Heart rate determination method and device, electronic equipment and storage medium

Abstract

The invention relates to a heart rate determination method and device, electronic equipment and a storage medium. The heart rate determination method comprises the following steps: acquiring a multi-channel PPG signal; filtering the multi-channel PPG signal to obtain a plurality of sub-band signals, the plurality of sub-band signals being obtained by a plurality of preset band-pass filters with different filtering ranges; determining a first correlation value of each reference signal in a plurality of reference signals and the plurality of sub-band signals, wherein the plurality of reference signals are determined according to a plurality of preset dominant frequencies; target dominant frequency information of the heart rate information is determined according to a target correlation value so as to determine the heart rate information, and the target correlation value is the maximum value in the multiple first correlation values. According to the method, before the heart rate information is determined, the target dominant frequency information corresponding to the current heart rate information of the user is accurately recognized through the correlation between the reference signal and the sub-band signal, it can be avoided that effective components of the pulse wave signals are removed during follow-up filtering, and the accuracy of the heart rate tracking result is ensured.

Description

Technical Field

[0001] This disclosure relates to the field of computer technology, and in particular to a method, apparatus, electronic device and storage medium for determining heart rate. Background Technology

[0002] Heart rate changes reflect a user's cardiac or physical condition, making heart rate tracking a crucial function of wearable devices. It's typically based on photoplethysmography (PPG) to track heart rate in areas with thinner skin, such as the wrist, fingers, and earlobes. Heart rate tracking mainly involves tracking heart rate at rest and during exercise. At rest, the heart rate is relatively stable, and detection is less affected by interference, resulting in generally accurate tracking results. However, during exercise, heart rate changes, and detection is more susceptible to interference, leading to potentially larger errors in tracking results and impacting the user experience. Summary of the Invention

[0003] To overcome the problems existing in the related technologies, this disclosure provides a method, apparatus, electronic device and storage medium for determining heart rate.

[0004] According to a first aspect of the present disclosure, a method for determining heart rate is provided, the method comprising:

[0005] Acquire multi-channel PPG signals;

[0006] The multi-channel PPG signal is filtered to obtain multiple sub-band signals, which are obtained by multiple preset bandpass filters with different filtering ranges.

[0007] A first correlation value is determined between each of the plurality of reference signals and the plurality of sub-band signals, wherein the plurality of reference signals are determined based on a plurality of preset main frequencies;

[0008] Based on the target correlation value, the target main frequency information of the heart rate information is determined to determine the heart rate information, wherein the target correlation value is the maximum value among a plurality of the first correlation values.

[0009] In an exemplary embodiment, filtering the multi-channel PPG signal to obtain multiple sub-band signals includes:

[0010] The multi-channel PPG signal is high-pass filtered to obtain the multi-channel first signal;

[0011] Based on the acceleration signal, the multi-channel first signal is adaptively filtered to obtain the multi-channel second signal;

[0012] The multi-channel second signal is filtered by multiple preset bandpass filters to obtain the multiple sub-band signals.

[0013] In an exemplary embodiment, the step of adaptively filtering the multi-channel first signal based on the acceleration signal to obtain the multi-channel second signal includes:

[0014] If it is determined that the electronic device is in motion based on the acceleration signal, the acceleration signal is used as the target signal, the multi-channel first signal is used as the observation signal, and the multi-channel first signal is adaptively filtered to obtain the multi-channel second signal;

[0015] If the electronic device is determined to be stationary based on the acceleration signal, the first multi-channel signal is used as the second multi-channel signal.

[0016] In one exemplary embodiment, the method further includes:

[0017] Based on the acceleration data of the acceleration signal at each sampling point within a preset time period, the action parameters are determined;

[0018] If the action parameter is greater than or equal to the first threshold, it is determined that the electronic device is in motion.

[0019] If the action parameter is less than the first threshold, the electronic device is determined to be in a stationary state.

[0020] In one exemplary embodiment, the process of setting the preset bandpass filter includes:

[0021] The effective frequency band range is determined based on the preset maximum heart rate value, preset minimum heart rate value, and preset harmonic parameters;

[0022] Based on the effective frequency band range, multiple preset bandpass filters with different filtering ranges are set.

[0023] In one exemplary embodiment, the process of setting the reference signal includes:

[0024] The effective frequency band range of the main frequency is determined based on the preset maximum heart rate value and the preset minimum heart rate value;

[0025] The preset main frequency is set according to the effective frequency band range of the main frequency;

[0026] The reference signal is determined based on the preset main frequency and preset harmonic parameters.

[0027] In an exemplary embodiment, setting the preset main frequency according to the effective frequency band range of the main frequency includes:

[0028] When the effective frequency band range of the main frequency includes a narrow frequency band range, a preset main frequency is set according to the narrow frequency band range. The preset main frequency includes a first preset main frequency and a second preset main frequency.

[0029] In an exemplary embodiment, determining a first correlation value between each of the plurality of reference signals and the plurality of sub-band signals includes:

[0030] Determine a second correlation value between each of the reference signals and each of the sub-band signals;

[0031] A first correlation value between each reference signal and the plurality of sub-band signals is determined based on a weighted sum of the second correlation values ​​of each reference signal and the plurality of sub-band signals.

[0032] In an exemplary embodiment, determining the target dominant frequency information of the heart rate information based on the target correlation value includes:

[0033] If it is determined that the electronic device is in a stationary state, and the target correlation value is greater than or equal to the second threshold, the target main frequency information of the heart rate information is determined according to the preset main frequency of the reference signal corresponding to the target correlation value;

[0034] If it is determined that the electronic device is in motion, or the target correlation value is less than the second threshold, the frequency band signal of the reference signal corresponding to the target correlation value is filtered to obtain a third signal, and the target frequency information of the heart rate information is determined based on the third signal.

[0035] According to a second aspect of the present disclosure, a heart rate determining device is provided, the device comprising:

[0036] The acquisition module is configured to acquire multi-channel PPG signals;

[0037] The filtering module is configured to filter the multi-channel PPG signal to obtain multiple sub-band signals, wherein the multiple sub-band signals are obtained by multiple preset bandpass filters with different filtering ranges.

[0038] The calculation module is configured to determine a first correlation value between each of the plurality of reference signals and the plurality of sub-band signals, the plurality of reference signals being determined based on a plurality of preset main frequencies;

[0039] The determination module is configured to determine the target main frequency information of the heart rate information based on the target correlation value, thereby determining the heart rate information, wherein the target correlation value is the maximum value among a plurality of the first correlation values.

[0040] According to a third aspect of the present disclosure, an electronic device is provided, comprising:

[0041] processor;

[0042] Memory used to store processor-executable instructions;

[0043] The processor is configured to perform the method described in the first aspect of the embodiments of this disclosure.

[0044] According to a fourth aspect of the present disclosure, a non-transitory computer-readable storage medium is provided, wherein when instructions in the storage medium are executed by a processor of an electronic device, the electronic device is enabled to perform the method described in the first aspect of the present disclosure.

[0045] The method described above has the following advantages: before determining the heart rate information, the method accurately identifies the target main frequency information corresponding to the user's current heart rate information by means of the correlation between the reference signal and the sub-band signal, which can avoid removing the effective components of the pulse wave signal during subsequent filtering and ensure the accuracy of the heart rate tracking results.

[0046] It should be understood that the above general description and the following detailed description are exemplary and explanatory only, and are not intended to limit this disclosure. Attached Figure Description

[0047] The accompanying drawings, which are incorporated in and form a part of this specification, illustrate embodiments consistent with this disclosure and, together with the description, serve to explain the principles of this disclosure.

[0048] Figure 1 This is a flowchart illustrating a heart rate determination method according to an exemplary embodiment;

[0049] Figure 2 This is a schematic diagram illustrating the distribution of multiple PPG sensors according to an exemplary embodiment;

[0050] Figure 3 This is a schematic diagram illustrating a multi-channel PPG signal according to an exemplary embodiment;

[0051] Figure 4 This is a schematic diagram illustrating the filtering range of a plurality of bandpass filters according to an exemplary embodiment;

[0052] Figure 5 This is a flowchart illustrating a heart rate determination method according to an exemplary embodiment;

[0053] Figure 6 This is a flowchart illustrating a heart rate determination method according to an exemplary embodiment;

[0054] Figure 7 This is a schematic flowchart illustrating a bandpass filter bank and canonical correlation analysis according to an exemplary embodiment;

[0055] Figure 8 This is a block diagram illustrating a heart rate determination device according to an exemplary embodiment;

[0056] Figure 9 This is a block diagram illustrating an electronic device according to an exemplary embodiment. Detailed Implementation

[0057] Exemplary embodiments will now be described in detail, examples of which are illustrated in the accompanying drawings. When the following description relates to the drawings, unless otherwise indicated, the same numerals in different drawings denote the same or similar elements. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with this disclosure. Rather, they are merely examples of apparatuses and methods consistent with some aspects of this disclosure as detailed in the appended claims.

[0058] In some embodiments, when measuring a user's pulse wave signal using a PPG sensor in the device to track the user's heart rate, the device's position on the user's skin cannot be guaranteed to remain constant during exercise. When the device's position changes, the position of the PPG sensor against the skin will shift to varying degrees depending on the exercise state, creating cavities of varying degrees. This will cause varying degrees of interference to the reflected light path of the PPG sensor. Furthermore, ambient light introduced by light leakage from the cavities will also interfere with the detection data of the PPG sensor. Therefore, the heart rate tracking results during exercise are prone to large errors, thus affecting the user's experience with the wearable device.

[0059] In related technologies, to ensure the accuracy of heart rate tracking results, the following two methods are typically used: First, the pulse wave signal acquired by the PPG sensor is filtered using common filtering methods to remove noise signals generated during exercise. However, when the noise signal is strong, multiple dominant frequency information may appear. Since it is impossible to accurately identify the dominant frequency information corresponding to the user's current heart rate, filtering may remove the effective components of the pulse wave signal while removing noise, resulting in significant errors in heart rate tracking results in some scenarios, such as when heart rate is measured in beats per minute. Minute (BPM): If a user's actual heart rate during exercise varies between 50 bpm and 60 bpm, the corresponding pulse wave signal spectrum will have a dominant frequency between 50 bpm-60 bpm, 100 bpm-120 bpm, and 25 bpm-30 bpm, respectively. Under the influence of noise, the dominant frequency between 25 bpm-30 bpm or 100 bpm-120 bpm is easily mistaken for the dominant frequency corresponding to the user's heart rate, thus leading to errors in heart rate detection results. The second method is to learn the user's heart rate change information over historical time periods through a deep learning model. However, due to the diversity of exercise states, it is difficult to optimize training for certain scenarios. In addition, the computational load of deep learning models is large, so this method is difficult to maintain relatively stable heart rate tracking results.

[0060] In an exemplary embodiment of this disclosure, to overcome the problem of large errors in heart rate tracking results during exercise in related technologies, a heart rate determination method is provided, comprising: acquiring multi-channel PPG signals; filtering the multi-channel PPG signals to obtain multiple sub-band signals, wherein the multiple sub-band signals are obtained by multiple preset bandpass filters with different filtering ranges; determining a first correlation value between each of the multiple reference signals and the multiple sub-band signals, wherein the multiple reference signals are determined based on multiple preset main frequencies; and determining the target main frequency information of the heart rate information based on the target correlation value, thereby determining the heart rate information, wherein the target correlation value is the maximum value among the multiple first correlation values. Before determining the heart rate information, this method accurately identifies the target main frequency information corresponding to the user's current heart rate information through the correlation between the reference signals and the sub-band signals, which can avoid removing the effective components of the pulse wave signal during subsequent filtering and ensure the accuracy of the heart rate tracking results.

[0061] In an exemplary embodiment of this disclosure, a method for determining heart rate is provided. Figure 1 This is a flowchart illustrating a heart rate determination method according to an exemplary embodiment, such as... Figure 1 As shown, it includes the following steps:

[0062] Step S101: Acquire multi-channel PPG signals;

[0063] Step S102: Filter the multi-channel PPG signal to obtain multiple sub-band signals. The multiple sub-band signals are obtained by multiple preset bandpass filters with different filtering ranges.

[0064] Step S103: Determine the first correlation value between each of the multiple reference signals and the multiple sub-band signals. The multiple reference signals are determined based on multiple preset main frequencies.

[0065] Step S104: Determine the target main frequency information of heart rate information based on the target correlation value, so as to determine the heart rate information. The target correlation value is the maximum value among multiple first correlation values.

[0066] The heart rate determination method in this disclosure is applied to electronic devices, including smart wearable devices such as smartphones, tablets, personal computers, smart bracelets, and smartwatches.

[0067] In step S101, multiple PPG sensors are used to collect multi-channel PPG signals from areas with thin skin, such as the user's wrist, fingers, and earlobes. These PPG sensors can be located within the current electronic device, allowing direct acquisition of the PPG signals, or they can be located in another device connected to the current electronic device, where the PPG signals are acquired and sent to the current electronic device. After acquiring the raw PPG signals, a spectrum transformation is performed to obtain the PPG signal's spectral information. In this embodiment, the multi-channel PPG signals all represent the spectral information of the multi-channel PPG signals.

[0068] In one example, a smartwatch is used as the electronic device for collecting PPG signals. Figure 2 This is a schematic diagram illustrating the distribution of multiple PPG sensors according to an exemplary embodiment, such as... Figure 2 As shown, four PPG sensors (PDs) for detecting PPG signals are set on the side of the electronic device that contacts the skin. Each PD corresponds to a light-emitting diode (LED) that emits green light. The four PDs can receive PPG green light from different areas of the skin contact surface to collect four-channel PPG signals. Since the blood vessel distribution density and adhesion under the PD are different, the use of multi-channel PPG signals can initially enhance the effective components in the PPG signal to improve the accuracy of heart rate determination results.

[0069] In one example, Figure 3 This is a schematic diagram of a multi-channel PPG signal according to an exemplary embodiment, such as... Figure 3As shown in Figure a, the raw PPG signals from four channels were acquired at different times, with the horizontal axis representing time and the vertical axis representing signal magnitude. Figure b shows the power spectrum of the raw PPG signals from the four channels, with the horizontal axis representing time and the vertical axis representing heart rate. Box 1 in Figure b represents the data under resting conditions, where the frequency of the heart rate information is clearly visible, directly indicating a heart rate value between 0 and 100, close to 100. Box 2 represents the data under exercise conditions, where noise affects the frequency of the heart rate information, making it impossible to directly determine the heart rate value. It should be noted that the heart rate value is the frequency value multiplied by 60. For example, when the frequency value is 2 Hz, the heart rate value is 120 bpm.

[0070] When determining heart rate, a preset signal length is used as a period. A heart rate determination is performed every time a multi-channel PPG signal of the preset length is acquired. The preset length is determined based on the sampling rate and the number of sampling points of the PPG signal. In one example, let the sampling rate of the PPG signal be fs, the spectral resolution be f0, and the number of sampling points be N: N = fs / f0. The preset length T is T = N / fs. For example, if the spectral resolution is 0.1, the signal sampling rate is 25Hz, the number of sampling points is 250, and the preset length is 10 seconds (s), then a heart rate determination is performed every 10 seconds of multi-channel PPG signal acquisition. The multi-channel PPG signal of the preset length can be represented as a multi-dimensional matrix. For example, a four-channel PPG signal within 10 seconds, including 4 × 250 PPG data points, can be represented as a 4 × 250 matrix.

[0071] By acquiring and using multi-channel PPG signals, synchronous enhancement of PPG signals can be achieved, avoiding large errors in heart rate tracking results when the quality of a single-channel signal is poor or unstable.

[0072] In step S102, the bandpass filter suppresses frequencies outside a specific frequency band, retaining only frequencies within that specific band. The filtering range of the bandpass filter indicates the allowed specific frequency band. Multiple preset bandpass filters with different filtering ranges are pre-stored in the electronic device. When bandpass filtering a multi-channel PPG signal using these preset bandpass filters, the initial, unprocessed multi-channel PPG signal can be bandpass filtered, or the initial multi-channel PPG signal can be pre-filtered before bandpass filtering. The pre-filtering method is not limited; examples include high-pass filtering and adaptive filtering. After filtering using multiple preset bandpass filters with different filtering ranges, multiple sub-band signals with different frequency bands are obtained. The number of sub-band signals is the same as the number of preset bandpass filters, and each sub-band signal can also be represented as a multi-dimensional matrix with the same dimension as the multi-channel PPG signal.

[0073] The filtering range of the preset bandpass filter is set according to the possible frequency range of the heart rate, and the setting method is not limited. In some implementations, the setting process of the preset bandpass filter includes the following steps:

[0074] The effective frequency band range is determined based on the preset maximum heart rate value, preset minimum heart rate value, and preset harmonic parameters;

[0075] Based on the effective frequency band range, set multiple preset bandpass filters with different filtering ranges.

[0076] A PPG signal includes a dominant frequency component and harmonic components. The harmonic frequencies are typically multiples of the dominant frequency, such as the second or third harmonic. Figure 3 As shown in Figure b, the lower signal line in box 1 represents the main frequency, and the upper signal line represents the harmonics. It can be seen that the harmonic frequency is twice the main frequency. The maximum heart rate value represents the user's highest heart rate during exercise, and the minimum heart rate value represents the user's lowest heart rate at rest. The harmonic parameters represent the maximum harmonic frequency and the number of harmonics. Preset maximum heart rate values, preset minimum heart rate values, and preset harmonic parameters are set according to actual needs. Based on these preset values, the heart rate range can be determined. Furthermore, based on the relationship between heart rate and frequency values, the effective frequency band range can be determined.

[0077] In one example, the preset maximum heart rate is 120 bpm, the preset minimum heart rate is 50 bpm, the preset number of harmonics is 3, and the preset maximum harmonic frequency is 3 times the main frequency. Therefore, the maximum value of the heart rate range is also 3 times the preset maximum heart rate, i.e., 360 bpm. Thus, the heart rate range can be set to [50 bpm, 360 bpm]. To ensure the accuracy of the final result, the heart rate range can be slightly expanded. For example, if the heart rate range is set to [48 bpm, 372 bpm], the effective frequency band range is [0.8 Hz, 6.2 Hz].

[0078] Multiple preset bandpass filters with different filtering ranges are set within the effective frequency band. The number of preset bandpass filters and the filtering range of each bandpass filter can be set according to actual needs, such as setting the filtering range based on the octave of the minimum frequency and the maximum frequency within the effective frequency band. In one example, when the effective frequency band of the multi-channel PPG signal is [0.8Hz, 6.2Hz], Figure 4 This is a schematic diagram illustrating the filtering range of a plurality of bandpass filters according to an exemplary embodiment, such as... Figure 4As shown, SB1-SB7 represent bandpass filters. The maximum filtering frequency of each bandpass filter is the maximum frequency in its effective frequency band, which is 6.2Hz. Using the minimum frequency in the effective frequency band (0.8Hz) as a reference, the filtering range of SB1 is [0.8Hz, 6.2Hz]. The minimum filtering frequency of SB2 is 1.2Hz, which is between the minimum frequency of 0.8Hz and its second harmonic (1.6Hz). Therefore, the filtering range of SB2 is [1.2Hz, 6.2Hz]. The minimum filtering frequency of SB3 is 2Hz, which is between the second harmonic (1.6Hz) and the third harmonic (2.4Hz). Therefore, the filtering range of SB3 is [2Hz, 6.2Hz]. And so on, we can deduce... Figure 4 The filtering range of each bandpass filter is shown.

[0079] The preset bandpass filter considers both the main frequency component and the harmonic component of the PPG signal, ensuring that all sub-band signals after bandpass filtering contain both main frequency and harmonic information.

[0080] In step S103, the dominant frequency represents the frequency corresponding to the heart rate value. The preset dominant frequency is determined based on the heart rate tracking range. Each preset dominant frequency corresponds to a reference signal, and the number of reference signals is determined by the number of preset dominant frequencies. The number of preset dominant frequencies is determined based on the expected heart rate change value during heart rate tracking. For example, if the heart rate tracking range is 50 bpm-120 bpm and the expected heart rate change value is 6 bpm, the preset dominant frequency range is 0.8 Hz-2 Hz, and the preset dominant frequency change value is 0.1 Hz. Or, if the heart rate tracking range is 30 bpm-150 bpm and the expected heart rate change value is 3 bpm, the preset dominant frequency range is 0.5 Hz-2.5 Hz, and the preset dominant frequency change value is 0.05 Hz. The heart rate tracking range and the expected heart rate change value can be set according to actual needs, and this disclosure does not impose any restrictions. The preset dominant frequency is used as the dominant frequency of the reference signal to determine the reference signal. Then, correlation analysis is performed on the reference signal and the sub-band signal to obtain the first correlation value between each reference signal and the multiple sub-band signals. The correlation analysis method is not limited.

[0081] In some implementations, the process of setting the reference signal includes the following steps:

[0082] The effective frequency band range of the main frequency is determined based on the preset maximum heart rate value and the preset minimum heart rate value;

[0083] Set the preset main frequency based on the effective frequency band range of the main frequency;

[0084] The reference signal is determined based on the preset main frequency and preset harmonic parameters.

[0085] The maximum heart rate value represents the user's highest heart rate during exercise, while the minimum heart rate value represents the user's lowest heart rate at rest. Preset maximum and minimum heart rate values ​​are set according to actual needs. The heart rate range can be determined based on these preset values ​​and preset harmonic parameters. Furthermore, the effective frequency band range of the master frequency can be determined based on the relationship between heart rate and frequency values. In one example, the preset maximum heart rate is 120 bpm, and the preset minimum heart rate is 50 bpm. Therefore, the heart rate range can be set to [50 bpm, 120 bpm], corresponding to an effective frequency band range of [0.8 Hz, 2 Hz]. Within the effective frequency band range, a preset master frequency is set for each reference signal based on the preset heart rate variation value. In one example, when the preset heart rate change is 6 bpm, the preset main frequency change is 0.1 Hz. Therefore, within the effective frequency band of the main frequency [0.8 Hz, 2 Hz], 12 reference signals can be set, each with a preset main frequency of 0.8 Hz, 0.9 Hz, 1 Hz, 1.2 Hz…2 Hz. Harmonic parameters include the number of harmonics. The preset number of harmonics is set according to actual needs; for example, the preset number of harmonics can be 3.

[0086] After determining the preset main frequency, the reference signal is represented by the following matrix:

[0087]

[0088] Among them, Y f This represents the reference signal for the preset main frequency f, where f represents the frequency of the preset main frequency, and N... h The preset number of harmonic components in the reference signal is represented by t, which represents the preset time length of the reference signal. The sin and cosine signals can supplement the phase of the reference signal.

[0089] In some implementations, when the effective frequency band range of the main frequency includes a narrow frequency band range, a preset main frequency is set according to the narrow frequency band range. The preset main frequency includes a first preset main frequency and a second preset main frequency.

[0090] Since the PPG signal can be either single-frequency or mixed-frequency, a mixed-frequency signal can also be generated in the reference signal when setting the reference signal. Therefore, when determining the effective frequency band range of the master frequency based on the preset maximum heart rate value and the preset minimum heart rate value, the preset frequency band can be set to a narrow frequency band range. Outside the narrow frequency band range, the reference signal is determined in the above manner. Within the narrow frequency band range, two preset master frequencies are set in each reference signal according to the expected heart rate change value within the narrow frequency band range. The preset master frequencies include a first preset master frequency and a second preset master frequency, and may also include more preset master frequencies. In one example, when the heart rate tracking range is [60 bpm, 70 bpm], the effective frequency band range of the corresponding master frequency is a narrow band range, that is, the narrow band range is [1.00 Hz, 1.25 Hz]. When the preset heart rate change value within the narrow band range is 3 bpm, the preset master frequency change value of a reference signal is 0.05 Hz. Therefore, three reference signals can be set within the narrow band range, and the preset master frequencies of each reference signal are 1.00 Hz and 1.05 Hz, 1.10 Hz and 1.15 Hz, and 1.20 Hz and 1.25 Hz, respectively.

[0091] After determining the preset main frequency, the reference signal within the narrow bandwidth range is represented by the following matrix:

[0092]

[0093] Among them, Y f The reference signals represent the preset main frequencies f0 and f1, where f0 represents the frequency of the first preset main frequency and f1 represents the frequency of the second preset main frequency. N h The preset number of harmonics in the reference signal is indicated by t, which represents the preset time length of the mixing signal in the reference signal. The sinusoidal signal (sin) and cosine signal (cos) can supplement the phase of the reference signal.

[0094] The reference signal setting also takes into account the main frequency component and harmonic components of the PPG signal, which can make full use of harmonic information to indirectly enhance the frequency band where the main frequency information is located, and reduce some interference caused by irregular movement or skin contact with the optical path.

[0095] In step S104, the larger the first correlation value between the reference signal and multiple sub-band signals, the greater the similarity between the reference signal and the multi-channel PPG signal. Therefore, based on the preset dominant frequency of the reference signal corresponding to the maximum first correlation value, i.e., the target correlation value, the target dominant frequency information of the heart rate information can be determined. The preset dominant frequency of the reference signal corresponding to the target correlation value can be used as the target dominant frequency information of the heart rate information, or the preset dominant frequency of the reference signal corresponding to the target correlation value can be used as the center frequency point to further filter the multi-channel PPG signal. The target dominant frequency information of the heart rate information is then determined based on the filtered signal. After determining the target dominant frequency information of the heart rate information, the heart rate value corresponding to the target dominant frequency information is the heart rate information. When determining the dominant frequency information corresponding to the heart rate information, a typical correlation calculation method is used. Compared with other methods, the computational load of correlation calculation is small, ensuring stable heart rate tracking results.

[0096] In the exemplary embodiments of this disclosure, a multi-channel PPG signal is acquired, filtered, and multiple sub-band signals are obtained through multiple preset bandpass filters with different filtering ranges. Multiple reference signals are determined based on multiple preset dominant frequencies. A first correlation value is determined between each reference signal and the multiple sub-band signals. The target dominant frequency information of the heart rate information is determined based on the maximum value among the multiple first correlation values, thereby determining the heart rate information. Before determining the heart rate information, this method accurately identifies the target dominant frequency information corresponding to the user's current heart rate information through the correlation between the reference signals and the sub-band signals. This avoids removing the effective components of the pulse wave signal during subsequent filtering, ensuring the accuracy of the heart rate tracking results. Furthermore, determining the target dominant frequency information through correlation analysis is simpler to implement and requires less computation compared to deep learning models, maintaining relatively stable heart rate tracking results.

[0097] In an exemplary embodiment of this disclosure, a method for determining heart rate is provided. Figure 5 This is a flowchart illustrating a heart rate determination method according to an exemplary embodiment, such as... Figure 5 As shown, it includes the following steps:

[0098] Step S501: Acquire multi-channel PPG signals;

[0099] Step S502: High-pass filtering is performed on the multi-channel PPG signal to obtain the first multi-channel signal;

[0100] Step S503: Based on the acceleration signal, perform adaptive filtering on the multi-channel first signal to obtain the multi-channel second signal;

[0101] Step S504: Filter the multi-channel second signal according to multiple preset bandpass filters to obtain multiple sub-band signals;

[0102] Step S505: Determine the second correlation value between each reference signal and each sub-band signal;

[0103] Step S506: Determine the first correlation value between each reference signal and the multiple sub-band signals based on the weighted sum of multiple second correlation values ​​of each reference signal and the multiple sub-band signals;

[0104] Step S507: Determine the target main frequency information of heart rate information based on the target correlation value, so as to determine the heart rate information. The target correlation value is the maximum value among multiple first correlation values.

[0105] The specific implementation of step S501 is described in step S101, and will not be repeated here.

[0106] In step S502, a high-pass non-recursive (Finite Impulse Response, FIR) filter is used to filter the PPG signal of each channel, suppressing low-frequency components in the signal to obtain the filtered PPG signal for each channel, i.e., the first signal of the multi-channel signal. For example, a 0.5Hz high-pass FIR filter is used to filter the 25Hz four-channel PPG signal. High-pass filtering removes low-frequency drift and baseline components from the signal, while preserving the high frequencies and the frequencies corresponding to the heart rate in the original signal with maximum linearity.

[0107] In step S503, acceleration signals are acquired using sensors such as accelerometers to measure acceleration. These acceleration signals are triaxial, including acceleration data in the horizontal, vertical, and longitudinal axes. Because the position of the PPG sensor against the skin changes with the movement, creating varying degrees of cavities, which can interfere with the PPG sensor's detection data, adaptive filtering of the acceleration signal on the multi-channel first signal removes noise interference from the movement state.

[0108] Because acceleration interferes with the PPG signal when the electronic device is in motion, but not when it is stationary, it is necessary to determine whether the electronic device is in motion based on the acceleration signal. In some implementations, the determination of whether the electronic device is in motion is achieved through the following steps:

[0109] The action parameters are determined based on the acceleration data of the acceleration signal at each sampling point within a preset time period;

[0110] If the motion parameter is greater than or equal to the first threshold, the electronic device is determined to be in motion.

[0111] If the action parameter is less than the first threshold, the electronic device is determined to be in a stationary state.

[0112] The preset duration is the length of the time window, for example, 0.4 seconds. The number of sampling points within the preset duration is determined based on the sampling rate of the acceleration signal. For example, if the sampling rate is 25Hz and includes 25 sampling points per second, then 0.4 seconds includes 25 × 0.4 = 10 sampling points. The magnitude of the triaxial acceleration data at each sampling point within the preset duration is calculated, i.e., the arithmetic square root of the triaxial acceleration data. Then, median filtering is applied to the magnitude signal within the preset duration to remove noise from the acceleration signal, obtaining motion parameters. These motion parameters are used to determine the motion state. A first threshold is a preset empirical value. If the motion parameter is greater than or equal to the first threshold, it indicates a large motion, determining that the electronic device is in motion; if the motion parameter is less than the first threshold, it indicates a small motion or no motion, determining that the electronic device is stationary.

[0113] In some implementations, if the electronic device is determined to be in motion based on the acceleration signal, the acceleration signal is used as the target signal, the multi-channel first signal is used as the observation signal, and the multi-channel first signal is adaptively filtered to obtain the multi-channel second signal.

[0114] When the electronic device is in motion, the acceleration signal is used as the target signal, and the first signal of each channel is used as the observation signal. Adaptive filtering is applied to each passing first signal, for example, using the Least Mean Square (LMS) filtering algorithm, to obtain the filtered first signal for each channel, which is the second signal for each channel. Adaptive filtering removes the interference of acceleration noise on the PPG signal, thus avoiding the interference of motion on the PPG signal.

[0115] In some implementations, if the electronic device is determined to be stationary based on the acceleration signal, the multi-channel first signal is used as the multi-channel second signal.

[0116] Since acceleration in a stationary state does not interfere with the PPG signal, when the electronic device is stationary, the adaptive filtering can be skipped, and the first multi-channel signal can be used as the second multi-channel signal.

[0117] In step S504, the multi-channel second signal is filtered by multiple preset bandpass filters with different filtering ranges to obtain multiple sub-band signals. For specific implementation details, please refer to step S102, which will not be repeated here.

[0118] In steps S505-S506, the second correlation value between each reference signal and each sub-band signal is determined using the following formula:

[0119]

[0120] Where, ρ kThis represents the second correlation matrix between the k-th reference signal and each sub-band signal. This represents the second correlation value between the k-th reference signal and the 1st sub-band signal. This represents the second correlation value between the k-th reference signal and the second sub-band signal. This represents the second correlation value between the k-th reference signal and the m-th sub-band signal. This represents the subband signal matrix obtained after filtering by bandpass filter SB1. This represents the subband signal matrix obtained after filtering by the bandpass filter SB2. Y represents the subband signal matrix obtained after filtering by the bandpass filter SBm. fk W represents the k-th reference signal matrix with a preset main frequency f. X and W Y This represents the weight vector that maximizes the correlation between X and Y. Since both X and Y are multi-dimensional matrices—for example, when the PPG signal is a four-channel signal acquired over 10 seconds at a sampling rate of 25Hz, the dimensions of X and Y are both 4×250—W is used to simplify the correlation calculation. X and W Y Reduce its dimension to a one-dimensional matrix.

[0121] W is calculated using the following formula. X and W Y :

[0122]

[0123] The first correlation value between each reference signal and multiple sub-band signals is determined using the following formula:

[0124]

[0125] in, This represents the first correlation value between each reference signal and multiple sub-band signals, where n takes values ​​of 1, 2, ..., m. Let w(n) represent the second correlation value between the k-th reference signal and the n-th sub-band signal, and w(n) represent the weight of the n-th sub-band signal, expressed by the formula w(n) = n -a +b is obtained, where a and b represent hyperparameters, which are preset values, such as a = 1.25 and b = 0.2.

[0126] In step S507, in some embodiments, if the dominant frequency of the acceleration signal is the same as the preset dominant frequency of the reference signal corresponding to the target correlation value, the target correlation value is updated to the second largest value among multiple first correlation values. After acquiring the acceleration signal, the acceleration signal is subjected to spectrum transformation to determine the energy of each frequency point. The frequency point with the highest energy is the dominant frequency of the acceleration signal. If the preset dominant frequency of the reference signal corresponding to the target correlation value is the same as the dominant frequency of the acceleration signal, that is, the frequency points are the same, it indicates that there is co-frequency interference between the PPG signal and the acceleration signal. At this time, the target correlation value is updated to the second largest value among multiple first correlation values.

[0127] In some implementations, determining heart rate information based on target-related values ​​includes the following two cases:

[0128] The first method involves determining the target frequency of the heart rate information based on the preset frequency of the reference signal corresponding to the target correlation value, if the electronic device is determined to be stationary and the target correlation value is greater than or equal to the second threshold.

[0129] The second threshold is a large value. If the target correlation value is greater than or equal to the second threshold, it means that the correlation between the reference signal and multiple sub-band signals is high. If the electronic device is in a stationary state and the correlation between the reference signal and multiple sub-band signals is high, it indicates that the quality of the current PPG signal is good and the frequency is stable. At this time, the preset main frequency of the reference signal corresponding to the target correlation value is used as the target main frequency information of the heart rate information, that is, the frequency point of the target main frequency. The heart rate information can be determined based on the target main frequency information.

[0130] The second method involves filtering the frequency band signal of the reference signal corresponding to the target correlation value if it is determined that the electronic device is in motion or the target correlation value is less than the second threshold, to obtain a third signal, and then determining the target frequency information of the heart rate information based on the third signal.

[0131] If the target correlation value is less than the second threshold, it indicates that the correlation between the reference signal and multiple sub-band signals is low. If the electronic device is in motion or the correlation between the reference signal and multiple sub-band signals is low, it indicates that the quality of the current PPG signal is poor and the frequency is unstable. At this time, further filtering is required. The preset main frequency of the reference signal corresponding to the target correlation value is used as the center frequency point. The frequency band signal is obtained based on the second signal. The frequency band signal is filtered to obtain the third signal. The frequency point with the highest energy in the third signal is used as the target main frequency information, that is, the frequency point of the target main frequency. The heart rate information can be determined based on the target main frequency information.

[0132] The filtering operation can employ any filtering algorithm, such as wavelet denoising or bandpass FIR filtering. When using wavelet denoising, the frequency band signal centered at the preset dominant frequency of the reference signal corresponding to the target correlation value is decomposed into multiple wavelet coefficients. The first correlation value between each reference signal and multiple sub-band signals is used as the denoising threshold for each wavelet coefficient. The wavelet layer corresponding to the target correlation value is the layer containing the effective signal. If each wavelet layer corresponds to the preset dominant frequency of multiple reference signals, the average of the first correlation values ​​between the multiple reference signals and multiple sub-band signals is used as the denoising threshold for each wavelet coefficient. Wavelet reconstruction is performed based on the product of the average value of each wavelet coefficient and the denoising threshold, thus obtaining the third signal. When using bandpass FIR filtering, the preset dominant frequency of the reference signal corresponding to the target correlation value is used as the center frequency to determine the filtering range of the bandpass filter. This bandpass filter is then used to perform bandpass filtering on the frequency band signal centered at the preset dominant frequency of the reference signal corresponding to the target correlation value to obtain the third signal.

[0133] Based on the preset main frequency of the reference signal corresponding to the target correlation value, further filtering operations are performed, which can accurately remove interference signals and noise. At the same time, it can improve the efficiency of the filtering algorithm, and the algorithm can perform the most reasonable calculations according to different scenarios to save power consumption.

[0134] In an exemplary embodiment of this disclosure, a method for determining heart rate is provided. Figure 6 This is a flowchart illustrating a heart rate determination method according to an exemplary embodiment, such as... Figure 6 As shown, it includes the following steps:

[0135] S6-1, acquire triaxial acceleration signals;

[0136] S6-2, calculate the magnitude of the triaxial acceleration data at each sampling point within the sliding time window, perform median filtering on the magnitude signal, and obtain the motion parameters;

[0137] S6-3, quantify the magnitude of the action based on the action parameters to determine whether the electronic device is in motion or stationary state;

[0138] S6-4, Calculate the main frequency information of the acceleration signal;

[0139] S6-5, acquire multi-channel green light signals, i.e. PPG signals;

[0140] S6-6 performs high-pass FIR filtering on the multi-channel PPG signal to obtain the first signal;

[0141] S6-7, In motion, the first signal is adaptively filtered based on the acceleration signal to obtain the second signal;

[0142] S6-8, bandpass filtering is performed on the second signal through a bandpass filter bank to obtain the sub-band signal;

[0143] S6-9, perform canonical correlation analysis on the reference signal and the sub-band signal to obtain the third signal;

[0144] S6-10: After performing wavelet denoising or bandpass FIR filtering on the third signal, perform main frequency analysis to obtain the target main frequency information of the heart rate information in order to determine the heart rate information;

[0145] S6-11 directly outputs the main frequency information of the third signal as the target main frequency information of the heart rate information to determine the heart rate information.

[0146] The execution order of S6-1 to S6-3 and S6-5 to S6-6 is not restricted.

[0147] in, Figure 7 This is a schematic diagram illustrating a bandpass filter bank and canonical correlation analysis according to an exemplary embodiment, such as... Figure 7 As shown, the process includes: applying bandpass filtering to the second signal using m bandpass filters to obtain m sub-band signals; and performing canonical correlation analysis on the reference signal and each sub-band signal to obtain m second correlation values. n is set to 1, 2, ..., m. Then, the weighted sum of the m second correlation values ​​is calculated to obtain the first correlation values ​​of the reference signal and multiple sub-band signals. Where k represents the k-th reference signal, and the maximum value of multiple first correlation values ​​is determined. Check if the preset main frequency of the corresponding reference signal is the same as the main frequency of the acceleration signal. If they are not the same, then use the maximum value in the first correlation value. The target correlation value is determined; if they are of the same frequency, the second largest value in the first correlation value is determined as the target correlation value. The preset main frequency of the reference signal of the target correlation value is determined as the center frequency point of the third signal.

[0148] In an exemplary embodiment of this disclosure, a heart rate determination device is provided. Figure 8 This is a block diagram illustrating a heart rate determination device according to an exemplary embodiment, such as... Figure 8 As shown, the heart rate determination device includes:

[0149] Acquisition module 801 is configured to acquire multi-channel PPG signals;

[0150] The filtering module 802 is configured to filter the multi-channel PPG signal to obtain multiple sub-band signals, which are obtained by multiple preset bandpass filters with different filtering ranges.

[0151] The calculation module 803 is configured to determine a first correlation value between each of a plurality of reference signals and a plurality of sub-band signals, wherein the plurality of reference signals are determined based on a plurality of preset main frequencies;

[0152] The determination module 804 is configured to determine the target main frequency information of the heart rate information based on the target correlation value, so as to determine the heart rate information. The target correlation value is the maximum value among multiple first correlation values.

[0153] In one exemplary embodiment, the filtering module 802 is further configured to:

[0154] High-pass filtering is applied to the multi-channel PPG signal to obtain the first multi-channel signal;

[0155] Based on the acceleration signal, adaptive filtering is performed on the multi-channel first signal to obtain the multi-channel second signal;

[0156] Multiple sub-band signals are obtained by filtering the multi-channel second signal using multiple preset bandpass filters.

[0157] In one exemplary embodiment, the filtering module 802 is further configured to:

[0158] If the electronic device is determined to be in motion based on the acceleration signal, the acceleration signal is used as the target signal, the first multi-channel signal is used as the observation signal, and the first multi-channel signal is adaptively filtered to obtain the second multi-channel signal.

[0159] If the electronic device is determined to be stationary based on the acceleration signal, the first multi-channel signal is used as the second multi-channel signal.

[0160] In one exemplary embodiment, the filtering module 802 is further configured to:

[0161] The action parameters are determined based on the acceleration data of the acceleration signal at each sampling point within a preset time period;

[0162] If the motion parameter is greater than or equal to the first threshold, the electronic device is determined to be in motion.

[0163] If the action parameter is less than the first threshold, the electronic device is determined to be in a stationary state.

[0164] In one exemplary embodiment, the heart rate determining device includes a setting module 805 configured to:

[0165] The effective frequency band range is determined based on the preset maximum heart rate value, preset minimum heart rate value, and preset harmonic parameters;

[0166] Based on the effective frequency band range, set multiple preset bandpass filters with different filtering ranges.

[0167] In one exemplary embodiment, the setting module 805 is further configured to:

[0168] The effective frequency band range of the main frequency is determined based on the preset maximum heart rate value and the preset minimum heart rate value;

[0169] Set the preset main frequency based on the effective frequency band range of the main frequency;

[0170] The reference signal is determined based on the preset main frequency and preset harmonic parameters.

[0171] In one exemplary embodiment, the setting module 805 is further configured to:

[0172] When the effective frequency band of the main frequency includes the narrow frequency band, a preset main frequency is set according to the narrow frequency band. The preset main frequency includes a first preset main frequency and a second preset main frequency.

[0173] In one exemplary embodiment, the computing module 803 is further configured to:

[0174] Determine the second correlation value between each reference signal and each sub-band signal;

[0175] The first correlation value between each reference signal and the multiple sub-band signals is determined by weighting the sum of multiple second correlation values ​​between each reference signal and the multiple sub-band signals.

[0176] In one exemplary embodiment, the determining module 804 is further configured to:

[0177] If it is determined that the electronic device is stationary and the target correlation value is greater than or equal to the second threshold, the target main frequency information of the heart rate information is determined according to the preset main frequency of the reference signal corresponding to the target correlation value.

[0178] If it is determined that the electronic device is in motion, or the target correlation value is less than the second threshold, the frequency band signal of the reference signal corresponding to the target correlation value is filtered to obtain the third signal, and the target frequency information of the heart rate information is determined based on the third signal.

[0179] Regarding the apparatus in the above embodiments, the specific manner in which each module performs its operation has been described in detail in the embodiments related to the method, and will not be elaborated upon here.

[0180] Figure 9 This is a block diagram illustrating an electronic device 900 according to an exemplary embodiment.

[0181] Reference Figure 9The electronic device 900 may include one or more of the following components: a processing component 902, a memory 904, a power supply component 906, a multimedia component 908, an audio component 910, an input / output (I / O) interface 912, a sensor component 914, and a communication component 916.

[0182] Processing component 902 typically controls the overall operation of electronic device 900, such as operations associated with display, telephone calls, data communication, camera operation, and recording operations. Processing component 902 may include one or more processors 920 to execute instructions to perform all or part of the steps of the methods described above. Furthermore, processing component 902 may include one or more modules to facilitate interaction between processing component 902 and other components. For example, processing component 902 may include a multimedia module to facilitate interaction between multimedia component 908 and processing component 902.

[0183] Memory 904 is configured to store various types of data to support the operation of electronic device 900. Examples of this data include instructions for any application or method operating on electronic device 900, contact data, phonebook data, messages, pictures, videos, etc. Memory 904 can be implemented by any type of volatile or non-volatile storage device or a combination thereof, such as static random access memory (SRAM), electrically erasable programmable read-only memory (EEPROM), erasable programmable read-only memory (EPROM), programmable read-only memory (PROM), read-only memory (ROM), magnetic storage, flash memory, magnetic disk, or optical disk.

[0184] Power supply component 906 provides power to various components of electronic device 900. Power supply component 906 may include a power management system, one or more power supplies, and other components associated with generating, managing, and distributing power to electronic device 900.

[0185] Multimedia component 908 includes a screen that provides an output interface between the electronic device 900 and the user. In some embodiments, the screen may include a liquid crystal display (LCD) and a touch panel (TP). If the screen includes a touch panel, the screen may be implemented as a touchscreen to receive input signals from the user. The touch panel includes one or more touch sensors to sense touches, swipes, and gestures on the touch panel. The touch sensors may sense not only the boundaries of the touch or swipe action but also the duration and pressure associated with the touch or swipe operation. In some embodiments, multimedia component 908 includes a front-facing camera and / or a rear-facing camera. When the electronic device 900 is in an operating mode, such as a shooting mode or a video mode, the front-facing camera and / or the rear-facing camera may receive external multimedia data. Each front-facing camera and rear-facing camera may be a fixed optical lens system or have focal length and optical zoom capabilities.

[0186] Audio component 910 is configured to output and / or input audio signals. For example, audio component 910 includes a microphone (MIC) configured to receive external audio signals when electronic device 900 is in an operating mode, such as call mode, recording mode, and voice recognition mode. The received audio signals may be further stored in memory 904 or transmitted via communication component 916. In some embodiments, audio component 910 also includes a speaker for outputting audio signals.

[0187] I / O interface 912 provides an interface between processing component 902 and peripheral interface modules, such as keyboards, click wheels, buttons, etc. These buttons may include, but are not limited to, home buttons, volume buttons, power buttons, and lock buttons.

[0188] Sensor assembly 914 includes one or more sensors for providing state assessments of various aspects of electronic device 900. For example, sensor assembly 914 can detect the on / off state of electronic device 900, the relative positioning of components such as the display and keypad of electronic device 900, changes in position of electronic device 900 or a component of electronic device 900, the presence or absence of user contact with electronic device 900, orientation or acceleration / deceleration of electronic device 900, and temperature changes of electronic device 900. Sensor assembly 914 may include a proximity sensor configured to detect the presence of nearby objects without any physical contact. Sensor assembly 914 may also include a light sensor, such as a CMOS or CCD image sensor, for use in imaging applications. In some embodiments, sensor assembly 914 may also include an accelerometer, gyroscope, magnetometer, pressure sensor, or temperature sensor.

[0189] Communication component 916 is configured to facilitate wired or wireless communication between electronic device 900 and other devices. Electronic device 900 can access wireless networks based on communication standards, such as WiFi, 2G, or 3G, or combinations thereof. In one exemplary embodiment, communication component 916 receives broadcast signals or broadcast-related information from an external broadcast management system via a broadcast channel. In one exemplary embodiment, communication component 916 also includes a near-field communication (NFC) module to facilitate short-range communication. For example, the NFC module may be implemented based on radio frequency identification (RFID) technology, Infrared Data Association (IrDA) technology, ultra-wideband (UWB) technology, Bluetooth (BT) technology, and other technologies.

[0190] In an exemplary embodiment, the electronic device 900 may be implemented by one or more application-specific integrated circuits (ASICs), digital signal processors (DSPs), digital signal processing devices (DSPDs), programmable logic devices (PLDs), field-programmable gate arrays (FPGAs), controllers, microcontrollers, microprocessors, or other electronic components to perform the methods described above.

[0191] In an exemplary embodiment, a non-transitory computer-readable storage medium including instructions is also provided, such as a memory 904 including instructions, which can be executed by a processor 920 of an electronic device 900 to perform the above-described method. For example, the non-transitory computer-readable storage medium may be a ROM, random access memory (RAM), CD-ROM, magnetic tape, floppy disk, and optical data storage device, etc.

[0192] A non-transitory computer-readable storage medium, when the instructions in the storage medium are executed by a processor of an electronic device, enables the electronic device to perform a heart rate determination method, including any of the methods described above.

[0193] Other embodiments of this disclosure will readily occur to those skilled in the art upon consideration of the specification and practice of the invention disclosed herein. This application is intended to cover any variations, uses, or adaptations of this disclosure that follow the general principles of this disclosure and include common knowledge or customary techniques in the art not disclosed herein. The specification and examples are to be considered exemplary only, and the true scope and spirit of this disclosure are indicated by the following claims.

[0194] It should be understood that this disclosure is not limited to the precise structures described above and shown in the accompanying drawings, and various modifications and changes can be made without departing from its scope. The scope of this disclosure is limited only by the appended claims.

Claims

1. A method for determining heart rate, characterized in that, The method includes: Acquire multi-channel PPG signals; The multi-channel PPG signal is filtered to obtain multiple sub-band signals, which are obtained by multiple preset bandpass filters with different filtering ranges. A first correlation value is determined between each of the plurality of reference signals and the plurality of sub-band signals, wherein the plurality of reference signals are determined based on a plurality of preset main frequencies; Based on the target correlation value, the target main frequency information of the heart rate information is determined to determine the heart rate information, wherein the target correlation value is the maximum value among a plurality of the first correlation values.

2. The heart rate determination method according to claim 1, characterized in that, The filtering of the multi-channel PPG signal to obtain multiple sub-band signals includes: The multi-channel PPG signal is high-pass filtered to obtain the multi-channel first signal; Based on the acceleration signal, the multi-channel first signal is adaptively filtered to obtain the multi-channel second signal; The multi-channel second signal is filtered by multiple preset bandpass filters to obtain the multiple sub-band signals.

3. The heart rate determination method according to claim 2, characterized in that, The step of adaptively filtering the multi-channel first signal based on the acceleration signal to obtain the multi-channel second signal includes: If it is determined that the electronic device is in motion based on the acceleration signal, the acceleration signal is used as the target signal, the multi-channel first signal is used as the observation signal, and the multi-channel first signal is adaptively filtered to obtain the multi-channel second signal; If the electronic device is determined to be stationary based on the acceleration signal, the first multi-channel signal is used as the second multi-channel signal.

4. The heart rate determination method according to claim 3, characterized in that, The method further includes: Based on the acceleration data of the acceleration signal at each sampling point within a preset time period, the action parameters are determined; If the action parameter is greater than or equal to the first threshold, it is determined that the electronic device is in motion. If the action parameter is less than the first threshold, the electronic device is determined to be in a stationary state.

5. The heart rate determination method according to claim 1, characterized in that, The process of setting the preset bandpass filter includes: The effective frequency band range is determined based on the preset maximum heart rate value, preset minimum heart rate value, and preset harmonic parameters; Based on the effective frequency band range, multiple preset bandpass filters with different filtering ranges are set.

6. The heart rate determination method according to claim 1, characterized in that, The process of setting the reference signal includes: The effective frequency band range of the main frequency is determined based on the preset maximum heart rate value and the preset minimum heart rate value; The preset main frequency is set according to the effective frequency band range of the main frequency; The reference signal is determined based on the preset main frequency and preset harmonic parameters.

7. The heart rate determination method according to claim 6, characterized in that, The step of setting the preset main frequency based on the effective frequency band range of the main frequency includes: When the effective frequency band range of the main frequency includes a narrow frequency band range, a preset main frequency is set according to the narrow frequency band range. The preset main frequency includes a first preset main frequency and a second preset main frequency.

8. The heart rate determination method according to claim 1, characterized in that, Determining a first correlation value between each of the plurality of reference signals and the plurality of sub-band signals includes: Determine a second correlation value between each of the reference signals and each of the sub-band signals; A first correlation value between each reference signal and the plurality of sub-band signals is determined based on a weighted sum of the second correlation values ​​of each reference signal and the plurality of sub-band signals.

9. The heart rate determination method according to claim 1, characterized in that, The step of determining the target dominant frequency information of heart rate information based on the target correlation value includes: If it is determined that the electronic device is in a stationary state, and the target correlation value is greater than or equal to the second threshold, the target main frequency information of the heart rate information is determined according to the preset main frequency of the reference signal corresponding to the target correlation value; If it is determined that the electronic device is in motion, or the target correlation value is less than the second threshold, the frequency band signal of the reference signal corresponding to the target correlation value is filtered to obtain a third signal, and the target frequency information of the heart rate information is determined based on the third signal.

10. A heart rate determination device, characterized in that, The device includes: The acquisition module is configured to acquire multi-channel PPG signals; The filtering module is configured to filter the multi-channel PPG signal to obtain multiple sub-band signals, wherein the multiple sub-band signals are obtained by multiple preset bandpass filters with different filtering ranges. The calculation module is configured to determine a first correlation value between each of the plurality of reference signals and the plurality of sub-band signals, the plurality of reference signals being determined based on a plurality of preset main frequencies; The determination module is configured to determine the target main frequency information of the heart rate information based on the target correlation value, thereby determining the heart rate information, wherein the target correlation value is the maximum value among a plurality of the first correlation values.

11. An electronic device, characterized in that, include: processor; Memory used to store processor-executable instructions; The processor is configured to perform the method as described in any one of claims 1-9.

12. A non-transitory computer-readable storage medium, characterized in that, When the instructions in the storage medium are executed by the processor of the electronic device, the electronic device is able to perform the method as described in any one of claims 1-9.

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