Simulation of muscle activity in sports training based on optical coherence tomography technology

Simulation of muscle activity in sports training based on optical coherence tomography technology | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Simulation of muscle activity in sports training based on optical coherence tomography technology Lingzi Yao, Ye Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-3849504/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract The aim of this study is to explore the method of combining optical coherence tomography and EMG sensors to effectively analyze muscle activity patterns in sports. Optical coherence tomography is used to monitor the surface morphological changes of muscles in a non-invasive manner in real time, and electromyography sensors are combined to record the electrical activity signals of muscles. Through experiments on multiple athletes, a large number of optical coherence tomography and EMG data were collected to analyze muscle activity patterns in different sports. Optical coherence tomography provides precise information on muscle morphological changes, while EMG sensors provide signals of muscle electrical activity. Through the comprehensive analysis of these two kinds of data, more comprehensive and accurate results of muscle activity patterns can be obtained. This method helps to understand the performance of muscles during exercise, and provides scientific basis for exercise training and rehabilitation treatment. optical coherence tomography Electromyography sensor Sports Muscle activity mode Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 1 Introduction In the field of sports, traditional methods often rely on the collection of electromyographic signals from surface electrodes. This method is prone to interference and can only obtain the activity of the muscle surface, unable to directly observe the activity of deep muscles [ 1 ]. Therefore, it is necessary to seek a new technological means to conduct in-depth research on muscle activity patterns, in order to better guide sports training and prevent sports injuries. Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technique that obtains internal structural information of biological tissues by measuring the interference phenomenon of light [ 2 ]. Based on this, this article aims to explore a new method for analyzing and studying muscle activity patterns in sports by combining optical coherence tomography technology and electromyography sensors. Utilizing the high spatial resolution and real-time imaging capabilities of OCT technology, observe and record changes in deep muscle activity, in order to gain a more comprehensive understanding of muscle collaborative activity and fatigue levels [ 3 – 4 ]. Electromyography sensors can be used to collect electrical signals on the surface of muscles and provide information on their surface activity [ 5 ]. By combining these two techniques, comprehensive information about muscle activity patterns can be obtained. This study can provide reference for the training of athletes and the prevention of sports injuries. By analyzing muscle activity patterns, evaluate athletes' muscle coordination and stability, and guide their training and technical improvement [ 6 ]. By monitoring changes in muscle activity patterns, it is also possible to detect signs of sports injury in advance, take corresponding preventive measures, and reduce the risk of sports injury. 2 Related work The literature briefly describes the differences in muscle activity by analyzing the characteristics of A-mode ultrasound and surface electromyography signals of the core muscle group [ 7 ]. Secondly, the literature fused A-mode ultrasound signals and surface signal features to analyze the synergistic patterns of core muscle groups, in order to confirm the differences in the synergistic patterns of core muscle groups between normal individuals and patients with low back pain [ 8 ]. Finally, through the collaborative mode of core muscle groups, the literature modeled the degree of muscle activation and studied the degree and timing of core muscle activation in normal individuals and patients with low back pain [ 9 ]. By analyzing the characteristics of A-mode ultrasound and surface electromyography signals, the literature aims to understand the differences in core muscle group activity between normal individuals and patients with low back pain. A-mode ultrasound can provide detailed muscle structure information, and surface electromyographic signals can reflect the electrical activity of muscles [ 10 ]. The literature selected the collection scheme of A-mode ultrasound and surface electromyography signals based on the research characteristics. The literature collected A-mode ultrasound and surface electromyography signals, and extracted the features of both signals [ 11 ]. Next, they fused the features of these two signals and proposed a method for extracting muscle thickness features from A-mode ultrasound signals. They also utilized the non negative matrix factorization (NMF) algorithm to achieve feature fusion analysis of core muscle group activity patterns. Literature collection of A-mode ultrasound signals and surface electromyography signals, and feature extraction of these two signals [ 12 ]. For A-type ultrasound signals, the literature proposes a muscle thickness feature extraction method that can extract muscle thickness information from ultrasound images [ 13 ]. For surface electromyography signals, literature adopts commonly used feature extraction methods, such as time-domain and frequency-domain features, to extract the features of electromyography signals. The literature fused the muscle thickness features of A-mode ultrasound signals with the features of surface electromyography signals. In order to achieve feature fusion, they adopted the Non Negative Matrix Factorization (NMF) algorithm [ 14 ]. The NMF algorithm can decompose the original data matrix into two non negative matrices, one representing the characteristics of the signal and the other representing the weight of the signal [ 15 ]. By performing NMF decomposition on the feature matrices of A-type ultrasound signals and surface electromyography signals, the literature obtained the fusion results of the two signal features [ 16 ]. The literature has conducted feature fusion analysis on the activity patterns of core muscle groups [ 17 ]. By analyzing the fused features, they can understand the activity patterns of the core muscle group, as well as the relationship between muscle thickness and surface electromyographic signals. 3 Analysis of the principles of optical coherence tomography technology 3.1 The transmission principle of light in optical fibers Numerical aperture is an indicator used to measure the ability of a fiber to receive incident light, and also reflects the difficulty of coupling between the fiber and the light source. The numerical aperture definition of traditional optical fibers is shown in formula (1): $$\text{N}\text{A}=\sqrt{{\text{n}}_{1}^{2}-{\text{n}}_{2}^{2}}=\text{s}\text{i}\text{n}{{\theta }}_{\text{max }}$$ 1 Traditional optical fibers are limited by the characteristics of the material itself, making it difficult to achieve larger numerical apertures. In contrast, PCF has greater design flexibility and can design structures with different numerical apertures based on different materials. The numerical aperture of PCF can be precisely controlled by adjusting the size and arrangement of air holes in photonic crystals, designing a PCF structure with a larger numerical aperture, thereby improving the receiving ability and coupling efficiency of the fiber. The characteristics of traditional optical fibers are usually described by two parameters, namely the relative refractive index difference and the normalized frequency. The relative refractive index difference can be calculated using formula (2): $${\Delta }=\frac{{\text{n}}_{1}-{\text{n}}_{2}}{{\text{n}}_{1}}$$ 2 The normalized frequency is calculated using formula (3): $$\text{V}={\text{k}}_{0}\text{a}{\left({\text{n}}_{1}^{2}-{\text{n}}_{2}^{2}\right)}^{\frac{1}{2}}$$ 3 Nonlinear effects occur when light waves propagate in optical fibers, and their intensity can be represented by nonlinear parameters. The calculation formula for nonlinear parameters is as follows: $${\gamma }\left({\lambda }\right)=\frac{2{\pi }{\text{n}}_{3}}{{\lambda }{\text{A}}_{\text{eff }}}$$ 4 Among them: $${\text{n}}_{3}=\frac{3}{8\text{n}}\text{R}\text{e}\left({{\chi }}_{\text{x}\text{x}\text{x}\text{x}}^{\left(3\right)}\right)$$ 5 $${\text{A}}_{\text{e}\text{f}\text{f}}=\frac{{\left(\iint \left|\text{F}\right(\text{x},\text{y}){|}^{2}\text{d}\text{x}\text{d}\text{y}\right)}^{2}}{\iint \left|\text{F}\right(\text{x},\text{y}){|}^{4}\text{d}\text{x}\text{d}\text{y}}$$ 6 For traditional optical fibers, nonlinear effects are uncontrollable because their nonlinear parameters are fixed. However, for PCF, due to its structural flexibility, nonlinear effects can be adjusted by adjusting the design parameters of the fiber. Dispersion in traditional optical fibers refers to the phenomenon of signal distortion caused by the different propagation speeds of light waves at different frequencies after a certain distance of propagation in the fiber. For single-mode fibers, dispersion can be described by dispersion parameters. $$\text{D}\left({\lambda }\right)={\text{D}}_{\text{m}}\left({\lambda }\right)+{\text{D}}_{\text{w}}\left({\lambda }\right)$$ 7 For dispersion, it can be divided into material dispersion and waveguide dispersion. Material dispersion refers to the dispersion phenomenon caused by the relationship between different frequencies of light waves and the refractive index of the material when light waves propagate in the material. Material dispersion can be described through dispersion parameters. $${\text{D}}_{\text{m}}\left({\lambda }\right)=-\frac{{\lambda }}{\text{c}}\frac{{\partial }^{2}\left({\text{n}}_{1}\right)}{\partial {{\lambda }}^{2}}$$ 8 Waveguide dispersion refers to the dispersion phenomenon caused by the geometric shape and refractive index distribution of the waveguide when light waves propagate in the waveguide structure. Waveguide dispersion can be controlled through the design parameters of the waveguide. Formula (9) provides the calculation formula for waveguide dispersion. $${\text{D}}_{\text{w}}\left({\lambda }\right)=-\frac{{\lambda }}{\text{c}}\frac{{\partial }^{2}\left[\text{Re}\left({\text{n}}_{\text{e}\text{f}\text{f}}\right)\right]}{\partial {{\lambda }}^{2}}$$ 9 The structure of PCF is flexible and has many adjustable parameters. Waveguide dispersion can be controlled by adjusting parameters such as the diameter of the fiber core and the refractive index difference between the fiber core and the cladding. This gives PCF the ability to flexibly control the total dispersion size. These characteristics of PCF enable it to generate supercontinuum spectra with good performance and wide spectrum, and can achieve full band single mode output. 3.2 Principles of Optical Coherence Tomography As shown in Fig. 1 , the Michelson interferometer is a structure commonly used in optical coherence tomography (OCT) technology. The frequency sweep OCT technology has gradually developed from the initial time-domain OCT. In the OCT imaging process, the intensity of the interference signal on the detector can be represented by formula (10), which describes the relationship between the intensity of the interference signal and the frequency difference of the sweep signal, light source power, sample reflectivity, etc. $$\begin{array}{c}{\text{I}}_{\text{D}}\left(k\right)=\frac{{\rho }}{4}\left[\text{S}\left(\text{k}\right)\left[{\text{R}}_{\text{R}}+{\text{R}}_{\text{S}1}+{\text{R}}_{\text{S}2}+\dots \right]\right]\\ +\frac{{\rho }}{4}\left[\text{S}\left(\text{k}\right)\sum _{\text{n}=\text{m}\ne 1}^{\text{N}} \sqrt{{\text{R}}_{\text{S}\text{n}}{\text{R}}_{\text{S}\text{m}}}\left({\text{e}}^{\text{i}2\text{k}\left({\text{z}}_{\text{R}}-{\text{z}}_{{\text{S}}_{\text{n}}}\right)}+{\text{e}}^{-\text{i}2\text{k}\left({\text{z}}_{\text{R}}-{\text{z}}_{\text{S}\text{n}}\right)}\right)\right]\\ +\frac{{\rho }}{4}\left[\text{S}\left(\text{k}\right)\sum _{\text{n}=1}^{\text{N}} \sqrt{{\text{R}}_{\text{R}}{\text{R}}_{\text{S}\text{n}}}\left({\text{e}}^{\text{i}2\text{k}\left({\text{z}}_{\text{S}\text{n}}-{\text{z}}_{\text{S}\text{m}}\right)}+{\text{e}}^{-\text{i}2\text{k}\left({\text{z}}_{\text{n}}-{\text{z}}_{\text{S}\text{m}}\right)}\right)\right]\end{array}$$ 10 According to the Venasinchin theorem, for a wide stationary stochastic process, the power spectral density function is the Fourier transform of its autocorrelation function. The inverse Fourier transform of the normalized Gaussian function S (k) can be calculated using formula (11). $${\gamma }\left(\text{z}\right)={\text{e}}^{-{\text{z}}^{2}{\Delta }{\text{k}}^{2}}\stackrel{\text{F}}{⟷}\text{S}\left(\text{k}\right)=\frac{1}{{\Delta }\text{k}\sqrt{{\pi }}}{\text{e}}^{-{\left[\frac{\left(\text{k}-{\text{k}}_{0}\right)}{{\Delta }\text{k}}\right]}^{2}}$$ 11 $$\begin{array}{c}i\left(z\right)=\rho F{\text{T}}^{-1}\left[{\text{I}}_{\text{D}}\left(\text{k}\right)\right]=\frac{{\rho }}{8}\left[{\gamma }\left(\text{z}\right)\left[{\text{R}}_{\text{R}}+{\text{R}}_{\text{S}1}+{\text{R}}_{\text{S}2}+\dots \right]\right]\\ +\frac{{\rho }}{8}\left[{\gamma }\left(\text{z}\right)\otimes \sum _{\text{n}=\text{m}\ne 1}^{\text{N}} \sqrt{{\text{R}}_{\text{S}\text{n}}{\text{R}}_{\text{S}\text{m}}}\left({\delta }\left(\text{z}\pm 2\left({\text{z}}_{\text{S}\text{n}}-{\text{z}}_{\text{S}\text{m}}\right)\right)\right)\right]\\ +\frac{{\rho }}{4}\left[{\gamma }\left(\text{z}\right)\otimes \sum _{\text{n}=1}^{\text{N}} \sqrt{{\text{R}}_{\text{R}}{\text{R}}_{\text{S}\text{n}}}\left({\delta }\left(\text{z}\pm 2\left({\text{z}}_{\text{S}\text{n}}-{\text{z}}_{\text{S}\text{m}}\right)\right)\right)\right]\end{array}$$ 12 By performing Fourier transform on the interference spectrum signal, scattering intensity information at different depths can be obtained. These scattering intensity information can be used to generate structural imaging of the sample. For the detected k-spectrum, inverse Fourier transform can be performed to obtain a deep z-encoded complex OCT function. This complex OCT function contains amplitude and phase information, which can be represented by formula (13): $$\text{B}\left(\text{z}\right)=\text{A}\left(\text{z}\right){\text{e}}^{-\text{i}{\upvarphi }\left(\text{z}\right)}$$ 13 In order to extract blood flow dynamic signals, N B-scans can be repeated at the same location, and then differential operations can be performed on the OCT complex information of adjacent B-scans. This differential operation can be represented by formula (14): $$\text{F}\left(\text{z}\right)=\frac{1}{\text{N}-1}\sum _{\text{i}=1}^{\text{N}} \left|{\text{B}}_{\text{i}+1}\left(\text{z}\right)-{\text{B}}_{\text{i}}\left(\text{z}\right)\right|$$ 14 3.3 Construction of Optical Coherence Tomography System The SS-OCT (sweep source optical coherence tomography) system utilizes the frequency modulation characteristics of the light source to achieve deep scanning of the sample by scanning different wavelengths of light. By collecting interference signals and utilizing the principle of Fourier transform, a depth profile image of the sample can be obtained. The SS-OCT system has high imaging resolution and sensitivity, and can achieve fast imaging speed. As shown in Fig. 2 , the handheld SS-OCT system is mainly composed of the following parts: scanning light source, fiber coupler, reference arm, sample arm, focusing lens, planar reflector, scanning galvanometer, photodetector, data acquisition card, and computer. 3.4 Optical coherence tomography data acquisition and calculation The power spectral density of the interference spectrum is the product of the power spectral density of a broadband light source and the composite effect of the optical path and reflection coefficient of each reflection point. The phase difference corresponding to the optical path of each reflection point will cause interference effects, and the interference effects of multiple reflection points will be combined to obtain the final interference spectrum. The interference spectrum formula (15) is as follows: $$\begin{array}{c}S\left(\lambda \right)={\text{R}}_{\text{R}}^{2}{\text{S}}_{0}\left(\lambda \right)+\sum _{\text{i}=1}^{\text{N}} \left\{{\text{R}}_{\text{i}}^{2}{\text{S}}_{0}\left({\lambda }\right)\right\}\\ +\sum _{\text{i}\ne \text{j}}^{\text{N}} \left\{2{\text{R}}_{\text{i}}{\text{R}}_{\text{j}}{\text{S}}_{0}\left({\lambda }\right)\text{c}\text{o}\text{s}\left[2{\pi }\left({\text{l}}_{\text{i}}-{\text{l}}_{\text{j}}\right)/{\lambda }\right]\right\}\\ +\sum _{\text{i}=1}^{\text{N}} \left\{2{\text{R}}_{\text{i}}{\text{R}}_{\text{R}}{\text{S}}_{0}\left({\lambda }\right)\text{c}\text{o}\text{s}\left[2{\pi }\left({\text{l}}_{\text{i}}-{\text{l}}_{\text{R}}\right)/{\lambda }\right]\right\}\end{array}$$ 15 The wave number represents the number of periods of a wave per unit length. In optics, there is a reciprocal relationship between wavenumber and wavelength. The shorter the wavelength, the greater the wavenumber; The longer the wavelength, the smaller the wavenumber. By formula (16), the wavelength of light can be determined λ Convert to wavenumber k, and use wavenumber to represent the frequency and optical path difference of light in interference spectral analysis. $$\text{k}=\frac{2{\pi }}{{\lambda }}$$ 16 By using formula (17), the analysis results of the interference spectrum in the wavelength domain can be converted into the analysis results in the wavenumber domain. $$\begin{array}{c}S\left(k\right)={\text{R}}_{\text{R}}^{2}{\text{S}}_{0}\left(k\right)+\sum _{\text{i}=1}^{\text{N}} \left\{{\text{R}}_{\text{i}}^{2}{\text{S}}_{0}\left(\text{k}\right)\right\}\\ +\sum _{\text{i}\ne \text{j}}^{\text{N}} \left\{2{\text{R}}_{\text{i}}{\text{R}}_{\text{j}}{\text{S}}_{0}\left(\text{k}\right)\text{c}\text{o}\text{s}\left[\text{k}\left({\text{l}}_{\text{i}}-{\text{l}}_{\text{j}}\right)\right]\right\}\\ +\sum _{\text{i}=1}^{\text{N}} \left\{2{\text{R}}_{\text{i}}{\text{R}}_{\text{R}}{\text{S}}_{0}\left(\text{k}\right)\text{c}\text{o}\text{s}\left[\text{k}\left({\text{l}}_{\text{i}}-{\text{l}}_{\text{R}}\right)\right]\right\}\end{array}$$ 17 According to the Wiener Sinchen theorem, the autocorrelation function C( τ) It is the result of Fourier transform of the power spectral density S (k) of the interference spectrum in the wavenumber domain. Fourier transform converts wavenumber k into time delay τ, And the spectral components of the interference spectrum in the wavenumber domain are weighted and overlaid to obtain the autocorrelation function. By using formula (18), the autocorrelation information of the interference spectrum can be obtained. $$\begin{array}{c}\varGamma \left(\varDelta l\right)={\text{R}}_{\text{R}}^{2}{{\Gamma }}_{0}\left(\varDelta l\right)+\sum _{\text{i}=1}^{\text{N}} \left\{{\text{R}}_{\text{i}}^{2}{{\Gamma }}_{0}\left({\Delta }\text{l}\right)\right\}\\ +\sum _{\text{i}\ne \text{j}}^{\text{N}} \left\{{\text{R}}_{\text{i}}{\text{R}}_{\text{j}}{{\Gamma }}_{0}\left[{\Delta }\text{l}\pm \left({\text{l}}_{\text{i}}-{\text{l}}_{\text{j}}\right)\right]\right\}\\ +\sum _{\text{i}=1}^{\text{N}} \left\{{\text{R}}_{\text{i}}{\text{R}}_{\text{R}}{{\Gamma }}_{0}\left[{\Delta }\text{l}\pm \left({\text{l}}_{\text{i}}-{\text{l}}_{\text{R}}\right)\right]\right\}\end{array}$$ 18 3.5 Simulation Results of Optical Coherence Tomography The optical path lR of the reference light is 1 mm, and the reflection coefficient RR of the reference mirror is 1. There are 2 reflection points in the sample, with a central wavelength set λ 0 is 810 nm, spectral line width ∆ λ Set to 70 nm, the power spectral density S0 of a Gaussian broadband light source( λ) As shown in Fig. 3 . Before conducting uniform sampling, the wavelength domain interference spectrum S' is obtained by Fourier transform of the wavenumber domain interference spectrum S' (k)( λ)。 Then use spline interpolation function to calculate the wavelength domain interference spectrum S'༈ λ) Perform uniform sampling to obtain a uniformly distributed wavelength domain interference spectrum S༈ λ)。 The spline interpolation function can obtain interpolation results on new uniform sampling points by performing interpolation calculations on known data points. Finally, the uniformly distributed wavelength domain interference spectrum S༈ λ) By converting the echo number domain, a uniformly distributed wavenumber domain interference spectrum S (k) can be obtained, as shown in Fig. 4 . 4 Research on muscle activity patterns in sports 4.1 Analysis of Muscle Collaborative Activity Patterns The surface electromyography analysis of muscle synergy patterns uses matrix decomposition dimensionality reduction techniques to identify the regularity of spatial and temporal patterns of multiple muscle activation. Formula (19) represents this process. $${\text{E}}_{\text{n}\times \text{m}}={\text{W}}_{\text{n}\times \text{s}}\times {\text{C}}_{\text{s}\times \text{m}}+\text{e}$$ 19 In formula (19), E represents the multidimensional muscle surface electromyography signal matrix, n represents the number of receiving channels for surface electromyography signals, and m represents the number of sampling points for surface electromyography signals. By decomposing matrix E, the muscle synergistic structure matrix W and muscle activation coefficient matrix C are obtained. Among them, the structural synergy matrix W reflects the spatial characteristics of synergistic muscles, namely the relationship and distribution between synergistic muscles. The muscle activation coefficient matrix C reflects the temporal characteristics of synergistic muscles, namely the temporal changes in muscle activation. By matrix decomposition, the dimensionality of the data is reduced and spatial and temporal patterns in muscle collaboration patterns are extracted. This helps to understand the synergistic effects between multiple muscles and can be used for the analysis and evaluation of muscle activity. The residual term e represents the part that has not been explained by the synergistic structure matrix and muscle activation coefficient matrix, and may contain noise or other unknown factors. The quantitative evaluation of muscle synergy and the similarity evaluation of muscle synergy are quantitative evaluations of the results of muscle synergy analysis. The evaluation of muscle synergy can be done using the VAF curve method. VAF (Variance Accounted For) is an indicator that measures the variance of collaborative pattern interpretation. The VAF curve method evaluates the amount of muscle synergy by calculating the VAF values in matrix dimensionality reduction decomposition, as follows: $$\text{V}\text{A}\text{F}=1-\frac{\sum _{\text{i}=1}^{\text{X}} \sum _{\text{j}=1}^{\text{N}} {\left(\text{M}-{\text{M}}_{0}\right)}^{2}}{\sum _{\text{i}=1}^{\text{X}} \sum _{\text{j}=1}^{\text{N}} (\text{M}-\text{M}{)}^{2}}$$ 20 In the study of similarity in muscle synergy. The Pearson correlation coefficient is used to measure linear correlation and is commonly used in similarity studies of collaborative patterns. The calculation formula for Pearson correlation coefficient is as follows: $$\text{r}=\frac{\text{n}\sum _{\text{i}}^{\text{n}} {\text{x}}_{\text{i}}{\text{y}}_{\text{i}}-\sum _{\text{i}}^{\text{n}} {\text{x}}_{\text{i}}\sum _{\text{i}}^{\text{n}} {\text{x}}_{\text{i}}}{\sqrt{\text{n}\sum _{\text{i}}^{\text{n}} {\text{x}}_{\text{i}}^{2}-{\left(\sum _{\text{i}}^{\text{n}} {\text{x}}_{\text{i}}\right)}^{2}}\sqrt{\text{n}\sum _{\text{i}}^{\text{n}} {\text{y}}_{\text{i}}^{2}-{\left(\sum _{\text{i}}^{\text{n}} {\text{y}}_{\text{i}}\right)}^{2}}}$$ 21 4.2 Extraction of lower limb electromyographic signals The research and analysis of the characteristics of surface electromyography signals mainly include time-domain and frequency-domain analysis methods. Among them, time-domain features mainly reflect the changes in muscle signals in the time dimension, while mean absolute value (MAV) features are independent of the calculated time, and can better reflect the dominant output of surface electromyography signals. $$\text{M}\text{A}\text{V}=\frac{\sum _{\text{i}=0}^{\text{N}} |\text{D}\text{a}\text{t}\text{a}[\text{i}\left]\right|}{\text{N}}$$ 22 Integrated electromyographic value (IEMG) refers to the area under the rectification curve, which can reflect the fluctuation and entropy value of the electromyographic signal, and is used to analyze the energy distribution of the electromyographic signal. $$\text{I}\text{E}\text{M}\text{G}=\sum _{\text{i}=0}^{\text{N}} \mid \text{ Data }\left[\text{i}\right]\mid \times {\Delta }\text{t}$$ 23 The root mean square value (RMS) of electromyography is directly related to the energy of the signal. The formula for calculating the root mean square value of electromyography is as follows: $$\text{R}\text{M}\text{S}=\sqrt{\frac{\sum _{\text{i}=0}^{\text{N}} \text{ Data }[\text{i}{]}^{2}}{\text{N}}}$$ 24 4.3 Processing of lower limb electromyographic signals The EMD decomposition process is shown below, which can generally be divided into the following processes: Select all extreme points for processing signal X (t); Perform spline curve fitting on the maximum value to obtain the upper envelope curve emax (t); Perform spline curve fitting on the minimum value to obtain the lower envelope curve emin (t); Calculate the average curve m (t) of the upper envelope curve emax (t) and the lower envelope curve emin (t); Subtract the average curve m (t) from the signal X (t) to obtain the residual signal h (t), where h (t) = X (t) - m (t); Determine whether the residual signal h (t) meets the termination condition. If it does, stop decomposition. Otherwise, use the residual signal h (t) as a new processing signal and repeat steps 1–5 until the termination condition is met; Add all the obtained components IMFs to obtain a decomposed representation of the original signal. Among them, formula (25) represents the average curve of the upper and lower envelope curves, and formula (26) represents the residual signal obtained by subtracting the average curve from the signal. $$\text{m}\left(\text{t}\right)=\frac{{\text{e}}_{\text{m}\text{a}\text{x}}\left(\text{t}\right)+{\text{e}}_{\text{m}\text{i}\text{n}}\left(\text{t}\right)}{2}$$ 25 $$\text{h}\left(\text{t}\right)=\text{x}\left(\text{t}\right)-\text{m}\left(\text{t}\right)$$ 26 4.4 Analysis of Lower Limb Muscle Activity Data Table 1 Surface EMG root mean square amplitude values (uV) of muscles measured during the buffer period Falling height (cm) 20 40 Fatigue (40) 80 100 Rectus femoris 377.840 ± 87.393 427.873 ± 91.343 388.903 ± 90.634 488.570 ± 111.697 417.607 ± 100.659 Vastus medialis 491.954 ± 89.213 562.934 ± 111.715 434.189 ± 102.953 572.044 ± 96.979 558.228 ± 97.178 Vastus lateralis 430.014 ± 104.162 496.916 ± 106.659 402.501 ± 112.651 522.450 ± 104.561 509.881 ± 104.461 Tibialis anterior muscle 153.784 ± 43.335 141.823 ± 48.250 194.593 ± 76.739 167.756 ± 60.589 185.546 ± 54.470 Gluteus maximus 299.698 ± 84.020 296.114 ± 89.402 278.490 ± 93.787 314.434 ± 92.890 356.153 ± 87.109 Biceps femoris muscle 206.220 ± 84.775 228.468 ± 57.476 236.250 ± 68.991 265.382 ± 88.844 315.665 ± 99.665 Gastrocnemius muscle (medial) 405.573 ± 94.582 483.447 ± 100.814 376.814 ± 100.010 505.524 ± 103.729 506.638 ± 106.443 Soleus muscle 325.816 ± 87.999 345.778 ± 90.202 326.417 ± 99.712 381.088 ± 100.313 386.505 ± 88.600 From the data in Table 1 , it can be seen that there is a certain trend in the electromyographic activity level of the lower limb muscles of athletes when jumping deep at different falling heights. At a drop height of 40–100 cm, the root mean square values of most muscles during the buffering period are higher than those during the stretching period. At a height of 20 cm, the average electromyographic activity level of some muscles during the buffering period is relatively close to the average electromyographic activity level during the stretching period. This change may be related to the stretching reflex activity of the lower limb extensor muscles when they are stretched. At higher falling heights, the lower limb muscles undergo prolonged stretching during the buffering period, resulting in sustained muscle stretch reflex activity and higher levels of electromyographic activity. At smaller drop heights, the stretching time of lower limb muscles during the buffering period is shorter, and the duration of stretch reflex activity is shorter, resulting in lower levels of electromyographic activity. Table 2 Surface EMG root mean square amplitude values (uV) of muscles measured during the stretching period Falling height (cm) 20 40 Fatigue (40) 80 100 Rectus femoris 357.408 ± 89.800 328.704 ± 99.967 309.567 ± 94.884 374.850 ± 120.996 290.930 ± 107.840 Vastus medialis 442.999 ± 120.235 459.217 ± 102.215 402.553 ± 102.415 485.947 ± 87.999 437.993 ± 121.537 Vastus lateralis 439.119 ± 119.058 447.234 ± 99.264 368.951 ± 108.963 443.672 ± 113.021 412.497 ± 104.114 Tibialis anterior muscle 153.784 ± 56.589 174.993 ± 71.565 282.742 ± 89.758 206.453 ± 79.304 275.405 ± 95.789 Gluteus maximus 191.070 ± 93.892 225.421 ± 89.710 221.637 ± 100.762 239.759 ± 70.295 290.837 ± 79.156 Biceps femoris muscle 192.452 ± 84.104 237.447 ± 79.515 269.572 ± 90.190 266.280 ± 79.215 289.226 ± 89.292 Gastrocnemius muscle (medial) 396.156 ± 90.532 422.080 ± 91.545 332.357 ± 80.507 461.675 ± 80.608 438.688 ± 111.697 Soleus muscle 326.116 ± 89.680 269.540 ± 85.667 247.271 ± 98.407 312.574 ± 99.711 311.371 ± 79.548 According to the data in Table 2 , under relative fatigue conditions, the surface EMG root mean square amplitude values of the measured muscles during the buffering and stretching stages of athletes during landing are lower than those under normal conditions. This indicates that fatigue can lead to a decrease in muscle electromyographic activity levels. This phenomenon is due to fatigue inducing the central nervous system to activate self-protection mechanisms to prevent muscle injury. As shown in Table 3 . Table 3 Surface integrated electromyographic values (UVs) of muscles measured during the buffer period Falling height (cm) 20 40 Fatigue (40) 80 100 Rectus femoris 33.722 ± 10.266 35.545 ± 9.867 34.026 ± 10.864 40.608 ± 14.252 38.279 ± 10.166 Vastus medialis 43.611 ± 8.209 48.191 ± 10.111 40.126 ± 13.315 53.966 ± 19.322 51.377 ± 11.513 Vastus lateralis 38.249 ± 8.016 41.745 ± 7.818 40.147 ± 9.996 47.837 ± 11.777 47.737 ± 13.559 Tibialis anterior muscle 12.639 ± 6.835 12.238 ± 3.920 15.950 ± 7.941 14.214 ± 6.734 16.150 ± 9.247 Gluteus maximus 26.512 ± 9.160 27.309 ± 8.463 26.013 ± 7.069 28.555 ± 10.455 32.691 ± 10.753 Biceps femoris muscle 17.173 ± 5.886 20.151 ± 9.179 21.541 ± 10.276 23.586 ± 3.592 28.887 ± 6.186 Gastrocnemius muscle (medial) 36.285 ± 7.291 42.014 ± 10.228 34.174 ± 10.329 44.929 ± 10.836 41.210 ± 9.215 Soleus muscle 29.033 ± 3.110 31.335 ± 10.433 28.232 ± 9.831 34.038 ± 10.834 35.440 ± 8.125 4.5 Analysis of muscle activity patterns in sports According to the curve in Fig. 5 , changes in the activation level of different muscles were observed under the action of flat support. As shown in Fig. 5 , most of the time, the activation level of these muscles exceeds 50%. The activation level of the erector spinalis (ES) and quadratus psoas (QL) muscles is relatively low, and they are basically in an inactive state. 5 Conclusion This article uses optical coherence tomography technology and electromyography sensors to study muscle activity patterns in sports. Through the application of these technologies, muscle activity can be more accurately observed and recorded, thereby better understanding the activity patterns of the core muscle group and the abnormalities of the core muscle group activity patterns in patients with low back pain. The results of this study will help to develop and implement more effective sports training plans. By understanding the activity patterns of the core muscle group and combining them with the characteristics of athletes, targeted training programs can be designed to improve the strength and stability of the core muscles, reduce back pain symptoms, and prevent related muscle and bone injuries. Declarations Author contributions Lingzi Yao has done the first version, Ye Zhang has done the simulations. All authors have contributed to the paper’s analysis, discussion, writing, and revision. Fund The authors have not disclosed any funding. Data availability The data will be available upon request. Conflict of interest The authors declare that they have no competing interests. Ethical approval Not applicable. References Farago, E., MacIsaac, D., Suk, M., & Chan, A. D. (2022). A review of techniques for surface electromyography signal quality analysis. IEEE Reviews in Biomedical Engineering, 16, 472-486. Psomadakis, C. E., Marghoob, N., Bleicher, B., & Markowitz, O. (2021). Optical coherence tomography. Clinics in Dermatology, 39(4), 624-634. Lains, I., Wang, J. C., Cui, Y., Katz, R., Vingopoulos, F., Staurenghi, G., ... & Miller, J. B. (2021). Retinal applications of swept source optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA). Progress in retinal and eye research, 84, 100951. Aytulun, A., Cruz-Herranz, A., Aktas, O., Balcer, L. J., Balk, L., Barboni, P., ... & Albrecht, P. (2021). APOSTEL 2.0 recommendations for reporting quantitative optical coherence tomography studies. Neurology, 97(2), 68-79. Mekruksavanich, S., & Jitpattanakul, A. (2020, March). Exercise activity recognition with surface electromyography sensor using machine learning approach. In 2020 Joint International Conference on Digital Arts, Media and Technology with ECTI Northern Section Conference on Electrical, Electronics, Computer and Telecommunications Engineering (ECTI DAMT & NCON) (pp. 75-78). IEEE. Taborri, J., Keogh, J., Kos, A., Santuz, A., Umek, A., Urbanczyk, C., ... & Rossi, S. (2020). Sport biomechanics applications using inertial, force, and EMG sensors: A literature overview. Applied bionics and biomechanics, 2020. Xia, W., Zhou, Y., Yang, X., He, K., & Liu, H. (2019). Toward portable hybrid surface electromyography/a-mode ultrasound sensing for human–machine interface. IEEE Sensors Journal, 19(13), 5219-5228. He, J., Luo, H., Jia, J., Yeow, J. T., & Jiang, N. (2018). Wrist and finger gesture recognition with single-element ultrasound signals: A comparison with single-channel surface electromyogram. IEEE Transactions on Biomedical Engineering, 66(5), 1277-1284. Yang, X., Sun, X., Zhou, D., Li, Y., & Liu, H. (2018). Towards wearable a-mode ultrasound sensing for real-time finger motion recognition. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 26(6), 1199-1208. Mendez, J., Murray, R., Gabert, L., Fey, N. P., Liu, H., & Lenzi, T. (2023). A-Mode Ultrasound-Based Prediction of Transfemoral Amputee Prosthesis Walking Kinematics via an Artificial Neural Network. IEEE Transactions on Neural Systems and Rehabilitation Engineering, 31, 1511-1520. Zou, Y., Cheng, L., & Li, Z. (2022). A multimodal fusion model for estimating human hand force: Comparing surface electromyography and ultrasound signals. IEEE Robotics & Automation Magazine, 29(4), 10-24. Guo, L., Lu, Z., Yao, L., & Cai, S. (2022). A gesture recognition strategy based on A-mode ultrasound for identifying known and unknown gestures. IEEE Sensors Journal, 22(11), 10730-10739. Kim, K. B. (2015). A fully automatic measurement of lumbar multifidus muscle thickness from ultrasound image. Journal of Medical Imaging and Health Informatics, 5(1), 1-6. Lopes, N., Ribeiro, B., Lopes, N., & Ribeiro, B. (2015). Non-negative matrix factorization (NMF). Machine Learning for Adaptive Many-Core Machines-A Practical Approach, 127-154. Biswas, J., Kayal, P., & Samanta, D. (2021). Reducing approximation error with rapid convergence rate for non-negative matrix factorization (NMF). Mathematics and Statistics, 9(3), 285-289. Quintero, A., Hübschmann, D., Kurzawa, N., Steinhauser, S., Rentzsch, P., Krämer, S., ... & Herrmann, C. (2020). ShinyButchR: interactive NMF-based decomposition workflow of genome-scale datasets. Biology Methods and Protocols, 5(1), bpaa022. Li, D., & Chen, C. (2022). Research on exercise fatigue estimation method of Pilates rehabilitation based on ECG and sEMG feature fusion. BMC Medical Informatics and Decision Making, 22(1), 67. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-3849504","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":266379323,"identity":"75ccc7d1-ec56-4d99-9790-6f178c76043c","order_by":0,"name":"Lingzi Yao","email":"","orcid":"","institution":"Guangzhou Maritime University","correspondingAuthor":false,"prefix":"","firstName":"Lingzi","middleName":"","lastName":"Yao","suffix":""},{"id":266379324,"identity":"378469c5-4bdd-43d1-a1bf-2360a5b4a137","order_by":1,"name":"Ye Zhang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+UlEQVRIiWNgGAWjYDACCQgpx8/ffPDBhwoJOXkitVgYS844lmw444yFsWEDcVoqEjccyFET5myrSGQ4QECH/OzmZw+/tkkwNhw4w8bMOE8igbGB+eGjG3i0MM45Zm4s2ybBzNjce+xx4TaJPHYGNmPjHDxamCUSzKQl2yTYmBnOpRvP3CZRzNjAwyaNTwubRPo3kBYeNoYcM2neORKJDQcIaOGRyDGT/NgmIcED1tJAhBYJiZwyaYZzEgYSEqBAPiZhbNhMwC/yM9K3Sf4oq6vffx4UlTV1cvLszQ8f49MCAsy8bChcAspBgPHHHyJUjYJRMApGwcgFANLvSWZc2OycAAAAAElFTkSuQmCC","orcid":"","institution":"Guangdong Polytechnic Normal University","correspondingAuthor":true,"prefix":"","firstName":"Ye","middleName":"","lastName":"Zhang","suffix":""}],"badges":[],"createdAt":"2024-01-10 06:14:11","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-3849504/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-3849504/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":49477504,"identity":"852e53d0-3832-4da1-a820-6794728ab7da","added_by":"auto","created_at":"2024-01-11 13:54:38","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":124382,"visible":true,"origin":"","legend":"\u003cp\u003eSchematic diagram of the structure of the Michelson interferometer\u003c/p\u003e","description":"","filename":"1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3849504/v1/74a16f4d59e0361a9682c3cc.jpg"},{"id":49478243,"identity":"07a0f831-a383-451b-8450-a40bba8fc3c0","added_by":"auto","created_at":"2024-01-11 14:02:38","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":220288,"visible":true,"origin":"","legend":"\u003cp\u003eHandheld SS-OCT System\u003c/p\u003e","description":"","filename":"2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3849504/v1/b9b36d97d3a72be31daf8f13.jpg"},{"id":49477505,"identity":"1a0306e3-6a28-4384-a5a8-a6ca40e0fc9e","added_by":"auto","created_at":"2024-01-11 13:54:38","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":123282,"visible":true,"origin":"","legend":"\u003cp\u003eWavelength domain light source power spectral density\u003c/p\u003e","description":"","filename":"3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3849504/v1/13be2369253455cc74534d9f.jpg"},{"id":49477506,"identity":"5732141b-21e4-4eea-a4eb-fb34a66902f0","added_by":"auto","created_at":"2024-01-11 13:54:38","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":185318,"visible":true,"origin":"","legend":"\u003cp\u003eInterference Spectrum with Uniform Sampling in the Wavenumber Domain\u003c/p\u003e","description":"","filename":"4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3849504/v1/1ebc8e572024c41cbe3ba397.jpg"},{"id":49477508,"identity":"5984dbf0-01d9-448f-b51d-18f0745bde39","added_by":"auto","created_at":"2024-01-11 13:54:39","extension":"jpg","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":184031,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in activation levels of different muscles during plate support movements\u003c/p\u003e","description":"","filename":"5.jpg","url":"https://assets-eu.researchsquare.com/files/rs-3849504/v1/11558dd25d8865c08e55db40.jpg"},{"id":57962576,"identity":"74389b1e-2ea7-47a9-afd7-95157cd0b1be","added_by":"auto","created_at":"2024-06-08 05:02:14","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1478860,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-3849504/v1/2ddb8aba-a6c3-49ec-aca1-c1e71d77a5a9.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Simulation of muscle activity in sports training based on optical coherence tomography technology","fulltext":[{"header":"1 Introduction","content":"\u003cp\u003eIn the field of sports, traditional methods often rely on the collection of electromyographic signals from surface electrodes. This method is prone to interference and can only obtain the activity of the muscle surface, unable to directly observe the activity of deep muscles [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Therefore, it is necessary to seek a new technological means to conduct in-depth research on muscle activity patterns, in order to better guide sports training and prevent sports injuries. Optical coherence tomography (OCT) is a non-invasive, high-resolution imaging technique that obtains internal structural information of biological tissues by measuring the interference phenomenon of light [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eBased on this, this article aims to explore a new method for analyzing and studying muscle activity patterns in sports by combining optical coherence tomography technology and electromyography sensors. Utilizing the high spatial resolution and real-time imaging capabilities of OCT technology, observe and record changes in deep muscle activity, in order to gain a more comprehensive understanding of muscle collaborative activity and fatigue levels [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Electromyography sensors can be used to collect electrical signals on the surface of muscles and provide information on their surface activity [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. By combining these two techniques, comprehensive information about muscle activity patterns can be obtained. This study can provide reference for the training of athletes and the prevention of sports injuries. By analyzing muscle activity patterns, evaluate athletes' muscle coordination and stability, and guide their training and technical improvement [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. By monitoring changes in muscle activity patterns, it is also possible to detect signs of sports injury in advance, take corresponding preventive measures, and reduce the risk of sports injury.\u003c/p\u003e"},{"header":"2 Related work","content":"\u003cp\u003eThe literature briefly describes the differences in muscle activity by analyzing the characteristics of A-mode ultrasound and surface electromyography signals of the core muscle group [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Secondly, the literature fused A-mode ultrasound signals and surface signal features to analyze the synergistic patterns of core muscle groups, in order to confirm the differences in the synergistic patterns of core muscle groups between normal individuals and patients with low back pain [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Finally, through the collaborative mode of core muscle groups, the literature modeled the degree of muscle activation and studied the degree and timing of core muscle activation in normal individuals and patients with low back pain [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e]. By analyzing the characteristics of A-mode ultrasound and surface electromyography signals, the literature aims to understand the differences in core muscle group activity between normal individuals and patients with low back pain. A-mode ultrasound can provide detailed muscle structure information, and surface electromyographic signals can reflect the electrical activity of muscles [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe literature selected the collection scheme of A-mode ultrasound and surface electromyography signals based on the research characteristics. The literature collected A-mode ultrasound and surface electromyography signals, and extracted the features of both signals [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Next, they fused the features of these two signals and proposed a method for extracting muscle thickness features from A-mode ultrasound signals. They also utilized the non negative matrix factorization (NMF) algorithm to achieve feature fusion analysis of core muscle group activity patterns. Literature collection of A-mode ultrasound signals and surface electromyography signals, and feature extraction of these two signals [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. For A-type ultrasound signals, the literature proposes a muscle thickness feature extraction method that can extract muscle thickness information from ultrasound images [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. For surface electromyography signals, literature adopts commonly used feature extraction methods, such as time-domain and frequency-domain features, to extract the features of electromyography signals. The literature fused the muscle thickness features of A-mode ultrasound signals with the features of surface electromyography signals. In order to achieve feature fusion, they adopted the Non Negative Matrix Factorization (NMF) algorithm [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. The NMF algorithm can decompose the original data matrix into two non negative matrices, one representing the characteristics of the signal and the other representing the weight of the signal [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. By performing NMF decomposition on the feature matrices of A-type ultrasound signals and surface electromyography signals, the literature obtained the fusion results of the two signal features [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. The literature has conducted feature fusion analysis on the activity patterns of core muscle groups [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. By analyzing the fused features, they can understand the activity patterns of the core muscle group, as well as the relationship between muscle thickness and surface electromyographic signals.\u003c/p\u003e"},{"header":"3 Analysis of the principles of optical coherence tomography technology","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e3.1 The transmission principle of light in optical fibers\u003c/h2\u003e \u003cp\u003eNumerical aperture is an indicator used to measure the ability of a fiber to receive incident light, and also reflects the difficulty of coupling between the fiber and the light source. The numerical aperture definition of traditional optical fibers is shown in formula (1):\u003cdiv id=\"Equ1\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ1\" name=\"EquationSource\"\u003e\n$$\\text{N}\\text{A}=\\sqrt{{\\text{n}}_{1}^{2}-{\\text{n}}_{2}^{2}}=\\text{s}\\text{i}\\text{n}{{\\theta }}_{\\text{max }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e1\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eTraditional optical fibers are limited by the characteristics of the material itself, making it difficult to achieve larger numerical apertures. In contrast, PCF has greater design flexibility and can design structures with different numerical apertures based on different materials. The numerical aperture of PCF can be precisely controlled by adjusting the size and arrangement of air holes in photonic crystals, designing a PCF structure with a larger numerical aperture, thereby improving the receiving ability and coupling efficiency of the fiber.\u003c/p\u003e \u003cp\u003eThe characteristics of traditional optical fibers are usually described by two parameters, namely the relative refractive index difference and the normalized frequency. The relative refractive index difference can be calculated using formula (2):\u003cdiv id=\"Equ2\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ2\" name=\"EquationSource\"\u003e\n$${\\Delta }=\\frac{{\\text{n}}_{1}-{\\text{n}}_{2}}{{\\text{n}}_{1}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e2\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe normalized frequency is calculated using formula (3):\u003cdiv id=\"Equ3\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ3\" name=\"EquationSource\"\u003e\n$$\\text{V}={\\text{k}}_{0}\\text{a}{\\left({\\text{n}}_{1}^{2}-{\\text{n}}_{2}^{2}\\right)}^{\\frac{1}{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e3\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eNonlinear effects occur when light waves propagate in optical fibers, and their intensity can be represented by nonlinear parameters. The calculation formula for nonlinear parameters is as follows:\u003cdiv id=\"Equ4\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ4\" name=\"EquationSource\"\u003e\n$${\\gamma }\\left({\\lambda }\\right)=\\frac{2{\\pi }{\\text{n}}_{3}}{{\\lambda }{\\text{A}}_{\\text{eff }}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e4\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAmong them:\u003cdiv id=\"Equ5\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ5\" name=\"EquationSource\"\u003e\n$${\\text{n}}_{3}=\\frac{3}{8\\text{n}}\\text{R}\\text{e}\\left({{\\chi }}_{\\text{x}\\text{x}\\text{x}\\text{x}}^{\\left(3\\right)}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e5\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ6\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ6\" name=\"EquationSource\"\u003e\n$${\\text{A}}_{\\text{e}\\text{f}\\text{f}}=\\frac{{\\left(\\iint \\left|\\text{F}\\right(\\text{x},\\text{y}){|}^{2}\\text{d}\\text{x}\\text{d}\\text{y}\\right)}^{2}}{\\iint \\left|\\text{F}\\right(\\text{x},\\text{y}){|}^{4}\\text{d}\\text{x}\\text{d}\\text{y}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e6\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor traditional optical fibers, nonlinear effects are uncontrollable because their nonlinear parameters are fixed. However, for PCF, due to its structural flexibility, nonlinear effects can be adjusted by adjusting the design parameters of the fiber. Dispersion in traditional optical fibers refers to the phenomenon of signal distortion caused by the different propagation speeds of light waves at different frequencies after a certain distance of propagation in the fiber. For single-mode fibers, dispersion can be described by dispersion parameters.\u003cdiv id=\"Equ7\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ7\" name=\"EquationSource\"\u003e\n$$\\text{D}\\left({\\lambda }\\right)={\\text{D}}_{\\text{m}}\\left({\\lambda }\\right)+{\\text{D}}_{\\text{w}}\\left({\\lambda }\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e7\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eFor dispersion, it can be divided into material dispersion and waveguide dispersion. Material dispersion refers to the dispersion phenomenon caused by the relationship between different frequencies of light waves and the refractive index of the material when light waves propagate in the material. Material dispersion can be described through dispersion parameters.\u003cdiv id=\"Equ8\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ8\" name=\"EquationSource\"\u003e\n$${\\text{D}}_{\\text{m}}\\left({\\lambda }\\right)=-\\frac{{\\lambda }}{\\text{c}}\\frac{{\\partial }^{2}\\left({\\text{n}}_{1}\\right)}{\\partial {{\\lambda }}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e8\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWaveguide dispersion refers to the dispersion phenomenon caused by the geometric shape and refractive index distribution of the waveguide when light waves propagate in the waveguide structure. Waveguide dispersion can be controlled through the design parameters of the waveguide. Formula (9) provides the calculation formula for waveguide dispersion.\u003cdiv id=\"Equ9\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ9\" name=\"EquationSource\"\u003e\n$${\\text{D}}_{\\text{w}}\\left({\\lambda }\\right)=-\\frac{{\\lambda }}{\\text{c}}\\frac{{\\partial }^{2}\\left[\\text{Re}\\left({\\text{n}}_{\\text{e}\\text{f}\\text{f}}\\right)\\right]}{\\partial {{\\lambda }}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e9\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe structure of PCF is flexible and has many adjustable parameters. Waveguide dispersion can be controlled by adjusting parameters such as the diameter of the fiber core and the refractive index difference between the fiber core and the cladding. This gives PCF the ability to flexibly control the total dispersion size. These characteristics of PCF enable it to generate supercontinuum spectra with good performance and wide spectrum, and can achieve full band single mode output.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Principles of Optical Coherence Tomography\u003c/h2\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, the Michelson interferometer is a structure commonly used in optical coherence tomography (OCT) technology. The frequency sweep OCT technology has gradually developed from the initial time-domain OCT. In the OCT imaging process, the intensity of the interference signal on the detector can be represented by formula (10), which describes the relationship between the intensity of the interference signal and the frequency difference of the sweep signal, light source power, sample reflectivity, etc.\u003cdiv id=\"Equ10\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ10\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}{\\text{I}}_{\\text{D}}\\left(k\\right)=\\frac{{\\rho }}{4}\\left[\\text{S}\\left(\\text{k}\\right)\\left[{\\text{R}}_{\\text{R}}+{\\text{R}}_{\\text{S}1}+{\\text{R}}_{\\text{S}2}+\\dots \\right]\\right]\\\\ +\\frac{{\\rho }}{4}\\left[\\text{S}\\left(\\text{k}\\right)\\sum _{\\text{n}=\\text{m}\\ne 1}^{\\text{N}} \\sqrt{{\\text{R}}_{\\text{S}\\text{n}}{\\text{R}}_{\\text{S}\\text{m}}}\\left({\\text{e}}^{\\text{i}2\\text{k}\\left({\\text{z}}_{\\text{R}}-{\\text{z}}_{{\\text{S}}_{\\text{n}}}\\right)}+{\\text{e}}^{-\\text{i}2\\text{k}\\left({\\text{z}}_{\\text{R}}-{\\text{z}}_{\\text{S}\\text{n}}\\right)}\\right)\\right]\\\\ +\\frac{{\\rho }}{4}\\left[\\text{S}\\left(\\text{k}\\right)\\sum _{\\text{n}=1}^{\\text{N}} \\sqrt{{\\text{R}}_{\\text{R}}{\\text{R}}_{\\text{S}\\text{n}}}\\left({\\text{e}}^{\\text{i}2\\text{k}\\left({\\text{z}}_{\\text{S}\\text{n}}-{\\text{z}}_{\\text{S}\\text{m}}\\right)}+{\\text{e}}^{-\\text{i}2\\text{k}\\left({\\text{z}}_{\\text{n}}-{\\text{z}}_{\\text{S}\\text{m}}\\right)}\\right)\\right]\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e10\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAccording to the Venasinchin theorem, for a wide stationary stochastic process, the power spectral density function is the Fourier transform of its autocorrelation function. The inverse Fourier transform of the normalized Gaussian function S (k) can be calculated using formula (11).\u003cdiv id=\"Equ11\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ11\" name=\"EquationSource\"\u003e\n$${\\gamma }\\left(\\text{z}\\right)={\\text{e}}^{-{\\text{z}}^{2}{\\Delta }{\\text{k}}^{2}}\\stackrel{\\text{F}}{⟷}\\text{S}\\left(\\text{k}\\right)=\\frac{1}{{\\Delta }\\text{k}\\sqrt{{\\pi }}}{\\text{e}}^{-{\\left[\\frac{\\left(\\text{k}-{\\text{k}}_{0}\\right)}{{\\Delta }\\text{k}}\\right]}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e11\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ12\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ12\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}i\\left(z\\right)=\\rho F{\\text{T}}^{-1}\\left[{\\text{I}}_{\\text{D}}\\left(\\text{k}\\right)\\right]=\\frac{{\\rho }}{8}\\left[{\\gamma }\\left(\\text{z}\\right)\\left[{\\text{R}}_{\\text{R}}+{\\text{R}}_{\\text{S}1}+{\\text{R}}_{\\text{S}2}+\\dots \\right]\\right]\\\\ +\\frac{{\\rho }}{8}\\left[{\\gamma }\\left(\\text{z}\\right)\\otimes \\sum _{\\text{n}=\\text{m}\\ne 1}^{\\text{N}} \\sqrt{{\\text{R}}_{\\text{S}\\text{n}}{\\text{R}}_{\\text{S}\\text{m}}}\\left({\\delta }\\left(\\text{z}\\pm 2\\left({\\text{z}}_{\\text{S}\\text{n}}-{\\text{z}}_{\\text{S}\\text{m}}\\right)\\right)\\right)\\right]\\\\ +\\frac{{\\rho }}{4}\\left[{\\gamma }\\left(\\text{z}\\right)\\otimes \\sum _{\\text{n}=1}^{\\text{N}} \\sqrt{{\\text{R}}_{\\text{R}}{\\text{R}}_{\\text{S}\\text{n}}}\\left({\\delta }\\left(\\text{z}\\pm 2\\left({\\text{z}}_{\\text{S}\\text{n}}-{\\text{z}}_{\\text{S}\\text{m}}\\right)\\right)\\right)\\right]\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e12\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBy performing Fourier transform on the interference spectrum signal, scattering intensity information at different depths can be obtained. These scattering intensity information can be used to generate structural imaging of the sample.\u003c/p\u003e \u003cp\u003eFor the detected k-spectrum, inverse Fourier transform can be performed to obtain a deep z-encoded complex OCT function. This complex OCT function contains amplitude and phase information, which can be represented by formula (13):\u003cdiv id=\"Equ13\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ13\" name=\"EquationSource\"\u003e\n$$\\text{B}\\left(\\text{z}\\right)=\\text{A}\\left(\\text{z}\\right){\\text{e}}^{-\\text{i}{\\upvarphi }\\left(\\text{z}\\right)}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e13\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn order to extract blood flow dynamic signals, N B-scans can be repeated at the same location, and then differential operations can be performed on the OCT complex information of adjacent B-scans. This differential operation can be represented by formula (14):\u003cdiv id=\"Equ14\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ14\" name=\"EquationSource\"\u003e\n$$\\text{F}\\left(\\text{z}\\right)=\\frac{1}{\\text{N}-1}\\sum _{\\text{i}=1}^{\\text{N}} \\left|{\\text{B}}_{\\text{i}+1}\\left(\\text{z}\\right)-{\\text{B}}_{\\text{i}}\\left(\\text{z}\\right)\\right|$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e14\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Construction of Optical Coherence Tomography System\u003c/h2\u003e \u003cp\u003eThe SS-OCT (sweep source optical coherence tomography) system utilizes the frequency modulation characteristics of the light source to achieve deep scanning of the sample by scanning different wavelengths of light. By collecting interference signals and utilizing the principle of Fourier transform, a depth profile image of the sample can be obtained. The SS-OCT system has high imaging resolution and sensitivity, and can achieve fast imaging speed.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, the handheld SS-OCT system is mainly composed of the following parts: scanning light source, fiber coupler, reference arm, sample arm, focusing lens, planar reflector, scanning galvanometer, photodetector, data acquisition card, and computer.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.4 Optical coherence tomography data acquisition and calculation\u003c/h2\u003e \u003cp\u003eThe power spectral density of the interference spectrum is the product of the power spectral density of a broadband light source and the composite effect of the optical path and reflection coefficient of each reflection point. The phase difference corresponding to the optical path of each reflection point will cause interference effects, and the interference effects of multiple reflection points will be combined to obtain the final interference spectrum. The interference spectrum formula (15) is as follows:\u003cdiv id=\"Equ15\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ15\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}S\\left(\\lambda \\right)={\\text{R}}_{\\text{R}}^{2}{\\text{S}}_{0}\\left(\\lambda \\right)+\\sum _{\\text{i}=1}^{\\text{N}} \\left\\{{\\text{R}}_{\\text{i}}^{2}{\\text{S}}_{0}\\left({\\lambda }\\right)\\right\\}\\\\ +\\sum _{\\text{i}\\ne \\text{j}}^{\\text{N}} \\left\\{2{\\text{R}}_{\\text{i}}{\\text{R}}_{\\text{j}}{\\text{S}}_{0}\\left({\\lambda }\\right)\\text{c}\\text{o}\\text{s}\\left[2{\\pi }\\left({\\text{l}}_{\\text{i}}-{\\text{l}}_{\\text{j}}\\right)/{\\lambda }\\right]\\right\\}\\\\ +\\sum _{\\text{i}=1}^{\\text{N}} \\left\\{2{\\text{R}}_{\\text{i}}{\\text{R}}_{\\text{R}}{\\text{S}}_{0}\\left({\\lambda }\\right)\\text{c}\\text{o}\\text{s}\\left[2{\\pi }\\left({\\text{l}}_{\\text{i}}-{\\text{l}}_{\\text{R}}\\right)/{\\lambda }\\right]\\right\\}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e15\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe wave number represents the number of periods of a wave per unit length. In optics, there is a reciprocal relationship between wavenumber and wavelength. The shorter the wavelength, the greater the wavenumber; The longer the wavelength, the smaller the wavenumber. By formula (16), the wavelength of light can be determined λ Convert to wavenumber k, and use wavenumber to represent the frequency and optical path difference of light in interference spectral analysis.\u003cdiv id=\"Equ16\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ16\" name=\"EquationSource\"\u003e\n$$\\text{k}=\\frac{2{\\pi }}{{\\lambda }}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e16\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eBy using formula (17), the analysis results of the interference spectrum in the wavelength domain can be converted into the analysis results in the wavenumber domain.\u003cdiv id=\"Equ17\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ17\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}S\\left(k\\right)={\\text{R}}_{\\text{R}}^{2}{\\text{S}}_{0}\\left(k\\right)+\\sum _{\\text{i}=1}^{\\text{N}} \\left\\{{\\text{R}}_{\\text{i}}^{2}{\\text{S}}_{0}\\left(\\text{k}\\right)\\right\\}\\\\ +\\sum _{\\text{i}\\ne \\text{j}}^{\\text{N}} \\left\\{2{\\text{R}}_{\\text{i}}{\\text{R}}_{\\text{j}}{\\text{S}}_{0}\\left(\\text{k}\\right)\\text{c}\\text{o}\\text{s}\\left[\\text{k}\\left({\\text{l}}_{\\text{i}}-{\\text{l}}_{\\text{j}}\\right)\\right]\\right\\}\\\\ +\\sum _{\\text{i}=1}^{\\text{N}} \\left\\{2{\\text{R}}_{\\text{i}}{\\text{R}}_{\\text{R}}{\\text{S}}_{0}\\left(\\text{k}\\right)\\text{c}\\text{o}\\text{s}\\left[\\text{k}\\left({\\text{l}}_{\\text{i}}-{\\text{l}}_{\\text{R}}\\right)\\right]\\right\\}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e17\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eAccording to the Wiener Sinchen theorem, the autocorrelation function C( τ) It is the result of Fourier transform of the power spectral density S (k) of the interference spectrum in the wavenumber domain. Fourier transform converts wavenumber k into time delay τ, And the spectral components of the interference spectrum in the wavenumber domain are weighted and overlaid to obtain the autocorrelation function. By using formula (18), the autocorrelation information of the interference spectrum can be obtained.\u003cdiv id=\"Equ18\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ18\" name=\"EquationSource\"\u003e\n$$\\begin{array}{c}\\varGamma \\left(\\varDelta l\\right)={\\text{R}}_{\\text{R}}^{2}{{\\Gamma }}_{0}\\left(\\varDelta l\\right)+\\sum _{\\text{i}=1}^{\\text{N}} \\left\\{{\\text{R}}_{\\text{i}}^{2}{{\\Gamma }}_{0}\\left({\\Delta }\\text{l}\\right)\\right\\}\\\\ +\\sum _{\\text{i}\\ne \\text{j}}^{\\text{N}} \\left\\{{\\text{R}}_{\\text{i}}{\\text{R}}_{\\text{j}}{{\\Gamma }}_{0}\\left[{\\Delta }\\text{l}\\pm \\left({\\text{l}}_{\\text{i}}-{\\text{l}}_{\\text{j}}\\right)\\right]\\right\\}\\\\ +\\sum _{\\text{i}=1}^{\\text{N}} \\left\\{{\\text{R}}_{\\text{i}}{\\text{R}}_{\\text{R}}{{\\Gamma }}_{0}\\left[{\\Delta }\\text{l}\\pm \\left({\\text{l}}_{\\text{i}}-{\\text{l}}_{\\text{R}}\\right)\\right]\\right\\}\\end{array}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e18\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.5 Simulation Results of Optical Coherence Tomography\u003c/h2\u003e \u003cp\u003eThe optical path lR of the reference light is 1 mm, and the reflection coefficient RR of the reference mirror is 1. There are 2 reflection points in the sample, with a central wavelength set λ 0 is 810 nm, spectral line width ∆ λ Set to 70 nm, the power spectral density S0 of a Gaussian broadband light source( λ) As shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003eBefore conducting uniform sampling, the wavelength domain interference spectrum S' is obtained by Fourier transform of the wavenumber domain interference spectrum S' (k)( λ)。 Then use spline interpolation function to calculate the wavelength domain interference spectrum S'༈ λ) Perform uniform sampling to obtain a uniformly distributed wavelength domain interference spectrum S༈ λ)。 The spline interpolation function can obtain interpolation results on new uniform sampling points by performing interpolation calculations on known data points. Finally, the uniformly distributed wavelength domain interference spectrum S༈ λ) By converting the echo number domain, a uniformly distributed wavenumber domain interference spectrum S (k) can be obtained, as shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e.\u003c/p\u003e \u003c/div\u003e"},{"header":"4 Research on muscle activity patterns in sports","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Analysis of Muscle Collaborative Activity Patterns\u003c/h2\u003e \u003cp\u003eThe surface electromyography analysis of muscle synergy patterns uses matrix decomposition dimensionality reduction techniques to identify the regularity of spatial and temporal patterns of multiple muscle activation. Formula (19) represents this process.\u003cdiv id=\"Equ19\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ19\" name=\"EquationSource\"\u003e\n$${\\text{E}}_{\\text{n}\\times \\text{m}}={\\text{W}}_{\\text{n}\\times \\text{s}}\\times {\\text{C}}_{\\text{s}\\times \\text{m}}+\\text{e}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e19\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn formula (19), E represents the multidimensional muscle surface electromyography signal matrix, n represents the number of receiving channels for surface electromyography signals, and m represents the number of sampling points for surface electromyography signals. By decomposing matrix E, the muscle synergistic structure matrix W and muscle activation coefficient matrix C are obtained. Among them, the structural synergy matrix W reflects the spatial characteristics of synergistic muscles, namely the relationship and distribution between synergistic muscles. The muscle activation coefficient matrix C reflects the temporal characteristics of synergistic muscles, namely the temporal changes in muscle activation. By matrix decomposition, the dimensionality of the data is reduced and spatial and temporal patterns in muscle collaboration patterns are extracted. This helps to understand the synergistic effects between multiple muscles and can be used for the analysis and evaluation of muscle activity. The residual term e represents the part that has not been explained by the synergistic structure matrix and muscle activation coefficient matrix, and may contain noise or other unknown factors.\u003c/p\u003e \u003cp\u003eThe quantitative evaluation of muscle synergy and the similarity evaluation of muscle synergy are quantitative evaluations of the results of muscle synergy analysis. The evaluation of muscle synergy can be done using the VAF curve method. VAF (Variance Accounted For) is an indicator that measures the variance of collaborative pattern interpretation. The VAF curve method evaluates the amount of muscle synergy by calculating the VAF values in matrix dimensionality reduction decomposition, as follows:\u003cdiv id=\"Equ20\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ20\" name=\"EquationSource\"\u003e\n$$\\text{V}\\text{A}\\text{F}=1-\\frac{\\sum _{\\text{i}=1}^{\\text{X}} \\sum _{\\text{j}=1}^{\\text{N}} {\\left(\\text{M}-{\\text{M}}_{0}\\right)}^{2}}{\\sum _{\\text{i}=1}^{\\text{X}} \\sum _{\\text{j}=1}^{\\text{N}} (\\text{M}-\\text{M}{)}^{2}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e20\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIn the study of similarity in muscle synergy. The Pearson correlation coefficient is used to measure linear correlation and is commonly used in similarity studies of collaborative patterns. The calculation formula for Pearson correlation coefficient is as follows:\u003cdiv id=\"Equ21\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ21\" name=\"EquationSource\"\u003e\n$$\\text{r}=\\frac{\\text{n}\\sum _{\\text{i}}^{\\text{n}} {\\text{x}}_{\\text{i}}{\\text{y}}_{\\text{i}}-\\sum _{\\text{i}}^{\\text{n}} {\\text{x}}_{\\text{i}}\\sum _{\\text{i}}^{\\text{n}} {\\text{x}}_{\\text{i}}}{\\sqrt{\\text{n}\\sum _{\\text{i}}^{\\text{n}} {\\text{x}}_{\\text{i}}^{2}-{\\left(\\sum _{\\text{i}}^{\\text{n}} {\\text{x}}_{\\text{i}}\\right)}^{2}}\\sqrt{\\text{n}\\sum _{\\text{i}}^{\\text{n}} {\\text{y}}_{\\text{i}}^{2}-{\\left(\\sum _{\\text{i}}^{\\text{n}} {\\text{y}}_{\\text{i}}\\right)}^{2}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e21\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Extraction of lower limb electromyographic signals\u003c/h2\u003e \u003cp\u003eThe research and analysis of the characteristics of surface electromyography signals mainly include time-domain and frequency-domain analysis methods. Among them, time-domain features mainly reflect the changes in muscle signals in the time dimension, while mean absolute value (MAV) features are independent of the calculated time, and can better reflect the dominant output of surface electromyography signals.\u003cdiv id=\"Equ22\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ22\" name=\"EquationSource\"\u003e\n$$\\text{M}\\text{A}\\text{V}=\\frac{\\sum _{\\text{i}=0}^{\\text{N}} |\\text{D}\\text{a}\\text{t}\\text{a}[\\text{i}\\left]\\right|}{\\text{N}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e22\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eIntegrated electromyographic value (IEMG) refers to the area under the rectification curve, which can reflect the fluctuation and entropy value of the electromyographic signal, and is used to analyze the energy distribution of the electromyographic signal.\u003cdiv id=\"Equ23\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ23\" name=\"EquationSource\"\u003e\n$$\\text{I}\\text{E}\\text{M}\\text{G}=\\sum _{\\text{i}=0}^{\\text{N}} \\mid \\text{ Data }\\left[\\text{i}\\right]\\mid \\times {\\Delta }\\text{t}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e23\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eThe root mean square value (RMS) of electromyography is directly related to the energy of the signal. The formula for calculating the root mean square value of electromyography is as follows:\u003cdiv id=\"Equ24\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ24\" name=\"EquationSource\"\u003e\n$$\\text{R}\\text{M}\\text{S}=\\sqrt{\\frac{\\sum _{\\text{i}=0}^{\\text{N}} \\text{ Data }[\\text{i}{]}^{2}}{\\text{N}}}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e24\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Processing of lower limb electromyographic signals\u003c/h2\u003e \u003cp\u003eThe EMD decomposition process is shown below, which can generally be divided into the following processes:\u003c/p\u003e \u003cp\u003eSelect all extreme points for processing signal X (t);\u003c/p\u003e \u003cp\u003ePerform spline curve fitting on the maximum value to obtain the upper envelope curve emax (t);\u003c/p\u003e \u003cp\u003ePerform spline curve fitting on the minimum value to obtain the lower envelope curve emin (t);\u003c/p\u003e \u003cp\u003eCalculate the average curve m (t) of the upper envelope curve emax (t) and the lower envelope curve emin (t);\u003c/p\u003e \u003cp\u003eSubtract the average curve m (t) from the signal X (t) to obtain the residual signal h (t), where h (t)\u0026thinsp;=\u0026thinsp;X (t) - m (t);\u003c/p\u003e \u003cp\u003eDetermine whether the residual signal h (t) meets the termination condition. If it does, stop decomposition. Otherwise, use the residual signal h (t) as a new processing signal and repeat steps 1\u0026ndash;5 until the termination condition is met;\u003c/p\u003e \u003cp\u003eAdd all the obtained components IMFs to obtain a decomposed representation of the original signal.\u003c/p\u003e \u003cp\u003eAmong them, formula (25) represents the average curve of the upper and lower envelope curves, and formula (26) represents the residual signal obtained by subtracting the average curve from the signal.\u003cdiv id=\"Equ25\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ25\" name=\"EquationSource\"\u003e\n$$\\text{m}\\left(\\text{t}\\right)=\\frac{{\\text{e}}_{\\text{m}\\text{a}\\text{x}}\\left(\\text{t}\\right)+{\\text{e}}_{\\text{m}\\text{i}\\text{n}}\\left(\\text{t}\\right)}{2}$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e25\u003c/div\u003e\u003c/div\u003e\u003cdiv id=\"Equ26\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equ26\" name=\"EquationSource\"\u003e\n$$\\text{h}\\left(\\text{t}\\right)=\\text{x}\\left(\\text{t}\\right)-\\text{m}\\left(\\text{t}\\right)$$\u003c/div\u003e\u003cdiv class=\"EquationNumber\"\u003e26\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Analysis of Lower Limb Muscle Activity Data\u003c/h2\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurface EMG root mean square amplitude values (uV) of muscles measured during the buffer period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalling height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFatigue (40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectus femoris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e377.840\u0026thinsp;\u0026plusmn;\u0026thinsp;87.393\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e427.873\u0026thinsp;\u0026plusmn;\u0026thinsp;91.343\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e388.903\u0026thinsp;\u0026plusmn;\u0026thinsp;90.634\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e488.570\u0026thinsp;\u0026plusmn;\u0026thinsp;111.697\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e417.607\u0026thinsp;\u0026plusmn;\u0026thinsp;100.659\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVastus medialis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e491.954\u0026thinsp;\u0026plusmn;\u0026thinsp;89.213\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e562.934\u0026thinsp;\u0026plusmn;\u0026thinsp;111.715\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e434.189\u0026thinsp;\u0026plusmn;\u0026thinsp;102.953\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e572.044\u0026thinsp;\u0026plusmn;\u0026thinsp;96.979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e558.228\u0026thinsp;\u0026plusmn;\u0026thinsp;97.178\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVastus lateralis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e430.014\u0026thinsp;\u0026plusmn;\u0026thinsp;104.162\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e496.916\u0026thinsp;\u0026plusmn;\u0026thinsp;106.659\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e402.501\u0026thinsp;\u0026plusmn;\u0026thinsp;112.651\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e522.450\u0026thinsp;\u0026plusmn;\u0026thinsp;104.561\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e509.881\u0026thinsp;\u0026plusmn;\u0026thinsp;104.461\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibialis anterior muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e153.784\u0026thinsp;\u0026plusmn;\u0026thinsp;43.335\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e141.823\u0026thinsp;\u0026plusmn;\u0026thinsp;48.250\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e194.593\u0026thinsp;\u0026plusmn;\u0026thinsp;76.739\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e167.756\u0026thinsp;\u0026plusmn;\u0026thinsp;60.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e185.546\u0026thinsp;\u0026plusmn;\u0026thinsp;54.470\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGluteus maximus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e299.698\u0026thinsp;\u0026plusmn;\u0026thinsp;84.020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e296.114\u0026thinsp;\u0026plusmn;\u0026thinsp;89.402\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e278.490\u0026thinsp;\u0026plusmn;\u0026thinsp;93.787\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e314.434\u0026thinsp;\u0026plusmn;\u0026thinsp;92.890\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e356.153\u0026thinsp;\u0026plusmn;\u0026thinsp;87.109\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps femoris muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e206.220\u0026thinsp;\u0026plusmn;\u0026thinsp;84.775\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e228.468\u0026thinsp;\u0026plusmn;\u0026thinsp;57.476\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e236.250\u0026thinsp;\u0026plusmn;\u0026thinsp;68.991\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e265.382\u0026thinsp;\u0026plusmn;\u0026thinsp;88.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e315.665\u0026thinsp;\u0026plusmn;\u0026thinsp;99.665\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrocnemius muscle (medial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e405.573\u0026thinsp;\u0026plusmn;\u0026thinsp;94.582\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e483.447\u0026thinsp;\u0026plusmn;\u0026thinsp;100.814\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e376.814\u0026thinsp;\u0026plusmn;\u0026thinsp;100.010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e505.524\u0026thinsp;\u0026plusmn;\u0026thinsp;103.729\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e506.638\u0026thinsp;\u0026plusmn;\u0026thinsp;106.443\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoleus muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e325.816\u0026thinsp;\u0026plusmn;\u0026thinsp;87.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e345.778\u0026thinsp;\u0026plusmn;\u0026thinsp;90.202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e326.417\u0026thinsp;\u0026plusmn;\u0026thinsp;99.712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e381.088\u0026thinsp;\u0026plusmn;\u0026thinsp;100.313\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e386.505\u0026thinsp;\u0026plusmn;\u0026thinsp;88.600\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eFrom the data in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, it can be seen that there is a certain trend in the electromyographic activity level of the lower limb muscles of athletes when jumping deep at different falling heights. At a drop height of 40\u0026ndash;100 cm, the root mean square values of most muscles during the buffering period are higher than those during the stretching period. At a height of 20 cm, the average electromyographic activity level of some muscles during the buffering period is relatively close to the average electromyographic activity level during the stretching period. This change may be related to the stretching reflex activity of the lower limb extensor muscles when they are stretched. At higher falling heights, the lower limb muscles undergo prolonged stretching during the buffering period, resulting in sustained muscle stretch reflex activity and higher levels of electromyographic activity. At smaller drop heights, the stretching time of lower limb muscles during the buffering period is shorter, and the duration of stretch reflex activity is shorter, resulting in lower levels of electromyographic activity.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurface EMG root mean square amplitude values (uV) of muscles measured during the stretching period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalling height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFatigue (40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectus femoris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e357.408\u0026thinsp;\u0026plusmn;\u0026thinsp;89.800\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e328.704\u0026thinsp;\u0026plusmn;\u0026thinsp;99.967\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e309.567\u0026thinsp;\u0026plusmn;\u0026thinsp;94.884\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e374.850\u0026thinsp;\u0026plusmn;\u0026thinsp;120.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e290.930\u0026thinsp;\u0026plusmn;\u0026thinsp;107.840\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVastus medialis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e442.999\u0026thinsp;\u0026plusmn;\u0026thinsp;120.235\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e459.217\u0026thinsp;\u0026plusmn;\u0026thinsp;102.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e402.553\u0026thinsp;\u0026plusmn;\u0026thinsp;102.415\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e485.947\u0026thinsp;\u0026plusmn;\u0026thinsp;87.999\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e437.993\u0026thinsp;\u0026plusmn;\u0026thinsp;121.537\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVastus lateralis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e439.119\u0026thinsp;\u0026plusmn;\u0026thinsp;119.058\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e447.234\u0026thinsp;\u0026plusmn;\u0026thinsp;99.264\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e368.951\u0026thinsp;\u0026plusmn;\u0026thinsp;108.963\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e443.672\u0026thinsp;\u0026plusmn;\u0026thinsp;113.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e412.497\u0026thinsp;\u0026plusmn;\u0026thinsp;104.114\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibialis anterior muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e153.784\u0026thinsp;\u0026plusmn;\u0026thinsp;56.589\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e174.993\u0026thinsp;\u0026plusmn;\u0026thinsp;71.565\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e282.742\u0026thinsp;\u0026plusmn;\u0026thinsp;89.758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e206.453\u0026thinsp;\u0026plusmn;\u0026thinsp;79.304\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e275.405\u0026thinsp;\u0026plusmn;\u0026thinsp;95.789\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGluteus maximus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e191.070\u0026thinsp;\u0026plusmn;\u0026thinsp;93.892\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e225.421\u0026thinsp;\u0026plusmn;\u0026thinsp;89.710\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e221.637\u0026thinsp;\u0026plusmn;\u0026thinsp;100.762\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e239.759\u0026thinsp;\u0026plusmn;\u0026thinsp;70.295\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e290.837\u0026thinsp;\u0026plusmn;\u0026thinsp;79.156\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps femoris muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e192.452\u0026thinsp;\u0026plusmn;\u0026thinsp;84.104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e237.447\u0026thinsp;\u0026plusmn;\u0026thinsp;79.515\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e269.572\u0026thinsp;\u0026plusmn;\u0026thinsp;90.190\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e266.280\u0026thinsp;\u0026plusmn;\u0026thinsp;79.215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e289.226\u0026thinsp;\u0026plusmn;\u0026thinsp;89.292\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrocnemius muscle (medial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e396.156\u0026thinsp;\u0026plusmn;\u0026thinsp;90.532\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e422.080\u0026thinsp;\u0026plusmn;\u0026thinsp;91.545\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e332.357\u0026thinsp;\u0026plusmn;\u0026thinsp;80.507\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e461.675\u0026thinsp;\u0026plusmn;\u0026thinsp;80.608\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e438.688\u0026thinsp;\u0026plusmn;\u0026thinsp;111.697\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoleus muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e326.116\u0026thinsp;\u0026plusmn;\u0026thinsp;89.680\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e269.540\u0026thinsp;\u0026plusmn;\u0026thinsp;85.667\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e247.271\u0026thinsp;\u0026plusmn;\u0026thinsp;98.407\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e312.574\u0026thinsp;\u0026plusmn;\u0026thinsp;99.711\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e311.371\u0026thinsp;\u0026plusmn;\u0026thinsp;79.548\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eAccording to the data in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, under relative fatigue conditions, the surface EMG root mean square amplitude values of the measured muscles during the buffering and stretching stages of athletes during landing are lower than those under normal conditions. This indicates that fatigue can lead to a decrease in muscle electromyographic activity levels. This phenomenon is due to fatigue inducing the central nervous system to activate self-protection mechanisms to prevent muscle injury.\u003c/p\u003e \u003cp\u003eAs shown in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSurface integrated electromyographic values (UVs) of muscles measured during the buffer period\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\"\u0026plusmn;\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFalling height (cm)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e20\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e40\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eFatigue (40)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e80\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRectus femoris\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e33.722\u0026thinsp;\u0026plusmn;\u0026thinsp;10.266\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e35.545\u0026thinsp;\u0026plusmn;\u0026thinsp;9.867\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e34.026\u0026thinsp;\u0026plusmn;\u0026thinsp;10.864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e40.608\u0026thinsp;\u0026plusmn;\u0026thinsp;14.252\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e38.279\u0026thinsp;\u0026plusmn;\u0026thinsp;10.166\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVastus medialis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e43.611\u0026thinsp;\u0026plusmn;\u0026thinsp;8.209\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e48.191\u0026thinsp;\u0026plusmn;\u0026thinsp;10.111\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e40.126\u0026thinsp;\u0026plusmn;\u0026thinsp;13.315\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e53.966\u0026thinsp;\u0026plusmn;\u0026thinsp;19.322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e51.377\u0026thinsp;\u0026plusmn;\u0026thinsp;11.513\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVastus lateralis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e38.249\u0026thinsp;\u0026plusmn;\u0026thinsp;8.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e41.745\u0026thinsp;\u0026plusmn;\u0026thinsp;7.818\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e40.147\u0026thinsp;\u0026plusmn;\u0026thinsp;9.996\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e47.837\u0026thinsp;\u0026plusmn;\u0026thinsp;11.777\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e47.737\u0026thinsp;\u0026plusmn;\u0026thinsp;13.559\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTibialis anterior muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e12.639\u0026thinsp;\u0026plusmn;\u0026thinsp;6.835\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e12.238\u0026thinsp;\u0026plusmn;\u0026thinsp;3.920\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e15.950\u0026thinsp;\u0026plusmn;\u0026thinsp;7.941\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e14.214\u0026thinsp;\u0026plusmn;\u0026thinsp;6.734\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e16.150\u0026thinsp;\u0026plusmn;\u0026thinsp;9.247\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGluteus maximus\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e26.512\u0026thinsp;\u0026plusmn;\u0026thinsp;9.160\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e27.309\u0026thinsp;\u0026plusmn;\u0026thinsp;8.463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e26.013\u0026thinsp;\u0026plusmn;\u0026thinsp;7.069\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e28.555\u0026thinsp;\u0026plusmn;\u0026thinsp;10.455\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e32.691\u0026thinsp;\u0026plusmn;\u0026thinsp;10.753\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBiceps femoris muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e17.173\u0026thinsp;\u0026plusmn;\u0026thinsp;5.886\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e20.151\u0026thinsp;\u0026plusmn;\u0026thinsp;9.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e21.541\u0026thinsp;\u0026plusmn;\u0026thinsp;10.276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e23.586\u0026thinsp;\u0026plusmn;\u0026thinsp;3.592\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e28.887\u0026thinsp;\u0026plusmn;\u0026thinsp;6.186\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGastrocnemius muscle (medial)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e36.285\u0026thinsp;\u0026plusmn;\u0026thinsp;7.291\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e42.014\u0026thinsp;\u0026plusmn;\u0026thinsp;10.228\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e34.174\u0026thinsp;\u0026plusmn;\u0026thinsp;10.329\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e44.929\u0026thinsp;\u0026plusmn;\u0026thinsp;10.836\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e41.210\u0026thinsp;\u0026plusmn;\u0026thinsp;9.215\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSoleus muscle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c2\"\u003e \u003cp\u003e29.033\u0026thinsp;\u0026plusmn;\u0026thinsp;3.110\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c3\"\u003e \u003cp\u003e31.335\u0026thinsp;\u0026plusmn;\u0026thinsp;10.433\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c4\"\u003e \u003cp\u003e28.232\u0026thinsp;\u0026plusmn;\u0026thinsp;9.831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c5\"\u003e \u003cp\u003e34.038\u0026thinsp;\u0026plusmn;\u0026thinsp;10.834\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\"\u0026plusmn;\" colname=\"c6\"\u003e \u003cp\u003e35.440\u0026thinsp;\u0026plusmn;\u0026thinsp;8.125\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.5 Analysis of muscle activity patterns in sports\u003c/h2\u003e \u003cp\u003eAccording to the curve in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, changes in the activation level of different muscles were observed under the action of flat support.\u003c/p\u003e \u003cp\u003eAs shown in Fig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, most of the time, the activation level of these muscles exceeds 50%. The activation level of the erector spinalis (ES) and quadratus psoas (QL) muscles is relatively low, and they are basically in an inactive state.\u003c/p\u003e \u003c/div\u003e"},{"header":"5 Conclusion","content":"\u003cp\u003eThis article uses optical coherence tomography technology and electromyography sensors to study muscle activity patterns in sports. Through the application of these technologies, muscle activity can be more accurately observed and recorded, thereby better understanding the activity patterns of the core muscle group and the abnormalities of the core muscle group activity patterns in patients with low back pain. The results of this study will help to develop and implement more effective sports training plans. By understanding the activity patterns of the core muscle group and combining them with the characteristics of athletes, targeted training programs can be designed to improve the strength and stability of the core muscles, reduce back pain symptoms, and prevent related muscle and bone injuries.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAuthor contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eLingzi Yao has done the first version, Ye Zhang has done the simulations. All authors have contributed to the paper\u0026rsquo;s analysis, discussion, writing, and revision.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFund\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors have not disclosed any funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data will be available upon request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthical approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eFarago, E., MacIsaac, D., Suk, M., \u0026amp; Chan, A. D. (2022). A review of techniques for surface electromyography signal quality analysis. 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BMC Medical Informatics and Decision Making, 22(1), 67.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"optical coherence tomography, Electromyography sensor, Sports, Muscle activity mode","lastPublishedDoi":"10.21203/rs.3.rs-3849504/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-3849504/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThe aim of this study is to explore the method of combining optical coherence tomography and EMG sensors to effectively analyze muscle activity patterns in sports. Optical coherence tomography is used to monitor the surface morphological changes of muscles in a non-invasive manner in real time, and electromyography sensors are combined to record the electrical activity signals of muscles. Through experiments on multiple athletes, a large number of optical coherence tomography and EMG data were collected to analyze muscle activity patterns in different sports. Optical coherence tomography provides precise information on muscle morphological changes, while EMG sensors provide signals of muscle electrical activity. Through the comprehensive analysis of these two kinds of data, more comprehensive and accurate results of muscle activity patterns can be obtained. This method helps to understand the performance of muscles during exercise, and provides scientific basis for exercise training and rehabilitation treatment.\u003c/p\u003e","manuscriptTitle":"Simulation of muscle activity in sports training based on optical coherence tomography technology","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-01-11 13:54:34","doi":"10.21203/rs.3.rs-3849504/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"1d3e70b6-dbfa-4f7a-a559-b3815399c9e5","owner":[],"postedDate":"January 11th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2024-06-08T04:54:05+00:00","versionOfRecord":[],"versionCreatedAt":"2024-01-11 13:54:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-3849504","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-3849504","identity":"rs-3849504","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}