CN107348943B - Wearable, portable and alarm human sleep quality monitoring system and method - Google Patents
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Abstract
A wearable, portable and alarming human sleep quality monitoring system and method adopts a sleep quality detector and comprises an acceleration sensor, an oxyhemoglobin saturation probe, an analog-to-digital conversion module, a microcontroller and a wireless communication module, wherein the acceleration sensor is used for collecting body movement data of a human body, the oxyhemoglobin saturation probe is used for collecting oxyhemoglobin saturation data and pulse data of the human body, signal output ends of the oxyhemoglobin saturation probe are connected with input ends of the analog-to-digital conversion module, signal output ends of the analog-to-digital conversion module are connected with the microcontroller, and the microcontroller analyzes and processes the collected data so as to judge whether an abnormal alarm is triggered. The sleep quality monitor has the characteristics of no wound, portability, simple operation and abnormal alarm. The adoption of multiple conductive electrodes is avoided, so that the psychological burden of a user and the influence on the sleep quality are reduced, and the using steps of the instrument are simplified.
Description
Technical Field
The invention relates to the field of medical/household medical equipment, in particular to a sleep quality monitoring system and a sleep quality monitoring method.
Background
At present, for monitoring human sleep, electrodes distributed on various parts of a human body are mainly used for acquiring relevant information of the human body, analyzing and processing the information or acquiring a single variable, and processing and analyzing data of the single variable. However, the distributed multi-conductive electrodes have great influence on the sleep of people, and the result obtained by a single variable has large error.
At present, research on sleep stages shows that the coincidence degree of the body movement data and the pulse data with the sleep stages of human beings is higher, the coincidence degree of the falling edge of the blood oxygen saturation with the REM stage in the sleep stages is higher, and more accurate sleep stage data can be obtained by integrating the data of the three aspects.
Disclosure of Invention
The invention aims to overcome the current situation that multiple conductive electrodes and single variables are used in the current sleep monitoring, and provides a sleep monitoring device, which avoids using multiple conductive electrodes, uses a small amount of sensors to collect more data and provides more accurate sleep monitoring information. The invention integrates the data of the three aspects of physical activity, the blood oxygen saturation and the pulse, analyzes the sleep data of the user all night more accurately, and alarms and reminds abnormal pulse data. The invention has good wearability and portability, and the simple using mode can be used for clinic and is more beneficial to household medical treatment.
The technical scheme of the invention is as follows:
a wearable, portable and alarm human sleep quality monitoring system adopts a sleep quality detector, the sleep quality monitor comprises an acceleration sensor, an oxyhemoglobin saturation probe, an analog-to-digital conversion module, a microcontroller and a wireless communication module, the acceleration sensor is used for collecting body movement data of a human body, the oxyhemoglobin saturation probe is used for collecting oxyhemoglobin saturation data and pulse data of the human body, signal output ends of the acceleration sensor and the oxyhemoglobin saturation probe are connected with an input end of the analog-to-digital conversion module, a signal output end of the analog-to-digital conversion module is connected with the microcontroller, and the microcontroller analyzes and processes the collected data so as to judge whether an abnormal alarm is triggered; and the signal output end of the wireless communication module is used as the data output of the sleep quality detector to communicate with the post-stage equipment.
Furthermore, the oxyhemoglobin saturation probe adopts a fingertip wound oxyhemoglobin saturation probe; the acceleration sensor is integrated on the wrist strap together with the microcontroller and is worn on the wrist of the user.
Further, the abnormity alarm comprises a buzzer and a vibrator, and is triggered when the heart rate is smaller than a preset value, namely a low value, namely preferably 50 times per minute, or larger than a preset value, namely a high value, namely preferably 120 times per minute.
A wearable, portable and alarming human sleep quality monitoring method comprises the following steps:
s1, acquiring body movement data of a human body by adopting an acceleration sensor and sending the body movement data to a microcontroller, and acquiring blood oxygen saturation data and pulse data of the human body by adopting a blood oxygen saturation probe and sending the blood oxygen saturation data and the pulse data to the microcontroller;
s2, setting six threshold regions for body movement data, blood oxygen saturation data and pulse data in the microcontroller respectively, wherein the six threshold regions correspond to six periods of human sleep states, namely an Awake period, a REM period and NREM 1-NREM 4 periods;
s3, converting the three types of data into sleep states according to the threshold divided by the S2;
and S4, comprehensively processing the body movement data, the blood oxygen saturation data and the pulse data acquired in S1 by adopting a naive Bayes-sliding window method to obtain the sleep quality data of the human body.
Further, in step S2, the threshold value division rule of the pulse and body motion data is: taking 30 seconds as a time interval, taking data in every 30 seconds as one page, and taking the data as six periods corresponding to the sleep state of the human, namely an Awake period, a REM period and NREM 1-NREM 4 periods, wherein the preset values of the body movement data are respectively 15 times/page, 30 times/page, 100 times/page, 125 times/page, 150 times/page and 200 times/page according to statistical data; the preset values of the pulse data are respectively 60 times/minute/page, 70 times/minute/page, 85 times/minute/page, 90 times/minute/page, 105 times/minute/page and 115 times/minute/page; quantifying the data of each page according to the threshold value to sleep stages as follows: awake 6, REM 5, NREM1 4, NREM2 3, NREM3 2, and NREM4 1.
Further, in step S2, the threshold value division rule of the blood oxygen saturation is: the position of REM is determined by finding the falling edge of the blood oxygen saturation, and the specific steps are as follows: according to the periodicity of human sleep, namely, the period from REM period to NREM 1-NREM 4, the period from NREM 4-NREM 1 and finally the period back to REM period, the data in two adjacent REM periods are quantitatively divided according to the statistical data according to the following ratio, and the quantitative sleep stage of the data outside the REM period is defined as 5:
stage NREM 1: the ratio is 5%; sleep stage 4;
stage NREM 2: the ratio is 25%; sleep stage 3;
stage NREM 3: the ratio is 10%; sleep stage 2;
stage NREM 4: the ratio is 10%; sleep stage 1;
stage NREM 4: the ratio is 10%; sleep stage 1;
stage NREM 3: the ratio is 10%; sleep stage 2;
stage NREM 2: the ratio is 25%; sleep stage 3;
stage NREM 1: the ratio is 5%; sleep stage 4.
Further, in step S4, the naive bayes-sliding window method comprises:
the method comprises the following steps: taking n pages of Data as a group, establishing a Data matrix Data and a sleep staging matrix Stage as shown in the specification;
wherein a isi，bi，ciI is a positive integer from 1 to n and respectively represents the body motion data, the blood oxygen saturation data and the pulse data of each page; a. thei，Bi，CiThe positive integers of i-1-n are respectively expressed as the body movement data, the blood oxygen saturation data and the sleep stage after the pulse data are quantized;
step two: calculating according to the Data matrix and the Stage matrix in the step one
Matrix sum
As follows:
step three: obtaining a relationship according to a calculation formula of the correlation coefficient:
calculating a correlation coefficient of each type of data and sleep stages, and acquiring a correlation coefficient matrix r:
wherein r is a 3 x 1 column vector, and the three elements are respectively body motion data, blood oxygen saturation data and correlation coefficients of pulse data and sleep stages; diag is a function of taking out diagonal elements of a matrix from MATLAB to form a column vector; sqrt is a function in the MATLAB for squaring each element in the matrix; the operational symbols/and represent the division and multiplication of corresponding elements in two matrices of the same size, respectively;
step four: selecting the sleep stage corresponding to the largest data from the three correlation coefficients according to a naive Bayes classification method as the sleep stage of the first page of data of the current window; if the correlation coefficients of two or more types of data are the same, the data of the time is abandoned, and the data of the previous page is used as the calculation result of the time;
step five: the window is moved one page back and the same calculation is done until the end of the data is reached.
The invention has the beneficial effects that:
the sleep quality monitor has the characteristics of no wound, portability, simple operation and abnormal alarm. The adoption of multiple conductive electrodes is avoided, so that the psychological burden of a user and the influence on the sleep quality are reduced, and the using steps of the instrument are simplified. The blood oxygen saturation probe adopts a fingertip winding type, so that falling off in the using process is avoided. The invention uses as few sensors as possible, collects enough data and provides more accurate sleep data for users.
The invention has the function of abnormal alarm, and is triggered when the heart rate is far beyond the normal range, wherein the normal range is set to be 50-120 times/min. Audible and tactile alerts are provided by a buzzer and vibrator.
The mobile application platform provides sleep data monitoring application software for a user, displays the processed data through the application software of the mobile terminal, and provides a good human-computer interaction interface. The sleep stage of each time point on the day can be consulted on application software, and the sleep state of a week or a month in the near future can be consulted at the same time, so that data support is provided for the user to adjust the life state of the user.
Drawings
Fig. 1 is a schematic block diagram of the present invention.
Detailed Description
The invention is further described below with reference to the figures and examples.
As shown in fig. 1, a wearable, portable and alarmable human sleep quality monitoring system adopts a sleep quality detector, the sleep quality monitor comprises an acceleration sensor, an oxyhemoglobin saturation probe, an analog-to-digital conversion module, a microcontroller and a wireless communication module, the acceleration sensor is used for collecting body movement data of a human body, the oxyhemoglobin saturation probe is used for collecting oxyhemoglobin saturation data and pulse data of the human body, signal output ends of the acceleration sensor and the oxyhemoglobin saturation probe are both connected with an input end of the analog-to-digital conversion module, a signal output end of the analog-to-digital conversion module is connected with the microcontroller, and the microcontroller analyzes and processes the collected data so as to judge whether an abnormal alarm is triggered; and the signal output end of the wireless communication module is used as the data output of the sleep quality detector to communicate with the post-stage equipment.
The oxyhemoglobin saturation probe adopts a fingertip wound oxyhemoglobin saturation probe; the acceleration sensor is integrated on the wrist strap together with the microcontroller and is worn on the wrist of the user.
The abnormity alarm comprises a buzzer and a vibrator, and is triggered when the heart rate is smaller than a preset value, namely a low value and a high value, or is larger than the preset value, namely the low value is preferably 50 times/minute, and the high value is preferably 120 times/minute.
In the specific implementation:
a wearable, portable and alarming human sleep quality monitoring method comprises the following steps:
s1, acquiring body movement data of a human body by adopting an acceleration sensor and sending the body movement data to a microcontroller, and acquiring blood oxygen saturation data and pulse data of the human body by adopting a blood oxygen saturation probe and sending the blood oxygen saturation data and the pulse data to the microcontroller;
s2, setting six threshold regions for body movement data, blood oxygen saturation data and pulse data in the microcontroller respectively, wherein the six threshold regions correspond to six periods of human sleep states, namely an Awake period, a REM period and NREM 1-NREM 4 periods;
the threshold value division rule of the pulse and body motion data is as follows: taking 30 seconds as a time interval, taking data in every 30 seconds as one page, and taking the data as six periods corresponding to the sleep state of the human, namely an Awake period, a REM period and NREM 1-NREM 4 periods, wherein the preset values of the body movement data are respectively 15 times/page, 30 times/page, 100 times/page, 125 times/page, 150 times/page and 200 times/page according to statistical data; the preset values of the pulse data are respectively 60 times/minute/page, 70 times/minute/page, 85 times/minute/page, 90 times/minute/page, 105 times/minute/page and 115 times/minute/page; quantifying the data of each page according to the threshold value to sleep stages as follows: awake 6, REM 5, NREM1 4, NREM2 3, NREM3 2, and NREM4 1.
The threshold partitioning rule for blood oxygen saturation is: (since there is a good correspondence between the falling edge of the blood oxygen saturation level and the REM period of the sleep stage), the position of the REM is determined by finding the falling edge of the blood oxygen saturation level, and the specific steps are as follows: according to the periodicity of human sleep, the period from REM period to NREM 1-4 period, then from NREM 4-1 period and finally back to REM period. Therefore, the data in two adjacent REM periods are divided according to the statistical data in proportion, and the quantified sleep stages are shown in table 1, and the quantified sleep stages of the data outside the REM periods are defined as 5.
TABLE 1 blood oxygen saturation
| REM | NREM1 | NREM2 | NREM3 | NREM4 | NREM4 | NREM3 | NREM2 | NREM1 | REM |
| Ratio of | 5％ | 25％ | 10％ | 10％ | 10％ | 10％ | 25％ | 5％ | |
| Staging of sleep | 4 | 3 | 2 | 1 | 1 | 2 | 3 | 4 |
S3, converting the three types of data into sleep states according to the threshold divided by the S2;
s4, comprehensively processing the body movement data, the blood oxygen saturation data and the pulse data acquired in the S1 by adopting a naive Bayes-sliding window method to obtain human sleep quality data;
wherein, the window size of the naive Bayes-sliding window method is 16 pages, and the basis is that the NREM4 period time in the human sleep cycle is the shortest and only accounts for about 10 percent; the normal sleeping time of one person is 8 hours, the sleeping period is 4-6 periods,
thus the minimum window is: size 8 x 3600s/6 x 0.1/30 s/page 16 page
The naive Bayes-sliding window method comprises the following specific steps:
the method comprises the following steps: taking 16 pages of Data as a group, establishing a Data matrix Data and a sleep staging matrix Stage as shown in the specification;
wherein a isi，bi，ciI is a positive integer of 1 to 16,respectively representing the body movement data, the blood oxygen saturation data and the pulse data of each page; a. thei，Bi，CiThe i is a positive integer of 1-16 and is respectively expressed as the body movement data, the blood oxygen saturation data and the sleep stage after the pulse data are quantized;
step two: calculating according to the Data matrix and the Stage matrix in the step one
Matrix sum
As follows:
step three: according to a calculation formula of the correlation coefficient:
and calculating the correlation coefficient of each type of data and the sleep stage, and designing a calculation formula of a correlation coefficient matrix r as follows:
wherein r is a 3 x 1 column vector, and the three elements are respectively body motion data, blood oxygen saturation data and correlation coefficients of pulse data and sleep stages; diag is a function of taking out diagonal elements of a matrix from MATLAB to form a column vector; sqrt is a function in the MATLAB for squaring each element in the matrix; the operational symbols/and represent the division and multiplication of corresponding elements in two matrices of the same size, respectively;
step four: selecting the sleep stage corresponding to the largest data from the three correlation coefficients according to a naive Bayes classification method as the sleep stage of the first page of data of the current window; if the correlation coefficients of two or more types of data are the same, the data of the time is abandoned, and the data of the previous page is used as the calculation result of the time;
step five: the window is moved one page back and the same calculation is done until the end of the data is reached. In this way, the influence caused by abnormal data is reduced, and the accuracy is improved.
The parts not involved in the present invention are the same as or can be implemented using the prior art.
Claims (9)
1. A wearable, portable and alarming human sleep quality monitoring method is characterized by comprising the following steps:
s1, acquiring body movement data of a human body by adopting an acceleration sensor and sending the body movement data to a microcontroller, and acquiring blood oxygen saturation data and pulse data of the human body by adopting a blood oxygen saturation probe and sending the blood oxygen saturation data and the pulse data to the microcontroller;
s2, setting six threshold regions for body movement data, blood oxygen saturation data and pulse data in the microcontroller respectively, wherein the six threshold regions correspond to six periods of human sleep states, namely an Awake period, a REM period and NREM 1-NREM 4 periods;
s3, converting the three types of data into sleep states according to the threshold divided by the S2;
s4, comprehensively processing the body movement data, the blood oxygen saturation data and the pulse data acquired in the S1 by adopting a naive Bayes-sliding window method to obtain human sleep quality data;
in step S4, the naive bayes-sliding window method comprises:
the method comprises the following steps: taking 30 seconds as a time interval, taking Data in every 30 seconds as one page, taking n pages of Data as a group, and establishing a Data matrix Data and a sleep Stage matrix Stage as shown in the specification;
wherein the content of the first and second substances,
the positive integers respectively represent the body movement data, the blood oxygen saturation data and the pulse data of each page;
the positive integers are respectively expressed as the sleep stage after the body movement data, the blood oxygen saturation data and the pulse data of each page are quantized;
step two: calculating according to the Data matrix and the Stage matrix in the step one
Matrix sum
As follows:
step three: calculating the correlation coefficient of each kind of data and sleep stage to obtain correlation coefficient matrix
：
Wherein the content of the first and second substances,
the three elements are respectively body movement data, blood oxygen saturation data and correlation coefficients of pulse data and sleep stages; diag is a function of taking out diagonal elements of matrix to form a column vector; sqrt is a function in the MATLAB for squaring each element in the matrix; the operational symbols/and represent the division and multiplication of corresponding elements in two matrices of the same size, respectively;
step four: selecting the sleep stage corresponding to the maximum data from the three correlation coefficients as the sleep stage of the current page data; if the correlation coefficients of more than two types of data are the same, abandoning the data, and taking the data as the calculation result of the time;
step five: the window is moved one page back and the same calculation is done until the end of the data is reached.
2. The method for monitoring sleep quality of human body according to claim 1, wherein in step S2, the threshold classification rule of pulse and body movement data is as follows: taking 30 seconds as a time interval, taking data in every 30 seconds as one page, taking the data as six periods corresponding to the sleep state of the human, namely an Awake period, a REM period and NREM 1-NREM 4 periods, and respectively setting preset values of the body movement data as 15 times per page, 30 times per page, 100 times per page, 125 times per page, 150 times per page and 200 times per page according to statistical data; the preset values of the pulse data are respectively 60 times/minute/page, 70 times/minute/page, 85 times/minute/page, 90 times/minute/page, 105 times/minute/page and 115 times/minute/page; quantifying the data of each page according to the threshold value to sleep stages as follows: awake =6, REM =5, NREM1=4, NREM2=3, NREM3=2, NREM4= 1.
3. The method for monitoring sleep quality of human body with functions of wearable, portable and alarming as claimed in claim 1, wherein in step S2, the threshold value of blood oxygen saturation is divided into: the position of REM is determined by finding the falling edge of the blood oxygen saturation, and the specific steps are as follows: according to the periodicity of human sleep, namely, entering stages NREM 1-NREM 4 from REM stage, then returning to REM stage from NREM 4-NREM 1, quantitatively dividing data in two adjacent REM stages according to statistical data according to the following ratio, and defining the quantitative sleep stage of the data in REM stage as 5:
stage NREM 1: the ratio is 5%; sleep stage 4;
stage NREM 2: the ratio is 25%; sleep stage 3;
stage NREM 3: the ratio is 10%; sleep stage 2;
stage NREM 4: the ratio is 10%; sleep stage 1;
stage NREM 4: the ratio is 10%; sleep stage 1;
stage NREM 3: the ratio is 10%; sleep stage 2;
stage NREM 2: the ratio is 25%; sleep stage 3;
stage NREM 1: the ratio is 5%; sleep stage 4.
4. The method for monitoring sleep quality of human body according to claim 2, wherein the naive Bayes-sliding window method in step S4 has a window size of 16 pages.
5. A system adopted by the wearable, portable and alarmable human sleep quality monitoring method of claim 1 is characterized in that a sleep quality detector is adopted, the sleep quality monitor comprises an acceleration sensor, an oxyhemoglobin saturation probe, an analog-to-digital conversion module, a microcontroller and a wireless communication module, the acceleration sensor is used for collecting body movement data of a human body, the oxyhemoglobin saturation probe is used for collecting oxyhemoglobin saturation data and pulse data of the human body, signal output ends of the acceleration sensor and the oxyhemoglobin saturation probe are connected with an input end of the analog-to-digital conversion module, a signal output end of the analog-to-digital conversion module is connected with the microcontroller, and the microcontroller analyzes and processes the collected data so as to judge whether an abnormal alarm is triggered; and the signal output end of the wireless communication module is used as the data output of the sleep quality detector to communicate with the post-stage equipment.
6. The system of claim 5, wherein said oximetry probe is a finger tip wrap oximetry probe.
7. The system of claim 5, wherein the acceleration sensor is integrated with the microcontroller in a wrist band worn on the wrist of the user.
8. The system of claim 5, wherein the abnormality alarm includes a buzzer and a vibrator, and is triggered when the heart rate is lower than a preset value one or higher than a preset value two.
9. The system of claim 8, wherein the low value of the first predetermined value is 50 times/minute and the high value of the second predetermined value is 120 times/minute.
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| CN108671359A (en) * | 2018-03-26 | 2018-10-19 | 亘冠智能技术（杭州）有限公司 | Sleep householder method |
| CN109567749B (en) * | 2018-11-09 | 2022-04-15 | 速眠创新科技（深圳）有限公司 | Sleep data processing method and device, computer equipment and storage medium |
| CN113491517A (en) * | 2020-03-19 | 2021-10-12 | 北京优理医疗器械有限公司 | Blood oxygen saturation monitoring system and application thereof |
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| CN102068261A (en) * | 2011-01-21 | 2011-05-25 | 上海弘周电子科技有限公司 | Safety monitor |
| CN203828910U (en) * | 2013-12-23 | 2014-09-17 | 上海华博信息服务有限公司 | Sleep cabin capable of monitoring human body signs in real time |
| CN205379309U (en) * | 2015-12-09 | 2016-07-13 | 深圳市新元素医疗技术开发有限公司 | Sleep guardianship device |
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2017
- 2017-07-03 CN CN201710530693.9A patent/CN107348943B/en active Active
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