CN112716449B - Method and system for monitoring human sleep state based on mobile equipment - Google Patents
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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Abstract
The invention discloses a method for monitoring sleep state of human body based on mobile equipment, belonging to the technical field of intelligent detection, comprising the following steps: basic movement information of a first object is collected, and before collection, the detection equipment is restored to an initial state; judging whether to start a sonar system according to the basic movement information of the first object: if the sonar system is started, the sonar system detects an environmental noise variable; and outputting sleep report information in combination with REM sleep according to the basic movement information of the first object and the environmental noise variable. According to the method and the system for monitoring the sleep state of the human body based on the mobile equipment, the sleep state information of the first object is determined based on the human body state information of the first object and the preset standard information, the state of the first object is automatically calculated according to the data, the sleep state information is deduced, only the basic data is acquired through the mobile equipment, other hardware support is not needed, and the problems of high hardware support cost and high use threshold in the prior art are solved.
Description
Technical Field
The invention relates to the technical field of intelligent detection, in particular to a method and a system for monitoring human sleep state based on mobile equipment.
Background
Along with the improvement of living standard, people pay more attention to self health. Among them, sleep quality is a topic of increasing attention. Modern people are influenced by working pressure and entertainment project diversification, and have become bad living habits with the largest range in night sleep and stay up.
In the prior art, people need to resort to medical equipment or medical drugs in order to improve sleep quality. The medical equipment is huge in size, and when in use, an external circuit is required to be attached to a human body, and the medicine is difficult to adhere to the medical equipment for a long time. The percentage of people who are truly willing to resort to medical instruction is small.
Along with the popularization of intelligent mobile equipment, the intelligent mobile equipment is used for acquiring the mobile information of a human body, the sonar system can effectively identify the variable size of environmental noise, and the sleep state can be acquired more actively, simply and conveniently by combining the data of the intelligent mobile equipment and the sleep scientific data.
Disclosure of Invention
The invention aims at the problems and provides a method for monitoring the sleep state of a human body based on mobile equipment.
The invention provides a method for monitoring sleep state of human body based on mobile equipment, which comprises the following steps:
basic movement information of a first object is collected, and before collection, detection equipment is restored to an initialized state;
judging whether to start a sonar system according to the basic movement information of the first object:
If the sonar system is started, the sonar system detects an environmental noise variable;
And outputting sleep report information in combination with REM sleep according to the basic movement information of the first object and the environmental noise variable.
Further, the basic movement information of the first object includes: displacement data;
the displacement data includes: acceleration data, gyroscope data, distance data of a device held by the first object.
Still further, the obtaining of the displacement data:
The detection object moves upwards in the X-axis direction, a group of data (X1, X2, X3 … Xn) is obtained in n seconds or n minutes of movement, the displacement difference in the X-axis direction is a number of columns M (M1= |X1-X2|, M2= |X2-X3| … Mn-1= |Xn-1-Xn|), and the value in the X direction is MAX or AVERGAE (M1, M2 … Mn), and the larger value in the X direction is finally obtained;
meanwhile, corresponding number columns N and number columns P are obtained in the Y-axis direction and the Z-axis direction by using the same calculation method;
The number sequence N (N1= |Y1-Y2|, N2= |Y2-Y3| … N-1= |Yn-1-Yn|), and the value in the Y direction is MAX or AVERGAE (N1, N2 … N), the larger value is given out;
The number of columns P (P1= |Z1-Z2|, P2= |Z2-Z3| … Pn-1= |Zn-1-Zn|), and finally the value in the Z-axis direction is MAX or AVERGAE (M1, M2 … Mn), wherein the larger value is the larger value.
Further, the sonar system is started; the displacement difference of the first object in the previous second and the next second in three directions of the coordinate X-axis, the Y-axis and the Z-axis is less than 0.05m-1m.
Further, when the displacement difference of the first object in the previous second and the next second in three directions of the X axis, the Y axis and the Z axis of the coordinates is more than 0.05m-1m; the sonar system is not activated.
Further, the preset information is performed before the basic movement information of the first object is collected;
The preset information includes: the first object displacement magnitude and the environmental noise variable in which the first object is located.
Still further, the displacement preset size is-15 m/s 2 to 15m/s 2, and the ambient noise preset size is 0db to 90db.
Still further, a system for monitoring a sleep state of a human body by a mobile device includes: a collection device;
the acquisition device comprises: the mobile APP, the hardware sensor, the central processing unit and the output device;
the mobile APP is used for collecting displacement data;
the hardware sensor is used for detecting environmental decibel data;
the central processing unit is used for processing related information transmitted by the mobile APP and the hardware sensor;
the output device is used for outputting the data obtained by the processing of the central processing unit.
The invention has the advantages that:
1. According to the method and the system for monitoring the sleep state of the human body based on the mobile equipment, the sleep state information of the first object is determined based on the human body state information of the first object and the preset standard information, the state of the first object is automatically calculated according to the data, the sleep state information is deduced, only the basic data is acquired through the mobile equipment, other hardware support is not needed, and the problems of high hardware support cost and high use threshold in the prior art are solved.
2. The invention does not need special or expensive information acquisition hardware, mobile equipment such as smart phones, bracelets and other daily equipment, and has no manufacturing cost of a user side.
3. The invention adopts the mobile equipment to obtain the original data and directly carries out modeling training, thereby having low manufacturing cost and greatly improving flexibility, adaptability and popularity.
4. The invention is an attempt made in the field of sleep health, and timely feeds back the sleep track of the user, so as to assist the user to timely adjust sleep habits and ensure sleep health.
5. According to the invention, through measuring data, the sleeping behaviors of the user can be analyzed and evaluated, and the great improvement and continuous tracking in the aspect of functionality are realized.
Drawings
The accompanying drawings, which are included to provide a further understanding of the application and are incorporated in and constitute a part of this specification, illustrate embodiments of the application and together with the description serve to explain the application.
FIG. 1 is a workflow diagram of the present invention;
FIG. 2 is a flow chart of the method for detecting the wakefulness of a subject
FIG. 3 is a flow chart of the present invention for detecting that a subject has been asleep.
Detailed Description
The present invention will be described in further detail with reference to the drawings and examples, in order to make the objects, technical solutions and advantages of the present invention more apparent. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
The term "first" and the like (if any) in the description and claims of the invention and the above figures is used for distinguishing between similar objects and not necessarily for describing a particular sequential or chronological order. It is to be understood that the data so used may be interchanged where appropriate such that the embodiments of the invention described herein may be implemented, for example, in sequences other than those illustrated or otherwise described herein. Furthermore, the terms "comprises," "comprising," and "having," and any variations thereof, are intended to cover a non-exclusive inclusion, such that a process, method, system, article, or apparatus that comprises a list of steps or elements is not necessarily limited to those steps or elements expressly listed but may include other steps or elements not expressly listed or inherent to such process, method, article, or apparatus.
Example 1
Referring to fig. 1 to 3, as shown in fig. 1 to 3, a method for monitoring a sleep state of a human body based on a mobile device includes: basic movement information of a first object is collected, and before collection, the detection equipment is restored to an initial state;
judging whether to start a sonar system according to the basic movement information of the first object:
If the sonar system is started, the sonar system detects an environmental noise variable;
And outputting sleep report information in combination with REM sleep according to the basic movement information of the first object and the environmental noise variable.
It should be noted that REM sleep, i.e. fast eye movement, is also known as out-of-phase sleep (Para-sleep) or also known as fast-phase sleep, out-of-phase sleep or fast wave sleep. Is a sleep stage during which the eye may appear to move rapidly involuntarily. At this stage, the brain neurons are active the same as when awake. Most life-like dreams that can be recalled after waking up occur during REM sleep. It is shallowest among all sleep stages, and a person who wakes up while REM sleeps may be full of vigilance and spirit, unlike in other sleep stages.
In addition, REM sleep usually occurs in the part of the interface with the shallow sleep later in the deep sleep period, so we use different calculation modes according to different data conditions:
When the first subject enters a shorter shallow sleep period after a longer deep sleep period, marking the second half of the deep sleep period as a time length of <50% of the first REM sleep, wherein the single REM sleep time length is not more than 15 minutes;
when the first subject enters a longer shallow sleep period after a shorter deep sleep period, the first half of the shallow sleep period is marked as second REM sleep for <50% of the time, and the length of single REM sleep is no more than 15 minutes.
It should be noted that REM sleep refers to the existing REM analysis system to output a sleep report in this scheme, and the specific process of analyzing data is not described herein, and the analysis principle refers to the description of the prior art.
In one embodiment of the present invention, the basic movement information includes: displacement data;
The displacement data includes: acceleration data, gyroscope data, distance data of a device held by the first object.
In one embodiment of the present invention, the basic movement information of the first object includes: displacement data;
The displacement data includes: acceleration data, gyroscope data, distance data of a device held by the first object.
In one embodiment of the present invention, displacement data is acquired:
Both the displacement data and the sonar system data are acquired at a frequency not lower than every second, and in some special cases, at a millisecond frequency. The mobile device test shows that the displacement difference is not obvious on the premise of accurate second movement speed of the first object, so that training data are respectively measured in units of every n seconds and every n minutes in the model, and the difference value of all detection data in n seconds or n is obtained by taking a MAX function or an AVERAGE function. After repeated pre-verification, the finally obtained modeling training formula is as follows:
The detection object moves upwards in the X-axis direction, a group of data (X1, X2, X3 … Xn) is obtained in n seconds or n minutes of movement, the displacement difference in the X-axis direction is a number of columns M (M1= |X1-X2|, M2= |X2-X3| … Mn-1= |Xn-1-Xn|), and the value in the X direction is MAX or AVERGAE (M1, M2 … Mn), and the larger value in the X direction is finally obtained;
meanwhile, corresponding number columns N and number columns P are obtained in the Y-axis direction and the Z-axis direction by using the same calculation method;
The number sequence N (N1= |Y1-Y2|, N2= |Y2-Y3| … N-1= |Yn-1-Yn|), and the value in the Y direction is MAX or AVERGAE (N1, N2 … N), the larger value is given out;
The number of columns P (P1= |Z1-Z2|, P2= |Z2-Z3| … Pn-1= |Zn-1-Zn|), and finally the value in the Z-axis direction is MAX or AVERGAE (M1, M2 … Mn), wherein the larger value is the larger value.
In one embodiment of the invention, the sonar system is started; the displacement difference of the first object in the previous second and the next second in three directions of the coordinate X-axis, the Y-axis and the Z-axis is less than 0.05m-1m.
In one embodiment of the present invention, when the displacement difference between the first object in the previous second and the next second in three directions of the X axis, the Y axis and the Z axis of the coordinates is greater than 0.05m-1m; the sonar system is not activated.
In one embodiment of the present invention, the preset information is performed before the basic movement information of the first object is collected; the preset information includes: the first object displacement magnitude and the environmental noise variable in which the first object is located.
In one embodiment of the invention, the displacement preset size is-15 m/s 2 to 15m/s 2, and the ambient noise preset size is 0db to 90db.
In one embodiment of the present invention, the method comprises: a collection device;
the acquisition device comprises: the mobile APP, the hardware sensor, the central processing unit and the output device;
the mobile APP is used for collecting displacement data;
the hardware sensor is used for detecting environmental decibel data;
the central processing unit is used for processing related information transmitted by the mobile APP and the hardware sensor;
the output device is used for outputting the data obtained by the processing of the central processing unit.
Example two
A method for monitoring sleep state of human body based on mobile equipment can be as follows:
In this embodiment, the displacement and the environmental noise decibel change data can be collected by the mobile device and the hardware sensor.
S101, starting sleep monitoring through a mobile terminal app, and ensuring that data can be acquired in the running process of equipment.
S102, acquiring displacement data, and acquiring acceleration data, gyroscope data and distance data of equipment held by a first object through detection equipment;
determining basic movement information of the first object according to the displacement, namely X-axis, Y-axis and Z-axis information data during the process of equipment held by the first object;
determining sonar system start time node information according to the basic movement information of the first object;
S103, deducing the human body state of the first object according to the displacement data: whether to fall asleep;
Specifically, according to displacement data, namely X-axis, Y-axis and Z-axis information during the period of equipment held by a first object, calculating displacement differences of the X-axis, the Y-axis and the Z-axis of the mobile equipment to obtain range values of the displacement differences, judging whether the human body state is static or falling asleep or awake or not according to whether the displacement data reaches the standard "
S104, if the first object does not carry mobile equipment, or the human body state is 'still/asleep', acquiring an environmental noise variable by adopting a sonar system; wherein the ambient noise variable can be collected by a device microphone
And according to the environment noise variable, accounting the noise variable difference of the area where the mobile equipment is positioned, and obtaining a range value of the variable difference.
S105, according to the obtained noise variation difference, combining basic models of deep sleep, shallow sleep and wakefulness in the sleeping period to have basic rules, and presetting the sleeping state of the first object to be deep sleep, shallow sleep and wakefulness; and continuously correcting the variable difference in the monitoring process, so that the sleep state of the first object is continuously approaching to the real sleep state.
S106, according to the sleep state of the first object in the last step, combining REM sleep, namely the stage and the trigger of the dream in the sleep process, and graphically marking the sleep state 'dream' of the first object.
And S107, combining the steps, deducing the acquired data of the first object, the environmental noise variable data and REM sleep, drawing a sleep track of the first object in the sleep monitoring process, and presenting the sleep track to the first object.
It should be noted that, in this embodiment, it is ensured that the sleep state of the first subject accords with the basic rule of sleep, accords with the due sleep period, deep sleep and shallow sleep time length ratio of the first subject, and continuously corrects and calculates.
It should be noted that, in order to more accurately detect the sleep state of the first object, the basic preview of the first object may be performed, that is, various states combined with the movement of the human body, including the displacement number reflected by the static, slight movement and huge movement, as a basic data frame, where the larger the data amount of the object, the more accurate the data.
For example, the first subject's environmental noise variance is poor and a preview of the sleep state, and the collected environmental noise variance belongs to one of deep sleep, shallow sleep, or awake in the first subject's personal behavior. In combination with the basic ambient noise, a threshold for deep sleep, shallow sleep is set, and the calculation is corrected in each complete monitoring of the first subject.
The specific process comprises the following steps:
The first step is to divide the test objects into N groups, and each group of test objects adopts mobile equipment to collect complete data for more than 24 hours and match with personal records of the test objects. And obtaining different displacement variables of the test object in the processes of walking, running, sleeping and daily use of the mobile phone, and dividing a rough living interval.
And a second step of: according to the rough life interval marks, the N groups of test objects mark the accuracy correction data.
And a third step of: and (3) issuing the correction data model on the line, and repeatedly verifying the model for a plurality of times by combining the feedback of the real object.
Fourth step: in combination with scientific sleep data, the deep sleep ratio was about 25%, the light sleep was about 55%, and the data during sleep was corrected.
In this example, the sleep track is recorded graphically, including a histogram, a waveform, etc.
Continuous monitoring of the first subject may form valid historical data that is analyzed to form sleep quality improvement programs, but is not limited to helping fall asleep, maintaining regular sleep, giving up behavioral factors or other factors that affect sleep, etc.
It should be noted that, as one of ordinary skill in the art can understand: all or part of the steps for implementing the method embodiments described above may be performed by hardware associated with program instructions. The foregoing program may be stored in a computer readable storage medium. The program, when executed, performs steps including the method embodiments described above; and the aforementioned storage medium includes: various media that can store program code, such as ROM, RAM, magnetic or optical disks.
The foregoing description of the preferred embodiments of the invention is not intended to limit the invention to the precise form disclosed, and any such modifications, equivalents, and alternatives falling within the spirit and scope of the invention are intended to be included within the scope of the invention.
Claims (5)
1. A method for monitoring sleep states of a human body based on a mobile device, the method comprising:
basic movement information of a first object is collected, and before collection, the detection equipment is restored to an initial state; wherein the basic movement information of the first object includes: displacement data;
the displacement data includes: acceleration data, gyroscope data, and distance data of a device held by the first object;
and acquiring the displacement data:
the detection object moves upwards in the X-axis direction, a group of data X1, X2 and X3 … Xn are obtained in n seconds or n minutes of movement, the displacement difference in the X-axis direction is a number of columns M, M1= |X 1-X2|, M2= |X 2-X3| … Mn-1= |Xn-1-Xn|, and finally the value in the X direction is MAX or AVERGAE (M1, M2 … Mn), and the larger value in the X direction is obtained;
meanwhile, corresponding number columns N and number columns P are obtained in the Y-axis direction and the Z-axis direction by using the same calculation method;
the number sequence N, N1 = |Y 1-Y2|, N2 = |Y 2-Y3| … N-1 = |Yn-1-Yn| and the value in the final Y direction is MAX or AVERGAE (N1, N2 … N), the larger one of the two;
the number of the rows P, P1= |Z 1-Z2|, P2= |Z 2-Z3| … Pn-1= |Zn-1-Zn| and finally the value in the Z-axis direction is MAX or AVERGAE (M1, M2 … Mn), the larger one of the two;
judging whether to start a sonar system according to the basic movement information of the first object:
If the sonar system is started, the sonar system detects an environmental noise variable;
The sonar system is started, and the displacement difference between the first object in the previous second and the next second in three directions of the coordinate X axis, the Y axis and the Z axis is smaller than 0.05m;
deducing the human body state of the first object according to the displacement data: whether to fall asleep;
specifically, according to displacement data, namely X-axis, Y-axis and Z-axis information during the period of equipment held by a first object, calculating displacement differences of the X-axis, Y-axis and Z-axis of the mobile equipment to obtain range values of the displacement differences, and judging whether the human body state is static or falling asleep or awake or not according to whether the displacement data reaches the standard;
If the first object does not carry mobile equipment or the human body state is 'still/asleep', acquiring an environmental noise variable by adopting a sonar system; wherein the ambient noise variable may be collected by the device microphone;
According to the environment noise variable, accounting the noise variable difference of the area where the mobile equipment is located, and obtaining a range value of the variable difference;
according to the obtained noise variation difference, combining basic models of deep sleep, shallow sleep and wakefulness in the sleeping period, presetting the sleeping state of a first object as deep sleep, shallow sleep and wakefulness; continuously correcting the variable difference in the monitoring process, so that the sleep state of the first object is continuously approaching to the real sleep state;
According to the sleep state of the first object in the last step, combining REM sleep, namely the stage of dream in the sleep process, graphically marking the sleep state of the first object as 'dream';
By combining the steps, the acquired data of the first object, the environmental noise variable data and REM sleep are deduced, and a sleep track of the first object in the sleep monitoring process is drawn;
outputting sleep report information in combination with REM sleep according to the basic movement information of the first object and the environmental noise variable;
The sleeping state of the first object can be accurately improved through the prediction of the environmental noise variable difference and the sleeping state of the first object;
In order to ensure that the sleep state of the first object accords with the basic rule of sleep, accords with the due sleep period, deep sleep and shallow sleep time length ratio of the first object, the basic previewing of the first object can be carried out, namely, the various states of human body movement are combined, the displacement number reflected by static, slight movement and huge movement is included as a basic data frame, and the real detection data of the first object are continuously corrected and calculated through the previewing data, so that the sleep state area of the first object accords with the basic rule most;
The previewing process comprises the following steps:
dividing the test objects into N groups, wherein each group of test objects adopts mobile equipment to perform complete data acquisition for more than 24 hours, and the complete data acquisition is matched with personal records of the test objects to obtain different displacement variables of the test objects in the processes of walking, running, sleeping and daily use of the mobile phone, so as to divide a rough living interval;
Secondly, marking accuracy correction data by N groups of test objects according to rough life interval marks;
Thirdly, the corrected data model is issued to the line, and the model is repeatedly verified for many times by combining the feedback of the real object;
fourth, in combination with scientific sleep data, the deep sleep ratio is about 25%, the light sleep is about 55%, and the data during sleep is corrected.
2. The method for monitoring sleep states of a human body based on a mobile device according to claim 1, wherein when the displacement difference of the first object in the previous second and the next second in three directions of the coordinate X axis, the Y axis and the Z axis is greater than 0.05m; the sonar system is not activated.
3. The method for monitoring sleep states of a human body based on a mobile device according to claim 2, wherein the preset information is performed before the basic movement information of the first object is collected;
The preset information includes: the first object displacement magnitude and the environmental noise variable in which the first object is located.
4. The method for monitoring sleep states of a human body based on a mobile device according to claim 3, wherein the first subject displacement is-15 m/s 2 to 15m/s 2, and the first subject is positioned at an environmental noise variable size of 0db-90db.
5. A system for monitoring human sleep states based on a mobile device, wherein the method for monitoring human sleep states based on a mobile device according to any one of claims 1-4 is used for human sleep state detection, the system comprising: a collection device;
the acquisition device comprises: the mobile APP, the hardware sensor, the central processing unit and the output device;
the mobile APP is used for collecting displacement data;
the hardware sensor is used for detecting environmental decibel data;
the central processing unit is used for processing related information transmitted by the mobile APP and the hardware sensor;
the output device is used for outputting the data obtained by the processing of the central processing unit.
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