A grain moisture detection method based on a wireless signal refraction model

Abstract

The application discloses a kind of grain moisture detection methods based on wireless signal refraction model, first with the dielectric constant of grain as bridge establishes the theoretical model of grain moisture and signal measurement phase difference.In the deployment of Wi-Fi transceiver node multi-antenna constraint phase difference does not exceed 2π, so as to solve the phase model problem.Then based on Wi-Fi beam forming algorithm enhances the refraction wireless signal useful for moisture detection.Signal characteristic Wi-Fi CSI beam forming ratio phase information is acquired, and finally based on theoretical model estimates grain moisture.The main work contents include signal acquisition and pretreatment, signal enhancement and model optimization, extract phase difference feature, grain moisture estimation.The application is good in the moisture detection experiment of different grains, and the average absolute error is less than 5%.

Description

Technical Field

[0001] This invention belongs to the field of detection technology, specifically relating to a method for detecting the moisture content of grains. Background Technology

[0002] Food is the foundation for human survival and sustainable social development, with grain playing a vital role. In recent years, smart grain storage technology has received widespread attention, aiming to reduce storage losses by monitoring relevant factors in the storage environment in real time. One crucial piece of information that needs to be detected to promote smart grain storage is the moisture content of the stored grain. Existing methods typically involve deploying numerous sensors in the warehouse, such as cameras and temperature / humidity / carbon dioxide sensors. However, this method is not only expensive but also inaccurate. For example, camera-based monitoring methods can only detect the surface of the grain, which is insufficient for moisture detection. Therefore, there is an urgent need to propose a high-accuracy and low-cost method for grain moisture detection. This paper proposes exploring a grain moisture detection method based on wireless sensing technology. Specifically, Channel State Information (CSI) of Wi-Fi signals is suggested due to its advantages in high sensing performance and low deployment costs. However, applying Wi-Fi CSI to grain moisture detection remains challenging due to the following difficulties.

[0003] First, the basic idea behind using Wi-Fi CSI to sense grain moisture is to measure the phase shift caused by differences in dielectric constant. Specifically, grains with different moisture levels have different dielectric constants, which affects the propagation speed of the Wi-Fi signal and leads to different phase shifts. Given that the dielectric constant of grains generally varies slightly within a small range between 2 and 3.5, a high-frequency wireless signal is needed to provide sufficient sensing resolution. However, high-frequency signals will result in large phase shifts, even if they only travel a short distance within the grain. Since the detectable phase shift is between 0 and 2π, a phase shift greater than 2π will cause phase ambiguity. Therefore, how to solve the problem of phase ambiguity is the first challenge in applying Wi-Fi CSI for grain moisture detection.

[0004] Secondly, in indoor environments, the presence of walls and other objects causes wireless signals to propagate in complex ways. Specifically, due to the coexistence of reflection and refraction, the receiver captures a mixed signal. Moisture detection methods rely on the refracted signal passing through the grain, but multipath reflection can severely interfere with detection accuracy. Furthermore, due to transceiver clock asynchrony, there is a time-varying phase shift between different data packets captured by the receiver. Therefore, the second challenge is how to reduce multipath interference and eliminate random phase shifts without compromising the phase shift caused by signal propagation through the grain. Summary of the Invention

[0005] To overcome the shortcomings of existing technologies, this invention provides a grain moisture detection method based on a wireless signal refraction model. First, a theoretical model of the grain moisture content and signal phase difference is established using the grain's dielectric constant as a bridge. Multiple antennas are deployed at the Wi-Fi transceiver node to constrain the phase difference to no more than 2π, thus solving the phase model problem. Then, the refractional wireless signal useful for moisture detection is enhanced based on a Wi-Fi beamforming algorithm. Phase information of the Wi-Fi CSI beamforming ratio is obtained, and finally, grain moisture is estimated based on the theoretical model. The main work includes signal acquisition and preprocessing, signal enhancement and model optimization, extraction of phase difference features, and grain moisture estimation. This invention performs well in moisture detection experiments with different grains, with an average absolute error of less than 5%.

[0006] The technical solution adopted by this invention to solve its technical problem includes the following steps:

[0007] Step 1: Use a desktop computer as a Wi-Fi transceiver node;

[0008] Step 2: Set the operating frequency and bandwidth of the transceiver signal, and set the duration of a single experiment;

[0009] Step 3: Preprocess the multiple Wi-Fi signals collected by the receiving end;

[0010] Step 4: Apply the Wi-Fi beamforming algorithm to further optimize the preprocessed signal, enhance the target refracted signal, and construct new signal features based on the optimized composite signal. Wi-Fi beamforming is superior to CSIB. ratio ;

[0011] Step 5: Select the optimal subcarrier for Wi-Fi beamforming ratio based on the minimum variance principle, and extract the phase difference.

[0012] Step 6: Estimate the dielectric constant ε of the grain based on the measured phase difference. g Furthermore, the grain moisture content M was estimated.

[0013] Furthermore, the desktop computer is equipped with an Intel 5300 network card.

[0014] Furthermore, the Wi-Fi transceiver node is equipped with the Linux 802.11n CSI tool platform.

[0015] Furthermore, the duration of a single experiment is 30 seconds.

[0016] Furthermore, the center frequency of the transceiver signal is 5.2 GHz, and the bandwidth is 20 MHz; the transmitting node sends data packets at a frequency of 100 Hz, which are then captured by the receiving node.

[0017] Furthermore, the preprocessing method in step 3 includes outlier removal based on the Hampel algorithm, noise reduction based on a low-pass filter algorithm, and data smoothing based on the Savitzky-Golay filter algorithm, wherein the cutoff frequency of the low-pass filter algorithm is 200Hz.

[0018] Furthermore, the Wi-Fi beamforming algorithm is as follows:

[0019]

[0020]

[0021] Among them, H beam1 and H beam2 It is a composite signal obtained by beamforming and enhancing the signals from the two transmitting antennas respectively, H mn (f,t) refers to the Wi-Fi signal received by the receiving antenna n from the transmitting antenna m, d is the distance between the receiving antennas, and θ is the target signal direction angle.

[0022] Furthermore, the Wi-Fi beamforming ratio is higher than that of CSIB. ratio for:

[0023]

[0024] Among them, H beam1 and H beam2 It is a composite signal obtained by beamforming and enhancing the signals from the two transmitting antennas respectively. It is the signal attenuation factor, which determines the amplitude change of the signal; f is the center frequency of the signal; τ 21 (t)-τ 11 (t) is the difference in propagation path time delay between transmitting antenna 1 and transmitting antenna 2 to receiving antenna 1, -j2πf(τ) 21 (t)-τ 11 (t) represents the signal phase information.

[0025] Furthermore, the phase difference for:

[0026]

[0027] Where f is the center frequency of the signal, τ 21 (t)-τ 11 (t) is the difference in propagation path time delay between transmitting antenna 1 and transmitting antenna 2 and receiving antenna 1.

[0028] Furthermore, the moisture content M of the grain is:

[0029]

[0030] Where f is the signal center frequency, Δd is the distance between the transmitting antennas, c0 is the signal speed in air, and θ g It is the signal refraction angle. It is the phase difference, and a, b, and c are constant parameters of the empirical model of grain moisture and dielectric constant.

[0031] The beneficial effects of this invention are as follows:

[0032] This invention optimizes model parameters based on prior knowledge to improve moisture estimation accuracy. Furthermore, it proposes a Wi-Fi beamforming algorithm to enhance the refracted signal and further proposes CSI beamforming ratio signal characteristics to eliminate the effects of subcarrier frequency deviation and cumulative phase deviation. This invention performs well in moisture detection experiments on different grains, with an average absolute error of less than 5%. Attached Figure Description

[0033] Figure 1 This is a flowchart of the method of the present invention.

[0034] Figure 2 This is a schematic diagram of the experimental design in an embodiment of the present invention.

[0035] Figure 3 This is a schematic diagram of the signal propagation path in an embodiment of the present invention.

[0036] Figure 4 This is a diagram of the Wi-Fi beamforming algorithm in an embodiment of the present invention.

[0037] Figure 5 This is the optimal subcarrier selection diagram in an embodiment of the present invention.

[0038] Figure 6 This is a schematic diagram illustrating the optimization of model parameters based on prior knowledge in an embodiment of the present invention.

[0039] Figure 7 This is an overall performance diagram in an embodiment of the present invention.

[0040] Figure 8 This is a diagram showing the phase difference and moisture estimation error of different grains in an embodiment of the present invention. Detailed Implementation

[0041] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0042] like Figure 1 As shown, a method for detecting grain moisture based on a wireless signal refraction model includes the following steps:

[0043] Step 1: Use two desktop computers equipped with Intel 5300 network cards as Wi-Fi transceiver nodes. To ensure accurate CSI measurements, both Wi-Fi nodes are equipped with the Linux 802.11n CSI tool platform and run on the Ubuntu 12.04 operating system.

[0044] Step 2: Based on the Linux 802.11n CSI tool platform, set the operating frequency and bandwidth of the transceiver signals, and set the duration of a single experiment to 30 seconds.

[0045] Step 3: Preprocess the multiple Wi-Fi signals collected by the receiving end.

[0046] Step 4: Apply the Wi-Fi beamforming algorithm to further optimize the preprocessed signal and enhance the target refracted signal. Based on the optimized composite signal, construct new signal features. Wi-Fi beamforming is superior to CSIB. ratio .

[0047] Step 5: Select the optimal subcarrier for Wi-Fi beamforming ratio based on the minimum variance principle, and extract the phase difference.

[0048] Step 6: Estimate the dielectric constant ε of the grain based on the measured phase difference. g Furthermore, the grain moisture content M was estimated.

[0049] Furthermore, in the grain moisture detection method based on the wireless signal refraction model, the center frequency of the transceiver signal in step 2 is 5.2 GHz, and the bandwidth is 20 MHz. The transmitting node sends data packets at a frequency of 100 Hz, which are then captured by the receiving node.

[0050] Furthermore, in the grain moisture detection method based on the wireless signal refraction model, the signal preprocessing in step 3 includes outlier removal based on the Hampel algorithm, noise reduction based on the low-pass filter algorithm, and data smoothing based on the Savitzky-Golay filter algorithm, wherein the cutoff frequency of the low-pass filter algorithm is 200Hz.

[0051] Furthermore, in the grain moisture detection method based on the wireless signal refraction model, the Wi-Fi beamforming algorithm for signal optimization in step 4 is as follows:

[0052]

[0053]

[0054] Furthermore, in the grain moisture detection method based on the wireless signal refraction model, the Wi-Fi beamforming ratio in step 4 is:

[0055]

[0056] in, It is the signal attenuation factor, which determines the amplitude change of the signal, -j2πf(τ) 21 (t)-τ 11 (t) represents the signal phase information.

[0057] Furthermore, in the grain moisture detection method based on the wireless signal refraction model, the phase difference mentioned in step 5 is:

[0058]

[0059] Furthermore, in the grain moisture detection method based on the wireless signal refraction model, the grain moisture content in step 6 is:

[0060]

[0061] Where f is the signal center frequency, Δd is the distance between the transmitting antennas, c0 is the signal speed in air, and θ g This is the signal refraction angle. Additionally, a, b, and c are constant parameters in the empirical model of grain moisture and dielectric constant.

[0062] Example:

[0063] Step 1: Use two desktop computers equipped with Intel 5300 network cards as Wi-Fi transceiver nodes. To ensure accurate CSI measurements, both Wi-Fi nodes are equipped with the Linux 802.11n CSI tool platform, running on Ubuntu 12.04. The transceiver node network cards are connected via antenna extension cables, and the setup is as follows: Figure 2 The experimental scenario shown.

[0064] like Figure 2 As shown, a grain moisture sensing model based on MIMO technology is first proposed, with the transmitter and receiver equipped with two and three antennas, respectively. Figure 3 The propagation path of the refracted signal in the multi-antenna experiment was analyzed. Therefore, the phase difference between the two transmitted signals from each receiving antenna can be measured, regardless of the signal propagation path. Furthermore, the spacing between the two transmitting antennas is carefully designed to avoid phase ambiguity (i.e., a phase difference greater than 2π) and antenna coupling problems. For example, the phase shift of the signal propagating from transmitting antenna TX1 to receiving antenna RX1 is:

[0065]

[0066] Where f is the center frequency of the signal, ε g It is the dielectric constant of the grain, d g1It is the propagation path of the signal emitted by transmitting antenna 1 inside the grain, d a1 C0 is the propagation path of the signal emitted by transmitting antenna 1 in the air, and C0 is the propagation speed of the signal in the air.

[0067] The phase shift of the signal propagated from transmitting antenna TX2 to receiving antenna RX1 is:

[0068]

[0069] Where f is the center frequency of the signal, ε g It is the dielectric constant of the grain, d g2 It is the propagation path of the signal emitted by transmitting antenna 2 inside the grain, d a2 c is the path of the signal emitted by transmitting antenna 2 in the air, and c0 is the speed of signal propagation in the air.

[0070] The phase difference between two signals is expressed as:

[0071]

[0072] in, It is the phase shift of the signal propagated from the transmitting antenna TX1 to the receiving antenna RX1. This refers to the phase shift of the signal propagating from the transmitting antenna TX2 to the receiving antenna RX1. f is the signal center frequency, and ε is the phase shift. g The dielectric constant of the grain, Δd is the distance between the transmitting antennas, and θ is the dielectric constant of the grain. g The angle of incidence of the refracted signal in the grain, d a1 It is the propagation path of the signal emitted by transmitting antenna 1 in the air, d a2 c is the path of the signal emitted by transmitting antenna 2 in the air, and c0 is the speed of signal propagation in the air.

[0073] Step 2: Set the center frequency of the transceiver signal to 5.2 GHz and the bandwidth to 20 MHz. The transmitting node sends data packets at a frequency of 100 Hz, which are then captured by the receiving node. Additionally, set the duration of each experiment to 30 seconds.

[0074] Step 3: Due to the influence of environmental noise on the received signal, preprocessing is required, including outlier removal based on the Hampel algorithm, noise reduction based on a low-pass filter algorithm, and data smoothing based on the Savitzky-Golay filter algorithm. The cutoff frequency of the low-pass filter algorithm is set to 200Hz.

[0075] Step 4: In indoor environments, the presence of walls and other objects causes wireless signals to propagate in complex ways. Specifically, due to the coexistence of reflection and refraction, the receiver captures a mixed signal. Furthermore, due to transceiver clock asynchrony, there is a time-varying phase shift between different data packets captured by the receiver. Since moisture detection methods rely solely on the refracted signal through the grain, a Wi-Fi beamforming algorithm is proposed to reduce multipath interference and eliminate random phase shifts without disrupting the phase shift caused by signal propagation through the grain. Figure 4 As shown, firstly, a Wi-Fi beamforming algorithm is applied to optimize and enhance the refracted signal based on a multi-antenna array.

[0076] The signal received by the antenna array can be represented as

[0077]

[0078] Among them, H mn (f,t) represents the Wi-Fi signal received by the nth receiving antenna and transmitted by the mth transmitting antenna, A m (f,t) represents the signal attenuation factor of the m-th transmitting antenna, which determines the amplitude variation of the received signal. f is the signal center frequency, e -j2πΔft It is a random phase shift, τ mn (t) represents the path propagation delay when the nth receiving antenna receives the signal transmitted by the mth transmitting antenna.

[0079] The multiple Wi-Fi signals received by the receiving antenna are further represented in matrix form as follows:

[0080]

[0081]

[0082] Where H1 and H2 represent the received signals from two different transmitting antennas, H mn (f,t) represents the Wi-Fi signal received by the nth receiving antenna and transmitted by the mth transmitting antenna, e -j2πΔft It represents random phase shift, A1(f,t) represents the signal attenuation factor of transmitting antenna 1, A2(f,t) represents the signal attenuation factor of transmitting antenna 2, f is the signal center frequency, λ is the signal wavelength, and τ is the signal wavelength. mn (t) represents the path propagation delay of the signal received by the nth receiving antenna from the mth transmitting antenna, where d is the distance between the receiving antennas and θ is the incident angle of the received signal. Let θ represent the direction vector, and then use the conjugate transpose of the direction vector as the weight vector ω(θ) of the delay summation beamforming algorithm.

[0083]

[0084] Where λ is the signal wavelength, d is the distance between the receiving antennas, and θ is the incident angle of the received signal.

[0085] Multiplying H1 and H2 by the weight vector ω(θ) on the left yields the signal enhanced by the beamforming algorithm, as shown below:

[0086]

[0087]

[0088] Among them, H beam1 and H beam2 It is a composite signal obtained by beamforming and enhancing the signals from the two transmitting antennas respectively, H mn (f,t) represents the Wi-Fi signal received by the nth receiving antenna and transmitted by the mth transmitting antenna, where f is the signal center frequency, λ is the signal wavelength, d is the distance between the receiving antennas, and θ is the incident angle of the received signal.

[0089] To enhance the refracted signal and improve the accuracy of phase difference estimation, it is necessary to determine the incident angle θ of the target signal. It was found that the refracted signal arrives perpendicularly to the receiving antenna array on the projection plane, meaning the target direction for beamforming is θ. ° Therefore, the enhanced refracted signal is expressed as:

[0090] H beam1 =H 11 (f,t)+H 12 (f,t)+H 13 (f,t)

[0091] H beam2 =H 21 (f,t)+H 22 (f,t)+H 23 (f,t)

[0092] Among them, H beam1 and H beam2 It is a composite signal obtained by beamforming and enhancing the signals from the two transmitting antennas respectively, H mn (f,t) represents the Wi-Fi signal received by the nth receiving antenna and transmitted by the mth transmitting antenna.

[0093] Furthermore, it was found that the enhanced signals based on the Wi-Fi beamforming algorithm had the same phase shift. Therefore, a division operation was used to eliminate these phase shifts, i.e., using H... beam2 and H beam1 The phase difference is estimated by the ratio of the two beamforming ratios, and the resulting signal is named the CSI beamforming ratio, which is formally defined as follows:

[0094]

[0095] Among them, H beam1 and H beam2 It is a composite signal obtained by beamforming and enhancing the signals from the two transmitting antennas respectively. It is the signal attenuation factor, which determines the amplitude change of the signal, and f is the center frequency of the signal, -j2πf(τ) 21 (t)-τ 11 (t) represents the estimated phase difference corresponding to the signal phase information.

[0096] Step 5: CSI has 30 subcarrier signals, and the signal stability varies significantly between different subcarriers. For example... Figure 5 As shown, CSIB is removed. ratio After processing some of the more volatile signals, most subcarriers exhibit good stability and consistency. Finally, the optimal subcarrier is selected based on the minimum variance principle.

[0097] Step 6: Based on the grain moisture sensing model established in Step 1 and the estimated phase difference, the grain dielectric constant ε can be obtained. g As shown below:

[0098]

[0099] Where c0 is the speed at which the signal travels through the air. The measured phase difference is f, the signal center frequency is f, Δd is the distance between the transmitting antennas, and θ is θ. g It is the angle of incidence of the refracted signal in the grain.

[0100] In addition, existing research has provided empirical models for the dielectric constant and moisture content of different grain materials, as shown below:

[0101] ε g =a+blgf+cM

[0102] Where, ε g The dielectric constant of the grain is represented by a, b, and c, which are constants as shown in Table 1. f is the frequency of the wireless signal, and M corresponds to the moisture content of the grain.

[0103] Therefore, the grain moisture sensing model based on phase difference can be derived as follows:

[0104]

[0105] Where f is the signal center frequency, Δd is the distance between the transmitting antennas, c0 is the signal speed in air, and θ g This is the signal refraction angle. Additionally, a, b, and c are constant parameters in the empirical model of grain moisture and dielectric constant.

[0106] Table 1. Dielectric constants of four different grains

[0107]

[0108] In addition, to improve the accuracy of the model, cosθ was further explored based on prior knowledge. g The range of values ​​for θ. First, according to the law of refraction, the incident angle θ g and the angle of refraction θ a Satisfy the following formula:

[0109]

[0110] When the angle of refraction reaches its maximum value of 90°, the corresponding angle of incidence also reaches its maximum value, i.e., the critical angle θ. gmax , can be represented as:

[0111]

[0112] As shown in the above equation, for a given dielectric constant ε g Only when the incident angle θ g Less than the critical angle θ gmax At this time, the Wi-Fi signal will be refracted into the air. Because it is difficult to directly determine the incident angle θ... g We choose to estimate its value based on trigonometric relationships. Specifically, cosθ g This can be further expressed as:

[0113]

[0114] Therefore, sinθ can be measured a To calculate cosθ g However, θ a The value of θ is also difficult to obtain directly. Therefore, θ is indirectly derived by utilizing prior knowledge. a The value of . For example Figure 6 As shown, for an antenna with height H′, in order to ensure signal acquisition, θ a The value of θ cannot be too small. Specifically, θ a The minimum value (i.e., θ) amin The condition is that the refracted signal just reaches the top of the antenna. In this case, the following geometric relationship is satisfied:

[0115]

[0116]

[0117] Based on the above formula, we can further obtain:

[0118]

[0119] Given the size of the container, the lengths of all variables in the above formula can be measured. For example, in the experiment, L = 27.7 cm, H = 15 cm, ΔH = 3 cm, H′ = 10 cm. When the dielectric constant of the grain ε... g When sinθ is 2, amin =0.88. When the dielectric constant ε of the grain is... g When it is 3.5, sinθ amin =0.90. Therefore, when the grain moisture content varies between 5% and 20% (i.e., the dielectric constant is between 2 and 3.5), sinθ amin The value is between 0.88 and 0.90. Furthermore, sinθ amax The value is 1.0, which corresponds to θ. a The case where the value is 90°. Also, cosθ g The expression can be further represented as:

[0120]

[0121] Furthermore, sinθ was empirically set in subsequent experiments. a =0.95, further improving the accuracy of grain moisture estimation.

[0122] During the experiment, the phase difference of soybeans under six different moisture conditions was measured and the soybean moisture content was estimated to evaluate the overall performance of the grain moisture model. Figure 7 As shown, the average absolute error of the grain moisture sensing model at different moisture levels is 3.88%, and its sensing accuracy is significantly higher than that of the baseline model. Figure 8 As shown, the average absolute error of the grain moisture sensing model in estimating the moisture content of grains such as wheat, sorghum, and rapeseed is less than 5%.

Claims

1. A method for detecting grain moisture based on a wireless signal refraction model, characterized in that, Includes the following steps: Step 1: Use a desktop computer as a Wi-Fi transceiver node; Step 2: Set the operating frequency and bandwidth of the transceiver signal, and set the duration of a single experiment; Step 3: Preprocess the multiple Wi-Fi signals collected by the receiving end; Step 4: Apply Wi-Fi beamforming algorithm to optimize the preprocessed signal, enhance the target refracted signal, and construct new signal features based on the optimized composite signal. Wi-Fi beamforming ratio... ; The Wi-Fi beamforming algorithm is as follows: in, and It is a composite signal obtained by beamforming and enhancing the signals from the two transmitting antennas respectively. This refers to the Wi-Fi signal received by the receiving antenna n from the transmitting antenna m. It is the spacing between the receiving antennas. It is the target signal direction angle; The Wi-Fi beamforming ratio for: in, It is the signal attenuation factor, which determines the amplitude change of the signal. It is the center frequency of the signal. It is the difference in propagation path time delay between transmitting antenna 1 and transmitting antenna 2 and reaching receiving antenna 1. It is signal phase information; Step 5: Select the optimal subcarrier for Wi-Fi beamforming ratio based on the minimum variance principle, and extract the phase difference. ; The phase difference for: ； Step 6: Estimate the dielectric constant of the grain based on the measured phase difference And estimate the grain moisture content M; The moisture content M of the grain is: in, It is the center frequency of the signal. It is the spacing between the transmitting antennas. It is the speed at which a signal travels through the air. It is the signal refraction angle. It's the phase difference. , , These are all constant parameters from an empirical model of grain moisture and dielectric constant.

2. The method for detecting grain moisture based on a wireless signal refraction model according to claim 1, characterized in that, The desktop computer is equipped with an Intel 5300 network card.

3. The method for detecting grain moisture based on a wireless signal refraction model according to claim 1, characterized in that, The Wi-Fi transceiver node is equipped with the Linux 802.11n CSI tool platform.

4. The method for detecting grain moisture based on a wireless signal refraction model according to claim 1, characterized in that, The duration of each experiment is 30 seconds.

5. The method for detecting grain moisture based on a wireless signal refraction model according to claim 1, characterized in that, The center frequency of the transceiver signal is 5.2 GHz, and the bandwidth is 20 MHz; the transmitting node sends data packets at a frequency of 100 Hz, which are then captured by the receiving node.

6. The method for detecting grain moisture based on a wireless signal refraction model according to claim 1, characterized in that, The preprocessing method in step 3 includes outlier removal based on the Hampel algorithm, noise reduction based on a low-pass filter algorithm, and data smoothing based on the Savitzky-Golay filter algorithm, wherein the cutoff frequency of the low-pass filter algorithm is 200Hz.

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