CN117524469A - Sleep data analysis method and device based on intelligent monitoring - Google Patents
Sleep data analysis method and device based on intelligent monitoring Download PDFInfo
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- 238000000034 method Methods 0.000 title claims abstract description 33
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- 230000008667 sleep stage Effects 0.000 claims abstract description 85
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- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000036385 rapid eye movement (rem) sleep Effects 0.000 description 1
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Abstract
A sleep data analysis method and device based on intelligent monitoring belong to the technical field of sleep monitoring, and aim to solve the problems that the existing sleep data analysis system is subjected to individual differences, differences of various data such as reference standards, reference values and the like, so that individual subjective feelings and objective data exist in and out; according to the invention, the heart rate, the respiratory rate, the body movement and the body temperature data of a user are acquired through the data acquisition unit in the wearable device, the respiratory rate, the respiratory mode and the relevant characteristics of the body temperature change of the acceleration signal are extracted through the data preprocessing unit and the characteristic extraction unit, rules in the cloud database are matched, the current sleep stage is determined according to the relevant characteristics through the sleep stage judging module, the sleep evaluation and the data visualization are carried out through the mobile terminal, corresponding improvement measures are provided, and compared with the existing sleep data detection system, a plurality of groups of rules for different people to select are judged, the uniformity between subjective evaluation and objective data is improved, and the intelligent degree is high.
Description
Technical Field
The invention relates to the technical field of sleep monitoring, in particular to a sleep data analysis method and device based on intelligent monitoring.
Background
Sleep data analysis is to collect physiological signal data of a user during sleep (such as a smart bracelet, a smart mattress and the like) and analyze and interpret the data through algorithms and models so as to provide information about sleep quality, sleep stage, sleep problem and the like of the user.
The sleep data analysis is mainly used for evaluating the sleep quality of a human body, and the like, but the difference of various data such as personal difference, reference standard, reference value and the like is easily caused by the sleep, so that the accuracy of the same monitoring device and analysis method to different people can be different, and finally, the subjective feeling and objective data of the individual are caused to come in and go out, and the intelligent degree is not high enough.
To solve the above problems. Therefore, a sleep data analysis method and device based on intelligent monitoring are provided.
Disclosure of Invention
The invention aims to provide a sleep data analysis method and device based on intelligent monitoring, which solve the problems that the existing sleep data analysis system in the background technology is subjected to the differences of various data such as personal differences, reference standards, reference values and the like, and finally leads to the subjective feeling of individuals and the existence of in-out and in-out of objective data.
In order to achieve the above purpose, the present invention provides the following technical solutions: the sleep data analysis method based on intelligent monitoring is characterized by comprising the following steps of:
s1: firstly, data acquisition is carried out through a data acquisition unit in the wearable equipment, wherein the data acquisition unit comprises the steps of acquiring data such as heart rate, respiratory rate, body movement, body temperature and the like;
s2: preprocessing the collected original data by a data preprocessing unit, including noise removal, outlier processing, interpolation and the like, so as to ensure the quality and accuracy of the data;
s3: extracting features related to sleep stages from the preprocessed data by a feature extraction unit, for example, calculating an average value of heart rate, variability index, extracting respiratory frequency and respiratory mode features, analyzing amplitude, frequency and the like of acceleration signals;
s4: through experiments and researches of various crowds, a plurality of groups of rules are designed and a cloud database is established, wherein the plurality of groups of rules comprise thresholds and conditions of different data and are used for judging sleep stages;
s5: selecting a group of applicable rules from a rule database by an algorithm selection unit according to the collected data such as heart rate, respiratory rate, body movement, body temperature and the like;
s6: judging data such as heart rate, respiratory rate, body movement, body temperature and the like according to the selected rule by a sleep stage judging module, and determining the current sleep stage;
s7: the sleep evaluation and the data visualization are carried out through the mobile terminal, the sleep quality and the sleep problems of the user, such as sleep efficiency, sleep interruption times, apnea and the like, are evaluated according to the sleep stage results and other characteristics, corresponding suggestions and improvement measures are provided, and analysis results are displayed to the user in the forms of charts, curves and the like, so that the user can intuitively know the sleep condition and trend change of the user.
Further, communication is carried out between the intelligent wearable device and the mobile terminal through Bluetooth, and the mobile terminal and the cloud database are communicated through 5G.
3. The intelligent monitoring-based sleep data analysis method as set forth in claim 1, wherein: the cloud database is a system or component for storing and managing sleep stage judging rules, is designed and built through experiments and researches of various crowds, comprises a plurality of groups of rules, and each rule comprises data characteristics, threshold values and conditions related to sleep stage judgment and is organized and ordered according to priority.
Further, the data preprocessing unit includes:
and a noise removal module: the method is used for removing noise interference in the data, and is realized through a filter and a noise reduction algorithm so as to improve the accuracy and the reliability of the data;
an outlier processing module: the method is used for detecting and processing abnormal values in data, and the abnormal values are identified and processed by adopting a data smoothing technology;
interpolation module: under the condition that missing values exist in the data, the interpolation module is used for filling the missing values so as to keep the continuity and the integrity of the data;
and a data calibration module: and calibrating the collected original data to ensure the accuracy and consistency of the original data, and adjusting the original data through a calibration algorithm to enable the original data to conform to the expected measurement result.
Further, the feature extraction unit includes;
heart rate feature extraction module: extracting features related to sleep stages from the preprocessed heart rate data, including but not limited to average, maximum, minimum, variability index, heart rate variability of heart rate;
the respiratory feature extraction module: for extracting features related to sleep stages, including but not limited to respiratory rate, respiratory pattern, and respiratory variability, from the preprocessed respiratory data;
body movement feature extraction module: for extracting features related to sleep stages from the preprocessed acceleration data, including but not limited to body movement amplitude, body movement frequency, body movement pattern;
the body temperature characteristic extraction module: for extracting features related to sleep stages, including but not limited to, average values of body temperature, fluctuation range, etc., from the preprocessed body temperature data.
Further, the sleep stage judging module includes:
and a data input module: the device is used for receiving the preprocessed data such as heart rate, respiratory rate, body movement, body temperature and the like and is used as input for judging sleep stages;
rule matching module: the application rule is used for selecting from the cloud database, and matching the input data;
sleep stage judging module: judging sleep stages by using a specific algorithm according to the matched rules and data characteristics;
and a result output module: and outputting the current sleep stage to the mobile terminal according to the result of the sleep stage judging algorithm, wherein the current sleep stage comprises a sleep stage label or a continuous sleep state index.
Further, in the rule matching module, the matching process is implemented by using if-else statements, the basic syntax is as follows:
if condition:
# if the condition is true, execute the code block here
else:
# if the condition is false, execute the code block here
The if-else statement is used for checking whether the input data meets the condition of the rule or not, and executing corresponding operation; the condition is a logic expression, which is used for judging whether a certain condition is true or false, and after a colon behind an if or else, writing a section of code block containing operation needed to be executed when the condition is met.
Further, the sleep stage judging module judges the sleep stage through a decision tree.
Further, the decision tree algorithm comprises the following steps:
and (3) constructing a decision tree: and constructing a decision tree model according to the matched rule and the data characteristic. Nodes of the decision tree represent feature conditions, branches represent feature values, and leaf nodes represent final classification results;
judging a sleep stage: and judging sample data to be judged along branches from the root node according to the constructed decision tree until the leaf node is reached, so as to obtain a final sleep stage.
The invention provides another technical scheme that: the utility model provides an intelligent monitoring device, intelligent wearing equipment is including wearing the undershirt to and set up collection module, processor module, power module and the communication module on wearing the back of the body, wear the undershirt and be used for the user to wear on one's body, collection module includes heart rate sensor, respiratory sensor, acceleration sensor and temperature sensor, processor module is used for carrying out real-time processing and analysis to the data that gathers, power module is used for supplying power for collection module and processor module, communication module is used for establishing bluetooth communication with mobile terminal between.
Compared with the prior art, the invention has the beneficial effects that:
according to the sleep data analysis method and device based on intelligent monitoring, the data of heart rate, respiratory rate, body movement and body temperature of a user are obtained through the data acquisition unit in the wearable device, the respiratory rate, respiratory mode and acceleration signal body temperature change related characteristics are extracted through the data preprocessing unit and the characteristic extraction unit, the current sleep stage is determined according to the related characteristics through the sleep stage judging module by matching rules in the cloud database, finally sleep evaluation and data visualization are performed through the mobile terminal, corresponding improvement measures are provided, and compared with the existing sleep data detection system, a plurality of groups of rules for different people to select are judged, the uniformity between subjective evaluation and objective data is improved, and the intelligent degree is high.
Drawings
FIG. 1 is a schematic diagram of the present invention;
FIG. 2 is a diagram of an analysis method according to the present invention;
FIG. 3 is a block diagram of a data preprocessing unit and a feature extraction unit according to the present invention;
fig. 4 is a block diagram of a sleep stage determination module according to the present invention.
In the figure: 1. an intelligent wearable device; 11. wearing a vest; 12. an acquisition module; 13. a processor module; 14. a power supply module; 15. a communication module; 2. a mobile terminal.
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.
In order to solve the technical problem that the existing sleep data analysis system is subject to the differences of various data such as personal differences, reference standards, reference values and the like, and finally causes the subjective feeling and objective data of the individual to come in and go out, as shown in fig. 1-4, the following preferable technical scheme is provided:
the sleep data analysis method based on intelligent monitoring comprises the following steps:
step one: firstly, data acquisition is carried out through a data acquisition unit in the wearable device 1, wherein the data acquisition comprises acquisition of data such as heart rate, respiratory rate, body movement, body temperature and the like;
step two: preprocessing the collected original data by a data preprocessing unit, including noise removal, outlier processing, interpolation and the like, so as to ensure the quality and accuracy of the data;
step three: extracting features related to sleep stages from the preprocessed data by a feature extraction unit, for example, calculating an average value of heart rate, variability index, extracting respiratory frequency and respiratory mode features, analyzing amplitude, frequency and the like of acceleration signals;
step four: through experiments and researches of various crowds, a plurality of groups of rules are designed and a cloud database is established, wherein the plurality of groups of rules comprise thresholds and conditions of different data and are used for judging sleep stages;
step five: selecting a group of applicable rules from a rule database by an algorithm selection unit according to the collected data such as heart rate, respiratory rate, body movement, body temperature and the like;
step six: judging data such as heart rate, respiratory rate, body movement, body temperature and the like according to the selected rule by a sleep stage judging module, and determining the current sleep stage;
step seven: the mobile terminal 2 is used for sleep evaluation and data visualization, and according to sleep stage results and other characteristics, the sleep quality and sleep problems of a user, such as sleep efficiency, sleep interruption times, apnea and the like, are evaluated, corresponding suggestions and improvement measures are provided, and analysis results are displayed to the user in the form of charts, curves and the like, so that the user can intuitively know sleep conditions and trend changes of the user.
The intelligent wearable device 1 and the mobile terminal 2 communicate through Bluetooth, and the mobile terminal 2 and the cloud database communicate through 5G.
The cloud database is a system or a component for storing and managing sleep stage judging rules, collects and analyzes a large amount of sleep data through experiments and researches of various crowds, and formulates rules according to data characteristics and statistical analysis results, wherein the rules database comprises a plurality of groups of rules, each rule comprises data characteristics, threshold values and conditions related to sleep stage judgment, for example, the rules for acceleration data may comprise amplitude threshold values and frequency threshold values; the rules for heart rate may include range conditions of average heart rate and variability index, and are organized and ranked according to priority, which rules should be applied first when judging sleep stage, priority may be set according to importance of data features and contribution degree to accuracy of sleep stage judgment, during sleep stage judgment, applicable rules are selected from rule database to match according to collected data features, and matching is generally based on comparison of threshold and conditions to determine whether current data meets a certain rule.
The data preprocessing unit includes:
and a noise removal module: the method is used for removing noise interference in the data, and is realized through a filter and a noise reduction algorithm so as to improve the accuracy and the reliability of the data;
an outlier processing module: the method is used for detecting and processing abnormal values in data, and the abnormal values are identified and processed by adopting a data smoothing technology;
interpolation module: under the condition that missing values exist in the data, the interpolation module is used for filling the missing values so as to keep the continuity and the integrity of the data;
and a data calibration module: and calibrating the collected original data to ensure the accuracy and consistency of the original data, and adjusting the original data through a calibration algorithm to enable the original data to conform to the expected measurement result.
The feature extraction unit includes;
heart rate feature extraction module: extracting features related to sleep stages from the preprocessed heart rate data, including but not limited to average, maximum, minimum, variability index, heart rate variability, variability index such as standard deviation, root mean square error, etc. of heart rate;
the respiratory feature extraction module: for extracting features related to sleep stages from the preprocessed breath data, including but not limited to respiratory rate, respiratory pattern, such as expiration time, inspiration time, depth of breath, etc., and respiratory variability;
body movement feature extraction module: for extracting features associated with sleep stages from the preprocessed acceleration data, including but not limited to body movement amplitude, body movement frequency, body movement patterns, such as continuous body movement or intermittent body movement;
the body temperature characteristic extraction module: for extracting features related to sleep stages, including but not limited to, average values of body temperature, fluctuation range, etc., from the preprocessed body temperature data.
The sleep stage judging module comprises:
and a data input module: the device is used for receiving the preprocessed data such as heart rate, respiratory rate, body movement, body temperature and the like and is used as input for judging sleep stages;
rule matching module: the application rule is used for selecting from the cloud database, and matching the input data;
sleep stage judging module: judging sleep stages by using a specific algorithm according to the matched rules and data characteristics;
and a result output module: according to the result of the sleep stage judgment algorithm, the current sleep stage is output to the mobile terminal 2, and the sleep stage comprises a sleep stage label or a continuous sleep state index, wherein the sleep stage label comprises a deep sleep stage, a light sleep stage and a REM sleep stage.
In the rule matching module, the matching process is implemented by using if-else statements, the basic syntax is as follows:
if condition:
# if the condition is true, execute the code block here
else:
# if the condition is false, execute the code block here
The if-else statement is used for checking whether the input data meets the condition of the rule or not, and executing corresponding operation; the condition is a logic expression, which is used for judging whether a certain condition is true or false, and writing a section of code block containing operation needed to be executed when the condition is satisfied after a colon behind an if or else;
for example, when the heart rate exceeds a threshold value, "heart rate abnormality" is output, otherwise "heart rate normal":
heart_rate＝80
threshold＝100
if heart_rate>threshold:
print ('heart rate abnormality')
else:
print ('heart rate is normal')
If-else statements are used to determine whether the heart rate exceeds a threshold. If the heart rate is greater than the threshold value, a heart rate abnormality is output; if the heart rate is not greater than the threshold value, the heart rate is output to be normal, and corresponding operation is executed according to the result, so that a flexible and accurate rule matching process can be realized.
The sleep stage judging module judges the sleep stage through a decision tree, and the decision tree algorithm comprises the following steps:
and (3) constructing a decision tree: and constructing a decision tree model according to the matched rule and the data characteristic. Nodes of the decision tree represent feature conditions, branches represent feature values, and leaf nodes represent final classification results, namely sleep stages;
judging a sleep stage: judging sample data to be judged along branches from a root node according to the constructed decision tree until leaf nodes are reached, so as to obtain a final sleep stage;
for example, the rule matching module selects a rule: if the heart rate is less than 60 and the respiratory rate is less than 10, determining a deep sleep period; otherwise, continuing to match according to other characteristics;
now there is one sample of data, the heart rate after pretreatment is 55, the breathing rate is 8; firstly, a rule matching module applies the rule to the sample data, and finds out that the condition is satisfied; and then judging sample data along branches from the root node according to the constructed decision tree model, and finally obtaining a deep sleep period as a sleep stage judgment result.
The invention provides another technical scheme that: the utility model provides an intelligent monitoring device, intelligent wearing equipment 1 includes wears undershirt 11 to and set up collection module 12, processor module 13, power module 14 and communication module 15 on wearing undershirt 11, wears undershirt 11 and is used for the user to wear on one's body, collection module 12 includes heart rate sensor, respiration sensor, acceleration sensor and temperature sensor, processor module 13 is used for carrying out real-time processing and analysis to the data that gathers, power module 14 is used for supplying power for collection module 12 and processor module 13, communication module 15 is used for establishing bluetooth communication with mobile terminal 2.
Specifically, the heart rate, the respiratory rate, the body movement and the body temperature data of the user are obtained through the data acquisition unit in the wearable device 1, the respiratory rate, the respiratory mode and the relevant characteristics of the acceleration signal body temperature change are extracted through the data preprocessing unit and the characteristic extraction unit, the current sleep stage is determined through the sleep stage judging module according to the relevant characteristics by matching rules in the cloud database, finally the sleep evaluation and the data visualization are carried out through the mobile terminal 2, corresponding improvement measures are provided, and compared with the existing sleep data detection system, a plurality of groups of rules for different people to select are judged, the uniformity between subjective evaluation and objective data is improved, and the intelligent degree is high.
It is noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus.
The foregoing is only a preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any person skilled in the art, who is within the scope of the present invention, should be covered by the protection scope of the present invention by making equivalents and modifications to the technical solution and the inventive concept thereof.
Claims (10)
1. The sleep data analysis method based on intelligent monitoring is characterized by comprising the following steps of:
s1: firstly, data acquisition is carried out through a data acquisition unit in the wearable equipment (1), wherein the data acquisition comprises acquisition of data such as heart rate, respiratory rate, body movement, body temperature and the like;
s2: preprocessing the collected original data by a data preprocessing unit, including noise removal, outlier processing, interpolation and the like, so as to ensure the quality and accuracy of the data;
s3: extracting features related to sleep stages from the preprocessed data by a feature extraction unit, for example, calculating an average value of heart rate, variability index, extracting respiratory frequency and respiratory mode features, analyzing amplitude, frequency and the like of acceleration signals;
s4: through experiments and researches of various crowds, a plurality of groups of rules are designed and a cloud database is established, wherein the plurality of groups of rules comprise thresholds and conditions of different data and are used for judging sleep stages;
s5: selecting a group of applicable rules from a rule database by an algorithm selection unit according to the collected data such as heart rate, respiratory rate, body movement, body temperature and the like;
s6: judging data such as heart rate, respiratory rate, body movement, body temperature and the like according to the selected rule by a sleep stage judging module, and determining the current sleep stage;
s7: the sleep evaluation and data visualization are carried out through the mobile terminal (2), the sleep quality and the sleep problems of the user, such as sleep efficiency, sleep interruption times, apnea and the like, are evaluated according to the sleep stage results and other characteristics, corresponding suggestions and improvement measures are provided, and analysis results are displayed to the user in the forms of charts, curves and the like, so that the user can intuitively know the sleep condition and trend change of the user.
2. The intelligent monitoring-based sleep data analysis method as set forth in claim 1, wherein: the intelligent wearable device is characterized in that communication is carried out between the intelligent wearable device (1) and the mobile terminal (2) through Bluetooth, and the mobile terminal (2) and the cloud database are communicated through 5G.
3. The intelligent monitoring-based sleep data analysis method as set forth in claim 1, wherein: the cloud database is a system or component for storing and managing sleep stage judging rules, is designed and built through experiments and researches of various crowds, comprises a plurality of groups of rules, and each rule comprises data characteristics, threshold values and conditions related to sleep stage judgment and is organized and ordered according to priority.
4. The intelligent monitoring-based sleep data analysis method as set forth in claim 1, wherein: the data preprocessing unit includes:
and a noise removal module: the method is used for removing noise interference in the data, and is realized through a filter and a noise reduction algorithm so as to improve the accuracy and the reliability of the data;
an outlier processing module: the method is used for detecting and processing abnormal values in data, and the abnormal values are identified and processed by adopting a data smoothing technology;
interpolation module: under the condition that missing values exist in the data, the interpolation module is used for filling the missing values so as to keep the continuity and the integrity of the data;
and a data calibration module: and calibrating the collected original data to ensure the accuracy and consistency of the original data, and adjusting the original data through a calibration algorithm to enable the original data to conform to the expected measurement result.
5. The intelligent monitoring-based sleep data analysis method as set forth in claim 1, wherein: the feature extraction unit includes;
heart rate feature extraction module: extracting features related to sleep stages from the preprocessed heart rate data, including but not limited to average, maximum, minimum, variability index, heart rate variability of heart rate;
the respiratory feature extraction module: for extracting features related to sleep stages, including but not limited to respiratory rate, respiratory pattern, and respiratory variability, from the preprocessed respiratory data;
body movement feature extraction module: for extracting features related to sleep stages from the preprocessed acceleration data, including but not limited to body movement amplitude, body movement frequency, body movement pattern;
the body temperature characteristic extraction module: for extracting features related to sleep stages, including but not limited to, average values of body temperature, fluctuation range, etc., from the preprocessed body temperature data.
6. The intelligent monitoring-based sleep data analysis method as set forth in claim 1, wherein: the sleep stage judging module comprises:
and a data input module: the device is used for receiving the preprocessed data such as heart rate, respiratory rate, body movement, body temperature and the like and is used as input for judging sleep stages;
rule matching module: the application rule is used for selecting from the cloud database, and matching the input data;
sleep stage judging module: judging sleep stages by using a specific algorithm according to the matched rules and data characteristics;
and a result output module: and outputting the current sleep stage to the mobile terminal (2) according to the result of the sleep stage judging algorithm, wherein the current sleep stage comprises a sleep stage label or a continuous sleep state index.
7. The intelligent monitoring-based sleep data analysis method as set forth in claim 6, wherein: in the rule matching module, the matching process is implemented by using if-else statements, the basic syntax is as follows:
if condition:
# if the condition is true, execute the code block here
else:
# if the condition is false, execute the code block here
The if-else statement is used for checking whether the input data meets the condition of the rule or not, and executing corresponding operation; the condition is a logic expression, which is used for judging whether a certain condition is true or false, and after a colon behind an if or else, writing a section of code block containing operation needed to be executed when the condition is met.
8. The intelligent monitoring-based sleep data analysis method as set forth in claim 7, wherein: the sleep stage judging module judges the sleep stage through a decision tree.
9. The intelligent monitoring-based sleep data analysis method as set forth in claim 8, wherein: the decision tree algorithm comprises the following steps:
and (3) constructing a decision tree: and constructing a decision tree model according to the matched rule and the data characteristic. Nodes of the decision tree represent feature conditions, branches represent feature values, and leaf nodes represent final classification results;
judging a sleep stage: and judging sample data to be judged along branches from the root node according to the constructed decision tree until the leaf node is reached, so as to obtain a final sleep stage.
10. An intelligent monitoring apparatus as claimed in claim 1, wherein: the intelligent wearing equipment (1) comprises a wearing vest (11), and an acquisition module (12), a processor module (13), a power supply module (14) and a communication module (15) which are arranged on the wearing vest (11), wherein the wearing vest (11) is used for being worn on a user, the acquisition module (12) comprises a heart rate sensor, a respiration sensor, an acceleration sensor and a temperature sensor, the processor module (13) is used for carrying out real-time processing and analysis on acquired data, the power supply module (14) is used for supplying power for the acquisition module (12) and the processor module (13), and the communication module (15) is used for establishing Bluetooth communication with the mobile terminal (2).
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