CN116965800A - Respiratory state evaluation method based on electrocardiographic data - Google Patents
Respiratory state evaluation method based on electrocardiographic data Download PDFInfo
- Publication number
- CN116965800A CN116965800A CN202310763267.5A CN202310763267A CN116965800A CN 116965800 A CN116965800 A CN 116965800A CN 202310763267 A CN202310763267 A CN 202310763267A CN 116965800 A CN116965800 A CN 116965800A
- Authority
- CN
- China
- Prior art keywords
- approximate
- respiratory
- peak value
- electrocardiographic
- confidence
- 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
- 230000000241 respiratory effect Effects 0.000 title claims abstract description 32
- 238000011156 evaluation Methods 0.000 title claims abstract description 23
- 230000029058 respiratory gaseous exchange Effects 0.000 claims abstract description 24
- 238000000034 method Methods 0.000 claims abstract description 23
- 238000012545 processing Methods 0.000 claims abstract description 15
- 238000007781 pre-processing Methods 0.000 claims abstract description 6
- 230000036387 respiratory rate Effects 0.000 claims description 11
- 230000001133 acceleration Effects 0.000 claims description 10
- 230000036391 respiratory frequency Effects 0.000 claims description 8
- 230000035565 breathing frequency Effects 0.000 claims description 5
- 230000008569 process Effects 0.000 claims description 5
- 238000001914 filtration Methods 0.000 claims description 4
- 238000000354 decomposition reaction Methods 0.000 claims description 3
- 238000013135 deep learning Methods 0.000 claims description 3
- 230000003044 adaptive effect Effects 0.000 claims description 2
- 230000005540 biological transmission Effects 0.000 abstract description 3
- 238000010586 diagram Methods 0.000 description 6
- 238000001514 detection method Methods 0.000 description 5
- 238000004364 calculation method Methods 0.000 description 3
- 238000012544 monitoring process Methods 0.000 description 3
- 238000011161 development Methods 0.000 description 2
- 238000005516 engineering process Methods 0.000 description 2
- 230000006870 function Effects 0.000 description 2
- 238000001228 spectrum Methods 0.000 description 2
- 238000007792 addition Methods 0.000 description 1
- 238000007405 data analysis Methods 0.000 description 1
- 238000013461 design Methods 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 238000009434 installation Methods 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000006467 substitution reaction Methods 0.000 description 1
- 238000011282 treatment Methods 0.000 description 1
Classifications
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
-
- 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
- A61B5/0205—Simultaneously evaluating both cardiovascular conditions and different types of body conditions, e.g. heart and respiratory condition
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/08—Detecting, measuring or recording devices for evaluating the respiratory organs
- A61B5/0816—Measuring devices for examining respiratory frequency
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/332—Portable devices specially adapted therefor
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/24—Detecting, measuring or recording bioelectric or biomagnetic signals of the body or parts thereof
- A61B5/316—Modalities, i.e. specific diagnostic methods
- A61B5/318—Heart-related electrical modalities, e.g. electrocardiography [ECG]
- A61B5/346—Analysis of electrocardiograms
- A61B5/349—Detecting specific parameters of the electrocardiograph cycle
- A61B5/352—Detecting R peaks, e.g. for synchronising diagnostic apparatus; Estimating R-R interval
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6801—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient specially adapted to be attached to or worn on the body surface
- A61B5/6802—Sensor mounted on worn items
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7235—Details of waveform analysis
- A61B5/7253—Details of waveform analysis characterised by using transforms
- A61B5/7257—Details of waveform analysis characterised by using transforms using Fourier transforms
Abstract
The invention provides a respiratory state evaluation method based on electrocardiographic data, and belongs to the technical field of wearable equipment. The method comprises the following steps: A. acquiring an electrocardiosignal of a target person through wearable equipment, and preprocessing to obtain an approximate electrocardiosignal; B. processing the approximate electrocardiosignal on the wearable equipment to obtain an R wave of the approximate electrocardiosignal; C. further calculating peak values and time values of each RR period of the R wave on the wearable equipment, and processing the peak values and the time values to obtain approximate respiration waveforms; D. performing fast fourier transform on the approximate respiration waveform on the wearable equipment to obtain a respiration rate, and uploading the result to a server. The method can greatly reduce the data transmission quantity between the wearable equipment and the server, and can accurately evaluate the breathing state.
Description
Technical Field
The invention belongs to the technical field of wearable equipment, and particularly relates to a respiratory state evaluation method based on electrocardiographic data.
Background
With the continuous development of technology, wearable vital sign detection technology is also continuously advancing. Currently, chest strap type detection devices have been widely used in the fields of health management, medical monitoring, fitness tracking, and the like. For example, although the breathing monitoring can be realized by the devices according to the chinese patents CN 214856778U, CN113925473a and CN103462592B, the principle is that the breathing belt made of an independent piezoelectric film is used for detecting the breathing rate, and the breathing belt itself has no elasticity and cannot be stretched, so that the arrangement and the installation of the breathing belt are inconvenient, and the comfort and the firmness of the whole chest belt are also affected. For example, in chinese patent CN208640697U, although it has electrocardiographic detection and GPS functions, but lacks respiratory monitoring, and can only be connected to a mobile phone through bluetooth, and cannot be normally used when the mobile phone is disconnected. That is, the detection of the electrocardiograph and the respiration are performed separately at present, and both require independent detection devices, which is disadvantageous for the miniaturization development of the wearable device. How to only acquire electrocardiographic data to evaluate the respiratory state of an acquisition target is a problem to be solved urgently.
Disclosure of Invention
Accordingly, the present invention is directed to a respiratory state evaluation method based on electrocardiographic data, which can evaluate respiratory state of a target person only by collecting electrocardiographic data of the target person.
The aim of the invention can be achieved by the following technical scheme: a respiratory state assessment method based on electrocardiographic data, comprising the steps of:
A. acquiring electrocardiosignals of a target person through wearable equipment, and preprocessing to obtain approximate electrocardiosignals;
B. processing the approximate electrocardiosignal on the wearable equipment to obtain an R wave of the approximate electrocardiosignal;
C. further calculating peak values and time values of each RR period of the R wave on the wearable equipment, and processing the peak values and the time values to obtain approximate respiration waveforms;
D. performing fast fourier transform on the approximate respiration waveform on the wearable equipment to obtain a respiration rate, and uploading the result to a server.
In the above respiratory state evaluation method based on electrocardiographic data, in the step C, a linear interpolation method is adopted to calculate each peak value and each time value.
In the above respiratory state evaluation method based on electrocardiographic data, the step C further includes: the average value of each RR period of the R-wave is calculated as heart rate data, which is uploaded to the server along with the respiratory rate.
In the above respiratory state evaluation method based on electrocardiographic data, the step C further includes: the standard deviation during each RR of the R-wave is calculated as the heart rate variability, which is uploaded to the server along with the respiratory rate.
In the above respiratory state evaluation method based on electrocardiographic data, the step D further includes calculating a confidence level of the respiratory frequency value.
In the above respiratory state evaluation method based on electrocardiographic data, the confidence coefficient is calculated as: calculating the ratio of the first peak value a1 to the second peak value a2 and the ratio of the first peak value a1 to the third peak value a3 in the Fourier transform result array, and judging as follows: if the ratio of the first peak value a1 to the second peak value a2 or the ratio of the first peak value a1 to the third peak value a3 is smaller than a ratio threshold value, judging that the confidence is low, and calculating the breathing frequency possibly by mistake; otherwise, the confidence is high, the respiratory frequency is correct, and the ratio threshold value is 1.5-2.
In the above respiratory state evaluation method based on electrocardiographic data, the confidence coefficient is calculated as: collecting acceleration of human trunk, and making the following judgment: if the average value of the absolute values of the accelerations of the human trunk is larger than an acceleration threshold value in the past 1 minute, judging that the human is moving, and that the electrocardiosignals are greatly interfered and the confidence of the respiratory frequency value is low; otherwise, the confidence is high, and the breathing frequency is correct. The acceleration threshold may be obtained by calibration through previous experiments.
In the respiratory state evaluation method based on electrocardiographic data, when the confidence is too low, the original data of the electrocardiographic signal is uploaded to a server through the wearable device for further operation processing, wherein the further operation processing comprises but is not limited to wavelet decomposition and deep learning.
In the respiratory state evaluation method based on electrocardiographic data, the preprocessing comprises filtering the original data of the electrocardiographic signals by adopting a baseline drift removal algorithm or a power frequency interference removal algorithm.
In the respiratory state evaluation method based on electrocardiographic data, in the step B, an adaptive sliding window peak finding algorithm or a p_t algorithm is adopted to process the approximate electrocardiographic signal to obtain an R wave of the approximate electrocardiographic signal.
Compared with the prior art, the respiratory state evaluation method based on the electrocardiographic data has the following advantages: (1) the respiratory state evaluation of the target personnel can be completed only by collecting the data of the electrocardiosignals, and the miniaturized design of the wearable equipment is facilitated. (2) The data volume of the electrocardiosignal waveform original data is very large, the vital sign operation process is carried out on the wearable equipment for local calculation, calculation result values such as heart rate and respiratory frequency are directly uploaded to the server, and the data traffic can be reduced by 99.9%. (3) The wearing equipment is limited by the use environment, the endurance and the comfort requirements of the user, a complex algorithm cannot be executed, and when the signal to noise ratio of the electrocardiosignal is low, the calculated respiratory rate value is possibly inaccurate. By adding confidence assessment, the amount and accuracy of data transmission can be balanced by transmitting raw data separately for important personnel and data with low confidence.
Drawings
Fig. 1 is an algorithm schematic diagram of a respiratory state evaluation method based on electrocardiographic data in an embodiment of the present invention.
Fig. 2 is a schematic diagram of raw data of an electrocardiograph signal acquired in an embodiment of the present invention.
FIG. 3 is a schematic diagram of an approximate electrocardiographic signal after preprocessing and filtering in an embodiment of the present invention.
Fig. 4 is a schematic diagram of R-waves obtained after processing an approximate electrocardiographic signal in an embodiment of the present invention.
Fig. 5 is a schematic diagram of an approximate respiration waveform obtained by performing an interpolation algorithm on R waves in the embodiment of the present invention.
Fig. 6 is a graph of a spectrum obtained after performing a fast fourier transform on an approximate respiration waveform in an embodiment of the invention.
FIG. 7 is a schematic workflow diagram incorporating confidence computation in an embodiment of the present invention.
Detailed Description
Embodiments of the present invention are described in detail below, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to like or similar elements or elements having like or similar functions throughout. The embodiments described below by way of example with reference to the accompanying drawings are intended to illustrate the invention and should not be construed as limiting the invention.
In the description of the present invention, it should be understood that the terms "length," "width," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," and the like indicate orientations or positional relationships based on the orientation or positional relationships shown in the drawings, merely to facilitate describing the present invention and simplify the description, and do not indicate or imply that the devices or elements referred to must have a specific orientation, be configured and operated in a specific orientation, and therefore should not be construed as limiting the present invention.
Furthermore, in the description of the present invention, unless explicitly stated otherwise, the algorithms involved are known algorithms and are therefore not specifically stated.
As shown in fig. 1, the respiratory state evaluation method based on electrocardiographic data provided by the invention acquires and performs data analysis and processing on electrocardiographic signals through wearable equipment to obtain R waves, and then obtains approximate respiratory waveforms through an interpolation algorithm. According to the method, the electrocardiosignal is directly processed by the controller of the wearable device, so that the respiratory state evaluation of the target personnel is realized, and the data transmission between the wearable device and the server can be greatly reduced under the unnecessary condition. In the implementation process of the method, the wearable device can be a series of portable intelligent wearable devices with electrocardiograph data acquisition and data processing functions, such as an intelligent chest strap, an intelligent vest and an intelligent bracelet.
The method comprises the following specific steps:
first, an electrocardiographic signal of 1kHz of the target person is acquired by a wearable apparatus, as shown in fig. 3. After the original data of the electrocardiosignal is obtained, the original data is filtered by adopting a baseline drift removal algorithm and a power frequency interference removal algorithm to obtain the approximate electrocardiosignal. By the filtering process, noise is reduced, accuracy of data is improved, and the obtained approximate electrocardiosignal is shown in fig. 3 and is smoother than that of fig. 2.
Then, the approximate electrocardiosignal is processed by adopting a self-adaptive sliding window peak finding algorithm to obtain an R wave of the approximate electrocardiosignal, so that an R wave graph of the approximate electrocardiosignal is obtained as shown in figure 4. In other embodiments of the invention, this step may be replaced with a P-T algorithm to find the R wave peak.
Then, on the basis of R wave, three further treatments are carried out:
1. calculating an average value of each RR period of the R wave as heart rate data;
2. by calculating the standard deviation during each RR of the R wave as heart rate variability;
3. calculating peak value and time value of each RR period of R wave, and calculating and processing each peak value and time value by adopting a linear difference method to obtain approximate respiration waveform, as shown in figure 5; then, performing fast Fourier transform on the approximate respiration waveform to obtain respiration frequency, namely a respiration signal spectrum, as shown in fig. 6;
and then transmitting the heart rate data, heart rate variability and respiratory rate to a server together for storage to complete the evaluation of respiratory state.
In another embodiment of the invention, shown in fig. 7, to provide accuracy in the assessment of respiratory status, the confidence of the respiratory rate values is further calculated on the basis of the steps shown in fig. 1. The confidence level can be calculated in one of the following two ways, or can be calculated and favored in two ways. Specific:
the first confidence measure is calculated as: when performing the fast fourier transform operation on the approximate respiration waveform, calculating the ratio of the first peak value a1 to the second peak value a2 and the ratio of the first peak value a1 to the third peak value a3 in the fast fourier transform result array, and performing the following judgment: if the ratio of the first peak value a1 to the second peak value a2 is smaller than 1.5 or the ratio of the first peak value a1 to the third peak value a3 is smaller than 2, the confidence is judged to be low, and the respiratory rate may be calculated incorrectly; otherwise, the confidence is high, and the respiratory frequency is correct. The proportional threshold in this embodiment may be implemented by self-setting, calibration implementation, and the like.
The second confidence level is calculated as: in addition, the acceleration of the human trunk is acquired through a gyroscope, the gyroscope is integrated on the wearable equipment, and then the following judgment is made: if the average value of the absolute values of the accelerations of the human trunk is larger than an acceleration threshold value in the past 1 minute, judging that the human is moving, and that the electrocardiosignals are greatly interfered and the confidence of the respiratory frequency value is low; otherwise, the confidence is high, and the breathing frequency is correct.
Confidence in the two ways described above or in the breathing rate. When the confidence is high or the target person is judged to be healthy, the server receives the data and stores the data. When the confidence level is too low, the server issues the instruction to require the original data of the electrocardiosignal, the wearable device uploads the original data of the electrocardiosignal to the server after receiving the instruction, and the server further carries out complex processing on the original data of the electrocardiosignal, including but not limited to adopting methods such as wavelet decomposition, deep learning and the like, and carries out further operation to obtain more accurate respiratory rate data.
The respiratory rate evaluation method is simple, has small data operand, is suitable for being executed on portable wearable equipment with low power consumption and low calculation force, and further calculates through a remote server when necessary to improve the accuracy and reliability of the evaluation result.
The specific embodiments described herein are offered by way of example only to illustrate the spirit of the invention. Those skilled in the art may make various modifications or additions to the described embodiments or substitutions thereof without departing from the spirit of the invention or exceeding the scope of the invention as defined in the accompanying claims.
Claims (10)
1. A respiratory state assessment method based on electrocardiographic data, comprising the steps of:
A. acquiring electrocardiosignals of a target person through wearable equipment, and preprocessing to obtain approximate electrocardiosignals;
B. processing the approximate electrocardiosignal on the wearable equipment to obtain an R wave of the approximate electrocardiosignal;
C. further calculating peak values and time values of each RR period of the R wave on the wearable equipment, and processing the peak values and the time values to obtain approximate respiration waveforms;
D. performing fast fourier transform on the approximate respiration waveform on the wearable equipment to obtain a respiration rate, and uploading the result to a server.
2. The method according to claim 1, wherein the step C calculates the peak value and the time value by linear interpolation.
3. The method for evaluating respiratory status based on electrocardiographic data according to claim 1 or 2, wherein the step C further comprises: the average value of each RR period of the R-wave is calculated as heart rate data, which is uploaded to the server along with the respiratory rate.
4. The method for evaluating the respiratory state based on electrocardiographic data according to claim 3, wherein the step C further comprises: the standard deviation during each RR of the R-wave is calculated as the heart rate variability, which is uploaded to the server along with the respiratory rate.
5. The method according to claim 1 or 2, wherein the step D further comprises calculating a confidence level of the respiratory rate value.
6. The method of claim 5, wherein the confidence level is calculated as: calculating the ratio of the first peak value a1 to the second peak value a2 and the ratio of the first peak value a1 to the third peak value a3 in the Fourier transform result array, and judging as follows: if the ratio of the first peak value a1 to the second peak value a2 or the ratio of the first peak value a1 to the third peak value a3 is smaller than a ratio threshold value, judging that the confidence is low, and calculating the breathing frequency possibly by mistake; otherwise, the confidence is high, the respiratory frequency is correct, and the ratio threshold value is 1.5-2.
7. The method of claim 5, wherein the confidence level is calculated as: collecting acceleration of human trunk, and making the following judgment: if the average value of the absolute values of the accelerations of the human trunk is larger than an acceleration threshold value in the past 1 minute, judging that the human is moving, and that the electrocardiosignals are greatly interfered and the confidence of the respiratory frequency value is low; otherwise, the confidence is high, and the breathing frequency is correct.
8. The method for evaluating the respiratory state based on electrocardiographic data according to claim 5, wherein when the confidence is too low, the raw data of the electrocardiographic signal is uploaded to a remote server through a wearable device for further operation processing, wherein the further operation processing comprises, but is not limited to, wavelet decomposition and deep learning.
9. The method for evaluating the respiratory state based on the electrocardiographic data according to claim 1 or 2, wherein the preprocessing comprises filtering the raw data of the electrocardiographic signal by adopting a baseline drift removal algorithm and/or a power frequency interference removal algorithm.
10. The respiratory state evaluation method based on electrocardiographic data according to claim 1 or 2, wherein in the step B, an adaptive sliding window peaking algorithm or a p_t algorithm is adopted to process the approximate electrocardiographic signal to obtain an R wave of the approximate electrocardiographic signal.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310763267.5A CN116965800A (en) | 2023-06-27 | 2023-06-27 | Respiratory state evaluation method based on electrocardiographic data |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310763267.5A CN116965800A (en) | 2023-06-27 | 2023-06-27 | Respiratory state evaluation method based on electrocardiographic data |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN116965800A true CN116965800A (en) | 2023-10-31 |
Family
ID=88470367
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310763267.5A Pending CN116965800A (en) | 2023-06-27 | 2023-06-27 | Respiratory state evaluation method based on electrocardiographic data |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116965800A (en) |
-
2023
- 2023-06-27 CN CN202310763267.5A patent/CN116965800A/en active Pending
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| AU2021203784B2 (en) | Multivariate residual-based health index for human health monitoring | |
| CN106725488B (en) | Wireless field intensity respiration detection method and device and respiration detector | |
| US9462956B2 (en) | Calculating heart rate from acceleration signals containing cardiac activity signals | |
| US20160228069A1 (en) | Processing apparatus, processing method and system for processing a physiological signal | |
| Liu et al. | Automatic detection of respiratory rate from electrocardiogram, respiration induced plethysmography and 3D acceleration signals | |
| US20230091163A1 (en) | Heart activity monitoring during physical exercise | |
| EP3764896B1 (en) | Method and apparatus for monitoring a human or animal subject | |
| CN113017559A (en) | Vital sign extraction algorithm and system based on piezoelectric film sensor | |
| CN114176564A (en) | Method for extracting respiratory state based on radar signal | |
| CN111685760B (en) | Human body respiratory frequency calculation method based on radar measurement | |
| CN111839520A (en) | Human respiration monitoring method and device based on CSI signal power response autocorrelation | |
| CN116965800A (en) | Respiratory state evaluation method based on electrocardiographic data | |
| CN111839521A (en) | Human body respiration monitoring method and device based on double-antenna WiFi signal | |
| US20220361798A1 (en) | Multi sensor and method | |
| US20180333075A1 (en) | Respiratory rate measuring method and apparatus, and wearable device | |
| WO2024013714A1 (en) | A wearable finger ring device with multiple biomarkers and method thereof | |
| CN117582203A (en) | Non-contact continuous blood pressure monitoring device, computer equipment and storage medium for night sleep state | |
| CN116804893A (en) | System for health analysis and suggestion based on intelligent wearing detection motion data | |
| CN112043256A (en) | Radar-based multi-target heart rate real-time measurement method | |
| CN112957022A (en) | Fast self-adaptive acquisition device and acquisition method for PPG (photoplethysmography) signals |
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 |