CN115770029A - Heart rate monitoring method and system and storage medium - Google Patents
Heart rate monitoring method and system and storage medium Download PDFInfo
- Publication number
- CN115770029A CN115770029A CN202111057732.0A CN202111057732A CN115770029A CN 115770029 A CN115770029 A CN 115770029A CN 202111057732 A CN202111057732 A CN 202111057732A CN 115770029 A CN115770029 A CN 115770029A
- Authority
- CN
- China
- Prior art keywords
- signal
- heart rate
- motion
- frequency
- target
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Landscapes
- Measuring Pulse, Heart Rate, Blood Pressure Or Blood Flow (AREA)
- Ultra Sonic Daignosis Equipment (AREA)
Abstract
The present specification discloses a heart rate monitoring method. The method comprises the following steps: acquiring a first signal, wherein the first signal comprises a target heart rate signal in a motion state; acquiring a motion signal corresponding to the motion state; identifying a second signal with a target frequency from the first signal based on a motion frequency corresponding to the motion signal, wherein the target frequency is derived from superposition of the motion frequency and a heart rate frequency corresponding to the target heart rate signal; and processing the first signal to determine the target heart rate signal based on the motion signal and the second signal. According to the heart rate monitoring method, the original heart rate data monitored by the heart rate sensor is denoised based on the superposition relationship between the motion signals and the heart rate signals, and meanwhile, the target heart rate signals can be determined based on the superposition relationship, so that the motion artifacts and the influence of the motion noise on the heart rate signals can be effectively removed, and a more accurate heart rate monitoring result is obtained.
Description
Technical Field
The present disclosure relates to the field of data monitoring, and in particular, to a heart rate monitoring method, a heart rate monitoring system, and a storage medium.
Background
Along with the popularization of intelligent wearing equipment, electronic equipment that wrist-watch, bracelet etc. have heart rate monitor function more and more receives the user to welcome. In the heart rate monitoring process, motion Artifacts (MA) may be present in the acquired heart rate signal due to interference from Motion, etc. The MA signal may be removed from the acquired heart rate signal by signal processing methods resulting in a "clean" (i.e., relatively high signal-to-noise ratio) heart rate signal. There may also be a coupling relationship, such as a superposition relationship, between the MA signal and the heart rate signal. This coupling relationship may also affect the accuracy of heart rate monitoring.
It is therefore desirable to provide a heart rate monitoring method for processing an acquired heart rate signal based on a superposition characteristic between an MA signal and the heart rate signal, which can denoise the acquired heart rate signal based on the superposition characteristic between the MA signal and the heart rate signal, while a target heart rate signal can be determined based on the relationship to improve the accuracy of the heart rate monitoring result.
Disclosure of Invention
One embodiment of the present specification provides a heart rate monitoring method. The method can comprise the following steps: acquiring a first signal, wherein the first signal can comprise a target heart rate signal in a motion state; acquiring a motion signal corresponding to the motion state; identifying a second signal with a target frequency from the first signal based on a motion frequency corresponding to the motion signal, wherein the target frequency can be derived from superposition of the motion frequency and a heart rate frequency corresponding to the target heart rate signal; and processing the first signal to determine the target heart rate signal based on the motion signal and the second signal.
In some embodiments, the acquiring a motion signal corresponding to the motion state may include: filtering the first signal; and determining the motion signal based on the filtered signal.
In some embodiments, the acquiring the motion signal corresponding to the motion state may include acquiring the motion signal by an acceleration sensor.
In some embodiments, the acquiring a motion signal corresponding to the motion state may include: acquiring two or more first signals through two or more optical paths; and determining the motion signal based on the two or more first signals.
In some embodiments, the second signal may comprise a superimposed signal between the motion signal and the target heart rate signal.
In some embodiments, the target frequency may be equal to a sum of the motion frequency and the heart rate frequency.
In some embodiments, the target frequency may be equal to a difference between the motion frequency and the heart rate frequency.
In some embodiments, the processing the first signal to determine the target heart rate signal based on the motion signal and the second signal may include: removing the motion signal and the second signal in the first signal, and determining the target heart rate signal.
In some embodiments, said processing the first signal to determine the target heart rate signal based on the motion signal and the second signal may further comprise: determining a signal amplitude of the motion signal; judging whether the signal amplitude is larger than an amplitude threshold value or not; and in response to the signal amplitude being greater than the amplitude threshold, processing the first signal to determine the target heart rate signal based on the motion signal and the second signal.
In some embodiments, said processing the first signal to determine the target heart rate signal based on the motion signal and the second signal may further comprise: determining a signal frequency of the motion signal; judging whether the signal frequency is greater than a frequency threshold value; and in response to the signal frequency being greater than the frequency threshold, processing the first signal to determine the target heart rate signal based on the motion signal and the second signal.
Additional features will be set forth in part in the description which follows and in part will become apparent to those having ordinary skill in the art upon examination of the following and the accompanying drawings or may be learned by production or operation of the examples. The features of the present invention may be realized and attained by practice or use of various aspects of the methodologies, instrumentalities and combinations particularly pointed out in the following detailed examples.
Drawings
The present description will be further explained by way of exemplary embodiments, which will be described in detail by way of the accompanying drawings. These embodiments are not intended to be limiting, and in these embodiments like numerals are used to indicate like structures, wherein:
fig. 1 is a schematic diagram of an application scenario of a heart rate monitoring system according to some embodiments of the present description;
FIG. 2 is a schematic diagram of exemplary hardware and/or software components of an exemplary computing device, shown in accordance with some embodiments of the present description;
FIG. 3 is a schematic diagram of exemplary hardware and/or software components of an exemplary mobile device, shown in accordance with some embodiments of the present description;
fig. 4 is an exemplary block diagram of a heart rate monitoring system, shown in accordance with some embodiments of the present description;
FIG. 5 is an exemplary flow diagram of a heart rate monitoring method, shown in some embodiments herein;
FIG. 6 is a schematic diagram of functional relationships shown in accordance with some embodiments of the present description;
FIG. 7 is a schematic diagram of a spectrum of a first signal shown in accordance with some embodiments of the present description;
FIG. 8 is an exemplary flow diagram of a heart rate monitoring method according to some embodiments of the present description;
fig. 9 is an exemplary flow diagram of a heart rate monitoring method according to some embodiments of the present description.
Detailed Description
In order to more clearly illustrate the technical solutions of the embodiments of the present specification, the drawings used in the description of the embodiments will be briefly described below. It is obvious that the drawings in the following description are only examples or embodiments of the present description, and that for a person skilled in the art, the present description can also be applied to other similar scenarios on the basis of these drawings without inventive effort. It is understood that these exemplary embodiments are given solely to enable those skilled in the relevant art to better understand and implement the present invention, and are not intended to limit the scope of the invention in any way. Unless otherwise apparent from the context, or otherwise indicated, like reference numbers in the figures refer to the same structure or operation.
As used in this specification and the appended claims, the terms "a," "an," "the," and/or "the" are not intended to be inclusive in the singular, but rather are intended to be inclusive in the plural, unless the context clearly dictates otherwise. In general, the terms "comprises" and "comprising" merely indicate that steps and elements are included which are explicitly identified, that the steps and elements do not form an exclusive list, and that a method or apparatus may include other steps or elements. The term "based on" is "based at least in part on". The term "one embodiment" means "at least one embodiment"; the term "another embodiment" means "at least one additional embodiment".
Flow charts are used in this description to illustrate operations performed by a system according to embodiments of the present description. It should be understood that the preceding or following operations are not necessarily performed in the exact order in which they are performed. Rather, the various steps may be processed in reverse order or simultaneously. Meanwhile, other operations may be added to the processes, or a certain step or several steps of operations may be removed from the processes.
The heart rate monitoring system and method provided by the embodiments of the present disclosure are described in detail below with reference to the accompanying drawings.
Fig. 1 is a schematic diagram of an application scenario of a heart rate monitoring system according to some embodiments of the present description. The heart rate monitoring system 100 shown in the embodiments of the present disclosure may be applied to various software, systems, platforms, and devices to implement heart rate signal monitoring and heart rate signal processing. For example, the method can be applied to noise reduction processing of the heart rate signals acquired by various software, systems, platforms and devices to remove motion signals doped in the heart rate signals, so that the accuracy of the heart rate signals monitored by a user in a motion state is improved.
When the user is in motion, the heart rate data collected by the heart rate monitoring device (e.g., the collecting device 120 shown in fig. 1) is not a clean heart rate signal, which also includes a motion signal caused by the motion of the user or a superimposed signal (also referred to as a second signal) of the heart rate signal and the motion signal. Therefore, in order to improve the accuracy of the heart rate monitoring result, the motion signal and the second signal included therein need to be removed to obtain a clean heart rate signal (which may also be referred to as a target heart rate signal). The embodiment of the present specification provides a heart rate monitoring system and a method, which can implement noise reduction processing on a heart rate signal in the above-mentioned motion scene, for example.
As shown in fig. 1, the heart rate monitoring system 100 may include a processing device 110, an acquisition device 120, a terminal 130, a storage device 140, and a network 150.
In some embodiments, processing device 110 may process data and/or information obtained from other devices or system components. Processing device 110 may execute program instructions based on the data, information, and/or processing results to perform one or more of the functions described herein. For example, the processing device 110 may obtain a first signal that the user is in a motion state and a motion signal corresponding to the motion state. As another example, the processing device 110 may identify a second signal having a target frequency from the first signals based on a motion frequency corresponding to the motion signal and a heart rate frequency corresponding to the target heart rate signal. As another example, the processing device 110 may process the first signal to derive a target heart rate signal based on the motion signal and the second signal.
In some embodiments, the processing device 110 may be a single processing device or a group of processing devices, such as a server or a group of servers. The set of processing devices may be centralized or distributed (e.g., processing device 110 may be a distributed system). In some embodiments, the processing device 110 may be local or remote. For example, the processing device 110 may access information and/or data in the acquisition device 120, the terminal 130, and the storage device 140 via the network 150. As another example, the processing device 110 may be directly connected to the acquisition device 120, the terminal 130, the storage device 140 to access stored information and/or data. In some embodiments, the processing device 110 may be implemented on a cloud platform. By way of example only, the cloud platform may include a private cloud, a public cloud, a hybrid cloud, a community cloud, a distributed cloud, between clouds, multiple clouds, the like, or any combination thereof. In some embodiments, the processing device 110 may be implemented on a computing device as illustrated in FIG. 2 of the present specification.
In some embodiments, processing device 110 may include a processing engine 112. The processing engine 112 may process data and/or information related to the heart rate signal or the motion signal to perform one or more of the methods or functions described herein. For example, the processing engine 112 may obtain a first signal that the user is in a motion state and a motion signal corresponding to the motion state. In some embodiments, the processing engine 112 may process the first and/or motion signals to remove the motion signal and/or the second signal due to user motion to arrive at a target heart rate signal.
In some embodiments, processing engine 112 may include one or more processing engines (e.g., a single chip processing engine or a multi-chip processor). By way of example only, the processing engine 112 may include a Central Processing Unit (CPU), an Application Specific Integrated Circuit (ASIC), an application specific instruction set processor (ASIP), an image processing unit (GPU), a physical arithmetic processing unit (PPU), a Digital Signal Processor (DSP), a Field Programmable Gate Array (FPGA), a Programmable Logic Device (PLD), a controller, a microcontroller unit, a Reduced Instruction Set Computer (RISC), a microprocessor, or the like, or any combination thereof. In some embodiments, the processing engine 112 may be integrated in the harvesting device 120 or the terminal 130.
In some embodiments, the acquisition device 120 may be used for acquiring a heart rate signal of the user and/or a motion signal for characterizing a motion state of the user, for example for acquiring the first signal and/or the motion signal described above. In some embodiments, the acquisition device 120 may be a single acquisition device, or an acquisition device set of multiple acquisition devices (e.g., 120-1.., 120-n). In some embodiments, the acquisition device 120 may be a device (e.g., a smart bracelet, a smart foot ring, a smart collar, a smart watch, a smart glove, etc.) that contains one or more sensors (e.g., an accelerometer sensor, a gyroscope, a heart rate sensor (e.g., a PPG photosensor, etc.) or other signal acquisition components.
The capture device 120 may convert the captured heart rate signals and/or motion signals to electrical signals and send the electrical signals to the processing device 110 for processing. In some embodiments, the heart rate signal acquired by the acquisition device 120 may contain MA signals caused by user motion. The processing device 110 may perform noise reduction processing on the acquired heart rate signal based on the heart rate signal and the motion signal acquired by the acquisition device 120 to remove interference due to the motion of the user, resulting in a clean heart rate signal.
In some embodiments, the collection device 120 may communicate information and/or data with the processing device 110, the terminal 130, and the storage device 140 via the network 150. In some embodiments, the acquisition device 120 may be directly connected to the processing device 110 or the storage device 140 to transfer information and/or data. For example, the acquisition device 120 and the processing device 110 may be different parts on the same electronic device (e.g., a smart bracelet, a smart watch, etc.) and connected by a metal wire.
In some embodiments, terminal 130 may be a terminal used by a user or other entity. For example, the terminal 130 may be a terminal carrying the above-described acquisition device 120. Also for example, the terminal 130 may be a terminal that communicates with any one or more components of the acquisition device 120 or the heart rate monitoring system 100 over the network 150. In some embodiments, the capture device 120 may be part of the terminal device 130.
In some embodiments, the terminal 130 may include a mobile device 130-1, a tablet computer 130-2, a laptop computer 130-3, the like, or any combination thereof.
In some embodiments, the terminal 130 may acquire/receive the heart rate signal and/or the motion signal acquired by the acquisition device 120. In some embodiments, the terminal 130 may acquire/receive a target heart rate signal obtained by processing the heart rate signal and/or the motion signal by the processing device 110. In some embodiments, the terminal 130 may obtain/receive signals or data directly from the acquisition device 120, the storage device 140, such as a first signal comprising a superposition of a heart rate signal and a motion signal, and a motion signal for characterizing a state of motion of the user. In some embodiments, the terminal 130 may retrieve/receive the noise-reduced clean heart rate signal from the storage device 140 or the processing device 110 via the network 150.
In some embodiments, the terminal 130 may send instructions to the processing device 110 and/or the collection device 120, and the processing device 110 and/or the collection device 120 may execute the instructions from the terminal 130. For example, the terminal 130 may send one or more instructions implementing the heart rate monitoring methods described herein to the processing device 110 and/or the acquisition device 120 to cause the processing device 110 and/or the acquisition device 120 to perform one or more operations/steps of the heart rate monitoring methods.
In some embodiments, the storage device 140 may be connected to the network 150 to communicate with one or more components in the heart rate monitoring system 100 (e.g., the processing device 110, the acquisition device 120, the terminal 130). One or more components in the heart rate monitoring system 100 may access data or instructions stored in the storage device 140 over the network 150. In some embodiments, the storage device 140 may be directly connected or in communication with one or more components in the heart rate monitoring system 100 (e.g., the processing device 110, the acquisition device 120, the terminal 130). In some embodiments, the storage device 140 may be part of the processing device 110.
In some embodiments, one or more components of the heart rate monitoring system 100 (e.g., processing device 110, acquisition device 120, terminal 130) may have permission to access the storage device 140. In some embodiments, one or more components of heart rate monitoring system 100 may read and/or modify information related to the data when one or more conditions are met.
The network 150 may facilitate the exchange of information and/or data. In some embodiments, one or more components in the heart rate monitoring system 100 (e.g., the processing device 110, the acquisition device 120, the terminal 130, and the storage device 140) may send/receive information and/or data to/from other components in the heart rate monitoring system 100 via the network 150. For example, the processing device 110 may obtain the first signal and/or the motion signal from the acquisition device 120 or the storage device 140 via the network 150, and the terminal 130 may obtain any one or more of the first signal, the motion signal, or the target heart rate signal from the processing device 110 or the storage device 140 via the network 150. In some embodiments, the heart rate monitoring system 100 may include one or more network access points. For example, the heart rate monitoring system 100 may include wired or wireless network access points, such as base stations and/or wireless access points 150-1, 150-2, …, through which one or more components of the heart rate monitoring system 100 may connect to the network 150 to exchange data and/or information.
One of ordinary skill in the art will appreciate that when elements or components of heart rate monitoring system 100 are executed, the components may be executed via electrical and/or electromagnetic signals. For example, when the acquisition device 120 transmits the first signal and/or the motion signal to the processing device 110, the acquisition device 120 may generate a coded electrical signal. The collection device 120 may then send the electrical signal to an output port. If the harvesting device 120 communicates with the harvesting device 120 via a wired network or data transmission line, the output port may be physically connected to a cable, which further transmits the electrical signal to the input port of the harvesting device 120. If the harvesting device 120 communicates with the harvesting device 120 via a wireless network, the output port of the harvesting device 120 may be one or more antennas that may convert the electrical signals to electromagnetic signals. Within an electronic device, such as the capture device 120 and/or the processing device 110, when processing instructions, issuing instructions, and/or performing actions, the instructions and/or actions may be performed via electrical signals. For example, when processing device 110 reads or writes data from a storage medium (e.g., storage device 140), it may send electrical signals to the storage medium's read/write device, which may read or write structured data in the storage medium. The structured data may be transmitted to the processor in the form of electrical signals over a bus of the electronic device. Herein, an electrical signal may refer to an electrical signal, a series of electrical signals, and/or at least two discrete electrical signals.
FIG. 2 is a schematic diagram of an exemplary computing device 200 shown in accordance with some embodiments of the present description. In some embodiments, the processing device 110 may be implemented on a computing device 200. As shown in FIG. 2, computing device 200 may include memory 210, processor 220, input/output (I/O) 230, and communication ports 240.
The memory 210 may store data/information obtained from the acquisition device 120, the terminal 130, the storage device 140, or any other component of the heart rate monitoring system 100. In some embodiments, memory 210 may include mass storage, removable storage, volatile read-write memory, read-only memory (ROM), and the like, or any combination thereof. Exemplary mass storage may include magnetic disks, optical disks, solid state disks, and the like. In some embodiments, memory 210 may store one or more programs and/or instructions to perform the example methods described in this specification. For example, the memory 210 may store a program executable by the processing device 110 to implement a heart rate monitoring method.
For purposes of illustration only, only one processor is depicted in computing device 200. However, it should be noted that the computing device 200 in this specification may also include multiple processors. Thus, operations and/or method steps described as being performed by one processor as described in this specification may also be performed by multiple processors, either jointly or separately. For example, if in this specification the processors of computing device 200 perform operation a and operation B simultaneously, it should be understood that operation a and operation B may also be performed by two or more different processors in the computing device, either jointly or separately. For example, a first processor performs operation a and a second processor performs operation B, or the first processor and the second processor collectively perform operations a and B.
I/O230 may input or output signals, data, and/or information. In some embodiments, I/O230 may enable a user to interact with processing device 110. In some embodiments, I/O230 may include input devices and output devices. Exemplary input devices may include a keyboard, mouse, touch screen, microphone, etc., or a combination thereof. Exemplary output devices may include a display device, speakers, printer, projector, etc., or a combination thereof. Exemplary display devices may include Liquid Crystal Displays (LCDs), light Emitting Diode (LED) based displays, flat panel displays, curved screens, television devices, cathode Ray Tubes (CRTs), speakers, and the like, or combinations thereof.
The communication port 240 may be connected to a network (e.g., network 150) to facilitate data communication. The communication port 240 may establish a connection between the processing device 110 and the acquisition device 120, the terminal 130, or the storage device 140. The connection may be a wired connection, a wireless connection, or a combination of both to enable data transmission and reception.
Fig. 3 is a schematic diagram illustrating exemplary hardware and/or software components of an exemplary mobile device 300 on which terminal 130 may be implemented according to some embodiments of the present description. As shown in fig. 3, mobile device 300 may include a communication unit 310, a display unit 320, a Graphics Processing Unit (GPU) 330, a Central Processing Unit (CPU) 340, input/output 350, memory 360, and storage 370.
Central Processing Unit (CPU) 340 may include interface circuits and processing circuits similar to processor 220. In some embodiments, any other suitable component, including but not limited to a system bus or a controller (not shown), may also be included in the mobile device 300. In some embodiments, the operating system 362 (e.g., IOS) is moved TM 、Andro TM 、Windows Phone TM Etc.) and one or more application programs 364 may be loaded from storage 370 into memory 360 for execution by Central Processing Unit (CPU) 340. The application 364 may include a browser or any other suitable mobile application for receiving and presenting information related to heart rate signals from a heart rate monitoring system on the mobile device 300. The interaction of signals and/or data may be accomplished via the input/output device 350 and provided to the processing engine 112 and/or other components of the heart rate monitoring system 100 via the network 150.
To implement the various modules, units and their functionality described above, a computer hardware platform may be used as a hardware platform for one or more elements (e.g., modules of processing device 110 described in fig. 1). Since these hardware elements, operating systems, and programming languages are common, it can be assumed that those skilled in the art are familiar with these techniques and that they are able to provide the information needed in route planning in accordance with the techniques described herein. A computer with a user interface may be used as a Personal Computer (PC) or other type of workstation or terminal device. After being properly programmed, a computer with a user interface may serve as a processing device such as a server. It is believed that one skilled in the art may also be familiar with this structure, programming, or general operation of this type of computer device. Therefore, no additional explanation is described with respect to the drawings.
Fig. 4 is an exemplary block diagram of a heart rate monitoring system according to some embodiments of the present description. In some embodiments, the heart rate monitoring system 100 may be implemented on the processing device 110. As shown in fig. 4, processing device 110 may include an acquisition module 410, a processing module 420, and a generation module 430.
The acquisition module 410 may be used to acquire the first signal. In some embodiments, the first signal may comprise a target heart rate signal in a state of motion. In some embodiments, the first signal may comprise a motion signal corresponding to said motion state. In some embodiments, the first signal may also include a superimposed signal between the motion signal and a target heart rate signal. In some embodiments, the first signal may be a heart rate signal acquired by an acquisition device (e.g., acquisition device 120) in a user motion state. In some embodiments, the acquisition device may acquire the heart rate signal based on photoplethysmography (PPG). The acquisition module 410 may acquire the first signal from the acquisition device. In some embodiments, the first signal may be stored in a storage device (e.g., storage device 140, memory 220, memory 370, or an external storage device). The retrieving module 410 may retrieve the first signal from the storage device.
The processing module 420 may be configured to obtain a motion signal corresponding to the motion state. In some embodiments, to acquire a motion signal corresponding to a motion state, the processing module 420 may be configured to filter the first signal to reduce or filter out noise signals (e.g., baseline wander, etc.) in the first signal. For example, the processing module 420 may filter the first signal based on a filtering algorithm to reduce or filter out baseline wander therein. Further, the processing module 420 may determine a motion signal corresponding to the motion state based on the filtered signal. For example, the processing module 420 may process the filtered signals based on an Independent Component Analysis (ICA) algorithm, statistically and independently process the filtered signals to obtain Independent Component components corresponding to the target heart rate signal and the motion signal, respectively, so as to determine the motion signal corresponding to the motion state. For another example, the processing module 420 may use a signal having a specific frequency component in the filtered signal as the motion signal.
In some embodiments, to acquire motion signals corresponding to a motion state, the processing module 420 may be configured to determine the motion signals based on a motion capture device (e.g., an acceleration sensor, a gyroscope, a magnetometer, etc.).
In some embodiments, to acquire a motion signal corresponding to the motion state, the processing module 420 may be configured to acquire two or more first signals through two or more optical paths. For example, the processing module 420 may cause the two or more optical paths to emit light having two or more spectral distributions (e.g., having two or more different wavelengths). The acquisition device may acquire two or more first signals corresponding to the two or more spectrally distributed lights, respectively. In some embodiments, the two or more different spectral distributions of light may have the same or similar correlation to the motion signal. Accordingly, the two or more first signals may have a common-mode signal. The common mode signal corresponds to the motion signal. In some embodiments, the two or more different spectral distributions of light may have different correlations to the target heart rate signal. Accordingly, the two or more first signals may have differential signals. The differential signal corresponds to a target heart rate signal. Further, the processing module 420 may be configured to determine the motion signal based on the two or more first signals. For example, the processing module 420 may separate the common-mode signal from the differential signal to obtain the common-mode signal. The common mode signal may be a motion signal.
The processing module 420 may be further configured to identify a second signal having a target frequency from the first signals based on a motion frequency corresponding to the motion signal and/or a heart rate frequency corresponding to the target heart rate signal. In some embodiments, after determining the motion signal, the processing module 420 may be configured to determine a motion frequency corresponding to the motion signal. In some embodiments, the processing module 420 may be configured to remove the motion signal from the first signal by filtering to obtain a preliminary target heart rate signal. Further, the processing module 420 may be configured to determine a heart rate frequency corresponding to the preliminary target heart rate signal, and use the heart rate frequency as the heart rate frequency corresponding to the target heart rate signal. In some embodiments, the processing module 420 may be further configured to convert the first signal into a frequency domain signal through Fast Fourier Transform (FFT), and determine a heart rate frequency corresponding to the motion signal and the target heart rate signal based on the frequency domain signal. In some embodiments, the second signal may correspond to a non-linear superposition signal between the target heart rate signal and the motion signal. The second signal may have a target frequency derived from a superposition of the motion frequency and the heart rate frequency. In some embodiments, the superposition of the motion frequency and the heart rate frequency may comprise a linear superposition. In some embodiments, the target frequency corresponding to the second signal may be equal to a sum of the motion frequency corresponding to the motion signal and the heart rate frequency corresponding to the target heart rate signal. In some embodiments, the target frequency corresponding to the second signal may be equal to a difference between the motion frequency corresponding to the motion signal and the heart rate frequency corresponding to the target heart rate signal. In some embodiments, the sum or difference of the exercise frequency and the heart rate frequency may comprise a sum or difference between a multiple of the exercise frequency and a multiple of the heart rate frequency. Thus, the processing module 420 may determine the target frequency based on the motion frequency corresponding to the motion signal and the heart rate frequency corresponding to the target heart rate signal. Further, the processing module 420 may identify a second signal from the first signal based on the target frequency.
The generation module 430 may be to process the first signal to determine the target heart rate signal based on the motion signal and the second signal. In some embodiments, to determine the target heart rate signal, the generation module 430 may be configured to remove the motion signal and/or the second signal from the first signal to determine the target heart rate signal. In some embodiments, the generation module 430 may filter the first signal after determining the motion signal to remove the motion signal. In some embodiments, the generation module 430 may directly delete the second signal corresponding to the target frequency to determine the target heart rate signal. Alternatively or additionally, the generation module 430 may also smooth the first signal after deleting the second signal, thereby determining the target heart rate signal. In some embodiments, to determine the target heart rate signal, the generation module 430 may also replace the second signal corresponding to the target frequency with the reference heart rate signal. The reference heart rate signal may be a signal or a range of signals predetermined based on heart rate signal statistics. In some embodiments, to determine the target heart rate signal, the generation module 430 may also determine a motion component and/or a heart rate component in the second signal and process the second signal based on the motion component and/or the heart rate component. The motion component and the heart rate component may respectively refer to the degree of influence of the motion signal and the target heart rate signal on the second signal, and may be determined based on methods such as data analysis.
It should be understood that the system and its modules shown in FIG. 4 may be implemented in a variety of ways. For example, in some embodiments, the system and its modules may be implemented in hardware, software, or a combination of software and hardware. Wherein the hardware portion may be implemented using dedicated logic; the software portions may be stored in a memory for execution by a suitable instruction execution system, such as a microprocessor or specially designed hardware. Those skilled in the art will appreciate that the methods and systems described above may be implemented using computer executable instructions and/or embodied in processor control code, such code being provided, for example, on a carrier medium such as a diskette, CD-or DVD-ROM, a programmable memory such as read-only memory (firmware), or a data carrier such as an optical or electronic signal carrier. The system and its modules in this specification may be implemented not only by hardware circuits such as very large scale integrated circuits or gate arrays, semiconductors such as logic chips, transistors, or programmable hardware devices such as field programmable gate arrays, programmable logic devices, etc., but also by software executed by various types of processors, for example, or by a combination of the above hardware circuits and software (e.g., firmware).
Fig. 5 is an exemplary flow diagram of a heart rate monitoring method according to some embodiments of the present description. In some embodiments, the method 500 may be performed by the processing device 110, the processing engine 112, the processor 220. For example, method 500 may be stored in a storage device (e.g., storage device 140 or a storage unit of processing device 110) in the form of a program or instructions, which when executed by processing device 110, processing engine 112, processor 220, or the modules shown in fig. 4, may implement method 500. In some embodiments, method 500 may utilize one or more additional operations/steps not described below, and/or may not be accomplished by one or more of the operations/steps discussed below. Additionally, the order of operations/steps as shown in FIG. 5 is not limiting. As shown in fig. 5, method 500 may include:
at step 510, the processing device 110 (e.g., the acquisition module 410) may acquire a first signal.
In some embodiments, the first signal may comprise a target heart rate signal in a state of motion. The target heart rate signal may refer to a heart rate signal (i.e., a clean heart rate signal) that is free of noise signals (e.g., motion artifacts, etc.). In some embodiments, the first signal may comprise a motion signal corresponding to said motion state. The Motion signal may generate interference in the signal acquisition process, so that the waveform of the heart rate signal acquired by the acquisition device is changed, and Motion Artifacts (MA) are formed. In some embodiments, the motion signal may also be referred to as the MA signal. In some embodiments, the first signal may also include a superimposed signal between the motion signal and a target heart rate signal. In some embodiments, the superimposed signal may comprise a non-linear superimposed signal.
In some embodiments, the first signal may be a heart rate signal acquired by an acquisition device (e.g., acquisition device 120) in a user motion state. The acquisition device may include a heart rate sensor. Exemplary sensors may include a photodiode sensor, a complementary metal oxide semiconductor sensor, and the like. In some embodiments, the processing device 110 may acquire the first signal from the acquisition device. In some embodiments, the first signal may be stored in a storage device (e.g., storage device 140, memory 220, memory 370, or an external storage device). The processing device 110 may retrieve the first signal from the memory device.
In some embodiments, the acquisition device may acquire the heart rate signal based on photoplethysmography (PPG). The PPG method may utilize the principle that a photo sensing element absorbs light energy, illuminate the skin of the subject with light (e.g., LED light), and record the change in blood flow in the blood vessel with the photo sensing element, thereby obtaining a heart rate signal.
Taking the example of acquiring the first signal using PPG as an example, in some embodiments, the light propagating in a substance may follow lambertian beer's law:
wherein T represents transmittance, N represents N substances, σ i Represents the loss cross section of the i-th substance, n i The density of the i-th substance is shown, and l represents the optical path length.
It should be noted that formula (1) shows the derivation of the transmission of light in a substance. In some embodiments, the reflection of light may also be analyzed with reference to transmission. In some embodiments, tissue (e.g., a wrist used to measure heart rate) may be divided into arteries (artey, a), veins (Vein, V), and other Tissue (Tissue, T, e.g., bone, muscle Tissue, etc., and assuming no blood in the other Tissue). The optical paths of the rays in these three tissues can be represented using A, V, T, respectively. Assuming that the three tissue densities are uniform, considering only the optical path problem, the transmitted light intensity received by the photoelectric sensing assembly without the influence of heart rate and motion can be expressed as:
wherein, I t Indicating the intensity of the transmitted light received by the photo-sensing element, I 0 Representing the incident light intensity and alpha representing the absorption coefficient.
In some embodiments, heart rate and motion may cause changes in the shape and position of the arterial vessel, which may cause changes in optical length/in equation (1), and may also cause changes in blood flow density, which may cause changes in absorption coefficient α in equation (2). Therefore, it can be assumed that the optical path length change due to the heart rate is Δ a and the absorption coefficient change due to the heart rate is Δ α A . Motion may have similar effects on arteries and veins, and thus, motion-induced changes in arterial and venous optical path lengths may be assumed to be Δ A, respectively m 、ΔV m The change of the absorption coefficient of artery and vein caused by movement is delta alpha respectively Am 、Δα vm . At this time, the transmitted light intensity received by the photoelectric sensing assembly can be expressed as:
wherein, I m Indicating the intensity of the transmitted light received by the photo-sensing assembly in the motion state. The transmitted light signal may be converted into an electrical signal, which may include a heart rate signal (i.e., a first signal) in a motion state.
In some embodiments, since the absorption of light by arteries changes while the absorption of light by other tissues is substantially unchanged during motion, the transmitted light signal may be divided into a direct current DC signal, which may be used to detect reflected light signals from tissues, bones, and muscles, and an alternating current AC signal, which may represent the change in blood volume that occurs between the systolic and diastolic phases of the cardiac cycle. Therefore, when the transmitted light signal received by the photoelectric sensing assembly is converted into an electric signal, an AC signal can be extracted from the transmitted light signal, the AC signal can reflect the characteristics of blood flow, and a heart rate signal in a motion state can be estimated based on the AC signal. Thus, the first signal may be represented as an AC signal.
In some embodiments, based on equation (3), the first signal may include a superimposed signal Δ α between the motion signal and the target heart rate signal A ΔA m (i.e., the second signal). The superimposed signal between the motion signal and the target heart rate signal refers to a noise signal generated by interaction between the motion signal and the target heart rate signal, and is related to the amplitude, frequency and the like of the motion signal and the target heart rate signal.
At step 520, the processing device 110 (e.g., the processing module 420) may obtain a motion signal corresponding to the motion state. The motion signal may be used to characterize the current motion state of the user. In some embodiments, the motion signal may include at least a motion frequency corresponding to the motion state.
In some embodiments, the first signal may include a noise signal therein. For example, the noise signal may include ambient noise, baseline drift, and the like. Ambient noise may refer to interference generated by signals in the environment (e.g., electromagnetic signals, ambient light signals), and the like. In some embodiments, the shielding component may be disposed on the acquisition device to shield the environmental signal from interference. The baseline drift may refer to a slow change in the orientation of the baseline in the first signal over time. Baseline drift may be caused by breathing fluctuations and/or relative friction between the skin surface and the acquisition device during measurement of the body. In some embodiments, the baseline wander may be low frequency noise. For example only, the frequency of baseline wander may be distributed in the range of 0-1 Hz.
In some embodiments, to obtain a motion signal corresponding to the motion state, the processing device 110 may filter the first signal to reduce or filter out noise signals in the first signal. For example, the processing device 110 may filter the first signal based on a filtering algorithm to reduce or filter out baseline wander therein. Exemplary filtering algorithms may include Finite Impulse Response (FIR) filtering algorithms, adaptive median filtering algorithms, infinite Impulse Response (IIR) filtering algorithms, and the like. For example only, the processing device 110 may high-pass filter the first signal based on a filtering algorithm to reduce or filter out baseline wander therein. In some embodiments, the cut-off frequency of the high-pass filtering may be determined based on the frequency of the baseline wander. For example, if the frequency of the baseline wander is in the range of 0-1Hz, the cut-off frequency of the high-pass filtering may be 1Hz. After filtering the first signal based on the cut-off frequency, baseline wander below a frequency of 1Hz may be reduced or filtered out. The filtered signal may include a motion signal and a target heart rate signal. Further, the processing device 110 may determine a motion signal corresponding to the motion state based on the filtered signal. For example, the processing device 110 may process the filtered signal based on an Independent Component Analysis (ICA) algorithm to determine the motion signal. The ICA algorithm may separate data or signals (e.g., the filtered signal) into independent components having statistical independence and non-gaussian based on statistical principles. The processing device 110 may statistically independent the filtered signals based on an ICA algorithm to obtain independent component components corresponding to the target heart rate signal and the motion signal, respectively, to determine a motion signal corresponding to the motion state. As another example, the processing device 110 may use a signal having a specific frequency component in the filtered signal as the motion signal. For example only, the processing device 110 may extract signals in the filtered signal having a frequency range within a particular frequency range (e.g., 3Hz-5Hz,3Hz-8 Hz) and identify motion signals based thereon. In some embodiments, the particular frequency range may be determined from a reference heart rate frequency of the user. For example only, the reference heart rate frequency of the user may be set directly by the system or extracted from historical heart rate data of the user or other users. The reference heart rate frequency of the user may be outside the particular frequency range. Alternatively, the processing device 110 may unify signals having a frequency range within a specific frequency range as the motion signal, or further extract a signal component having a specific characteristic (e.g., one or more frequency components corresponding to a maximum amplitude value) within the frequency range as the motion signal.
In some embodiments, to acquire a motion signal corresponding to the motion state, the processing device 110 may determine the motion signal based on a motion acquisition device. The motion capture device may be integrated into the capture device for capturing the first signal or may be a separate device for capturing the motion signal. By way of example only, the motion capture device may include an acceleration sensor, a gyroscope, a magnetometer, and the like. The processing device 110 may obtain parameters such as acceleration, angular velocity, and the like of the user in a motion state through the acceleration sensor, the gyroscope, the magnetometer, and the like, and process the parameters through a data fusion algorithm to determine the motion signal. In some embodiments, by determining the motion signal through the motion acquisition device, a more accurate motion signal may be obtained, and corresponding processing of the first signal may be avoided, thereby improving accuracy and acquisition efficiency of the motion signal.
In some embodiments, to acquire a motion signal corresponding to the motion state, the processing device 110 may acquire two or more first signals through two or more optical paths. In some embodiments, the collection device may have two or more optical paths that the processing device 110 may cause to emit light having two or more spectral distributions. The two or more spectral distributions may include two or more different wavelengths. Taking as an example that the collecting device has two optical paths including a first optical path and a second optical path, the first optical path and the second optical path may emit light of different wavelengths, respectively. For example, the first optical path may emit light of a shorter wavelength (e.g., green light) and the second optical path may emit light of a longer wavelength (e.g., red light). In some embodiments, the two or more different wavelengths of light may alternately illuminate the skin of the user. The collecting device can respectively obtain two or more first signals corresponding to light with different wavelengths. In some embodiments, the two or more different wavelengths of light may have the same or similar correlation to the motion signal. Accordingly, the two or more first signals may have a common-mode signal (i.e., a portion common to the two or more first signals). The common mode signal corresponds to the motion signal. In some embodiments, the two or more different wavelengths of light may have different correlations to the target heart rate signal. Accordingly, the two or more first signals may have differential signals (i.e., portions of the two or more first signals that are different). The differential signal corresponds to a target heart rate signal. Further, the processing device 110 may determine the motion signal based on the two or more first signals. For example, the processing device 110 may separate the common-mode signal from the differential signal to obtain the common-mode signal. The common mode signal may be a motion signal.
At step 530, the processing device 110 (e.g., the processing module 420) may identify a second signal having a target frequency from the first signals based on a motion frequency corresponding to the motion signal.
In some embodiments, the processing device 110 may identify a second signal having a target frequency from the first signal based on the motion frequency and a heart rate frequency corresponding to the target heart rate signal. In some embodiments, after determining the motion signal, the processing device 110 may further determine a motion frequency corresponding to the motion signal. In some embodiments, the processing device 110 may remove the motion signal from the first signal by a filtering process to obtain a preliminary target heart rate signal. The preliminary target heart rate signal may be a roughly calculated target heart rate signal, which may include a superimposed signal of the motion signal and the target heart rate signal. The processing device 110 may determine a heart rate frequency corresponding to the preliminary target heart rate signal and use it as the heart rate frequency corresponding to the target heart rate signal. In some embodiments, the processing device 110 may convert the first signal into a frequency domain signal through Fast Fourier Transform (FFT), and determine a motion frequency corresponding to the motion signal and a heart rate frequency corresponding to the target heart rate signal based on the frequency domain signal. For example, in determining the motion frequency and the heart rate frequency, the first signal may be considered approximately as a linear superposition of the target heart rate signal and the motion signal. The first signal may be decomposed into waveform components having a motion frequency and a heart rate frequency, respectively, by FFT. Thus, the processing device 110 may determine the motion frequency and the heart rate frequency based on the FFT transformation results.
In some embodiments, as shown in formula (3), the first signal may include the target heart rate signal, the motion signal, and the superimposed signal Δ α between the target heart rate signal and the motion signal A ΔA m . The superposition signal is a nonlinear multiplication superposition signal. In some embodiments, the non-linear superposition signal may be converted to a linear superposition signal according to an integration and difference law. The converted linear superposition signal may have a new signal frequency related to the motion frequency and a heart rate frequency corresponding to the target heart rate signal, wherein the converted linear superposition signal is the second signal. The second signal has a target frequency, the new signal frequency. In some embodiments, the target frequency corresponding to the second signal may be equal to a sum of the motion frequency corresponding to the motion signal and the heart rate frequency corresponding to the target heart rate signal. In some embodiments, the target frequency corresponding to the second signal may be equal to a difference between the motion frequency corresponding to the motion signal and the heart rate frequency corresponding to the target heart rate signal. In some embodiments, the sum or difference of the exercise frequency and the heart rate frequency may comprise a sum or difference between a multiple of the exercise frequency and a multiple of the heart rate frequency. Thus, the processing device 110 may determine the target frequency based on the motion frequency corresponding to the motion signal and the heart rate frequency corresponding to the target heart rate signal. Further, the processing device 110 may identify a second signal from the first signal based on the target frequency.
Fig. 7 is a schematic diagram of a spectrum of a first signal shown in accordance with some embodiments of the present description. For illustration purposes, the first signal may be a signal obtained by experimentally simulating motion and heart rate, where W1 represents a heart rate frequency corresponding to the heart rate signal, and W2 represents a motion frequency corresponding to the motion signal. In the experimental simulation, the heart rate frequency and the exercise frequency may be known parameters, where W1=1.3hz, W2=5hz. After the first signal is acquired, the first signal may be converted into a frequency domain signal as shown in fig. 7 by FFT transformation. As shown in fig. 7, the abscissa indicates the frequency of the first signal, and the ordinate indicates the amplitude intensity (for example, the amplitude intensity after logarithmic calculation) of the first signal. In some embodiments, according to the FFT principle, a frequency-doubled signal, i.e., a signal having frequency points at M × W1 and N × W2 (M =1,2,3,4,5,6,7,8 …; N =1,2,3,4,5,6,7,8 …), may appear in the first signal converted into the frequency domain. As shown in fig. 7, the position of each frequency peak in the first signal is indicated in fig. 7. As can be seen from fig. 7, in addition to having a frequency-doubled signal at a frequency-doubled point where the frequency point is located at 2W1 (2.583 Hz), 3W1 (3.883 Hz), 2W2 (10 Hz), 3W2 (15 Hz), etc., in the first signal, a frequency peak may also be present at a frequency point of abs (W1 ± W2) (e.g., W1+ W2, W2-W1, 2w1+ W2, W2-2W1, etc.). Therefore, as can be seen from fig. 7, the first signal also includes a non-linear superimposed signal (i.e., a second signal) between the motion signal and the target heart rate signal, and the superimposed signal has a frequency peak at the frequency point of abs (W1 ± W2). Thus, the processing device 110 may determine a target frequency based on the motion frequency corresponding to the motion signal and the heart rate frequency corresponding to the target heart rate signal, and identify the second signal from the first signal based on the target frequency.
In some embodiments, the heart rate frequency corresponding to the target heart rate signal may be an unknown frequency, and the processing device 110 may identify a second signal having the target frequency from the first signal based on the motion frequency corresponding to the motion signal. For example, the unknown frequency is X, the motion frequency is W2, and the second signal has a frequency peak at a frequency point abs (X ± W2) (i.e., a target frequency) according to a linear superposition relationship between the motion frequency and the heart rate frequency. Thus, the processing device 110 may identify the second signal having the target frequency abs (X ± W2) from the first signal according to a linear superposition relationship of the motion frequency and the heart rate frequency based on the motion frequency. In some embodiments, the processing device 110 may also determine the heart rate frequency X from a linear superposition of the motion frequency and the heart rate frequency based on the motion frequency.
At step 540, processing device 110 (e.g., generation module 430) may process the first signal to determine the target heart rate signal based on the motion signal and the second signal.
In some embodiments, the processing device 110 may remove the motion signal and/or the second signal from the first signal to determine the target heart rate signal. In some embodiments, the processing device 110 may filter the first signal after determining the motion signal to remove the motion signal. In some embodiments, the processing device 110 may directly delete the second signal corresponding to the target frequency to determine the target heart rate signal. Alternatively or additionally, the processing device 110 may also smooth the first signal after deleting the second signal, thereby determining the target heart rate signal.
In some embodiments, to determine the target heart rate signal, the processing device 110 may replace the second signal corresponding to the target frequency with the reference heart rate signal. For example, the reference heart rate signal may be a signal or a range of signals predetermined according to heart rate signal statistics. Different motion frequencies may correspond to different reference heart rate signals. After determining the movement frequency, the processing device 110 may determine a reference heart rate signal corresponding to the movement frequency. Further, the processing device 110 may replace the second signal with a reference heart rate signal to determine the target heart rate signal.
In some embodiments, to determine the target heart rate signal, the processing device 110 may determine a motion component and/or a heart rate component in the second signal and process the second signal based on the motion component and/or the heart rate component. The motion component and the heart rate component may refer to the degree of influence of the motion signal and the target heart rate signal, respectively, on the second signal. For example only, first signals of the same object in different motion states and/or first signals of different objects in the same motion state may be collected and/or simulated, and second signals of each first signal may be identified separately. Further, the relationship of the motion signal to the second signal may be determined by a data analysis method (e.g., a mathematical statistics algorithm, a machine learning algorithm, etc.). For example, the second signals corresponding to different motion signals may be analyzed by a data analysis method and the variation rule of the second signals with the motion signals may be determined. The law of change may reflect the effect of the motion signal on the motion component in the second signal. For example only, the change rule may include a mapping relationship between the motion signal and a proportion of the motion component in the second signal. The processing device 110 may determine the motion component in the second signal from the mapping.
It should be noted that the above description of heart rate monitoring method 500 is merely for convenience of description and is not intended to limit the scope of the present disclosure to the illustrated embodiments. It will be understood by those skilled in the art that, having the benefit of the teachings of this method, any combination of steps can be used or any steps can be added or deleted without departing from such teachings. For example, when the motion amplitude or frequency of the user is small, the influence of the motion state of the user on the heart rate of the user is relatively small, and whether the target heart rate signal is accurately calculated can be judged according to the motion state of the user. Accordingly, the method 500 may further include the step of determining a motion state. For another example, when the heart rate frequency is unknown, the processing device 110 may identify a second signal having the target frequency abs (X ± W2) from the first signal according to a linear superposition relationship between the motion frequency and the heart rate frequency based on the motion frequency and determine the heart rate frequency X. Thus, step 540 may be omitted and the processing device 110 may identify a target heart rate signal from the first signal based on the determined heart rate frequency.
FIG. 8 is an exemplary flow chart of a heart rate monitoring method according to further embodiments of the present description. In some embodiments, method 800 may be performed by processing device 110, processing engine 112, processor 220. For example, method 800 may be stored in a storage device (e.g., storage device 140 or a memory unit of processing device 110) in the form of a program or instructions, which when executed by processing device 110, processing engine 112, processor 220, or the modules shown in fig. 4, may implement method 800. In some embodiments, operation 520 described in method 500 may be implemented by method 800. In some embodiments, method 800 may utilize one or more additional operations/steps not described below, and/or may not be accomplished by one or more of the operations/steps discussed below. Additionally, the order of operations/steps as shown in FIG. 8 is not limiting. As shown in fig. 8, method 800 may include:
at step 810, the processing device 110 (e.g., the processing module 420) may determine a signal amplitude of the motion signal. In some embodiments, processing device 110 may determine the motion signal by performing steps 510 and/or 520 described in fig. 5, which is not described herein. Further, the processing device 110 may determine a signal amplitude of the motion signal.
At step 820, the processing device 110 (e.g., the processing module 420) may determine whether the signal amplitude of the motion signal is greater than an amplitude threshold. In some embodiments, the amplitude threshold may be a predetermined amplitude threshold based on historical heart rate data. For example, the effect of motion with different signal amplitudes on the target heart rate signal may be determined from historical heart rate data, with the motion signal amplitude corresponding to the greater degree of effect being determined as the amplitude threshold.
At step 830, in response to the signal amplitude being greater than the amplitude threshold, the processing device 110 (e.g., the generation module 430) may process the first signal to determine the target heart rate signal based on the motion signal and the second signal. In some embodiments, the processing device 110 may determine the target heart rate signal by performing step 540 described in fig. 5, which is not described in detail herein.
As shown in fig. 8, it may be determined whether to perform step 540 to accurately calculate the target heart rate signal by determining whether the signal amplitude corresponding to the motion signal is greater than a preset amplitude threshold. If the signal amplitude of the motion signal is greater than the amplitude threshold, performing the accurate heart rate calculation in step 540 to obtain a target heart rate signal of the user in a motion state; on the contrary, if the signal amplitude of the motion signal is smaller than or equal to the amplitude threshold, the heart rate signal corresponding to the first signal can be used as the target heart rate signal, so that the operation load of the processor is reduced, and the calculation speed of heart rate monitoring is improved while the accuracy of heart rate monitoring is ensured.
It should be noted that the above description of the heart rate monitoring method 800 is merely for convenience of description and is not intended to limit the scope of the present disclosure to the illustrated embodiments. It will be understood by those skilled in the art that, having the benefit of the teachings of this method, any combination of steps can be used or any steps can be added or deleted without departing from such teachings.
FIG. 9 is an exemplary flow chart of a heart rate monitoring method according to further embodiments of the present description. In some embodiments, method 900 may be performed by processing device 110, processing engine 112, processor 220. For example, method 900 may be stored in a storage device (e.g., storage device 140 or a storage unit of processing device 110) in the form of a program or instructions, which when executed by processing device 110, processing engine 112, processor 220, or the modules shown in fig. 4, may implement method 900. In some embodiments, operation 520 described in method 500 may be implemented by method 900. In some embodiments, method 900 may utilize one or more additional operations/steps not described below, and/or may not be accomplished by one or more of the operations/steps discussed below. Additionally, the order of operations/steps as shown in FIG. 9 is not limiting. As shown in fig. 9, method 900 may include:
at step 910, the processing device 110 (e.g., the processing module 420) may determine a signal frequency (i.e., a motion frequency) of the motion signal. In some embodiments, processing device 110 may determine the motion signal by performing steps 510 and/or 520 described in fig. 8, which is not described in detail herein. Further, the processing device 110 may determine a signal frequency of the motion signal.
At step 920, processing device 110 (e.g., processing module 420) may determine whether the signal frequency is greater than a frequency threshold. In some embodiments, the frequency threshold may be a predetermined frequency threshold based on historical heart rate data. For example, the effect of motion with different frequencies on the target heart rate signal may be determined from historical heart rate data, and the frequency of the motion signal with the corresponding greater degree of effect may be determined as the frequency threshold.
At step 930, the processing device 110 (e.g., the generation module 430) may process the first signal to determine the target heart rate signal based on the motion signal and the second signal in response to the signal frequency being greater than the frequency threshold.
Similar to the method 800, in other embodiments, it may also be determined whether to perform the step 540 by determining whether the signal frequency corresponding to the motion signal is greater than a preset frequency threshold. Specifically, if the signal frequency of the exercise signal is greater than the frequency threshold, the step 540 is executed to perform accurate heart rate calculation to obtain a target heart rate signal of the user in the exercise state; otherwise, if the signal frequency of the motion signal is less than or equal to the frequency threshold, the heart rate signal corresponding to the first signal is directly used as the target heart rate signal. In some embodiments, the processing device 110 may determine the target heart rate signal by performing step 540 described in fig. 5, which is not described in detail herein.
It should be noted that the above description of the heart rate monitoring method 900 is merely for convenience of description and is not intended to limit the scope of the present disclosure to the illustrated embodiments. It will be understood by those skilled in the art that, having the benefit of the teachings of this method, any combination of steps can be used or any steps can be added or deleted without departing from such teachings.
The beneficial effects that may be brought by the embodiments of the present description include, but are not limited to: (1) According to the heart rate monitoring method provided by the embodiment of the specification, the original heart rate data monitored by the heart rate sensor is denoised based on the superposition relationship between the motion signals and the heart rate signals, so that motion artifacts and the influence of the motion noise on the heart rate signals can be better removed, and a more accurate heart rate monitoring result is obtained; (2) The heart rate monitoring method provided by the embodiment of the specification can also determine the heart rate frequency corresponding to the target heart rate signal from the original heart rate data based on the superposition relationship between the motion signal and the heart rate signal, so as to determine the target heart rate signal and obtain a more accurate heart rate monitoring result; (3) According to the heart rate monitoring method provided by the embodiment of the specification, accurate or rough calculation of data monitored by the heart rate sensor is judged according to the movement amplitude or the movement frequency of the user, the operation load of the processor can be reduced when the movement amplitude of the user is small, and therefore the heart rate calculation speed is increased while the heart rate monitoring accuracy is ensured.
Having thus described the basic concept, it will be apparent to those skilled in the art that the foregoing disclosure is only illustrative and not limiting of the present specification. Various modifications, improvements and adaptations to the present description may occur to those skilled in the art, though not explicitly described herein. Such modifications, improvements and adaptations are proposed in the present specification and thus fall within the spirit and scope of the exemplary embodiments of the present specification.
Also, the description uses specific words to describe embodiments of the specification. Reference to "one embodiment," "an embodiment," and/or "some embodiments" means that a particular feature, structure, or characteristic described in connection with at least one embodiment of the specification. Therefore, it is emphasized and should be appreciated that two or more references to "an embodiment" or "one embodiment" or "an alternative embodiment" in various places throughout this specification are not necessarily all referring to the same embodiment. Furthermore, some features, structures, or characteristics of one or more embodiments of the specification may be combined as appropriate.
Moreover, those skilled in the art will appreciate that aspects of the present description may be illustrated and described in terms of any number of patentable categories or situations, including any new and useful combinations of processes, machines, manufacture, or materials, or any new and useful modifications thereof. Accordingly, aspects of this description may be performed entirely by hardware, entirely by software (including firmware, resident software, micro-code, etc.), or by a combination of hardware and software. The above hardware or software may be referred to as "data block," module, "" engine, "" unit, "" component, "or" system. Furthermore, aspects of the present description may be represented as a computer product, including computer readable program code, embodied in one or more computer readable media.
Additionally, the order in which the elements and sequences are handled, the use of alphanumeric or other designations in this specification is not intended to limit the order of the processes and methods in this specification, unless explicitly stated in the claims. While certain presently contemplated useful embodiments of the invention have been discussed in the foregoing disclosure by way of various examples, it is to be understood that such detail is solely for that purpose and that the appended claims are not limited to the disclosed embodiments, but, on the contrary, are intended to cover all modifications and equivalent arrangements that are within the spirit and scope of the embodiments herein described. For example, although the system components described above may be implemented by hardware devices, they may also be implemented by software-only solutions, such as installing the described system on an existing server or mobile device.
Similarly, it should be noted that in the preceding description of embodiments of the present specification, various features are sometimes grouped together in a single embodiment, figure, or description thereof for the purpose of streamlining the disclosure aiding in the understanding of one or more of the embodiments. This method of disclosure, however, is not intended to imply that more features than are expressly recited in a claim. Indeed, the embodiments may be characterized as having less than all of the features of a single embodiment disclosed above.
Where numbers describing quantities of ingredients, properties, etc. are used in some embodiments, it is understood that such numbers used in the description of the embodiments are modified in some instances by the use of the modifier "about", "approximately" or "substantially", etc. Unless otherwise indicated, "about", "approximately" or "substantially" indicates that the number allows a variation of ± 20%. Accordingly, in some embodiments, the numerical data used in the specification and claims are approximations that may vary depending upon the desired properties of the individual embodiments. In some embodiments, numerical data should take into account the specified significant digits and employ a general digit preservation approach. Notwithstanding that the numerical ranges and data setting forth the broad scope of the range presented in some of the examples of the specification are approximations, in specific examples, such numerical values are set forth as precisely as possible within the practical range.
Claims (10)
1. A method of heart rate monitoring, the method comprising:
acquiring a first signal, wherein the first signal comprises a target heart rate signal in a motion state;
acquiring a motion signal corresponding to the motion state;
identifying a second signal with a target frequency from the first signal based on a motion frequency corresponding to the motion signal, wherein the target frequency is derived from superposition of the motion frequency and a heart rate frequency corresponding to the target heart rate signal; and
processing the first signal to determine the target heart rate signal based on the motion signal and the second signal.
2. The heart rate monitoring method of claim 1, wherein the acquiring a motion signal corresponding to the motion state comprises:
filtering the first signal; and
determining the motion signal based on the filtered signal.
3. The heart rate monitoring method of claim 1, wherein the acquiring the motion signal corresponding to the motion state comprises acquiring the motion signal via an acceleration sensor.
4. The heart rate monitoring method of claim 1, wherein the acquiring a motion signal corresponding to the motion state comprises:
acquiring two or more first signals through two or more optical paths; and
determining the motion signal based on the two or more first signals.
5. The method of heart rate monitoring according to claim 1, wherein the second signal comprises a superimposed signal between the motion signal and the target heart rate signal.
6. The heart rate monitoring method of claim 1, wherein the target frequency is equal to a sum of the exercise frequency and the heart rate frequency.
7. The heart rate monitoring method of claim 1, wherein the target frequency is equal to a difference between the motion frequency and the heart rate frequency.
8. The heart rate monitoring method according to any one of claims 1-8, wherein the processing the first signal to determine the target heart rate signal based on the motion signal and the second signal comprises:
removing the motion signal and the second signal in the first signal, and determining the target heart rate signal.
9. The heart rate monitoring method of claim 1, wherein the processing the first signal to determine the target heart rate signal based on the motion signal and the second signal comprises:
determining a signal amplitude of the motion signal;
judging whether the signal amplitude is larger than an amplitude threshold value; and
in response to the signal amplitude being greater than the amplitude threshold, processing the first signal to determine the target heart rate signal based on the motion signal and the second signal.
10. The heart rate monitoring method of claim 1, wherein the processing the first signal to determine the target heart rate signal based on the motion signal and the second signal comprises:
determining a signal frequency of the motion signal;
judging whether the signal frequency is greater than a frequency threshold value; and
in response to the signal frequency being greater than the frequency threshold, process the first signal to determine the target heart rate signal based on the motion signal and the second signal.
Priority Applications (2)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111057732.0A CN115770029A (en) | 2021-09-09 | 2021-09-09 | Heart rate monitoring method and system and storage medium |
| TW111128309A TWI823500B (en) | 2021-09-09 | 2022-07-28 | Heart rate monitoring method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111057732.0A CN115770029A (en) | 2021-09-09 | 2021-09-09 | Heart rate monitoring method and system and storage medium |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115770029A true CN115770029A (en) | 2023-03-10 |
Family
ID=85387816
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111057732.0A Pending CN115770029A (en) | 2021-09-09 | 2021-09-09 | Heart rate monitoring method and system and storage medium |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN115770029A (en) |
| TW (1) | TWI823500B (en) |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2755551B1 (en) * | 2011-09-16 | 2016-08-17 | Koninklijke Philips N.V. | Device and method for estimating the heart rate during motion |
| US10004408B2 (en) * | 2014-12-03 | 2018-06-26 | Rethink Medical, Inc. | Methods and systems for detecting physiology for monitoring cardiac health |
| TW201801671A (en) * | 2016-07-11 | 2018-01-16 | 義明科技股份有限公司 | Method of heart rate detection |
| CN111616695B (en) * | 2020-06-29 | 2022-09-13 | 歌尔科技有限公司 | Heart rate acquisition method, device, system and medium |
| CN113261932B (en) * | 2021-06-28 | 2022-03-04 | 山东大学 | Heart rate measurement method and device based on PPG signal and one-dimensional convolutional neural network |
-
2021
- 2021-09-09 CN CN202111057732.0A patent/CN115770029A/en active Pending
-
2022
- 2022-07-28 TW TW111128309A patent/TWI823500B/en active
Also Published As
| Publication number | Publication date |
|---|---|
| TWI823500B (en) | 2023-11-21 |
| TW202312936A (en) | 2023-04-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104602594B (en) | Pulse wave detection method and pulse wave detection device | |
| US11039747B2 (en) | Signal obtaining method and system | |
| CN107949321A (en) | Time domain interference removes and improved heart rate measurement tracking mechanism | |
| US20170181680A1 (en) | Oxygen saturation measuring apparatus and oxygen saturation measuring method thereof | |
| CN105832289A (en) | Method and equipment using Hilbert transform to estimate biophysiological rates | |
| CN105249925B (en) | A kind of traditional Chinese medical pulse manifestation collecting device and noise reduction system and noise-reduction method | |
| CN109793506B (en) | Non-contact radial artery waveform extraction method | |
| WO2021054582A1 (en) | Electronic device and method of estimating physiological signal | |
| CN108937957A (en) | Detection method, device and detection device | |
| Awodeyi et al. | Median filter approach for removal of baseline wander in photoplethysmography signals | |
| CN114052693A (en) | Heart rate analysis method, device and equipment | |
| CN110876615B (en) | Real-time dynamic heart rate monitoring device and monitoring method | |
| Arunkumar et al. | CASINOR: Combination of adaptive filters using single noise reference signal for heart rate estimation from PPG signals | |
| CN115429251A (en) | Wearable device and monitoring method and monitoring device thereof | |
| CN106096589A (en) | The method carrying out pulse frequency Variability Analysis in short-term based on intelligent terminal's photographic head | |
| CN109620198B (en) | Cardiovascular index detection and model training method and device | |
| CN115770029A (en) | Heart rate monitoring method and system and storage medium | |
| CN116028809A (en) | Continuous blood pressure measurement model training and measuring method and device | |
| CN116327133A (en) | Multi-physiological index detection method, device and related equipment | |
| CN115624333A (en) | Human body electrical impedance signal acquisition system and working method thereof | |
| CN117320619A (en) | Heart rate monitoring method, system and storage medium | |
| CN106361350A (en) | Image processing-based blood oxygen saturation degree monitoring method | |
| CN109363630B (en) | Vital sign information measuring method and device | |
| CN115281641A (en) | Heart rate extraction method and device, electronic equipment, piezoelectric sensor and medium | |
| CN113842128B (en) | Non-contact heart rate detection device based on multiple filtering and mixed amplification |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |