecting athletes' effective performance and leading to poor compliance. Existing vital sign signal monitoring sensors based on fiber optics and piezoelectric vibration measurement principles still require indirect contact with the human body.

[0004] Therefore, how to provide a method and system for evaluating athletes' competitive state based on millimeter-wave radar is a problem that urgently needs to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention provides a method and system for evaluating the competitive state of athletes based on millimeter-wave radar, which can realize non-contact detection and evaluation of the competitive state of athletes without affecting their effective performance.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for assessing athlete performance based on millimeter-wave radar, comprising the following steps:

[0008] Transmit millimeter-wave detection wave signals and receive echo signals carrying chest displacement signals formed after the detection wave signals reach the area to be measured;

[0009] The detected wave signal and the echo signal are mixed and low-pass filtered to obtain an intermediate frequency signal;

[0010] The intermediate frequency signal is filtered and separated to obtain the respiratory signal and the heartbeat signal;

[0011] The competitive state is assessed based on the respiratory and heart rate signals.

[0012] Furthermore, the frequency of the intermediate frequency signal is the difference frequency between the detection wave signal and the echo signal, and the phase is the phase difference between the detection wave signal and the echo signal.

[0013] Furthermore, the assessment of athletic performance based on respiratory and heart rate signals includes obtaining beat-by-beat cardiac cycle signals using an adaptive matching template and clustering algorithm based on the heart rate signals, and assessing the athlete's athletic performance based on the beat-by-beat cardiac cycle signals and respiratory signals.

[0014] Furthermore, adaptive template matching and clustering algorithms are used to extract beat-by-beat cardiac cycles. The steps include:

[0015] S11: Preprocess the heartbeat signal;

[0016] S12: Obtain the initial signal from the preprocessed heartbeat signal, and use a clustering algorithm to extract the heartbeat template from the initial signal;

[0017] S13: Use the heartbeat template to detect subsequent heartbeat signals and obtain the positive maximum peak value of the heartbeat signal;

[0018] S14: Output beat-by-beat heart rate cycle.

[0019] Furthermore, S13 also includes calculating the matching degree between the heartbeat template and the heartbeat signal. When a decrease in the matching degree of the heartbeat signal is detected or a change in posture is detected by the user state determination, the heartbeat template is recalculated starting from the current signal.

[0020] Furthermore, the assessment of athletic performance based on respiratory signals and the beat-by-beat cardiac cycle also includes,

[0021] S21: Based on the beat-by-beat cardiac cycle and respiratory signals, analyze the breathing pattern, apnea, and heart rate variability; extract the time-domain, frequency-domain, and nonlinear features of the respiratory and heartbeat signals;

[0022] S22: The random forest algorithm is used to select features related to the athlete's competitive state from the time-domain features, frequency-domain features, and nonlinear features of the breathing signals and heartbeat signals for optimization;

[0023] S23: Use support vector machines, neural networks, or ensemble learning to comprehensively evaluate the athlete's competitive state using selected optimized features.

[0024] An athlete performance evaluation system based on millimeter-wave radar includes a signal detection unit, a data transmission unit, and a signal processing unit.

[0025] The signal detection unit is used to transmit millimeter-wave detection wave signals and echo signals formed and reflected back by the part to be tested;

[0026] The signal processing unit includes a mixer, a filter, and a data processor;

[0027] The mixer is used to mix and low-pass filter the detection wave signal and the echo signal to obtain an intermediate frequency signal.

[0028] The filter is used to filter and separate the intermediate frequency signal to obtain the respiratory signal and the heartbeat signal;

[0029] The information processor is used to assess the competitive state based on the respiratory and heartbeat signals.

[0030] Furthermore, the information processor is also used to acquire beat-by-beat cardiac cycle signals using adaptive template matching and clustering algorithms.

[0031] Furthermore, the information processor is also used to analyze indicators such as breathing patterns, apnea, and heart rate variability based on beat-by-beat cardiac cycles and respiratory signals, extract feature parameters, and evaluate the competitive state based on the feature parameters.

[0032] Furthermore, it also includes a data transmission unit and a human-computer interaction unit;

[0033] The data transmission unit is used to wirelessly transmit the raw monitoring data of the millimeter-wave radar to the signal processing unit via wired or wireless means, such as RJ45 wired or WiFi / Bluetooth wireless communication.

[0034] The human-computer interaction unit is used to receive external control signals and display the results of the athlete's competitive status monitoring and evaluation.

[0035] The beneficial effects of this invention are:

[0036] As can be seen from the above technical solution, compared with the prior art, the present invention discloses a method and system for evaluating the competitive state of athletes based on millimeter-wave radar. Based on millimeter-wave radar, it realizes non-contact and zero-load monitoring of the competitive state of athletes, while not affecting the effective performance of athletes. Attached Figure Description

[0037] To more clearly illustrate the technical solutions in the embodiments of the present invention or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on the provided drawings without creative effort.

[0038] Figure 1 The attached figure is a schematic diagram of the process of an athlete's competitive state assessment method based on millimeter-wave radar provided by the present invention;

[0039] Figure 2 The attached figure is a schematic diagram of the structure of an athlete's competitive state assessment system based on millimeter-wave radar provided by the present invention;

[0040] Figure 3 The attached figure is a schematic diagram of another embodiment of an athlete performance evaluation system based on millimeter-wave radar provided by the present invention; Detailed Implementation

[0041] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 This invention discloses a method for assessing an athlete's competitive state based on millimeter-wave radar, comprising the following steps:

[0043] The millimeter-wave radar is set up and a millimeter-wave detection signal is emitted; after the detection signal reaches the part to be measured, an echo signal carrying the chest displacement signal is generated.

[0044] The detection wave signal and the echo signal are mixed and low-pass filtered to obtain the intermediate frequency signal;

[0045] The intermediate frequency signal is filtered and separated to obtain the respiratory signal and the heartbeat signal. Since the physiological cycles are different, the heartbeat signal and the respiratory signal have certain differences in frequency band, so they can be separated by filtering.

[0046] Performance status is assessed based on respiratory and heart rate signals.

[0047] To further implement the above technical solution, the frequency of the intermediate frequency signal is the difference frequency between the detection wave signal and the echo signal, and the phase is the phase difference between the detection wave signal and the echo signal.

[0048] To further implement the above technical solution, the assessment of competitive status based on respiratory and heartbeat signals includes using adaptive matching templates and clustering algorithms to obtain beat-by-beat mid-cycle heart rate signals.

[0049] In this embodiment, template matching based on cross-correlation calculation and various types of clustering algorithms are employed to achieve accurate analysis of the cardiac cycle beat by beat. The first method for extracting the cardiac cycle template is the K-means method. First, the number of clusters and initial cluster centers are specified. Then, the data is divided into K classes based on the Euclidean distance between the cluster centers. The number of K classes is determined through actual testing. The cluster centers are recalculated. If the cluster centers change, a new K value is selected and the cluster centers are recalculated. If the cluster centers remain unchanged, the clustering results are directly output. The second method is the AffinityPropagation method. First, the algorithm is initialized, that is, both the attraction matrix and the membership matrix are initialized to zero matrices. The attraction (Responsibility) r(i,k) describes how well point k is suitable as the cluster center of data point i. The membership (Availability) a(i,k) describes how well point i chooses point k as its cluster center. Then, the attraction matrix and membership matrix are iteratively updated, and the two matrices are decayed using decay coefficients until the maximum number of iterations is reached.

[0050] To further implement the above technical solution, adaptive template matching and clustering algorithms are used to extract beat-by-beat cardiac cycles. The steps include:

[0051] S11: Preprocess the heartbeat signal;

[0052] S12: Obtain the initial signal from the preprocessed heartbeat signal, and use a clustering algorithm to extract the heartbeat template from the initial signal;

[0053] S13: Use the heartbeat template to detect subsequent heartbeat signals and obtain the positive maximum peak value of the heartbeat signal;

[0054] S14: Output beat-by-beat heart rate cycle.

[0055] To further implement the above technical solution, S13 also includes calculating the matching degree between the heartbeat template and the heartbeat signal. When the matching degree of the heartbeat signal is detected to decrease or the user state judgment detects a change in posture, the heartbeat template is recalculated starting from the current signal.

[0056] To further implement the above technical solutions, the assessment of athletic performance based on respiratory signals and beat-by-beat cardiac cycles also includes,

[0057] S21: Based on the beat-by-beat cardiac cycle and respiratory signals, analyze the breathing pattern, apnea, and heart rate variability; extract the time-domain, frequency-domain, and nonlinear features of the respiratory and heartbeat signals;

[0058] S22: The random forest algorithm is used to select features related to the athlete's competitive state from the time domain features, frequency domain features, and nonlinear features of respiratory and heartbeat signals for optimization;

[0059] S23: Based on the selected optimized features, a comprehensive evaluation of the athlete's competitive state is conducted using support vector machines, neural networks, and ensemble learning. The evaluation of competitive state based on feature parameters can employ support vector machines, neural networks, or ensemble learning to comprehensively assess the athlete's competitive state using selected optimized features. The following is a brief explanation using support vector machines as an example:

[0060] Support Vector Machines (SVMs), proposed by Vapnik et al., are a statistics-based classification algorithm. Its core idea is to map the feature space of samples to a higher dimension and then use a simple linear classifier to partition the mapped sample space. SVMs contain two important concepts: the optimal hyperplane and support vectors. The optimal hyperplane, also known as the optimal classification line, is called the optimal hyperplane if it can correctly partition the feature vectors in a sample set and maximize the classification margin. Support vectors are the sample points closest to the hyperplane that play a crucial role in the decision-making process. In this application case of evaluating athlete performance, athletes can be divided into two categories: excellent and poor. The goal of the SVM algorithm is to find an optimal hyperplane that maximizes the margin between the feature vectors of the two classes (excellent and poor performance).

[0061] like Figure 2 A system for assessing the competitive state of athletes based on millimeter-wave radar includes a signal detection unit, a data transmission unit, and a signal processing unit.

[0062] The signal detection unit is used to transmit millimeter-wave detection wave signals and echo signals formed and reflected back by the part under test;

[0063] The signal processing unit includes a mixer, filters, and a data processor;

[0064] A mixer is used to mix and low-pass filter the detection wave signal and the echo signal to obtain an intermediate frequency signal.

[0065] Filters are used to filter and separate intermediate frequency signals to obtain respiratory and heartbeat signals;

[0066] Information processors are used to assess athletic performance based on respiratory and heart rate signals.

[0067] The signal detection unit is a millimeter-wave bio-radar capable of measuring cardiac impaction and chest cavity movement. This millimeter-wave bio-radar uses frequency-modulated continuous wave (FMCW) radar to measure the mechanical vibrations of the chest cavity caused by respiration and heartbeat, acquiring raw respiratory and heartbeat signals. The phase of the intermediate frequency signal from the FMCW radar is highly sensitive to minute displacements, enabling the extraction of multi-parameter vital signs information such as heart rate and respiratory rate from chest displacement signals.

[0068] The FMCW radar periodically transmits a sinusoidal signal with a linearly increasing frequency. A mixer down-converts the transmitted detection signal and the received echo signal to obtain the intermediate frequency (IF) signal. The IF signal's frequency is the difference frequency between the two signals, and its phase is the phase difference between the two signals. The transmitted detection signal can be expressed as:

[0069] The received signal can be approximated as the delay of the transmitted signal. The delay, represented by the received signal, is as follows:

[0070] The intermediate frequency signal generated by a stationary object at a distance R from the radar can be expressed as:

[0071]

[0072] The target distance R determines the frequency and phase of the intermediate frequency signal. A single stationary object will generate a single-frequency signal, with a phase of f. s The sampling rate is used to sample the intermediate frequency signal, and the sampling interval is T. s If the number of sampling points is N, then the sampling duration is T. c =T s ×N, where the system's frequency resolution is:

[0073]

[0074] From the above formula, we can see that so That is, the distance resolution d ResDepending solely on the radar bandwidth, when an object moves beyond d... Res At that time, the position of the corresponding spectral peak will change:

[0075]

[0076] There is a linear relationship between the phase Φ and R of the IF signal. Therefore, there is also a linear relationship between ΔΦ and ΔR.

[0077]

[0078] The above formula demonstrates the radar's ability to measure minute displacements.

[0079] like Figure 3 The high-frequency millimeter-wave signal is generated by a voltage-controlled oscillator (Oscillator). A portion of the signal is amplified by a power divider and fed to the transmitting antenna (TX). The other portion is coupled to a mixer and mixed with the echo signal reflected by the human body received by the receiving antenna (RX). After being filtered by a low-pass filter (LPF), the resulting signal is a radar intermediate frequency signal that is highly sensitive to the weak vibration signals on the human body surface.

[0080] To further implement the above technical solution, the information processor is also used to acquire beat-by-beat mid-heart signals using adaptive template matching and clustering algorithms.

[0081] To further implement the above technical solution, the information processor is also used to analyze indicators such as breathing patterns, apnea, and heart rate variability based on the beat-by-beat cardiac cycle and respiratory signals, extract feature parameters, and evaluate the competitive state based on the feature parameters.

[0082] To further implement the above technical solution, a data transmission unit and a human-computer interaction unit are also included;

[0083] The data transmission unit is used to wirelessly transmit the raw monitoring data of the millimeter-wave radar to the signal processing unit via wired or wireless means, such as RJ45 wired or WiFi / Bluetooth wireless communication.

[0084] The human-computer interaction unit is used to receive external control signals and display the results of the athlete's competitive status monitoring and evaluation.

[0085] To further implement the above technical solution, a power management unit is also included to supply power to the signal processing unit and the data transmission unit.

[0086] The various embodiments in this specification are described in a progressive manner, with each embodiment focusing on its differences from other embodiments. Similar or identical parts between embodiments can be referred to interchangeably. For the apparatus disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple; relevant parts can be referred to the method section.

[0087] The above description of the disclosed embodiments enables those skilled in the art to make or use the invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the general principles defined herein may be implemented in other embodiments without departing from the spirit or scope of the invention. Therefore, the invention is not to be limited to the embodiments shown herein, but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.

Claims

1. A method for assessing athlete performance based on millimeter-wave radar, characterized in that, The steps include, Transmit millimeter-wave detection wave signals and receive the echo signals that arrive at the test site, forming an echo signal carrying chest displacement signals; The detected wave signal and the echo signal are mixed and low-pass filtered to obtain an intermediate frequency signal; The intermediate frequency signal is filtered and separated to obtain the respiratory signal and the heartbeat signal; Based on the heartbeat signal, an adaptive matching template and clustering algorithm are used to obtain the beat-by-beat cardiac cycle signal. The athlete's competitive state is then assessed based on the beat-by-beat cardiac cycle signal and respiratory signal. The steps include: S11: Preprocess the heartbeat signal; S12: Obtain the initial signal from the preprocessed heartbeat signal, and use a clustering algorithm to extract the heartbeat template from the initial signal; S13: Use the heartbeat template to detect subsequent heartbeat signals and obtain the positive maximum peak value of the heartbeat signal; calculate the matching degree between the heartbeat template and the subsequent heartbeat signal. When the matching degree of the subsequent heartbeat signal decreases or the user state judgment detects a change in posture, recalculate the heartbeat template starting from the current signal. S14: Output beat-by-beat heart rate cycle.

2. The method for evaluating athlete performance based on millimeter-wave radar according to claim 1, characterized in that, The frequency of the intermediate frequency signal is the difference frequency between the detection wave signal and the echo signal, and the phase is the phase difference between the detection wave signal and the echo signal.

3. The method for evaluating athlete performance based on millimeter-wave radar according to claim 1, characterized in that, The assessment of athletic performance based on respiratory signals and the beat-by-beat cardiac cycle also includes... S21: Based on the beat-by-beat cardiac cycle and respiratory signals, analyze the breathing pattern, apnea, and heart rate variability; extract the time-domain, frequency-domain, and nonlinear features of the respiratory and heartbeat signals; S22: The random forest algorithm is used to select features related to the athlete's competitive state from the time-domain features, frequency-domain features, and nonlinear features of the breathing signals and heartbeat signals for optimization; S23: Use support vector machines, neural networks, or ensemble learning to comprehensively evaluate the athlete's competitive state using selected optimized features.

4. An athlete performance evaluation system based on millimeter-wave radar, characterized in that, The method for evaluating the competitive state of athletes according to any one of claims 1-3 is adopted, including a detection unit and a signal processing unit; The signal detection unit is used to transmit millimeter-wave detection wave signals and echo signals formed and reflected back by the part to be tested; The signal processing unit includes a mixer, a filter, and a data processor; The mixer is used to mix and low-pass filter the detection wave signal and the echo signal to obtain an intermediate frequency signal. The filter is used to filter and separate the intermediate frequency signal to obtain the respiratory signal and the heartbeat signal; The data processor is used to assess the competitive state based on the respiratory and heartbeat signals. It is used to obtain beat-by-beat cardiac cycle signals using adaptive template matching and clustering algorithms.

5. The athlete performance evaluation system based on millimeter-wave radar according to claim 4, characterized in that, The data processor is also used to analyze breathing patterns, apnea, and heart rate variability indicators based on beat-by-beat cardiac cycles and respiratory signals, extract feature parameters, and evaluate competitive status based on the feature parameters.

6. The athlete performance evaluation system based on millimeter-wave radar according to claim 5, characterized in that, It also includes a data transmission unit and a human-computer interaction unit; The data transmission unit is used to wirelessly transmit the raw monitoring data of the millimeter-wave radar to the signal processing unit via wired or wireless means, such as RJ45 wired or WiFi / Bluetooth wireless communication. The human-computer interaction unit is used to receive external control signals and display the results of the athlete's competitive status monitoring and evaluation.

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