WO2020206661A1 - Procédé d'acquisition de paramètres de caractéristiques d'objet cible, terminal et support de stockage - Google Patents

Procédé d'acquisition de paramètres de caractéristiques d'objet cible, terminal et support de stockage Download PDF

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Publication number
WO2020206661A1
WO2020206661A1 PCT/CN2019/082330 CN2019082330W WO2020206661A1 WO 2020206661 A1 WO2020206661 A1 WO 2020206661A1 CN 2019082330 W CN2019082330 W CN 2019082330W WO 2020206661 A1 WO2020206661 A1 WO 2020206661A1
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point
candidate
extreme
value
digital signal
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PCT/CN2019/082330
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English (en)
Chinese (zh)
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严鑫洋
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深圳市柔宇科技有限公司
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Application filed by 深圳市柔宇科技有限公司 filed Critical 深圳市柔宇科技有限公司
Priority to PCT/CN2019/082330 priority Critical patent/WO2020206661A1/fr
Priority to CN201980079851.0A priority patent/CN113329685A/zh
Publication of WO2020206661A1 publication Critical patent/WO2020206661A1/fr

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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
    • A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
    • A61B5/00—Measuring for diagnostic purposes; Identification of persons
    • A61B5/02—Detecting, measuring or recording pulse, heart rate, blood pressure or blood flow; Combined pulse/heart-rate/blood pressure determination; Evaluating a cardiovascular condition not otherwise provided for, e.g. using combinations of techniques provided for in this group with electrocardiography or electroauscultation; Heart catheters for measuring blood pressure

Definitions

  • This application relates to the field of signal processing, in particular to a method, terminal and storage medium for acquiring characteristic parameters of a target object.
  • wearable devices can use sensors to detect the user's pulse rate, heart rate, and respiratory rate.
  • the process from the output of the sensor signal to the visualization parameter presentation of the human-computer interaction interface it is necessary to perform signal filtering and baseline correction on the electrical signal, and then perform analog-to-digital conversion to obtain a digital signal, and then obtain the visualization parameter based on the digital signal.
  • the periodic digital signal processing process is more complicated and consumes a lot of power.
  • the purpose of the present application is to provide a method, terminal and storage medium for acquiring characteristic parameters of a target object, which can reduce the complexity of the periodic digital signal processing process and reduce power consumption.
  • an embodiment of the present application provides a method for acquiring characteristic parameters of a target object, including:
  • the characteristic parameter of the periodic digital signal is determined according to at least one valid characteristic value.
  • an embodiment of the present application provides a terminal, the terminal includes a processor, and the processor is configured to:
  • the characteristic parameter of the periodic digital signal is determined according to at least one of the effective characteristic values.
  • an embodiment of the present application provides a storage medium that stores a characteristic parameter acquisition instruction of a target object, and when the characteristic parameter acquisition instruction of the target object runs on a computer, the computer executes the above The method of acquiring characteristic parameters of the target object.
  • the periodic digital signal of the target object is acquired; secondly, the first candidate extreme value point and the second candidate extreme value point are determined from the periodic digital signal; Third, determine at least one effective characteristic value of the periodic digital signal according to the first candidate extreme value point and the second candidate extreme value point; finally, determine the characteristic parameter of the periodic digital signal according to the at least one effective characteristic value.
  • the embodiment of the present application can determine the effective feature value according to the first candidate extreme value point and the second candidate extreme value point, and obtain the feature parameter according to the effective feature value .
  • the difference between the two candidate extremum points in an effective eigenvalue can be the peak value in a period, the first candidate extremum point (or the second candidate extremum point) of the two effective eigenvalues
  • the time difference can calculate the period of the periodic digital signal, and there is no need to perform signal filtering and baseline correction operations. While ensuring the signal-to-noise ratio of the signal, it reduces the complexity of the signal processing process and reduces power consumption.
  • Fig. 1 is a schematic diagram of a flow of signal collection and processing in the prior art
  • FIG. 2 is a schematic diagram of the signal collection and processing flow in this application.
  • FIG. 3 is a schematic flowchart of a method for acquiring characteristic parameters of a target object provided by an embodiment of the application
  • Figure 4 is a schematic diagram of a heart rhythm signal provided by an embodiment of the application.
  • FIG. 5 is a schematic diagram of a heart rhythm signal detection result provided by an embodiment of the application.
  • Fig. 6 is a schematic diagram of a pulse signal provided by an embodiment of the application.
  • Fig. 7 is a schematic diagram of a pulse signal detection result provided by an embodiment of the application.
  • FIG. 8 is a schematic diagram of a breathing signal provided by an embodiment of the application.
  • FIG. 9 is a schematic diagram of a respiratory signal detection result provided by an embodiment of the application.
  • FIG. 10 is a schematic structural diagram of a terminal provided by an embodiment of the application.
  • FIG. 1 is a flow chart of the current processing of signals detected by wearable devices.
  • wearable devices photoelectric sensors, piezoelectric sensors and other devices are used to obtain human health and motion signals.
  • Wearable devices include smart watches, smart bracelets, smart masks and other devices. The above-mentioned devices can measure human pulse rate, heart rate, and respiratory rate. From the output of the electrical signal of the sensor to the visual parameters such as pulse, heart rate, and respiration rate presented on the human-computer interaction interface, it usually requires a multi-step signal collection, processing, analysis and recognition process.
  • the signal collection module 101 processes the signal output by the sensor through the following functional units: 1) When the sensor captures a change in a certain feature of the human body, the sensor 11 outputs an electrical signal to The signal amplifying unit 12. 2) The signal amplifying unit 12 amplifies the signal. 3) The baseline correction unit 13 performs baseline correction on the amplified signal. 4) The signal filtering unit 14 performs signal filtering on the corrected signal. 5) The A/D conversion unit 15 converts the filtered signal from an analog signal to a digital signal and transmits it to the data processing module for analysis.
  • the signal processing module 102 After the signal processing module 102 receives the digital signal, the data scanning unit 16 scans the digital signal, the data feature value extraction unit 17 extracts the feature value in the digital signal, and the feature value analysis unit 18 analyzes the extracted feature value to obtain The analysis result, the man-machine interface 19 outputs the analysis result.
  • the signal processing module uses a specific algorithm to compare the signal value with the set baseline within a certain period of time, and uses the difference between the signal and the baseline to determine whether the required characteristic parameters exist within a certain period of time, thereby performing waveform recognition.
  • the characteristic parameters such as the peak value and frequency of the signal wave are extracted; finally, the characteristic parameter processing is further transformed into parameters such as pulse frequency, heart rate, and respiration frequency and output to the human-computer interaction interface.
  • the inventor found that the above-mentioned signal processing process has at least the following problems: because the sensors in the wearable equipment are in direct or indirect contact with the human body, the environment and motion state in which they are located are fickle, and it is extremely easy to cause the sensors due to environmental or motion interference
  • the sensor signal characteristic value is captured by the above-mentioned technical solution, it is very easy to miss or misidentify the characteristic value of the sensor signal due to the baseline drift.
  • the sensor signal processing needs to be filtered and baseline corrected, which makes the wearable equipment circuit more complicated, the structure becomes larger, the system power consumption becomes larger, and the system error is more likely to affect the accuracy. It can be seen that the current signal processing process is more complicated and consumes a lot of power.
  • the embodiment of the present application provides a method, terminal, and storage medium for acquiring characteristic parameters of a target object.
  • the system architecture may include a signal collection module 201 and a signal processing module 202.
  • the signal collection module 201 is used to receive the output signal of the sensor 21, the signal amplifying unit 22 performs signal amplification, and the analog signal is converted by the A/D conversion unit 23. Convert to digital signal.
  • the signal processing module 202 includes scanning the digital signal by the data scanning unit 24 to obtain the corresponding relationship between the time point and the data point, and the data feature value extraction and analysis unit 25 obtains the characteristic parameter of the periodic digital signal according to the data point.
  • the human-computer interaction interface 26 outputs characteristic parameters. It can be seen that Fig. 2 is compared with Fig. 1 in that the baseline correction unit and the signal filtering unit are reduced in the signal collection module. Figure 2 uses the feature value extraction and analysis unit to replace the two parts of data feature value extraction and feature value analysis in Figure 1. Through the corresponding processing of the digital signal in the feature value extraction and analysis, the digital signal can be denoised and corrected without signal filtering and baseline correction, reducing the complexity of the signal processing process and reducing power consumption. At the same time, avoid using the baseline for signal correction and improve the accuracy of signal processing.
  • the following examples describe in detail the feature value extraction and analysis methods provided in this application:
  • Figure 3 is a schematic flowchart of a method for acquiring characteristic parameters of a target object provided by an embodiment of the application.
  • the method can be executed by a terminal, which can be a wearable device, a smart phone, a tablet computer, or a personal computer, etc., where Wearable devices include smart watches, smart bracelets, smart masks, etc.
  • the method includes:
  • the target object can be a user wearing a wearable device, or a creature or object that can emit periodic signals.
  • the sensor on the wearable device can measure the pulse rate, heart rate, and respiration rate of the human body to obtain periodic digital signals.
  • obtain the heart rhythm periodic digital signal of the target object detected by the wearable device or obtain the pulse periodic digital signal of the target object detected by the wearable device; or obtain the pulse periodic digital signal of the target object detected by the wearable device Respiratory periodic digital signal.
  • the wearable device can detect the periodic digital signal of the target object's heart rhythm through the heart rhythm sensor, the periodic digital signal of the pulse of the target object can be detected by the pulse sensor, and the periodic digital signal of the breathing of the target object can be detected through the breathing sensor.
  • S120 Determine the first candidate extreme value point and the second candidate extreme value point from the periodic digital signal.
  • the interval between the second candidate extreme point and the first candidate extreme point is smaller than the first threshold, and the difference between the second candidate extreme point and the first candidate extreme point matches the second threshold.
  • the first candidate extremum point and the second candidate extremum point can be used as effective feature values that characterize the periodic digital signal.
  • the periodic digital signals are sorted according to the time information, and the signal value corresponding to each time point is obtained. Secondly, find all extreme points according to the change trend of the signal value.
  • the first candidate extreme value point and the second candidate extreme value point are determined according to the first threshold and the second threshold.
  • the corresponding relationship between the periodic digital signal and the first threshold and the second threshold can be preset.
  • the first threshold is determined according to the time difference between the effective peak and the effective valley of the periodic signal; the second threshold is determined according to the voltage difference between the effective peak and the effective valley.
  • the first threshold can be the number of extreme points between the first candidate extreme point and the second candidate extreme point, or between the first candidate extreme point and the second candidate extreme point The interval time threshold.
  • S120 can be implemented in the following manner:
  • Step 2-1 Traverse each data point in the periodic digital signal sequentially, and calculate the first judgment character and the second judgment character of the current data point traversed.
  • the first judgment character represents the value change relationship between the current data point and the previous data point adjacent to the current data point
  • the second judgment character represents the value change relationship between the current data point and the next data point adjacent to the current data point relationship.
  • Sequentially traversing the periodic digital signal can sequentially traverse the data points in the periodic digital signal in ascending order of time; or traverse the data points in the periodic digital signal in descending order of time.
  • the first judgment character is calculated according to the difference between the previous data point and the current data point; the second judgment character is calculated according to the difference between the current data point and the next data point.
  • the previous data point N-1 of the data point N can be read. Calculate the first difference between the last data point N-1 and the current data point N. If the first difference is a positive number, the first determinant is marked as 1. If the first difference is a negative number, the first determinant is marked as -1. If the first difference is zero, then the first determinant is marked as zero. Read the next data point N+1 of the data point N, and calculate the second difference between the current data point N and the next data point N+1. If the second difference is a positive number, the second determinant is marked as 1. If the second difference is a negative number, the second determinant is marked as -1. If the second difference is zero, then the second judgement flag is 0.
  • Step 2-2 If the first judgment character is different from the second judgment character, mark the current data point as an extreme point.
  • the marking method can be storing the current data point in the extreme point sequence, or adding extreme point labels to the current data point. If the first determinant is the same as the second determinant, the data point is not an extreme point.
  • Step 2-3 Determine the first candidate extreme value point and the second candidate extreme value point from the extreme value points.
  • steps 2-3 can be implemented in the following manner:
  • the extreme points in the periodic digital signal can be arranged in the ascending order of the acquisition time; the extreme points in the periodic digital signal can also be arranged in the descending order of the acquisition time.
  • the extreme points in the extreme point sequence can be read sequentially to obtain the extreme point sequence in ascending or descending order of time. Or sort the extreme points according to the extreme point labels and time information.
  • the extreme points can be traversed in sequence according to the ascending order or descending order of the acquisition time.
  • the next extreme point is the extreme point to be traversed next to the current extreme point when traversing according to the preset traversal mode.
  • the reference extreme point is determined as the second candidate extreme point.
  • the second threshold may be determined according to the type of digital signal.
  • the type of digital signal includes, but is not limited to, a heart rhythm signal, a respiration signal, or a pulse signal.
  • the reference extreme point is the next extreme point adjacent to the first candidate extreme point.
  • the difference between the first candidate extreme point and the next extreme point adjacent to the first candidate extreme point matches the second threshold, the next extreme point adjacent to the first candidate extreme point The point is determined as the second candidate extreme point. If it does not match, go to step 5).
  • the difference between the first candidate extreme point and the reference extreme point is less than or equal to (or less than) the second threshold, that is, the difference between the first candidate extreme point and the reference extreme point does not match the second threshold.
  • the extreme points are sequentially read as the new reference extreme points.
  • the first threshold is used as the number of extremum points between the first candidate extremum point and the reference extremum point, which can be 3-10.
  • step 5) is executed in sequence.
  • step 5) is executed in sequence.
  • the method further includes:
  • the preset traversal mode, the first threshold and the second threshold are determined according to the type of the periodic digital signal.
  • Types of periodic digital signals include, but are not limited to, heart rhythm signals, respiration signals, or pulse signals.
  • S130 Determine at least one effective characteristic value of the periodic digital signal according to the first candidate extreme value point and the second candidate extreme value point.
  • the effective feature value is used to represent the effective extreme point combination in the periodic digital signal.
  • the key-value pair is determined according to the first candidate extreme value point and the second candidate extreme value point, and the key-value pair is used as the effective characteristic value of the periodic digital signal.
  • the data structure of the first candidate extreme point and the second candidate extreme point may include time information and numerical parameters.
  • the first candidate extreme point can be expressed as (T1, V1)
  • the second candidate extreme point can be expressed as (T2, V2).
  • the effective feature value can be a key-value pair of the first candidate extreme value point and the second candidate extreme value point, such as [(T1, V1), (T2, V2)].
  • S140 Determine a characteristic parameter of the periodic digital signal according to at least one effective characteristic value.
  • the characteristic parameters of the periodic digital signal include the peak value of each period and so on.
  • the peak value of each cycle can be determined according to the numerical parameters of the first candidate extremum point and the second candidate extremum point.
  • S140 may be implemented as: obtaining the first effective feature value from at least one effective feature value, and according to the difference between the first candidate extreme value point and the second candidate extreme value point corresponding to the first effective feature value The value difference determines the peak characteristic parameter; the first effective characteristic value is any effective characteristic value. Then, according to the acquisition time of the first candidate extremum point and the second candidate extremum point corresponding to the first effective feature value, the time information of the peak feature parameter is determined.
  • the characteristic parameters of periodic digital signals also include signal frequency and so on.
  • the signal frequency can be determined according to the time interval of the peak or trough of the two effective characteristic values.
  • the time information of the first candidate extreme point (or the second candidate extreme point) can be extracted from the two effective feature values, and the signal frequency can be calculated based on the time information.
  • S140 may be implemented as: obtaining the first candidate extreme value point corresponding to the second effective feature value from the at least one effective feature value; then, obtaining the first candidate extreme value point corresponding to the second effective feature value; The second time of the first candidate extremum point corresponding to the third effective eigenvalue, the first candidate extremum point is used to represent the peak or trough, and the second effective eigenvalue is adjacent to the third effective eigenvalue; finally, according to The first time and the second time determine the frequency characteristic parameters.
  • a periodic digital signal of the target object is acquired; secondly, a first candidate extreme value point and a second candidate extreme value point are determined from the periodic digital signal, The interval between the second candidate extreme point and the first candidate extreme point is less than the first threshold, and the difference between the second candidate extreme point and the first candidate extreme point matches the second threshold; again, according to The first candidate extremum point and the second candidate extremum point are used to determine at least one effective characteristic value of the periodic digital signal, and the effective characteristic value is used to indicate the combination of the effective extreme points in the periodic digital signal; finally, according to at least A valid characteristic value determines the characteristic parameter of the periodic digital signal.
  • the embodiment of the present application can determine the effective feature value according to the first candidate extreme value point and the second candidate extreme value point, and obtain the feature parameter according to the effective feature value .
  • the difference between the two candidate extremum points in an effective eigenvalue can be the peak value in a period, the first candidate extremum point (or the second candidate extremum point) of the two effective eigenvalues
  • the time difference can calculate the period of the periodic digital signal, and there is no need to perform signal filtering and baseline correction operations. While ensuring the signal-to-noise ratio of the signal, it reduces the complexity of the signal processing process and reduces power consumption.
  • the user's heart rhythm recognition can be completed through the following steps, including:
  • the heart rate sensor was used to collect 78 human heartbeat signal changes over time. After the signal is amplified, as shown in Figure 4, the heart rate sensor detects the voltage value corresponding to each time point, and the voltage value represents the heartbeat intensity at that time point.
  • heart rate characteristic value processing and analysis are performed as follows:
  • the data includes the time information of all data points and the corresponding voltage information.
  • Output data such as time, instantaneous heart rate, average heart rate, and heart rate peak to the human-computer interaction interface, as shown in Figure 5.
  • the parameters in Figure 5 include peak value, time, instantaneous heart rhythm, average heart rhythm, and current heart rhythm peak value.
  • the extracted characteristic values such as peak value and time correspond to the image in Figure 4 one-to-one. Starting from the second peak, the instantaneous and average heart rate are calculated, and the results are accurate and reliable; a large number of interference signals in Figure 4 did not affect the accuracy of the recognition results.
  • the user's pulse frequency identification can be completed through the following steps, including:
  • the pulse sensor was used to collect 244 personal pulse signal data over time.
  • the amplified signal is shown in Figure 6.
  • the pulse sensor detects the voltage value corresponding to each time point, and the voltage value represents the pulse intensity at that time point.
  • the pulse characteristic value processing and analysis are performed as follows:
  • the parameters from Figure 7 include peak value, time, instantaneous pulse frequency, average pulse frequency, and current pulse peak value.
  • the extracted characteristic values such as peak value and time correspond one-to-one with the image in Figure 6.
  • the instantaneous and average pulse frequency are calculated from the second peak, and the results are accurate and reliable; the severe baseline offset in Figure 6 does not affect the recognition results accuracy.
  • the user's respiratory rate identification can be completed through the following steps, including:
  • the respiratory sensor is used to collect data of 120 human respiratory signals that change over time.
  • the signal is amplified as shown in Figure 8.
  • the respiratory sensor detects the voltage value corresponding to each time point, and the voltage value represents the value at that time point. Breathing intensity.
  • the respiratory characteristic value processing and analysis are performed as follows:
  • the parameters include peak value, time, instantaneous respiratory rate, average respiratory rate, and current peak respiratory value.
  • the extracted characteristic values such as peak value and time correspond one-to-one with the image in Figure 8.
  • the instantaneous and average respiratory frequency are calculated from the second peak, and the results are accurate and reliable; the severe baseline offset in Figure 8 does not affect the recognition results accuracy.
  • the method for acquiring the characteristic parameters of the target object provided by the embodiment of the present application has strong anti-interference ability, and the initial sensor signal does not need special filtering processing, which saves the circuit space and energy consumption of the sensor system.
  • there is no need to perform baseline correction on the initial sensing signal and the problem of baseline drift caused by signal interference has no effect on the signal feature value identification in this method, thereby reducing energy consumption while ensuring signal quality, which is stable and reliable.
  • FIG. 10 is a schematic structural diagram of a terminal provided by an embodiment of this application.
  • the terminal 200 may be configured to execute the method for acquiring characteristic parameters of a target object disclosed in the embodiment of this application.
  • the terminal can be an electronic device with signal processing functions such as a smart watch, a smart bracelet, a smart mask, a smart phone, a tablet computer, and a computer.
  • the terminal 200 may include a processor, and the processor is configured to:
  • the characteristic parameter of the periodic digital signal is determined according to at least one valid characteristic value.
  • processor is configured as:
  • the first candidate extreme value point and the second candidate extreme value point are determined from the extreme value points.
  • processor is configured as:
  • the second judgment character is calculated according to the difference between the current data point and the next data point.
  • processor is configured as:
  • the reference extreme point is determined as the second candidate extreme point
  • the new reference extreme point is determined as the second candidate extreme point, and The next extreme value point adjacent to the second candidate extreme value point is taken as the new first candidate extreme value point, and the previous steps are repeated to determine the new second candidate extreme value point.
  • the difference between all new reference extreme points and the first candidate extreme point does not match the second threshold, then the next extreme point adjacent to the first candidate extreme point Point as the new first candidate extreme point, and repeat the previous steps to determine the new second candidate extreme point.
  • processor is configured as:
  • the processor is configured to: before determining the first candidate extremum point and the second candidate extremum point from the periodic digital signal, determine the preset traversal mode, the first threshold, and the The second threshold.
  • the processor is configured to determine the key-value pair according to the first candidate extreme value point and the second candidate extreme value point, and use the key-value pair as the effective characteristic value of the periodic digital signal.
  • processor is configured as:
  • the first effective feature value from at least one effective feature value, and determine the peak feature parameter according to the numerical difference between the first candidate extreme value point and the second candidate extreme value point corresponding to the first effective feature value; the first effective feature The value is any valid characteristic value;
  • the time information of the peak feature parameter is determined.
  • processor is configured as:
  • the frequency characteristic parameter is determined according to the first time and the second time.
  • processor is configured as:
  • the terminal 200 may include at least one processor 201, at least one input device 202, at least one output device 203, a memory 204, a display unit 205 and other components. Among them, these components can be connected through one or more buses 206 for communication.
  • the structure of the terminal 200 shown in FIG. 10 does not constitute a limitation to the embodiment of the present application. It may be a bus-shaped structure or a star-shaped structure, and may include more Or fewer parts, or a combination of some parts, or a different arrangement of parts.
  • the processor 201 is the control center of the terminal 200, which uses various interfaces and lines to connect various parts of the entire terminal 200, and calls the programs and/or units stored in the memory 204 by running or executing programs and/or units stored in the memory 204. 204 to perform various functions of the terminal 200 and process data.
  • the processor 201 can be composed of an integrated circuit (Integrated Circuit, IC for short), for example, can be composed of a single packaged IC, or can be composed of multiple packaged ICs with the same function or different functions.
  • the processor 201 may only include a central processing unit, or may be a combination of a CPU, a digital signal processor (Digital Signal Processor, DSP for short), a GPU, and various control chips.
  • the CPU may be a single computing core, or may include multiple computing cores.
  • the input device 202 may include a standard touch screen, a keyboard, etc., and may also include a wired interface, a wireless interface, etc., and may be used to implement interaction between the user and the terminal 200.
  • the output device 203 may include a speaker, and may also include a wired interface, a wireless interface, and the like.
  • a computer program that can be run on a processor is stored in the memory, and the processor executes the computer program to implement the method shown in the foregoing embodiment.
  • the memory 204 includes at least one of the following: random access memory, non-volatile memory, and external memory.
  • the memory 204 can be used to store program codes.
  • the processor 201 executes any one of the above by calling the program codes stored in the memory 204.
  • the memory 204 mainly includes a program storage area and a data storage area, where the program storage area can store an operating system, an application program required by at least one function, and the like.
  • the data storage area can store data created according to the use of the terminal, etc.
  • the operating system may be an Android system, an iOS system, a Windows operating system, and so on.
  • the display unit 205 is used to display information such as images and text, and may be a light-emitting diode display unit, a liquid crystal display unit, or the like.
  • the initial sensor signal does not need special filtering processing, which saves the circuit space and energy consumption of the sensor system.
  • there is no need to perform baseline correction on the initial sensing signal and the problem of baseline drift caused by signal interference has no effect on the signal feature value identification in this method, thereby reducing energy consumption while ensuring signal quality, which is stable and reliable.
  • the embodiment of the present application also provides a storage medium in which a characteristic parameter acquisition instruction of a target object is stored.
  • the characteristic parameter acquisition instruction of the target object runs on a computer, the computer executes the characteristics of the target object.
  • the parameter acquisition method for specific implementation, please refer to the method embodiment, which will not be repeated here. It should be noted that in the several embodiments provided in this application, it should be understood that the disclosed system and method can be implemented in other ways.
  • the functional modules in the various embodiments of the present application may be integrated together to form an independent part, or each module may exist alone, or two or more modules may be integrated to form an independent part.
  • a periodic digital signal of the target object is acquired; secondly, a first candidate extreme value point and a second candidate extreme value point are determined from the periodic digital signal, The interval between the second candidate extreme point and the first candidate extreme point is less than the first threshold, and the difference between the second candidate extreme point and the first candidate extreme point matches the second threshold; again, according to The first candidate extreme value point and the second candidate extreme value point determine at least one effective characteristic value of the periodic digital signal; finally, the characteristic parameter of the periodic digital signal is determined according to the at least one effective characteristic value.
  • the embodiment of the present application can determine the effective feature value according to the first candidate extreme value point and the second candidate extreme value point, and obtain the feature parameter according to the effective feature value .
  • the difference between the two candidate extremum points in an effective eigenvalue can be the peak value in a period, the first candidate extremum point (or the second candidate extremum point) of the two effective eigenvalues
  • the time difference can calculate the period of the periodic digital signal, and there is no need to perform signal filtering and baseline correction operations. While ensuring the signal-to-noise ratio of the signal, it reduces the complexity of the signal processing process and reduces power consumption.
  • the method for acquiring the characteristic parameters of the target object provided by the embodiment of the present application has strong anti-interference ability, and the initial sensor signal does not need special filtering processing, which saves the circuit space and energy consumption of the sensor system.
  • there is no need to perform baseline correction on the initial sensing signal and the problem of baseline drift caused by signal interference has no effect on the signal feature value identification in this method, thereby reducing energy consumption while ensuring signal quality, which is stable and reliable.

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Abstract

L'invention concerne un procédé d'acquisition des paramètres de caractéristiques d'un objet cible, un terminal et un support de stockage. Le procédé consiste à : acquérir un signal numérique périodique d'un objet cible (110) ; déterminer un premier point de valeur extrême alternatif et un second point de valeur extrême alternatif dans le signal numérique périodique (120) ; sur la base du premier point de valeur extrême alternatif et du second point de valeur extrême alternatif, déterminer au moins une valeur de caractéristique valide du signal numérique périodique (130) ; et, sur la base de ladite valeur de caractéristique valide, déterminer des paramètres de caractéristiques du signal numérique périodique (140). Le procédé d'acquisition des paramètres de caractéristiques d'un objet cible selon la présente invention ne nécessite pas de réaliser des opérations de filtrage de signal et de correction de ligne de base et réduit la complexité de la procédure de traitement de signaux et réduit la consommation d'énergie tout en garantissant le rapport signal sur bruit du signal.
PCT/CN2019/082330 2019-04-11 2019-04-11 Procédé d'acquisition de paramètres de caractéristiques d'objet cible, terminal et support de stockage WO2020206661A1 (fr)

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