Heart rate detection method and device for body fat scale

Abstract

The present invention is applicable to the technical field of data recognition and provides a heart rate detection method and device for a body fat scale. The method comprises: obtaining first heartbeat data collected based on bioelectrical impedance analysis and second heartbeat data collected based on photoplethysmography; extracting multiple first peak positions from the first heartbeat data and multiple second peak positions from the second heartbeat data; extracting first difference peak positions from the multiple first peak positions and second difference peak positions from the multiple second peak positions; generating corrected peak positions based on the multiple first peak positions, the multiple second peak positions, the first difference peak positions, and the second difference peak positions; and calculating the user's heart rate based on the peak time intervals in the corrected peak positions. This solution overcomes the problems of high noise and poor accuracy in heart rate measurement associated with traditional body fat scales through differential peak analysis and correction.

Description

Technical Field

[0001] The present invention belongs to the technical field of data recognition, and in particular relates to a heart rate detection method and device for a body fat scale. Background Art

[0002] With increasing health awareness, a variety of smart devices capable of measuring body fat and heart rate have emerged on the market, particularly body fat scales. These scales use bioelectrical impedance analysis (BIA) technology to detect physiological parameters such as body fat percentage, and an increasing number of them are integrating heart rate monitoring. However, traditional body fat scales suffer from significant noise when measuring heart rate, impacting the accuracy and reliability of heart rate measurements.

[0003] The most common heart rate detection method currently used on body fat scales is based on bioelectrical impedance analysis (BIA). BIA uses electrodes placed on the scale to measure the current flowing through the body to estimate body composition and heart rate. However, due to variations in the electrode design of body fat scales and the user's contact with the device, the BIA method is susceptible to external factors such as changes in skin resistance and poor contact. This can lead to significant noise in the heart rate signal and, in severe cases, even inability to accurately identify the heart rate waveform, resulting in low heart rate detection accuracy. Summary of the Invention

[0004] In view of this, an embodiment of the present invention provides a heart rate detection method and device for a body fat scale to solve the technical problem that traditional technologies are easily affected by external factors, resulting in strong noise in the heart rate signal.

[0005] A first aspect of an embodiment of the present invention provides a heart rate detection method for a body fat scale. The heart rate detection method for a body fat scale is applied to a body fat scale. The body fat scale includes a bioimpedance acquisition module and a photoelectric capacitance acquisition module. The heart rate detection method for the body fat scale includes:

[0006] Acquiring first heartbeat data collected based on bioelectrical impedance analysis and second heartbeat data collected based on photoplethysmography;

[0007] Extracting a plurality of first peak positions from the first heartbeat data, and extracting a plurality of second peak positions from the second heartbeat data;

[0008] Extracting a first difference peak position from the plurality of first peak positions, and extracting a second difference peak position from the plurality of second peak positions; the first difference peak position or the second difference peak position refers to a peak position where the peak distribution of the first peak position and the second peak position in the same order is different;

[0009] generating a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position;

[0010] The user's heart rate is calculated based on the peak time interval in the corrected peak position.

[0011] Furthermore, the step of extracting a plurality of first peak positions in the first heartbeat data includes:

[0012] intercepting a fixed-length segment of waveform data from the first heartbeat data;

[0013] Collecting a plurality of first sampling point data in the segment waveform data based on a preset sampling frequency; wherein the segment waveform data includes the entire heartbeat cycle waveform data;

[0014] Arrange the amplitudes of the plurality of first sampling point data from large to small to obtain ordered first sampling point data;

[0015] Using the first sampling point data at a preset position in the ordered first sampling point data as a peak threshold; the preset position includes one tenth of the ordered sampling point data;

[0016] Collecting a plurality of second sampling point data from the first heartbeat data based on a preset sampling frequency;

[0017] Extracting target second sampling point data greater than the peak threshold from the plurality of second sampling point data;

[0018] Extracting adjacent sampling point data from a plurality of target second sampling point data as a peak data set; the adjacent sampling point data refers to target second sampling point data with adjacent sampling order in the first heartbeat data;

[0019] The average position of a plurality of continuous sampling point data in the peak data set is used as the first peak position.

[0020] Furthermore, the step of extracting a first difference peak position from the plurality of first peak positions and extracting a second difference peak position from the plurality of second peak positions includes:

[0021] Calculating a plurality of first horizontal coordinate differences between adjacent first wave peak positions in the first heartbeat data;

[0022] Calculating a plurality of second horizontal coordinate differences between adjacent second wave peak positions in the second heartbeat data;

[0023] Subtract the first horizontal coordinate difference from the second horizontal coordinate difference in the same order to obtain the error value;

[0024] Obtaining a first wave peak position to be corrected and a second wave peak position to be corrected corresponding to the error value being greater than a first threshold;

[0025] Obtaining a first peak position of the sample and a second peak position of the sample corresponding to the error being no greater than a first threshold;

[0026] Calculating a first similarity between a first wave peak data set corresponding to the first wave peak position to be corrected and a second wave peak data set corresponding to the first wave peak position of the sample;

[0027] Calculating a second similarity between the first wave peak data set corresponding to the second wave peak position to be corrected and the second wave peak data set corresponding to the second wave peak position of the sample;

[0028] If the first similarity is not greater than a second threshold, and the second similarity is greater than a second threshold, taking the first peak position corresponding to the error value as the first difference peak position;

[0029] If the first similarity is greater than a second threshold and the second similarity is not greater than the second threshold, the second peak position corresponding to the error value is used as the second difference peak position.

[0030] Furthermore, the step of calculating a first similarity between the first wave peak data set corresponding to the first wave peak position to be corrected and the second wave peak data set corresponding to the first wave peak position of the sample includes:

[0031] Calculating a plurality of first waveform features of the first peak data set; the plurality of first waveform features including peak value, mean value, rise time, fall time, skewness, and variance;

[0032] calculating a plurality of second waveform features of the second peak data set;

[0033] The similarity between the plurality of first waveform features and the plurality of second waveform features is used as the first similarity.

[0034] Furthermore, after the step of calculating the second similarity between the first wave peak data set corresponding to the second wave peak position to be corrected and the second wave peak data set corresponding to the second wave peak position of the sample, the method further includes:

[0035] If both the first similarity and the second similarity are smaller than the second threshold, new first heartbeat data and new second heartbeat data are reacquired, and a first difference peak position in the new first heartbeat data and a second difference peak position in the new second heartbeat data are extracted.

[0036] Furthermore, the step of generating a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position includes:

[0037] If the number of the first difference peak positions is less than the number of the second difference peak positions, correcting the first difference peak positions among the plurality of first peak positions based on the plurality of second peak positions, and using the corrected plurality of first peak positions as the corrected peak positions;

[0038] If the number of the first difference peak positions is not less than the number of the second difference peak positions, the second difference peak positions in the plurality of second peak positions are corrected based on the plurality of first peak positions, and the corrected plurality of second peak positions are used as the corrected peak positions.

[0039] Furthermore, if the number of the first difference peak positions is less than the number of the second difference peak positions, the step of correcting the first difference peak positions among the plurality of first peak positions based on the plurality of second peak positions and using the corrected plurality of first peak positions as the corrected peak positions includes:

[0040] Acquire a target second peak position having the same sequence as the first difference peak position;

[0041] replacing the first difference peak positions in the plurality of first peak positions with target second peak positions in the same order to obtain a plurality of corrected first peak positions;

[0042] The corrected plurality of first peak positions are used as the corrected peak positions.

[0043] A second aspect of an embodiment of the present invention provides a heart rate detection device for a body fat scale, comprising:

[0044] an acquiring unit, configured to acquire first heartbeat data acquired based on bioelectrical impedance analysis and second heartbeat data acquired based on photoplethysmography;

[0045] a first extraction unit, configured to extract a plurality of first peak positions from the first heartbeat data, and extract a plurality of second peak positions from the second heartbeat data;

[0046] a second extraction unit, configured to extract a first difference peak position from the plurality of first peak positions, and extract a second difference peak position from the plurality of second peak positions; the first difference peak position or the second difference peak position refers to a peak position where a difference exists in peak distribution between the first peak position and the second peak position in the same order;

[0047] a generating unit, configured to generate a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position;

[0048] A calculation unit is used to calculate the user's heart rate based on the peak time interval in the corrected peak position.

[0049] A third aspect of an embodiment of the present invention provides a terminal device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor. When the processor executes the computer program, the steps in the heart rate detection method for the body fat scale described in the first aspect are implemented.

[0050] A fourth aspect of an embodiment of the present invention provides a computer-readable storage medium, which stores a computer program. When the computer program is executed by a processor, it implements the steps in the heart rate detection method of the body fat scale described in the first aspect.

[0051] Compared to the prior art, the present invention offers the following advantages: by extracting multiple peak positions from the BIA and PPG signals and analyzing their differences (i.e., the first and second difference peak positions), the present invention can identify and correct differences between the two signal sources. This correction method based on difference peak positions effectively eliminates peak position deviations between the two data sources, thereby reducing errors caused by poor contact, interfering noise, or posture changes. Traditional methods often rely solely on the peaks of a single signal source and are susceptible to noise. However, by comparing the peak position differences between the two signals, the present invention provides a more robust and accurate peak correction solution. By comprehensively considering the data and difference peak positions of the two detection signals, the present invention significantly enhances the system's adaptability to external interference (such as poor skin contact, device movement, and ambient light changes). In actual use, BIA and PPG signals may be affected by different interference sources, but by fusing the data and performing peak difference analysis, the impact of interference can be effectively reduced. By combining BIA and PPG heart rate detection methods and performing differential peak analysis and correction, the problems of high noise and poor accuracy in heart rate measurement of traditional body fat scales are overcome, significantly improving the accuracy and reliability of heart rate detection. BRIEF DESCRIPTION OF THE DRAWINGS

[0052] In order to more clearly illustrate the technical solutions in the embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments or related technical descriptions. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0053] Figure 1 A schematic flow chart of a heart rate detection method for a body fat scale provided by the present invention is shown;

[0054] Figure 2 A schematic diagram of a heart rate detection device for a body fat scale provided by one embodiment of the present invention is shown;

[0055] Figure 3 A schematic diagram of a terminal device provided by an embodiment of the present invention is shown. DETAILED DESCRIPTION

[0056] In the following description, specific details such as particular system structures and techniques are provided for purposes of illustration, not limitation, to facilitate a thorough understanding of the embodiments of the present invention. However, it will be apparent to those skilled in the art that the present invention may be practiced in other embodiments without these specific details. In other cases, detailed descriptions of well-known systems, devices, circuits, and methods are omitted so as not to obscure the description of the present invention with unnecessary detail.

[0057] The embodiments of the present invention provide a heart rate detection method and device for a body fat scale to solve the technical problem of optimizing the power consumption of a beacon device while ensuring positioning accuracy.

[0058] First, the present invention provides a method for detecting heart rate on a body fat scale. Figure 1 , Figure 1 FIG1 shows a schematic flow chart of a heart rate detection method for a body fat scale provided by the present invention. Figure 1 As shown, the heart rate detection method of the body fat scale may include the following steps:

[0059] Step 101: Acquire first heartbeat data collected based on bioelectrical impedance analysis and second heartbeat data collected based on photoplethysmography;

[0060] Body fat scales use bioimpedance analysis (BIA) to measure changes in the body's resistance to electrical current. These changes are closely correlated with heart rhythm. While body fat scales primarily use this method to measure body fat and body water, they can also indirectly obtain heart rate information. A weak current is sent through electrodes to measure changes in the electrical resistance of body tissue. Heartbeats cause tiny fluctuations in the resistance of body tissue, and by analyzing these fluctuations, heart rate can be inferred.

[0061] Photoplethysmography (PPG) uses optical sensors (typically LEDs and photodiodes) to detect changes in blood flow. When the heart beats, blood flow causes tiny changes in skin color, and the sensor calculates heart rate by detecting these changes. When the user stands on the scale, the sensor measures changes in blood flow through the tiny blood vessels in the soles of the feet, thereby detecting heart rate. This method generally requires the user to remain still to ensure accurate measurement.

[0062] Step 102: extracting a plurality of first peak positions from the first heartbeat data, and extracting a plurality of second peak positions from the second heartbeat data;

[0063] In bioelectrical impedance data, peaks correspond to periodic changes in the heartbeat signal. Extracting multiple peak positions can help determine the heartbeat rhythm. Similarly, peak positions extracted from photoplethysmography data can also provide information about the heartbeat's periodicity.

[0064] Specifically, step 102 includes steps 1021 to 1028:

[0065] Step 1021: extracting fixed-length waveform data segments from the first heartbeat data;

[0066] The purpose of this step is to select a data segment that contains a complete heartbeat cycle from the original first heartbeat data. Usually, the heartbeat waveform is periodic, and intercepting a complete cycle data can help the algorithm accurately analyze the peak position.

[0067] Step 1022: collecting a plurality of first sampling point data in the segment waveform data based on a preset sampling frequency; wherein the segment waveform data includes the entire heartbeat cycle waveform data;

[0068] In this step, the waveform is sampled using a preset sampling frequency. The sampling frequency determines the temporal resolution of the data. High-frequency sampling allows for more accurate capture of heartbeat signal details.

[0069] Step 1023: Arrange the amplitudes of the plurality of first sampling point data from large to small to obtain ordered first sampling point data;

[0070] This operation sorts the sampled data by amplitude, from largest to smallest. This provides a basis for determining the peak threshold. The sorting process helps reveal the most significant fluctuations in the signal, namely the peaks with the largest amplitudes.

[0071] Step 1024: using the first sampling point data at a preset position in the ordered first sampling point data as a peak threshold; the preset position includes one tenth of the ordered sampling point data;

[0072] A point at a preset position in the sorted sampling point data is selected as the peak threshold. This preset position is typically one-tenth of the sorted data, or 10% of the sorted data. The preset position is used to "truncate" a peak in the sampling point data, and can be set at one-tenth, one-eighth, or so on, based on the actual application scenario.

[0073] Step 1025: collecting a plurality of second sampling point data from the first heartbeat data based on a preset sampling frequency;

[0074] This step continues to sample the original heartbeat waveform data (complete data) using the preset sampling frequency and obtains a second set of sampling point data. These sampling point data will be used to further analyze the peaks in the heartbeat waveform.

[0075] Step 1026: extracting target second sampling point data greater than the peak threshold from the plurality of second sampling point data;

[0076] From the second set of sampling points, we filter out those data points whose amplitudes are greater than the previously calculated peak threshold. As a peak is defined as the portion of the waveform with the largest amplitude, extracting data points with amplitudes greater than the threshold effectively identifies the key peaks in the heartbeat waveform.

[0077] Step 1027: extracting adjacent sampling point data from a plurality of target second sampling point data as a peak data set; the adjacent sampling point data refers to target second sampling point data that are adjacent in sampling order in the first heartbeat data;

[0078] In this step, the adjacent sampling points in the time series are selected from the target second sampling point data. These adjacent points represent that the sampling points are in the same peak area.

[0079] Step 1028: taking the average position of a plurality of consecutive sampling point data in the peak data set as the first peak position.

[0080] The first peak position is accurately determined by calculating the average position of adjacent peak data. This is because a peak is typically not a single sampling point, but rather an area, and the peak data set represents multiple sampling points within that area. By calculating the average position of these points, the center of the peak can be determined more stably and accurately.

[0081] In the embodiment corresponding to steps 1021 through 1028, by gradually processing signal data and combining sampling, sorting, threshold setting, and neighboring point calculation methods, the accurate peak location can be effectively extracted from the heartbeat waveform. Each step is designed to reduce noise interference and ensure that the peak location is verified and confirmed from different angles. The overall process emphasizes improving the accuracy of heart rate detection through data preprocessing, threshold selection, and precise calculation.

[0082] The extraction logic of the second peak position is the same as that of the first peak position. For the specific execution logic, please refer to steps 1021 to 1028.

[0083] Step 103: extracting a first difference peak position from the plurality of first peak positions, and extracting a second difference peak position from the plurality of second peak positions; the first difference peak position or the second difference peak position refers to a peak position where the peak distribution of the first peak position and the second peak position of the same order is different;

[0084] The positions of peaks in the same sequence may differ between the first heartbeat data (bioelectrical impedance data) and the second heartbeat data (photoplethysmography data). These differing peak positions are called "discrepant peak positions." These discrepancies may be caused by factors such as external noise, signal interference, and sensor errors.

[0085] Similar to the first difference peak, peaks that are inconsistent with the peak positions in the bioelectrical impedance data are extracted from the second heartbeat data to help identify potential errors in the signal.

[0086] Specifically, step 103 includes steps 1031 to 1039:

[0087] Step 1031: Calculate a plurality of first horizontal coordinate differences between adjacent first wave peak positions in the first heartbeat data;

[0088] First, the locations of multiple peaks are extracted from the bioelectrical impedance data. The horizontal coordinate differences between these peaks (i.e., the time intervals between adjacent peaks) are calculated. These differences reveal the periodicity differences between the different peaks, providing a quantitative analysis of the signal's periodicity.

[0089] Step 1032: Calculate a plurality of second horizontal coordinate differences between adjacent second wave peak positions in the second heartbeat data;

[0090] The same operation is performed on the heartbeat data collected by photoplethysmography to calculate the time interval between adjacent peaks (the difference in the horizontal axis). The purpose of this is to obtain the peak interval information in the second heartbeat data for subsequent comparison.

[0091] Step 1033: Subtract the first horizontal coordinate difference from the second horizontal coordinate difference in the same order to obtain an error value;

[0092] The abscissa differences calculated from the bioelectrical impedance and photoplethysmography data were paired one-to-one and the difference between them was calculated. This error value represents the difference in the peak position of the two signal data within the same time period, reflecting differences in signal acquisition or interference.

[0093] Step 1034: Obtain the first peak position to be corrected and the second peak position to be corrected corresponding to the error value being greater than the first threshold;

[0094] For peaks with an error greater than a preset first threshold, further correction is required. "Peaks to be corrected" here refer to those peaks where signal inconsistencies may be caused by noise or other interference. By setting a threshold, these peaks requiring correction are screened.

[0095] Step 1035: Obtain the first peak position and the second peak position of the sample corresponding to the error being no greater than the first threshold;

[0096] In contrast to the previous step, if the error value is less than or equal to the first threshold, the difference between the two peak positions is within an acceptable range, and these peaks can be used as "sample peak positions" for subsequent processing (it is understood that the sample peak positions are the peak positions in the normal state). The sample peak positions are used to compare with the peak positions to be corrected to determine whether correction is necessary.

[0097] Step 1036: Calculate a first similarity between the first wave peak data set corresponding to the first wave peak position to be corrected and the second wave peak data set corresponding to the first wave peak position of the sample;

[0098] Here, we need to calculate the similarity between the first peak position data set to be corrected (from the bioelectrical impedance data) and the first peak position data set of the sample (from the photoplethysmography data). Similarity can be calculated using a variety of methods, such as correlation, cross-validation, etc. The purpose is to quantify the degree of temporal matching between the two peak sets. This embodiment uses the following similarity calculation method:

[0099] Specifically, step 1036 includes steps A1 to A3:

[0100] Step A1: Calculating a plurality of first waveform features of the first wave peak data set; the plurality of first waveform features include peak value, mean value, rise time, fall time, skewness and variance;

[0101] In this step, multiple waveform features are first extracted from the first peak data set (usually the peak data in the bioelectrical impedance signal). These features are used to describe the shape and characteristics of the peak, including but not limited to:

[0102] Peak: The maximum amplitude of a wave, indicating the most significant point of the waveform.

[0103] Mean: The average value of a waveform within a period or interval, reflecting the overall trend of the waveform.

[0104] Rise time: The time required from the starting point of the waveform to the peak value, reflecting the rising speed of the waveform.

[0105] Fall time: The time required for the waveform to fall from its peak to its lowest point, reflecting the falling speed of the waveform.

[0106] Skewness: A measure of the skewness of a waveform, indicating whether it is symmetrical. A positive skewness indicates that the waveform is skewed toward one side of the peak; a negative skewness indicates that the waveform is skewed toward the other side.

[0107] Variance: The degree of fluctuation in waveform data, describing the dispersion of the waveform amplitude. A high variance usually means that the waveform has large variations.

[0108] These features can describe the shape, change speed, and symmetry of the peak data from different perspectives, thereby providing detailed feature information for subsequent similarity calculations.

[0109] Step A2: calculating a plurality of second waveform features of the second wave peak data set;

[0110] Next, we extract the same type of waveform features from the second peak data set. This process is similar to the feature extraction for the first peak data set, ensuring that the two data sets are compared on the same dimensions.

[0111] This includes calculating the peak value, mean, rise time, fall time, skewness, variance and other features of the second peak data. By extracting these features, it is possible to ensure that the two sets of peak data can be compared and analyzed from multiple aspects.

[0112] Step A3: taking the similarity between the plurality of first waveform features and the plurality of second waveform features as the first similarity.

[0113] After obtaining the features of the two peak data sets, we need to calculate the similarity between the two sets of features. The similarity calculation is to quantify the degree of matching between the two sets of peak data in terms of shape, amplitude, speed, etc.

[0114] The similarity between the plurality of first waveform features and the plurality of second waveform features is calculated using Euclidean distance.

[0115] The similarity between the plurality of first waveform features and the plurality of second waveform features may also be calculated as follows:

[0116] ;

[0117] ;

[0118] in, represents the peak value in the first waveform feature, represents the peak value in the second waveform feature, represents the difference between the two peaks, represents the maximum value between two peaks, represents the mean value in the first waveform feature, represents the mean value in the second waveform feature, represents the difference between two means, represents the maximum value between two means, represents the rise time in the first waveform feature, represents the rise time in the second waveform feature, represents the difference between the two rise times, represents the maximum value between two rise times, represents the fall time in the first waveform feature, represents the fall time in the second waveform feature, represents the difference between the two fall times, Indicates the maximum value between two fall times, represents the skewness in the first waveform feature, represents the skewness in the second waveform feature, represents the difference between the two skewnesses, represents the maximum value between two skewnesses, represents the variance in the first waveform feature, represents the variance in the second waveform feature, represents the difference between two variances, represents the maximum value between two variances, Indicates similarity.

[0119] The above calculation process integrates multiple waveform features and unifies them in a framework, which can more comprehensively reflect the similarity between two waveforms.

[0120] In the embodiment corresponding to steps A1 to A3, the degree of matching in terms of shape, amplitude, symmetry, etc. is evaluated by extracting features from two sets of peak data and calculating the similarity between these features. The extracted waveform features include peak value, mean, rise time, fall time, skewness, and variance, which reflect the nature of the peak from different angles. The similarity calculation comprehensively considers all features and derives a final similarity value through mathematical methods to determine whether the two peak data sets are similar, thereby helping to identify the difference peak positions. This method effectively improves the accuracy of peak matching and reduces the interference of errors and noise.

[0121] Step 1037: Calculate a second similarity between the first wave peak data set corresponding to the second wave peak position to be corrected and the second wave peak data set corresponding to the second wave peak position of the sample;

[0122] The same operation is performed to compare the second wave peak position data set to be corrected (from the photoplethysmography data) with the second wave peak position data set of the sample (from the bioelectrical impedance data) to calculate the second similarity. By comparing the similarity between the two data sets, the matching between the peaks is further confirmed.

[0123] Step 1038: If the first similarity is not greater than a second threshold, and the second similarity is greater than a second threshold, taking the first peak position corresponding to the error value as the first difference peak position;

[0124] In this step, a conditional judgment is used to determine whether a peak position is considered a difference peak position. If the first similarity is less than or equal to the second threshold (indicating that the current peak in the bioelectrical impedance data is significantly different from the normal peak), and the second similarity is greater than the second threshold (indicating that the current peak in the photoplethysmography data is closer to the normal peak), the bioelectrical impedance data peak is considered problematic and is considered the "first difference peak position."

[0125] Step 1039: If the first similarity is greater than a second threshold and the second similarity is not greater than the second threshold, the second peak position corresponding to the error value is used as the second difference peak position.

[0126] If the situation is the opposite, that is, the first similarity is larger (meaning that the current peak of the bioelectrical impedance data is closer to the normal peak), but the second similarity is smaller (meaning that there is a large difference between the current peak of the photoplethysmography data and the normal peak), then the peak position of the photoplethysmography data is considered to be incorrect and is used as the "second difference peak position".

[0127] In the embodiment corresponding to steps 1031 to 1039, the differences in the horizontal coordinates between adjacent peaks are calculated, the peak errors are analyzed, and similarity calculations are combined to accurately identify and correct the different peak positions in the two data sources. This method can effectively identify peaks affected by external interference or device errors and correct them, thereby improving the accuracy of heart rate detection. This multiple comparison and similarity analysis method enhances the robustness of the method, reduces the impact of noise and interference, and makes the final peak location more reliable.

[0128] As an optional embodiment of the present invention, after step 1037, it also includes: if the first similarity and the second similarity are both less than a second threshold, re-acquiring new first heartbeat data and new second heartbeat data, and extracting the first difference peak position in the new first heartbeat data and the second difference peak position in the new second heartbeat data.

[0129] The first similarity is used to measure the matching degree between the first peak data and the sample first peak data; the second similarity is used to measure the matching degree between the second peak data and the sample second peak data.

[0130] If both similarities are less than the preset second threshold, it indicates that there is a significant difference between the two sets of peak data, which may be due to interference, noise, or signal instability during data collection. In this case, data re-collection is required to ensure more accurate subsequent peak correction.

[0131] If the similarity value does not meet the requirements, the system will re-collect the data. The new first heartbeat data usually means re-collecting the heartbeat waveform data from the bioelectrical impedance device; the new second heartbeat data means re-collecting the heartbeat waveform data from the photoplethysmography.

[0132] The purpose of re-collecting data is to ensure that the obtained waveform data is of higher quality and more stable, reducing the impact of interference, noise, or equipment errors. By re-collecting, new peak data can be obtained, providing a more reliable data foundation for the next step of correction work.

[0133] After the new first heartbeat data and the new second heartbeat data are collected again, the system needs to extract the first difference peak position and the second difference peak position again.

[0134] Step 104: generating a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position;

[0135] By comparing the peak positions of the first and second heartbeat data, as well as their difference peak positions, the algorithm can comprehensively consider the peak information from both data sources and generate a corrected peak position. This process helps correct errors in the original data, making the generated peak position more accurate.

[0136] Specifically, step 104 includes steps 1041 to 1042:

[0137] Step 1041: If the number of the first difference peak positions is less than the number of the second difference peak positions, correcting the first difference peak positions among the plurality of first peak positions based on the plurality of second peak positions, and using the corrected plurality of first peak positions as the corrected peak positions;

[0138] The fact that the number of first difference peak positions is smaller than the number of second difference peak positions indicates that there are fewer abnormal points in the multiple first peak positions, resulting in less data noise. Therefore, the heart rate data calculated based on the first heartbeat data is more accurate. Therefore, the multiple first peak positions are used as reference data, and the multiple second peak positions are used to correct the first difference peak positions in the multiple first peak positions. (According to the above scheme, the second peak positions that have the same sequence as the first difference peak positions are considered normal data.)

[0139] Specifically, step 1041 includes steps B1 to B3:

[0140] Step B1: obtaining target second peak positions having the same sequence as the first difference peak positions;

[0141] The system needs to search for target second peak positions according to the order of the first difference peak positions. These target second peak positions refer to corresponding peak positions in the second heartbeat data that match the sample peak positions and are relative to the first difference peak positions.

[0142] This operation ensures that the first peak position and the second peak position maintain the same order and relationship during the calibration process. In other words, the system selects the corresponding position in the second peak data by matching the order of the first peak position, ensuring the corresponding relationship between the two.

[0143] Step B2: replacing the first difference peak positions in the plurality of first peak positions with target second peak positions in the same order to obtain a plurality of corrected first peak positions;

[0144] In this step, the system sequentially replaces the first difference peak position among the plurality of first peak positions with the target second peak position obtained from the second peak data. In this way, the system can reduce deviations or errors caused by the difference peak positions.

[0145] For example, if a particular first difference peak position deviates significantly, the system will replace that position with the corresponding target second peak position, and obtain a new, more accurate peak position after correction.

[0146] Step B3: using the corrected multiple first peak positions as the corrected peak positions.

[0147] The corrected peak position is obtained by correcting the first peak data and will be used for subsequent peak analysis or data processing. In this way, the corrected peak position will be more consistent with the actual waveform trend and sample data, thereby improving the accuracy and reliability of subsequent processing.

[0148] In the embodiments corresponding to steps B1 to B3, the first peak data can be corrected by accurately matching the second peak data, thereby ensuring that the final peak position is more accurate and reliable.

[0149] Step 1042: If the number of the first difference peak positions is not less than the number of the second difference peak positions, correct the second difference peak positions in the plurality of second peak positions based on the plurality of first peak positions, and use the corrected plurality of second peak positions as the corrected peak positions.

[0150] The fact that the number of first difference peak positions is not less than the number of second difference peak positions indicates that there are fewer abnormal points in the multiple second peak positions, resulting in less data noise. Therefore, the heart rate data calculated based on the second heartbeat data is more accurate. Therefore, the multiple second peak positions are used as reference data, and the multiple first peak positions are used to correct the second difference peak positions in the multiple second peak positions. (According to the above solution, first peak positions that have the same order as the second difference peak positions are considered normal data.)

[0151] The specific execution principle of step 1042 is similar to that of steps B1 to B3. Please refer to the execution logic of steps B1 to B3 for details, which will not be repeated here.

[0152] In the embodiment corresponding to step 1041 to step 1042 , the correction priority is determined by comparing the number of first difference peak positions and second difference peak positions, thereby selecting the most appropriate peak data set for correction.

[0153] Step 105: Calculate the user's heart rate based on the peak time interval in the corrected peak position.

[0154] By analyzing the peak time intervals within the corrected peak positions, the user's heart rate can be accurately calculated. Typically, heart rate is estimated by measuring the time intervals between adjacent peaks (i.e., heartbeat cycles). By applying corrected peak positions, this process can better remove noise and errors, resulting in a more accurate heart rate value. Calculating heart rate based on peak time intervals is a traditional technique and will not be further explained here.

[0155] In the embodiment corresponding to steps 101 to 105, the present invention identifies and corrects differences between the two signal sources by extracting multiple peak positions from the BIA and PPG signals and analyzing their differences (i.e., the first difference peak position and the second difference peak position). This correction method based on difference peak positions effectively eliminates peak position deviations between the two data sources, thereby reducing errors caused by poor contact, interfering noise, or posture changes. Traditional methods often rely solely on the peaks of a single signal source and are susceptible to noise. However, the present invention provides a more robust and accurate peak correction solution by comparing the peak position differences between the two signals. By comprehensively considering the data and difference peak positions of the two detection signals, the present invention significantly enhances the system's adaptability to external interference (such as poor skin contact, device movement, and ambient light changes). In actual use, BIA and PPG signals may be affected by different interference sources, but by fusing the data and performing peak difference analysis, the impact of interference can be effectively reduced. By combining BIA and PPG heart rate detection methods and performing differential peak analysis and correction, the problems of high noise and poor accuracy in heart rate measurement of traditional body fat scales are overcome, significantly improving the accuracy and reliability of heart rate detection.

[0156] like Figure 2 The present invention provides a heart rate detection device for a body fat scale, see Figure 2 , Figure 2 A schematic diagram of a heart rate detection device for a body fat scale provided by the present invention is shown. Figure 2 The heart rate detection device of a body fat scale shown includes:

[0157] An acquiring unit 21 is configured to acquire first heartbeat data acquired based on bioelectrical impedance analysis and second heartbeat data acquired based on photoplethysmography;

[0158] A first extraction unit 22 is configured to extract a plurality of first peak positions from the first heartbeat data and a plurality of second peak positions from the second heartbeat data;

[0159] A second extraction unit 23 is configured to extract a first difference peak position from the plurality of first peak positions, and extract a second difference peak position from the plurality of second peak positions; the first difference peak position or the second difference peak position refers to a peak position where a difference exists in peak distribution between the first peak position and the second peak position of the same order;

[0160] a generating unit 24 for generating a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position;

[0161] The calculation unit 25 is configured to calculate the user's heart rate based on the peak time interval in the corrected peak position.

[0162] The present invention provides a heart rate detection device for a body fat scale. By extracting multiple peak positions from BIA and PPG signals and analyzing their differences (i.e., first and second difference peak positions), the present invention can identify and correct differences between the two signal sources. This correction method based on difference peak positions effectively eliminates peak position deviations between the two data sources, thereby reducing errors caused by poor contact, interfering noise, or posture changes. Traditional methods often rely solely on the peaks of a single signal source and are susceptible to noise. However, the present invention provides a more robust and accurate peak correction solution by comparing the peak position differences between the two signals. By comprehensively considering the data and difference peak positions of the two detection signals, the present invention significantly enhances the system's adaptability to external interference (such as poor skin contact, device movement, and ambient light changes). In actual use, BIA and PPG signals may be affected by different interference sources. However, by fusing the data and performing peak difference analysis, the impact of interference can be effectively reduced. By combining BIA and PPG heart rate detection methods and performing differential peak analysis and correction, the problems of high noise and poor accuracy in heart rate measurement of traditional body fat scales are overcome, significantly improving the accuracy and reliability of heart rate detection.

[0163] Figure 3 FIG. 1 is a schematic diagram of a terminal device provided by an embodiment of the present invention. Figure 3 As shown, a terminal device 3 of this embodiment includes: a processor 30, a memory 31, and a computer program 32 stored in the memory 31 and executable on the processor 30, such as a heart rate detection program for a body fat scale. When the processor 30 executes the computer program 32, the steps of the above-mentioned embodiments of the heart rate detection method for a body fat scale are implemented, such as Figure 1 Alternatively, when the processor 30 executes the computer program 32, the functions of each unit in the above-mentioned device embodiments are realized, for example, Figure 2 Function of the unit shown.

[0164] Exemplarily, the computer program 32 may be divided into one or more units, which are stored in the memory 31 and executed by the processor 30 to implement the present invention. The one or more units may be a series of computer program instruction segments capable of performing specific functions, which are used to describe the execution process of the computer program 32 in the terminal device 3. For example, the specific functions of each unit of the computer program 32 are as follows:

[0165] an acquiring unit, configured to acquire first heartbeat data acquired based on bioelectrical impedance analysis and second heartbeat data acquired based on photoplethysmography;

[0166] a first extraction unit, configured to extract a plurality of first peak positions from the first heartbeat data, and extract a plurality of second peak positions from the second heartbeat data;

[0167] a second extraction unit, configured to extract a first difference peak position from the plurality of first peak positions, and extract a second difference peak position from the plurality of second peak positions; the first difference peak position or the second difference peak position refers to a peak position where a difference exists in peak distribution between the first peak position and the second peak position in the same order;

[0168] a generating unit, configured to generate a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position;

[0169] A calculation unit is used to calculate the user's heart rate based on the peak time interval in the corrected peak position.

[0170] The terminal device includes but is not limited to a processor 30 and a memory 31. Those skilled in the art will understand that Figure 3 It is only an example of a terminal device 3 and does not constitute a limitation on a terminal device 3. It may include more or fewer components than shown in the figure, or a combination of certain components, or different components. For example, the terminal device may also include input and output devices, network access devices, buses, etc.

[0171] The processor 30 may be a central processing unit (CPU), other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or any conventional processor.

[0172] The memory 31 can be an internal storage unit of the terminal device 3, such as a hard drive or memory of the terminal device 3. The memory 31 can also be an external storage device of the terminal device 3, such as a plug-in hard drive, a Smart Media Card (SMC), a Secure Digital (SD) card, a flash memory card, etc. equipped on the terminal device 3. Furthermore, the memory 31 can include both an internal storage unit of the terminal device 3 and an external storage device. The memory 31 is used to store the computer program and other programs and data required by the roaming control device. The memory 31 can also be used to temporarily store data that has been output or is about to be output.

[0173] It should be understood that the size of the serial numbers of the steps in the above embodiments does not mean the order of execution. The execution order of each process should be determined by its function and internal logic, and should not constitute any limitation on the implementation process of the embodiments of the present invention.

[0174] It should be noted that the information interaction, execution process, etc. between the above-mentioned devices / units are based on the same concept as the embodiment of the method of the present invention. Their specific functions and technical effects can be found in the method embodiment part and will not be repeated here.

[0175] Those skilled in the art can clearly understand that, for the convenience and brevity of description, only the division of the above-mentioned functional units and modules is used as an example for illustration. In actual applications, the above-mentioned functions can be distributed and completed by different functional units and modules as needed, that is, the internal structure of the device can be divided into different functional units or modules to complete all or part of the functions described above. The functional units and modules in the embodiment can be integrated into one processing unit, or each unit can exist physically alone, or two or more units can be integrated into one unit. The above-mentioned integrated unit can be implemented in the form of hardware or in the form of software functional units. In addition, the specific names of the functional units and modules are only for the convenience of distinguishing each other, and are not used to limit the scope of protection of the present invention. The specific working process of the units and modules in the above-mentioned system can refer to the corresponding process in the aforementioned method embodiment, and will not be repeated here.

[0176] An embodiment of the present invention further provides a computer-readable storage medium, wherein the computer-readable storage medium stores a computer program, and when the computer program is executed by a processor, the steps in the above-mentioned method embodiments can be implemented.

[0177] An embodiment of the present invention provides a computer program product. When the computer program product is run on a mobile terminal, the mobile terminal can implement the steps in the above-mentioned method embodiments when executing the computer program product.

[0178] If the integrated unit is implemented as a software functional unit and sold or used as a standalone product, it can be stored in a computer-readable storage medium. Based on this understanding, the present invention can implement all or part of the process steps in the above-mentioned method embodiments by using a computer program to instruct the relevant hardware. The computer program can be stored in a computer-readable storage medium. When executed by a processor, the computer program can implement the steps of each of the above-mentioned method embodiments. The computer program includes computer program code, which can be in source code form, object code form, executable file, or some intermediate form. The computer-readable medium can include at least: any entity or device capable of carrying the computer program code to the camera / terminal device, a recording medium, computer memory, read-only memory (ROM), random access memory (RAM), an electrical carrier signal, a telecommunications signal, and a software distribution medium. Examples include a USB flash drive, a removable hard drive, a magnetic disk, or an optical disk.

[0179] In the above embodiments, the description of each embodiment has its own focus. For parts that are not described or recorded in detail in a certain embodiment, reference can be made to the relevant description of other embodiments.

[0180] Those skilled in the art will appreciate that the units and algorithm steps of each example described in conjunction with the embodiments disclosed herein can be implemented in electronic hardware, or a combination of computer software and electronic hardware. Whether these functions are performed in hardware or software depends on the specific application and design constraints of the technical solution. Professionals and technicians can use different methods to implement the described functions for each specific application, but such implementation should not be considered beyond the scope of the present invention.

[0181] In the embodiments provided by the present invention, it should be understood that the disclosed devices / network equipment and methods can be implemented in other ways. For example, the device / network equipment embodiments described above are merely illustrative. For example, the division of the modules or units is merely a logical function division. In actual implementation, there may be other division methods, such as multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the mutual coupling or direct coupling or communication connection shown or discussed can be through some interfaces, indirect coupling or communication connection of devices or units, which can be electrical, mechanical or other forms.

[0182] The units described as separate components may or may not be physically separate, and the components shown as units may or may not be physical units, and may be located in one place or distributed across multiple network units.

[0183] It should be understood that when used in the present specification and the appended claims, the term "comprising" indicates the presence of described features, integers, steps, operations, elements and / or components, but does not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0184] It will also be understood that the term "and / or" used in the present description and appended claims refers to and includes any and all possible combinations of one or more of the associated listed items.

[0185] As used in the present specification and the appended claims, the term "if" may be interpreted as "when" or "upon" or "in response to determining" or "in response to monitoring," depending on the context. Similarly, the phrase "if it is determined" or "if [described condition or event] is monitored" may be interpreted as meaning "upon determination" or "in response to determining" or "upon monitoring [described condition or event]" or "in response to monitoring [described condition or event]," depending on the context.

[0186] In addition, in the description of the present specification and the appended claims, the terms "first", "second", "third", etc. are only used to distinguish the descriptions and cannot be understood as indicating or implying relative importance.

[0187] References to "one embodiment" or "some embodiments" in the present specification mean that a particular feature, structure, or characteristic described in conjunction with that embodiment is included in one or more embodiments of the present invention. Thus, phrases such as "in one embodiment," "in some embodiments," "in other embodiments," and "in yet other embodiments" appearing in various places in this specification do not necessarily refer to the same embodiment, but rather mean "one or more, but not all, embodiments," unless otherwise specifically emphasized. The terms "including," "comprising," "having," and variations thereof mean "including but not limited to," unless otherwise specifically emphasized.

[0188] The embodiments described above are only used to illustrate the technical solutions of the present invention, rather than to limit the same. Although the present invention has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the spirit and scope of the technical solutions of the various embodiments of the present invention, and should all be included in the scope of protection of the present invention.

Claims

1. A heart rate detection method for a body fat scale, characterized in that: The heart rate detection method of the body fat scale is applied to the body fat scale, which includes a bioimpedance acquisition module and a photoelectric capacitance acquisition module. The heart rate detection method of the body fat scale includes: Acquiring first heartbeat data collected based on bioelectrical impedance analysis and second heartbeat data collected based on photoplethysmography; intercepting a fixed-length segment of waveform data from the first heartbeat data; Collecting a plurality of first sampling point data in the segment waveform data based on a preset sampling frequency; wherein the segment waveform data includes the entire heartbeat cycle waveform data; Arrange the amplitudes of the plurality of first sampling point data from large to small to obtain ordered first sampling point data; Using the first sampling point data at a preset position in the ordered first sampling point data as a peak threshold; the preset position includes one tenth of the ordered sampling point data; Collecting a plurality of second sampling point data from the first heartbeat data based on a preset sampling frequency; Extracting target second sampling point data greater than the peak threshold from the plurality of second sampling point data; Extracting adjacent sampling point data from a plurality of target second sampling point data as a peak data set; the adjacent sampling point data refers to target second sampling point data with adjacent sampling order in the first heartbeat data; Taking the average position of a plurality of continuous sampling point data in the peak data set as the first peak position; extracting a plurality of second peak positions from the second heartbeat data; Calculating a plurality of first horizontal coordinate differences between adjacent first wave peak positions in the first heartbeat data; Calculating a plurality of second horizontal coordinate differences between adjacent second wave peak positions in the second heartbeat data; Subtract the first horizontal coordinate difference from the second horizontal coordinate difference in the same order to obtain the error value; Obtaining a first wave peak position to be corrected and a second wave peak position to be corrected corresponding to the error value being greater than a first threshold; Obtaining a first peak position of the sample and a second peak position of the sample corresponding to the error being no greater than a first threshold; Calculating a first similarity between a first wave peak data set corresponding to the first wave peak position to be corrected and a second wave peak data set corresponding to the first wave peak position of the sample; Calculating a second similarity between the first wave peak data set corresponding to the second wave peak position to be corrected and the second wave peak data set corresponding to the second wave peak position of the sample; If the first similarity is not greater than a second threshold, and the second similarity is greater than a second threshold, taking the first peak position corresponding to the error value as the first difference peak position; If the first similarity is greater than a second threshold value and the second similarity is not greater than the second threshold value, the second peak position corresponding to the error value is used as the second difference peak position; the first difference peak position refers to the first peak position where the peak distribution of the first peak position and the second peak position of the same order differs; the second difference peak position refers to the second peak position where the peak distribution of the first peak position and the second peak position of the same order differs; generating a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position; The user's heart rate is calculated based on the peak time interval in the corrected peak position.

2. The heart rate detection method of the body fat scale according to claim 1, characterized in that: The step of calculating the first similarity between the first wave peak data set corresponding to the first wave peak position to be corrected and the second wave peak data set corresponding to the first wave peak position of the sample comprises: Calculating a plurality of first waveform features of the first peak data set; the plurality of first waveform features including peak value, mean value, rise time, fall time, skewness, and variance; calculating a plurality of second waveform features of the second peak data set; The similarity between the plurality of first waveform features and the plurality of second waveform features is used as the first similarity.

3. The heart rate detection method of the body fat scale according to claim 1, characterized in that: After the step of calculating the second similarity between the first wave peak data set corresponding to the second wave peak position to be corrected and the second wave peak data set corresponding to the second wave peak position of the sample, the method further includes: If both the first similarity and the second similarity are smaller than the second threshold, new first heartbeat data and new second heartbeat data are reacquired, and a first difference peak position in the new first heartbeat data and a second difference peak position in the new second heartbeat data are extracted.

4. The heart rate detection method of a body fat scale according to claim 1, wherein: The step of generating a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position comprises: If the number of the first difference peak positions is less than the number of the second difference peak positions, correcting the first difference peak positions among the plurality of first peak positions based on the plurality of second peak positions, and using the corrected plurality of first peak positions as the corrected peak positions; If the number of the first difference peak positions is not less than the number of the second difference peak positions, the second difference peak positions in the plurality of second peak positions are corrected based on the plurality of first peak positions, and the corrected plurality of second peak positions are used as the corrected peak positions.

5. The heart rate detection method of the body fat scale according to claim 4, characterized in that: If the number of the first difference peak positions is less than the number of the second difference peak positions, the step of correcting the first difference peak positions among the plurality of first peak positions based on the plurality of second peak positions and using the corrected plurality of first peak positions as the corrected peak positions includes: Acquire a target second peak position having the same sequence as the first difference peak position; replacing the first difference peak positions in the plurality of first peak positions with target second peak positions in the same order to obtain a plurality of corrected first peak positions; The corrected plurality of first peak positions are used as the corrected peak positions.

6. A heart rate detection device for a body fat scale, characterized in that: The heart rate detection device of the body fat scale comprises: an acquiring unit, configured to acquire first heartbeat data acquired based on bioelectrical impedance analysis and second heartbeat data acquired based on photoplethysmography; A first extraction unit is configured to intercept a fixed-length segment waveform data from the first heartbeat data; collect a plurality of first sampling point data from the segment waveform data based on a preset sampling frequency; wherein the segment waveform data includes the entire heartbeat cycle waveform data; arrange the amplitudes of the plurality of first sampling point data from large to small to obtain ordered first sampling point data; use the first sampling point data at a preset position in the ordered first sampling point data as a peak threshold; the preset position includes one-tenth of the ordered sampling point data; collect a plurality of second sampling point data from the first heartbeat data based on a preset sampling frequency; extract target second sampling point data greater than the peak threshold from the plurality of second sampling point data; extract adjacent sampling point data from the plurality of target second sampling point data as a peak data set; the adjacent sampling point data refers to target second sampling point data adjacent in sampling order in the first heartbeat data; use the average position of a plurality of consecutive sampling point data in the peak data set as a first peak position, and extract a plurality of second peak positions from the second heartbeat data; The second extraction unit is used to calculate multiple first horizontal coordinate differences between adjacent first peak positions in the first heartbeat data; calculate multiple second horizontal coordinate differences between adjacent second peak positions in the second heartbeat data; subtract the first horizontal coordinate differences and the second horizontal coordinate differences of the same order to obtain an error value; obtain the first peak position to be corrected and the second peak position to be corrected corresponding to the error value being greater than the first threshold; obtain the sample first peak position and the sample second peak position corresponding to the error being not greater than the first threshold; calculate the first similarity between the first peak data set corresponding to the first peak position to be corrected and the second peak data set corresponding to the first peak position of the sample; calculate the first peak position corresponding to the second peak position to be corrected a second similarity between the data set and the second peak data set corresponding to the second peak position of the sample; if the first similarity is not greater than a second threshold value, and the second similarity is greater than the second threshold value, the first peak position corresponding to the error value is used as the first difference peak position; if the first similarity is greater than the second threshold value, and the second similarity is not greater than the second threshold value, the second peak position corresponding to the error value is used as the second difference peak position; the first difference peak position refers to the first peak position where the peak distribution of the first peak position and the second peak position in the same order is different; the second difference peak position refers to the second peak position where the peak distribution of the first peak position and the second peak position in the same order is different; a generating unit, configured to generate a corrected peak position according to the plurality of first peak positions, the plurality of second peak positions, the first difference peak position, and the second difference peak position; A calculation unit is used to calculate the user's heart rate based on the peak time interval in the corrected peak position.

7. A terminal device, characterized in that: The terminal device includes: a memory, a processor, and a heart rate detection program for a body fat scale stored in the memory and executable on the processor. The heart rate detection program for the body fat scale is configured to implement the steps in the heart rate detection method for a body fat scale as described in any one of claims 1 to 5.

8. A computer-readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the steps of the heart rate detection method of the body fat scale according to any one of claims 1 to 5 are implemented.

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