CN117370769B - Intelligent wearable device data processing method suitable for sleep environment - Google Patents
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- G06F—ELECTRIC DIGITAL DATA PROCESSING
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- A—HUMAN NECESSITIES
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- 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
- A61B5/024—Detecting, measuring or recording pulse rate or heart rate
- A61B5/02416—Detecting, measuring or recording pulse rate or heart rate using photoplethysmograph signals, e.g. generated by infrared radiation
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Abstract
The invention discloses an intelligent wearable device data processing method suitable for a sleep environment, which relates to the field of intelligent wearable devices and comprises the following steps: according to the ACC acceleration data characteristics, whether the sleep environment meets typical sleep state characteristics is analyzed, if the sleep environment does not meet the typical sleep state characteristics, verification analysis is performed on PPG photoplethysmography data, accuracy and reliability of PPG measurement data are improved, ACC state influence is eliminated, PPG detection duration and period are adjusted according to heart rate detection results, conversion efficiency of LED light sources with different wavelengths is improved, PPG detection reliability and accuracy are improved, analysis signal classification is performed, and policy processing is performed on the sleep state of a user.
Description
Technical Field
The invention relates to the field of intelligent wearing equipment, in particular to an intelligent wearing equipment data processing method suitable for a sleeping environment.
Background
The intelligent wearable device for the sleep environment has wide application in the sleep monitoring field, can be used for monitoring and improving the sleep quality of a user, analyzes the sleep quality of the user by monitoring the factors such as the sleep time length, the sleep cycle, the respiration and the heart rate state of the user, and provides a dependable suggestion for improving the sleep state of the user.
In the sleep monitoring process of the existing intelligent wearable device, ACC acceleration characteristic state analysis and PPG photoelectric volume pulse wave tracing data are combined, the sleep state of a user is analyzed through an optical signal sensor and an acoustic signal sensor according to a CPC cardiopulmonary coupling algorithm, but in a special sleep environment such as vehicles like trains and ships with irregular vibration, the ACC acceleration characteristic does not accord with a typical sleep state rule, the specific sleep state of the user cannot be determined, the sleep duration of the user detected by the intelligent wearable device is short, and the sleep period is irregular, and the like.
In order to solve the above-mentioned drawbacks, a technical solution is proposed.
Disclosure of Invention
The invention aims to provide an intelligent wearable device data processing method suitable for a sleeping environment, which aims to solve the defects in the background technology.
In order to achieve the above object, the present invention provides the following technical solutions: a data processing method of intelligent wearable equipment suitable for sleeping environment comprises the following specific steps of;
analyzing whether the sleep environment meets typical sleep state characteristics according to the ACC acceleration data characteristics, if the sleep environment meets the typical sleep state characteristics, determining that an ACC verification value is 1, and monitoring the sleep state of a user according to the existing CPC cardiopulmonary coupling analysis algorithm by combining the ACC acceleration data characteristics and PGG photoplethysmography data according to the detection result of the optical signal sensor and the acoustic signal sensor;
if the characteristic of the typical sleep state is not met, determining that the ACC verification value is 0, performing verification analysis on the PPG photoplethysmography data, checking the accuracy and the credibility of the PPG measurement data, eliminating the influence of the ACC state, and analyzing the sleep state of the user by means of the PPG measurement data;
the reliability of the PPG detection duration and period is evaluated by calculating the blood volume reflection fluctuation coefficient, and the accuracy of the conversion efficiency of the LED light sources with different wavelengths in the PPG detection is verified by calculating the RG light intensity conversion efficiency coefficient;
and verifying single factor stability of PPG analysis by combining reliability and accuracy analysis results of PPG detection, classifying verification signals, and after obtaining a permission signal, carrying out sleep monitoring on a user by combining PPG data fluctuation according to a CPC cardiopulmonary coupling algorithm, otherwise, issuing an early warning prompt and uploading analysis data for tracing.
In a preferred embodiment, the acquisition logic of the ACC verification value;
when a user wears the intelligent wearing equipment to perform sleep behaviors, and the data of the optical signal sensor and the acoustic signal sensor meet the sleep state condition threshold, the ACC algorithm is applied to analyze the sleep state of the user, the acceleration data is subjected to band-pass filtering of low-frequency signals by collecting acceleration pulse limiting count values in the period time, the fine action performance of the user is amplified and extracted, the total number higher than the preset acceleration sensor limiting signal threshold is calculated in the period time, and when the ACC characteristics meet the typical sleep state, the ACC verification value is 1; when the ACC feature does not conform to the typical sleep state, the ACC verification value takes 0.
In a preferred embodiment, logic for performing sleep analysis on the user when the ACC verification value takes 1;
by collecting the light signal characteristics and the sound signal characteristics in the sleeping environment and combining the ACC acceleration data fluctuation and the PGG photoplethysmography data, the sleeping state of the user is monitored according to a CPC cardiopulmonary coupling analysis algorithm.
In a preferred embodiment, logic for performing sleep analysis on the user when the ACC verification value takes 0;
and if the ACC verification value is 0, the ACC characteristic does not accord with a typical sleep state, ACC characteristic analysis data is abandoned, the PPG measurement process is subjected to verification analysis, the accuracy and reliability of PPG measurement are improved by verifying the PPG measurement process, and sleep monitoring is performed by combining a CPC cardiopulmonary coupling algorithm by means of the PPG measurement result.
In a preferred embodiment, the logic for evaluating reliability of PPG detection duration and period by calculating blood volume reflection fluctuation coefficient is;
setting an optimal sampling period duration range for PPG measurement by the intelligent wearing equipment, acquiring an initial heart rate of a user by using the minimum sampling period duration, comparing an upper limit range and a lower limit range of the heart rate of an ordinary person, adjusting the time-to-time next sampling period duration according to the initial heart rate equal proportion, and so on, adjusting the time-to-time next sampling period duration according to the heart rate of the user in the previous monitoring period, acquiring continuous sampling period duration data of the intelligent wearing equipment in one measuring period T, performing difference making on the continuous two sampling period durations, calibrating the continuous two sampling period durations to be continuous period duration fluctuation amplitude values, integrating the continuous period duration fluctuation amplitude values in one measuring period T into a data set, performing difference accumulating on the optimal sampling period duration range for PPG measurement by combining the intelligent wearing equipment, and calculating a blood volume reflection fluctuation coefficient of the intelligent wearing equipment in one measuring period T.
In a preferred embodiment, the accuracy of the conversion efficiency of the different wavelength LED light sources in PPG detection is verified by calculating the RG light intensity conversion efficiency coefficient;
according to the existing PPG light source adjustment algorithm standard based on the implemented skin state, calibrating a red light conversion green light threshold value as an RG conversion dominant point, calibrating a green light conversion red light threshold value as a GR conversion dominant point, and calibrating a green light three-primary-color model light intensity index asR and B are both 0, and the light intensity index of the red light three-primary color model is +.>G and B are both 0;
calculating RG conversion frequency and GR conversion frequency of intelligent wearing equipment in one measurement period T, and calculating three primary color indexes of an LED light source when reaching RG conversion dominant pointCompletely switch to->The ratio of the conversion time to the RG conversion frequency is defined as RGn, wherein +.>For the number of RG conversion frequency of intelligent wearing equipment in a measurement period T, and z is a positive integer, when the GR conversion dominant point is reached, calculating the three primary color index of the LED light source from +.>Completely switch to->The ratio of the conversion time to the GR conversion frequency is defined as GR conversion frequency, and GR conversion frequency is defined as GRm, wherein +.>The method comprises the steps that the number of RG conversion frequency of intelligent wearable equipment in a measurement period T is given, and x is a positive integer;
RG light intensity conversion efficiency coefficient Lc of intelligent wearable equipment is calculated, and the calculation expression is thatIn the formula->。
In a preferred embodiment, a method of verifying single factor stability of PPG analytical data;
and comprehensively analyzing a blood volume reflection fluctuation coefficient representing the reliability of the PPG measurement result and an RG light intensity conversion efficiency coefficient representing the accuracy by a linear regression method in combination with the test result of the reliability and the accuracy of the PPG measurement, and evaluating the stability degree of the PPG measurement by calculating a blood volume change rate drift index.
In a preferred embodiment, an analytical method and processing strategy to evaluate the degree of stability of PPG measurements;
comparing the calculated blood volume variability drift index with a preset blood volume variability drift index threshold, and when the calculated blood volume variability drift index is smaller than or equal to the preset blood volume variability drift index threshold, performing PPG credibility verification, generating a trust signal by the intelligent wearable device, and performing sleep state analysis according to a PPG measurement result and a heart-lung coupling algorithm;
when the calculated blood volume change rate drift index is larger than a preset blood volume change rate drift index threshold, the PPG credibility verification fails, the intelligent wearable device generates a questioning signal, performs early warning prompt on sleep monitoring of a user, uploads analysis data to a server, and performs characteristic analysis and freezing for data backtracking verification.
In the technical scheme, the invention has the technical effects and advantages that:
according to the intelligent wearable device, the ACC data feature analysis of the sleep environment of the intelligent wearable device is checked, when the sleep environment feature is found to be not in accordance with the ACC algorithm processing mode, the PPG data processing accuracy and the reliability of the intelligent wearable device are checked, the data analysis feasibility of the sleep environment is judged, the analysis data is uploaded and an early warning prompt is sent out under the condition that the data analysis reliability is insufficient, the abnormal feature state can be found in time by a manager at the rear end of the intelligent wearable device, the abnormal state is detected in a targeted manner, and further the occurrence of data analysis failure risk or data safety failure of the intelligent wearable device is effectively prevented.
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For a clearer description of embodiments of the present application or of the solutions of the prior art, the drawings that are needed in the embodiments will be briefly described below, it being obvious that the drawings in the following description are only some embodiments described in the present invention, and that other drawings may be obtained according to these drawings for a person skilled in the art.
FIG. 1 is a flow chart of the method of the present invention.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
Referring to fig. 1, the invention provides a data processing method of an intelligent wearable device suitable for a sleeping environment, comprising the following steps:
analyzing whether the sleep environment meets typical sleep state characteristics according to the ACC acceleration data characteristics, if the sleep environment meets the typical sleep state characteristics, determining that an ACC verification value is 1, and monitoring the sleep state of a user according to the existing CPC cardiopulmonary coupling analysis algorithm by combining the ACC acceleration data characteristics and PGG photoplethysmography data according to the detection result of the optical signal sensor and the acoustic signal sensor;
if the characteristic of the typical sleep state is not met, determining that the ACC verification value is 0, performing verification analysis on the PPG photoplethysmography data, checking the accuracy and the credibility of the PPG measurement data, eliminating the influence of the ACC state, and analyzing the sleep state of the user by means of the PPG measurement data;
adjusting the PPG detection duration and period according to the heart rate detection result, verifying the reliability of PPG detection, verifying the accuracy of PPG detection through the conversion efficiency of the LED light sources with different wavelengths, and improving the reliability and accuracy of PPG measurement according to verification analysis;
verifying single factor stability of PPG analysis by combining reliability and accuracy analysis results of PPG detection, classifying analysis signals, and after obtaining a permission signal, carrying out sleep monitoring on a user by combining PPG data fluctuation according to a CPC cardiopulmonary coupling algorithm, otherwise, issuing an early warning prompt and uploading the analysis data for tracing;
the ACC characteristic change is collected through an acceleration sensor arranged in the intelligent wearing equipment, and PPG data fluctuation is collected through a photoelectric sensor arranged in the intelligent wearing equipment by utilizing a volume pulse wave tracing method;
when a user wears the intelligent wearing equipment to perform sleep behaviors, limbs of the user enter a relatively mild and stable state, the data of the optical signal sensor and the acoustic signal sensor meet a sleep state condition threshold, the ACC characteristic change detects limb action change of the user through the acceleration sensor, the sleep state of the user is analyzed according to the action state change of the user, a pure ACC algorithm is used for analyzing the existing mature technology application of the sleep state of the user, the acceleration data is subjected to band-pass filtering of low-frequency signals through collecting acceleration pulse amplitude limiting count values in period time, fine action performance of the user is amplified and extracted, and the total number higher than the preset amplitude limiting signal threshold of the acceleration sensor is calculated in the period time so as to evaluate the possibility that the user is in the sleep state;
the ACC characteristic change cannot be used as a data processing basis judgment decision factor for calculating the sleep state in a special sleep environment, the ACC characteristic can be used as an auxiliary sleep state judgment standard, when the ACC characteristic accords with a typical sleep state, an ACC verification value is 1, and the intelligent wearable equipment performs algorithm analysis according to a CPC cardiopulmonary coupling theory; when the ACC characteristics do not accord with the typical sleep state, the ACC verification value is 0, the intelligent wearable device performs verification analysis by combining with the measurement conclusion of the PPG, and performs cardiopulmonary coupling algorithm analysis according to the actual sleep performance of the user after the accuracy and the credibility of the PPG measurement are determined;
the effectiveness of ACC features in a sleep environment depends on the verification and judgment of the intelligent wearing equipment on PPG data fluctuation and cardiopulmonary coupling, and when the ACC features detected by the intelligent wearing equipment do not meet typical sleep state feature thresholds, whether a user enters a sleep state is checked according to the detection of the heart rate states of the user by the LED and the PD sensors and the fluctuation signal feature change under different states of a cardiopulmonary coupling algorithm;
the measurement principle of the PPG is that the pulse rate is measured according to the frequency state of the periodic change of the blood volume and the heart rate is calculated according to the rule that the pulse rate is consistent with the heart rate, the measurement time of the LED and the PD sensor for measuring the PPG directly relates to the stability and the accuracy of the measurement result, if the measurement time of the PPG is overlong, the measurement process is difficult to maintain an absolute consistency state due to the instability of the intelligent wearing equipment in contact with limbs, and the possibility of measurement distortion interruption is high; if the PPG measurement time is too short, the measurement result is uniformly classified into heart rate variation conditions per minute, and the too short section measurement result does not have universality and cannot represent continuous steady-state heart rate variation;
the optimal state of the PPG measurement time period is represented by a blood volume reflection fluctuation coefficient, and the calculation method of the blood volume reflection fluctuation coefficient is as follows:
s101, acquiring an optimal sampling period duration range of PPG measurement by intelligent wearable equipment, and calibrating the optimal sampling period duration range of PPG measurement by the intelligent wearable equipment as Sa1-Sa2;
it should be noted that, the optimal sampling period duration range of the intelligent wearable device for PPG measurement meets the requirements of accuracy and feasibility of PPG measurement according to PSG polysomnography contrast test on the premise of ensuring that the intelligent wearable device operates stably according to the requirements of endurance, performance pressure and response speed factors of different sampling period durations of the intelligent wearable device, and the specific optimal sampling period range of PPG measurement is not particularly limited herein and is adjusted according to the PSG contrast test result;
s102, taking the high-precision sampling period duration Sa1 as an initial sampling period duration, acquiring an initial heart rate Ih of a user, and adjusting a calculation method of the real-time sampling period duration according to the initial heart rate to obtain the following steps ofIn the formula, ih is an initial heart rate measured by high-precision sampling period duration, 220 is a normal heart rate high-limit value of an ordinary person, and 40 is a normal heart rate low-limit value of the ordinary person;
it should be noted that, the specific value of the sampling period duration is adjusted in real time according to the heart rate value obtained by measuring the previous period, when the obtained heart rate of the user is the high limit 220, the duration of the next sampling period is Sa1 according to the calculation result, when the obtained heart rate of the user is the low limit 40, the duration of the next sampling period is Sa2 according to the calculation result, so that the high-precision sampling requirement in the high heart rate state of the user and the high-stability requirement in the low heart rate state of the user can be simultaneously satisfied;
s103, acquiring continuous sampling period duration data in one measurement period T, differencing two continuous sampling period durations, calibrating the continuous period duration fluctuation amplitude, integrating the continuous period duration fluctuation amplitude into a data set, and taking each continuous period duration fluctuation amplitude as Cf, whereinNumbering the continuous period duration fluctuation amplitude, and g is a positive integer;
s104, calculating a blood volume reflection fluctuation coefficient Bv of the intelligent wearable device in a measurement period T, wherein the calculation expression is as follows；
As can be known from the calculation expression of the blood volume reflection fluctuation coefficient, the smaller the blood volume reflection fluctuation coefficient of the intelligent wearing equipment in one measurement period T is, the better the measurement stability and accuracy of the intelligent wearing equipment are, otherwise, the larger the blood volume reflection fluctuation coefficient of the intelligent wearing equipment in one measurement period T is, the worse the measurement stability and accuracy of the intelligent wearing equipment are;
the LED light sources used in the PPG measurement are set to be visible light with two different frequencies, namely green light and red light, the wavelength of the green light is shorter than that of the red light, the absorption efficiency of melanin in skin is higher than that of the red light, the absorption efficiency of red cells in blood is higher than that of the green light, the wavelength of the red light is longer, and the capability of penetrating through the skin is stronger, so that the green light and the red light respectively have different adaptability under the condition of different skin characteristic states of a user, the existing composite PPG measurement mode simultaneously considers the light sources with two different wavelengths of the green light and the red light for adjustment application according to different skin states of the user, the conversion efficiency of the green light source and the red light source directly influences the accuracy of PPG measurement and the effectiveness of heart rate calculation, and the RG light intensity conversion efficiency coefficient is used for intuitively representing the adjustment efficiency of the red-green light source LEDs in the PPG measurement, and the calculation method is as follows:
s201, according to the existing PPG light source adjustment algorithm standard based on the implemented skin state, calibrating a red light conversion green light threshold value as an RG conversion dominant point, calibrating a green light conversion red light threshold value as a GR conversion dominant point, and calibrating a green light three-primary-color model light intensity index asR and B are both 0, and the light intensity index of the red light three-primary color model is +.>G and B are both 0;
s202, calculating RG conversion frequency and GR conversion frequency of the intelligent wearable device in a measurement period T, and calculating three primary color indexes of the LED light source when the RG conversion dominant point is reachedCompletely switch to->The ratio of the conversion time to the RG conversion frequency is defined as RGn, wherein +.>For the number of RG conversion frequency of intelligent wearing equipment in a measurement period T, and z is a positive integer, when the GR conversion dominant point is reached, calculating the three primary color index of the LED light source from +.>Completely switch to->The ratio of the conversion time to the GR conversion frequency is defined as GR conversion frequency, and GR conversion frequency is defined as GRm, wherein +.>The method comprises the steps that the number of RG conversion frequency of intelligent wearable equipment in a measurement period T is given, and x is a positive integer;
it is pointed out that the three primary color index change of the LED light source is detected and controlled by the LED controller, and is recorded by the operation monitoring log of the intelligent wearable equipment;
s203, calculating an RG light intensity conversion efficiency coefficient Lc of the intelligent wearable device, wherein the calculation expression is as followsIn the formula->；
According to the expression of the RG light intensity conversion efficiency coefficient, the higher the RG light intensity conversion efficiency coefficient of the intelligent wearable device in one measurement period T is, the worse the measurement stability and accuracy of the intelligent wearable device are, otherwise, the lower the RG light intensity conversion efficiency coefficient of the intelligent wearable device in one measurement period T is, the better the measurement stability and accuracy of the intelligent wearable device are;
according to the calculated blood volume reflection fluctuation coefficient and RG light intensity conversion efficiency coefficient, verification analysis is carried out on the accuracy and the reliability of PPG measurement, so that the effectiveness of sleep state analysis of a user is improved under the condition that ACC characteristic fluctuation is unavailable, and the application accuracy of a heart-lung coupling algorithm is ensured;
the blood volume change rate drift verification index is used for analyzing the accuracy and the reliability of the PGG measurement, when the ACC verification value is 0, namely the ACC characteristic change does not accord with the typical sleep environment standard, the PPG measurement is further analyzed through the blood volume change rate drift verification index, so that the accuracy and the reliability of the PPG measurement are ensured without reliable ACC characteristic auxiliary judgment, and the calculation method of the blood volume change rate drift index is as follows:
calibrating the blood volume change rate drift index as Vd, calculating the blood volume change rate drift index according to the blood volume reflection fluctuation coefficient and the RG light intensity conversion efficiency coefficient in one measurement period T of the intelligent wearable device, wherein the calculation expression is thatIn the formula->And->Proportional coefficients of the blood volume reflection fluctuation coefficient and the RG light intensity conversion efficiency coefficient, respectively, and +.>And->Are all greater than 0;
comparing the calculated blood volume variability drift index with a preset blood volume variability drift index threshold to determine the reliability and accuracy of PPG measurement, and when the calculated blood volume variability drift index is smaller than or equal to the preset blood volume variability drift index threshold, passing the PPG reliability verification, generating a trust signal by the intelligent wearable device, and carrying out sleep state analysis according to a PPG measurement result and a heart-lung coupling algorithm; when the calculated blood volume change rate drift index is larger than a preset blood volume change rate drift index threshold, the PPG credibility verification fails, the intelligent wearable equipment generates a questioning signal, performs early warning prompt on sleep monitoring of a user, uploads analysis data to a server, and performs characteristic analysis and freezing for data backtracking verification;
according to the intelligent wearable device, the ACC data feature analysis of the sleep environment of the intelligent wearable device is checked, when the sleep environment feature is found to be not in accordance with the ACC algorithm processing mode, the PPG data processing accuracy and the reliability of the intelligent wearable device are checked, the data analysis feasibility of the sleep environment is judged, the analysis data is uploaded and an early warning prompt is sent out under the condition that the data analysis reliability is insufficient, the abnormal feature state can be found in time by a manager at the rear end of the intelligent wearable device, the abnormal state is detected in a targeted manner, and further the occurrence of data analysis failure risk or data safety failure of the intelligent wearable device is effectively prevented.
The above formulas are all formulas with dimensions removed and numerical values calculated, the formulas are formulas with a large amount of data collected for software simulation to obtain the latest real situation, and preset parameters in the formulas are set by those skilled in the art according to the actual situation.
The above embodiments may be implemented in whole or in part by software, hardware, firmware, or any other combination. When implemented in software, the above-described embodiments may be implemented in whole or in part in the form of computer program product. The computer program product comprises one or more computer instructions or computer programs. When the computer instructions or computer program are loaded or executed on a computer, the processes or functions described in accordance with the embodiments of the present application are all or partially produced. The computer may be a general purpose computer, a special purpose computer, a network of computers, or other programmable devices. The computer instructions may be stored in a computer-readable storage medium or transmitted from one computer-readable storage medium to another computer-readable storage medium, for example, the computer instructions may be transmitted from one website site, computer, server, or data center to another website site, computer, server, or data center by wired or wireless means (e.g., infrared, wireless, microwave, etc.). The computer readable storage medium may be any available medium that can be accessed by a computer or a data storage device such as a server, data center, etc. that contains one or more sets of available media. The usable medium may be a magnetic medium (e.g., floppy disk, hard disk, magnetic tape), an optical medium (e.g., DVD), or a semiconductor medium. The semiconductor medium may be a solid state disk.
It should be understood that, in various embodiments of the present application, the sequence numbers of the foregoing processes do not mean the order of execution, and the order of execution of the processes should be determined by the functions and internal logic thereof, and should not constitute any limitation on the implementation process of the embodiments of the present application.
Those of ordinary skill in the art will appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, or combinations of computer software and electronic hardware. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application.
It will be clear to those skilled in the art that, for convenience and brevity of description, reference may be made to the corresponding process in the foregoing method embodiment for the specific working process of the above-described system, which is not described herein again.
The functions, if implemented in the form of software functional units and sold or used as stand-alone goods, may be stored in a computer readable storage medium. Based on such understanding, the technical solution of the present application may be embodied essentially or in a part contributing to the prior art or in a part of the technical solution, in the form of software goods stored in a storage medium, including several instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to perform all or part of the steps of the method described in the embodiments of the present application. And the aforementioned storage medium includes: a U-disk, a removable hard disk, a read-only memory (ROM), a random access memory (random access memory, RAM), a magnetic disk, or an optical disk, or other various media capable of storing program codes.
The foregoing is merely specific embodiments of the present application, but the scope of the present application is not limited thereto, and any person skilled in the art can easily think about changes or substitutions within the technical scope of the present application, and the changes and substitutions are intended to be covered by the scope of the present application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (6)
1. The intelligent wearable device data processing method suitable for the sleeping environment is characterized by comprising the following steps of: the method comprises the following specific steps:
analyzing whether the sleep environment meets typical sleep state characteristics according to the ACC acceleration data characteristics, if the sleep environment meets the typical sleep state characteristics, determining that an ACC verification value is 1, and monitoring the sleep state of a user according to the existing CPC cardiopulmonary coupling analysis algorithm by combining the ACC acceleration data characteristics and PPG photoplethysmography data according to the detection result of the optical signal sensor and the acoustic signal sensor;
if the characteristic of the typical sleep state is not met, determining that the ACC verification value is 0, performing verification analysis on the PPG photoplethysmography data, checking the accuracy and the credibility of the PPG measurement data, eliminating the influence of the ACC state, and analyzing the sleep state of the user by means of the PPG measurement data;
the reliability of the PPG detection duration and period is evaluated by calculating the blood volume reflection fluctuation coefficient, a composite PPG measurement mode is adopted, meanwhile, two light sources with different wavelengths of green light and red light are taken into consideration, adjustment application is carried out according to different skin states of a user, the adjustment efficiency of the green light source and the red light source directly influences the accuracy of PPG measurement, and the adjustment efficiency of the red light source and the green light source in PPG measurement is intuitively represented through the RG light intensity conversion efficiency coefficient;
verifying single factor stability of PPG analysis by combining reliability and accuracy analysis results of PPG detection, classifying verification signals, after obtaining a permission signal, carrying out sleep monitoring on a user by combining PPG data fluctuation according to a CPC cardiopulmonary coupling algorithm, issuing an early warning prompt under the condition of insufficient data analysis reliability, and uploading analysis data for tracing;
the logic for evaluating the reliability of the PPG detection duration and period by calculating the blood volume reflection fluctuation coefficient is as follows:
setting an optimal sampling period duration range for PPG measurement by the intelligent wearing equipment, acquiring an initial heart rate of a user by using the minimum sampling period duration, comparing the upper limit range and the lower limit range of the heart rate of an ordinary person, adjusting the duration of the next sampling period in real time according to the initial heart rate, and so on, adjusting the duration of the next sampling period in real time by using the heart rate of the user in the previous monitoring period, acquiring continuous sampling period duration data of the intelligent wearing equipment in one measuring period T, performing difference calibration on the continuous sampling period duration data, calibrating the continuous period duration fluctuation amplitude obtained by calculation in the one measuring period T into a data set, performing difference accumulation by combining the optimal sampling period duration range for PPG measurement by the intelligent wearing equipment, calculating the blood volume reflection fluctuation coefficient of the intelligent wearing equipment in the one measuring period T, wherein the greater the measurement stability and the accuracy of the intelligent wearing equipment are, and the greater the blood volume reflection fluctuation coefficient of the intelligent wearing equipment in the one measuring period T are, and the greater the measurement stability and the accuracy of the intelligent wearing equipment are;
the method for calculating the RG light intensity conversion efficiency coefficient in the adjustment efficiency of the red light source and the green light source in the PPG measurement is specifically as follows:
according to the existing PPG light source adjustment algorithm standard based on the implemented skin state, calibrating a red light conversion green light threshold value as an RG conversion dominant point, calibrating a green light conversion red light threshold value as a GR conversion dominant point, and calibrating a green light three-primary-color model light intensity index asR and B are both 0, and the light intensity index of the red light three-primary color model is +.>G and B are both 0;
calculating RG conversion frequency and GR conversion frequency of intelligent wearing equipment in one measurement period T, and calculating three primary color indexes of an LED light source when reaching RG conversion dominant pointG and B are all 0 completely converted into +.>The ratio of the change amplitude of the gray value of the pixel with R and B being 0 and the time used for conversion is taken as RG conversion frequency, and the RG conversion frequency is calibrated to be RGn, whereinFor the number of RG conversion frequency of intelligent wearing equipment in a measurement period T, and z is a positive integer, when the GR conversion dominant point is reached, calculating the three primary color index of the LED light source from +.>R and B are each 0 completely converted into +.>The ratio of the change amplitude of the gray value of the pixel with G and B being 0 to the time used for conversion is used as GR conversion frequency, and the GR conversion frequency is calibrated to be GRm, wherein +.>The method comprises the steps that the number of RG conversion frequency of intelligent wearable equipment in a measurement period T is given, and x is a positive integer;
RG light intensity conversion efficiency coefficient Lc of intelligent wearable equipment is calculated, and the calculation expression is thatIn the formula->And->The higher the RG light intensity conversion efficiency coefficient of the intelligent wearable device in one measurement period T is, the worse the measurement stability and accuracy of the intelligent wearable device are, otherwise, the lower the RG light intensity conversion efficiency coefficient of the intelligent wearable device in one measurement period T is, the better the measurement stability and accuracy of the intelligent wearable device are.
2. The intelligent wearable device data processing method applicable to a sleeping environment according to claim 1, wherein: the acquisition logic of the ACC verification value is as follows:
when a user wears the intelligent wearing equipment to perform sleep behaviors and the optical signal sensor data and the acoustic signal sensor data meet the sleep state condition threshold, an ACC algorithm is applied to analyze the sleep state of the user, the acceleration data is subjected to band-pass filtering of low-frequency signals by collecting acceleration pulse limiting count values in the period time, fine action performance of the user is amplified and extracted, the total number of acceleration pulses higher than the preset acceleration sensor limiting signal threshold is calculated in the period time so as to evaluate the possibility that the user is in the sleep state, and when the ACC characteristics meet the typical sleep state, an ACC verification value is 1; when the ACC feature does not conform to the typical sleep state, the ACC verification value takes 0.
3. The intelligent wearable device data processing method applicable to a sleeping environment according to claim 2, wherein: when the ACC verification value is 1, the logic for carrying out sleep analysis on the user is as follows:
by collecting the light signal characteristics and the sound signal characteristics in the sleeping environment and combining the ACC acceleration data fluctuation and the PPG photoplethysmography data, the sleeping state of the user is monitored according to a CPC cardiopulmonary coupling analysis algorithm.
4. The intelligent wearable device data processing method applicable to a sleeping environment according to claim 2, wherein: when the ACC verification value is taken to be 0, the logic for carrying out sleep analysis on the user is as follows:
and if the ACC verification value is 0, the ACC characteristic does not accord with a typical sleep state, ACC characteristic analysis data is abandoned, the PPG measurement process is subjected to verification analysis, the accuracy and reliability of PPG measurement are improved by verifying the PPG measurement process, and sleep monitoring is performed by combining a CPC cardiopulmonary coupling algorithm by means of the PPG measurement result.
5. The intelligent wearable device data processing method applicable to a sleeping environment according to claim 1, wherein: the method for verifying the single factor stability of the PPG analysis data comprises the following steps:
and comprehensively analyzing a blood volume reflection fluctuation coefficient representing the reliability of the PPG measurement result and an RG light intensity conversion efficiency coefficient representing the accuracy by a linear regression method in combination with the test result of the reliability and the accuracy of the PPG measurement, and evaluating the stability degree of the PPG measurement by calculating a blood volume change rate drift index.
6. The intelligent wearable device data processing method applicable to a sleeping environment according to claim 5, wherein: the analytical method and the processing strategy for evaluating the stability degree of PPG measurement are as follows:
comparing the calculated blood volume variability drift index with a preset blood volume variability drift index threshold, and when the calculated blood volume variability drift index is smaller than or equal to the preset blood volume variability drift index threshold, performing PPG credibility verification, generating a trust signal by the intelligent wearable device, and performing sleep state analysis according to a PPG measurement result and a heart-lung coupling algorithm;
when the calculated blood volume change rate drift index is larger than a preset blood volume change rate drift index threshold, the PPG credibility verification fails, the intelligent wearable device generates a questioning signal, performs early warning prompt on sleep monitoring of a user, uploads analysis data to a server, and performs characteristic analysis and freezing for data backtracking verification.
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