CN113974567B - Method for calculating metabolic rate in sleeping process - Google Patents
Method for calculating metabolic rate in sleeping process Download PDFInfo
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- CN113974567B CN113974567B CN202111321446.0A CN202111321446A CN113974567B CN 113974567 B CN113974567 B CN 113974567B CN 202111321446 A CN202111321446 A CN 202111321446A CN 113974567 B CN113974567 B CN 113974567B
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- 238000000034 method Methods 0.000 title claims abstract description 63
- 230000037323 metabolic rate Effects 0.000 title claims abstract description 41
- 230000008569 process Effects 0.000 title claims abstract description 20
- 230000002159 abnormal effect Effects 0.000 claims abstract description 53
- 238000005259 measurement Methods 0.000 claims abstract description 44
- 239000013585 weight reducing agent Substances 0.000 claims abstract description 10
- 230000037396 body weight Effects 0.000 claims description 35
- 230000002503 metabolic effect Effects 0.000 abstract description 6
- 230000009467 reduction Effects 0.000 abstract description 4
- 230000004580 weight loss Effects 0.000 abstract 1
- 230000008859 change Effects 0.000 description 12
- 238000005303 weighing Methods 0.000 description 8
- 230000004060 metabolic process Effects 0.000 description 7
- 208000022249 Sleep-Wake Transition disease Diseases 0.000 description 3
- 230000000694 effects Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 208000031648 Body Weight Changes Diseases 0.000 description 1
- 230000003321 amplification Effects 0.000 description 1
- 230000004579 body weight change Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 230000035622 drinking Effects 0.000 description 1
- 239000003651 drinking water Substances 0.000 description 1
- 235000020188 drinking water Nutrition 0.000 description 1
- 230000007774 longterm Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 238000003199 nucleic acid amplification method Methods 0.000 description 1
- 230000004622 sleep time Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4866—Evaluating metabolism
Abstract
The invention relates to a method for calculating the metabolic rate of a sleeping process, which comprises measuring the weight of a bed once every first preset time, identifying a steady state according to the measured value, identifying the starting time and the ending time of sleeping according to the steady state, identifying an abnormal event and calculating the weight difference value generated during the abnormal event; obtaining an absolute weight reduction value during sleep according to the measured values of the pressure sensors at the beginning time and the ending time of sleep and the weight difference value generated by the abnormal event; sleep metabolic rate was calculated using the value of absolute weight loss during sleep. In the invention, the metabolic quantity is calculated by measuring the absolute reduction of the weight of the person in the period of sleeping in the bed and the weight reduction in the sleeping process, and the metabolic condition of the person can be reflected more truly because the weight measurement is more direct than the heat measurement; by introducing abnormal events, various interference factors affecting the weight in the sleeping process can be eliminated, and the weight measurement is more accurate.
Description
Technical Field
The invention belongs to the technical field of sleep process metabolic rate monitoring, and relates to a method for calculating the metabolic rate of a sleep process.
Background
The metabolic rate represents the energy consumed by the human body per unit time. In the prior art, the metabolic rate is generally calculated by using the metabolism of heat, but the monitoring of the heat is difficult, and the heat can be converted only by other indexes, so that a large error exists in a calculation result.
Because people consume energy at all times, under the condition of not drinking water and not eating food, the weight is gradually reduced, so the metabolic rate can be calculated through the change of the weight, but the change value of the weight is very small, the traditional weight measurement means cannot accurately monitor the reduced value, and the metabolic rate can also generate larger fluctuation due to the movement quantity of each period of time in the process of activities. However, the metabolism rate of a person is basically stable during sleep, and if the body weight during sleep can be monitored with high accuracy, various interference factors affecting the body weight during sleep are eliminated, and the absolute reduction of the body weight during the period of sleeping of the person in a bed is measured, the metabolism rate can be calculated through the reduction of the body weight during sleep.
Disclosure of Invention
In view of the above, an object of the present invention is to provide a method for calculating a metabolic rate during sleep.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a method for calculating metabolic rate of a sleep process, comprising the steps of:
s1, every interval of a first preset time T 0 Measuring the weight of the primary bed;
s2, defining the length as uT 0 The time period of the time frame is a long time window, whether the long time window taking the current measurement time as the end time is in a stable state is judged according to the measured value of the weight in the long time window, and if the long time window is in the stable state, the current state is judged; recognizing the starting time and the ending time of sleep according to the steady state, recognizing an abnormal event and calculating a weight difference value generated during the abnormal event;
s3, obtaining an absolute weight reduction value D in the sleeping period according to the measured values of the pressure sensor at the sleeping starting time and the sleeping ending time and the weight difference value generated by the abnormal event;
s4, calculating the sleep metabolic rate by using the absolute weight reduction value during the sleep period.
Further, in the step S1, a plurality of pressure sensors are disposed under the bed, each pressure sensor being spaced apart by a first preset time T 0 Measuring the weight of the primary bed; defining total weight S as the sum of the measurements of the pressure sensors, defining empty bed weight B as the sum of the measurements of the pressure sensors when the bed is empty, defining instantaneous weight w=s-B;
in the step S2, the method for identifying the sleep start time is as follows: judging whether a loading event occurs according to the measured total weight S; and judging whether to enter a steady state after the occurrence of the loading event, if so, determining to enter a sleep state, and taking the starting time of the steady state as the starting time of sleep.
Further, in the step S2, the method for identifying the abnormal event and the end time of sleep is as follows: dividing the abnormal event into an event of getting on/off the bed and other abnormal events; after entering a sleep state, if the steady state is ended and the off-bed event does not occur, entering the steady state again, and identifying other abnormal events; if the getting-out event occurs after the steady state is ended, and the getting-in event occurs within the getting-out threshold value and enters the steady state, the getting-in event is identified; if the off event occurs after the steady state is ended and the on event does not occur within the on-off threshold, or if the on event does not occur within the on-off threshold but does not enter the steady state, judging that the sleep is ended, and taking the ending time of the steady state before the off event as the ending time of the sleep.
Further, the step S2 includes the following substeps:
step S201, judging whether a loading event occurs according to the measured value of the pressure sensor; executing step S202 if a get-in event occurs;
step S202, detecting whether a steady state is entered and whether a getting-out event occurs, and executing step S203 if the steady state is entered; returning to execute step S201 if a get-off event occurs;
step S203, judging to enter a sleep state, and recording the weight at the beginning of a steady state as the weight at the beginning of the sleep;
step S204, detecting whether the steady state is ended, and executing step S205 if the steady state is ended;
step S205, detecting whether to reenter a steady state or whether to have an out-of-bed event, and executing step S206 if the steady state is reentered; executing step S207 if a get-off event occurs;
step S206, judging that other abnormal events are generated, calculating weight difference generated during the event, and returning to the step S204;
step S207, detecting whether a loading event occurs within the threshold of the loading and unloading time and entering a steady state, and executing step S208 if the loading event occurs within the threshold of the loading and unloading time and entering the steady state; otherwise, step S209 is performed;
step S208, judging that the patient gets on or off the bed, calculating the weight difference generated during the patient gets on or off the bed, and returning to the step S204;
step S209, determining that sleep is completed, and step S3 is executed.
Further, the method for judging whether the event of getting in or out of bed occurs is as follows: if a certain measuring moment of the pressure sensor is changed from an empty bed state to a bed state, judging that a loading event occurs at the measuring moment; if a certain measuring moment of the sensor is changed from the in-bed state to the empty-bed state, the occurrence of an out-of-bed event at the measuring moment is judged.
Further, the method for judging whether the bed is empty is as follows: define a length of vT 0 The time period of the moment W is a short time window, whether the short time window taking the current measuring moment as the ending moment is in a stable state or not is judged, if the short time window is in a stable state, the average value or the median of the weight W at each moment in the short time window is compared with an empty bed threshold value, and if the weight W is smaller than the empty bed threshold value, the measuring moment is judged to be in an empty bed state.
Further, the method for judging whether the bed is in the bed state is as follows: setting a reference weight W of the user during the measurement period r The method comprises the steps of carrying out a first treatment on the surface of the And sets a weight difference threshold value, and sets (W r -W) is compared with a weight difference threshold value if (W) r -W) is less than or equal to the weight difference threshold value.
Further, the method of calculating the sleep metabolic rate using the value of the absolute decrease in body weight during sleep includes:
the sleep metabolic rate m is calculated according to the following formula:
wherein W is s1 Indicating the body weight at the beginning of sleep.
Further, the method of calculating the sleep metabolic rate using the value of the absolute decrease in body weight during sleep further includes:
the 8-hour daily sleep metabolic rate m (8 h) was calculated according to the following:
where Δt represents the duration of sleep.
In the invention, the metabolic quantity is calculated by measuring the absolute reduction of the weight of the person in the period of sleeping in the bed and the weight reduction in the sleeping process, and the metabolic condition of the person can be reflected more truly because the weight measurement is more direct than the heat measurement; by introducing abnormal events, various interference factors affecting the weight in the sleeping process can be eliminated, and the weight measurement is more accurate.
Drawings
For the purpose of making the objects, technical solutions and advantages of the present invention more apparent, the present invention will be described in the following preferred detail with reference to the accompanying drawings, in which:
FIG. 1 is a flow chart of a preferred embodiment of a method of calculating metabolic rate during sleep according to the present invention.
Fig. 2 is a flowchart for identifying a start time, an end time, and an abnormal event of sleep from a steady state.
Fig. 3 is a graph showing the change in weighing readings for 8 hours.
Fig. 4 is a schematic representation of the details of the change in weighing readings for 8 hours.
Figure 5 is a graphical representation of steady state body weight change over 8 hours.
Detailed Description
Other advantages and effects of the present invention will become apparent to those skilled in the art from the following disclosure, which describes the embodiments of the present invention with reference to specific examples. The invention may be practiced or carried out in other embodiments that depart from the specific details, and the details of the present description may be modified or varied from the spirit and scope of the present invention. It should be noted that the illustrations provided in the following embodiments merely illustrate the basic idea of the present invention by way of illustration, and the following embodiments and features in the embodiments may be combined with each other without conflict.
As shown in fig. 1, a preferred embodiment of the method for calculating metabolic rate during sleep according to the present invention comprises the steps of:
step S1, arranging a plurality of pressure sensors under the bed, wherein each pressure sensor is arranged at intervals of a first preset time T 0 The weight of the primary bed was measured.
Because the weight change generated by metabolism is very weak in the sleeping process, the measurement is difficult, and uncertain interference factors exist in the sleeping process; in the embodiment, n (n is more than or equal to 4) pressure sensors are arranged under the bed, preferably four pressure sensors are respectively padded under four bed legs, of course, the pressure sensors can also be padded at the contact positions of the bed board and the bed frame, and the number of the pressure sensors can also be more than four; the measurement accuracy of the pressure sensor should be not less than 10g, preferably 1g. Every first preset time T of each pressure sensor 0 (T 0 Typically set to 0.1 to 2s, preferably 1 s) to measure the weight once. In the whole measurement process, the original absolute output values of the pressure sensors are set as S1, S2, … … and Sn, and the sum of the S and the Sn is S, which is called total weight; the weight output values measured by the pressure sensors when the bed is empty are set as B1, B2, … … and Bn, and the sum of the weight output values is B, and the weight output values are called empty bed weight; (the value of the empty bed weight can be updated at regular intervals, so that the removed bed product is not usually taken or calculated). Instantaneous body weight w=s-B is defined. The present embodiment introduces a steady state concept to determine whether the measured value of the weight of the bed is stable, and a steady state to make the determinationAnd measuring.
And S2, recognizing a steady state according to the measured value of the pressure sensor, recognizing the starting time and the ending time of sleep according to the steady state, recognizing an abnormal event and calculating a weight difference value generated during the abnormal event. The steady state can be identified by directly judging according to the value of the total weight S, or calculating the instant weight W according to the total weight S, and then judging according to the value of the instant weight W.
The abnormal events are events that cause a change in weight value in the bed state in addition to the natural decrease in weight due to metabolism, and in this embodiment are classified into an up-down bed event (an event affecting weight such as toilet, water drinking, clothes adding, etc. typically occurs additionally) and other abnormal events (an abnormal event other than an up-down bed event, such as a quilt, a mobile phone falling under a bed, etc.). Since an abnormal event may cause a change in body weight outside of metabolism, it is necessary to identify the abnormal event and remove the difference in body weight generated during the abnormal event.
It should be noted that the steady state and the steady state in the present embodiment are completely different, and the steady state refers to a state in which fluctuation of the measured value is small, and is a state; and steady state refers to a period of time in steady state, which is a period of time.
The method for judging the steady state comprises the following steps:
define a length of uT 0 Is a long time window, where u is a natural number, and the value of u is preferably u=30 (i.e., uT 0 For half a minute, u may take other values such as 60, etc.), and it is determined whether or not the long time window with the current measurement time as the end time is in a steady state, and if the long time window is in a steady state, it is determined that the current measurement time is in a steady state.
The following method can be used to determine whether the long window is in a steady state: defining the standard deviation of all total weight S values recorded over a long time window as sigma TWC Setting a long steady-state standard deviation threshold delta 1 (δ 1 The value of (c) is generally in the range of 5 to 1000, preferably 15 in the present embodiment), when a long time window ends, if sigma of the long time window TWC ≤δ 1 Then judgeThe long time window is in a stable state; if sigma TWC ＞δ 1 The long time window is determined to be in an unstable state.
In a specific combination example, the length of the long time window is set to 30 seconds, and each measurement interval is set to 1 second. The current time is 8 hours and 01 minutes and 00 seconds (i.e. 08:01:00), and a long time window taking the time as the end time is 08:00:31-8:01:00; since the long time window contains 30 measurement moments, there are 30 total weight S values, and the standard deviation of these 30 total weight S values is calculated, if not greater than 15, indicating that the long time window of 08:00:31-08:01:00 is in a steady state, and the moment of 8.01 minutes 00 seconds is in a steady state (i.e., the moment in steady state). Otherwise, the long time window of 08:00:31 to 08:01:00 is described as being in an unsteady state, and the time of 8.01 minutes 00 seconds is not in a steady state (i.e., is not in a steady state).
Each time of measurement, whether the current time of measurement is steady state or not needs to be judged according to the above examples, and the examples are specifically combined:
assume that the first measurement time after getting in bed: 8:01:00, the end time of the long time window is 8:01:00, the forward pushing is carried out for 30 seconds (the length of the long time window) to judge whether 8:00:00 is steady state or not, namely, whether the total 30 total weight standard deviation obtained from 08:00:31-8:01:00 in 30 seconds exceeds 15 or not is judged, if yes, 8:01:00 is in unsteady state.
Second measurement: 8:01:01, the end time of the long time window is 8:01:01, the time is pushed forward for 30 seconds to judge whether 8:01:01 is in a steady state or not, namely, whether 30 total weight standard deviations obtained from 08:00:32-8:01:01 in 30 seconds are more than 15 or not is judged, and if not, 8:01:01 is in a steady state.
Third measurement: 8:01:02, the end time of the long time window is 8:01:02, the time is pushed forward for 30 seconds to judge whether 8:01:02 is in a steady state or not, namely, whether 30 total weight standard deviations obtained from 08:00:33-8:01:02 in 30 seconds are more than 15 or not is judged, and if not, the time is 8:01:02 in the steady state.
Fourth measurement: 8:01:03, and pushing forward for 30 seconds to determine whether 8:01:03 is in a steady state or not, namely, whether 30 total weight standard deviations obtained from 08:00:34-8:01:03 in 30 seconds are more than 15, if not, 8:01:03 is in a steady state.
The fifth measurement, 8:01:04, was at steady state.
Sixth measurement, 8:01:05, is at steady state.
… … (in steady state)
8:31:59, in steady state.
8:32:00, in unsteady state.
… … (in unsteady state and no occurrence of a bedevent is detected).
8:35:00, in an unsteady state, and no occurrence of a bedevent is detected.
8:35:01, in steady state.
… … (in steady state)
11:00:00, in steady state.
11:00:01, in unsteady state.
… … (in unsteady state)
11:01:00, and detecting that an out-of-bed event occurs.
… … (in empty bed)
11:03:00, and detecting that a loading event occurs.
… … (in unsteady state)
11:03:10, in unsteady state.
11:03:11, in steady state.
… … (in steady state)
13:09:00, in steady state.
13:09:01, in unsteady state.
… … (in unsteady state)
13:10:00, detecting that an out-of-bed event occurs.
… … (in empty bed)
13:40:00, and detecting that no event of getting in bed occurs.
Steady state is defined as: if a steady state long time window is included between two adjacent non-steady state long time windows, the duration of the steady state long time window between the two adjacent non-steady state long time windows is defined as a steady state.
In the present embodiment, the time of the ith measurement is defined as t i I is a natural number; defining the starting and ending time as t i Is TW i Defining the ending time as t i+1 Is TW i+1 If the window TW is long i Is in an unstable state, and has a long time window TW i+1 Is in a steady state, then it is considered to be from the long time window TW i+1 End time t of (2) i+1 Start to enter steady state, let t i+1 Defining as the starting time of a steady state; if long time window TW i ～TW i+k Are all in a stable state, k is a natural number, and the long time window TW i+k+1 Is not stable, then it is considered to be in the long time window TW i+k End time t of (2) i+k Ending steady state, let t i+k Defined as the end time of the steady state.
For example: in the above example, the long time window with the end time 08:01:00 is in an unstable state, and the long time window with the end time 08:01:01 is in a stable state, so the end time 08:01:01 is the start time of the first steady state after getting on bed. Since the long time windows with the end time of 08:01:01 to 08:31:59 are all in a stable state and the long time window with the end time of 08:32:00 is in an unstable state, the time period of the end time of 08:01:01 to 08:31:59 is considered to be a steady state, and the end time of the steady state is considered to be the end time of the moment 08:31:59. The long time window with the end time of 8:32:00 to 8:35:00 is in an unstable state, and the long time window with the end time of 8:35:01 is in a stable state again, so that the end time of 8:35:01 is the start time of the next steady state.
The method for identifying the starting time of sleeping comprises the following steps: judging whether a loading event occurs according to the measured total weight S; and judging whether to enter a steady state after the occurrence of the loading event, if so, determining to enter a sleep state, and taking the starting time of the steady state as the starting time of sleep. For example: in the above example, after getting on bed, the long time window with the end time 08:01:00 is in an unstable state, and the long time window with the end time 08:01:01 is in a stable state, so the start time 08:01:01 is the first stable state after getting on bed, and is also the sleep start time.
The method for judging whether the event of getting in or out of bed occurs comprises the following steps: if a certain measuring moment of the sensor is changed from an empty bed state to a bed state, judging that a loading event occurs at the measuring moment; if a certain measuring moment of the sensor is changed from the in-bed state to the empty-bed state, the occurrence of an out-of-bed event at the measuring moment is judged. In this embodiment, the instant weight W is preferably used to determine the event of getting on and off, but it is also possible to determine the event directly from the total weight S.
The following method can be adopted for judging whether the bed is empty: define a length of vT 0 Wherein V is a natural number (V is generally not more than 10; V is set to 3, 5, etc.), and determining whether the short time window with the current measurement time as the end time is in a steady state, if the short time window is in a steady state, comparing the average value or median of the weight W at each instant in the short time window with an empty bed threshold, and if the average value or median is less than the empty bed threshold, determining that the measurement time is in an empty bed state.
The method for judging whether the short time window is in a stable state comprises the following steps: defining standard deviation of all instantaneous weight W values recorded in a short time window as sigma TWD Setting a short steady-state standard deviation threshold delta 0 When a short time window ends, if sigma of the short time window TWD ≤δ 0 Judging that the short time window is in a stable state; if sigma TWD ＞δ 0 It is determined that the short time window is in an unstable state. The method for judging whether the short time window is in a stable state is similar to the method for judging the long time window, except that the duration and standard deviation threshold of the time window are different.
The following method can be adopted for judging whether the bed is in a state: setting a reference weight W of the user during the measurement period r The method comprises the steps of carrying out a first treatment on the surface of the And sets a weight difference threshold value, and sets (W r -W) is compared with a weight difference threshold value if (W) r -W) is less than or equal to the weight difference threshold value. When in initial use, the body of the user should be usedResetting the reference weight W r The reference weight W can be compared with the measured value in the measuring process r Is adaptively adjusted.
The method for identifying the abnormal event and the end time of sleep comprises the following steps: after entering a sleep state, if the steady state is ended and the off-bed event does not occur, entering the steady state again, and identifying other abnormal events; if the getting-out event occurs after the steady state is ended, and the getting-in event occurs within the getting-out threshold value and enters the steady state, the getting-in event is identified; if the off event occurs after the steady state is ended and the on event does not occur within the on-off threshold, or if the on event does not occur within the on-off threshold but does not enter the steady state, judging that the sleep is ended, and taking the ending time of the steady state before the off event as the ending time of the sleep.
Continuing with the description of the above example: and the measurement result of the time 08:01:01 is a long time window for measuring the stable state for the first time after the patient gets in bed, and the patient enters a sleep state. Since the long time windows with the end time of 08:01:01 to 08:31:59 are all in a stable state and the long time window with the end time of 08:32:00 is in an unstable state, the time period of the end time of 08:01:01 to 08:31:59 is considered to be a steady state, and the end time of the steady state is the end time of the time 08:31:59.
The long time window with the end time of 8:32:00 to 8:35:00 is in an unstable state, and the long time window with the end time of 8:35:01 is in a stable state again, so that the end time of 8:35:01 is the start time of the next steady state. Since the first steady state is completed and no out-of-bed event occurs, the step of determining the sleep end and the out-of-bed event is not performed, and it is determined that other abnormal events occur between the times 8:32:00 and 8:35:00.
The occurrence of a bedevent is detected at time 11:01:00, followed by the occurrence of a bedevent at 11:03:00, and again at 11:03:11 in steady state. Since the time interval from the time of getting out of bed (11:01:00) to the time of getting in the steady state again (11:03:11) is smaller than the threshold value of getting in and out of bed, the occurrence of the event of getting in and out of bed is judged, and the sleep is not ended.
Detecting that a getting-off event occurs at the time 13:10:00, and then, not detecting the getting-on event until the time 11:40:00, wherein the time interval exceeds a getting-on and getting-off threshold value, so that the sleep is judged to be ended; time 13:09:00 is the end time of steady state before the occurrence of the beddown event, and is also taken as the end time of sleep.
As shown in fig. 2, in this embodiment, the step S2 is split into the following sub-steps:
step S201, judging whether a loading event occurs according to the total weight S; if a get-in event occurs, step S202 is performed to detect whether the user has gone to sleep after getting in bed.
To eliminate errors in the sensors due to interference factors such as temperature changes during empty bed conditions, the pressure sensors may be calibrated at intervals during steady state of the empty bed conditions, for example, at intervals of 30 minutes. The calibration method is to set the value of the instantaneous weight W at the current measurement time to 0 by taking the value of the total weight S currently measured (i.e., the average or median of the total weight at each measurement time in a short time window having the current measurement time as the end time) as the value of the empty bed weight B. The influence of temperature change on the pressure sensor can be reduced through calibration, the measurement accuracy is improved, and the interference generated after adding or reducing articles on the bed can be eliminated.
Step S202, detecting whether a steady state is entered and whether a getting-out event occurs, if so, indicating that the user enters a sleep state, and executing step S203; if a get-off event occurs, it is indicated that the user is not sleeping, and the process returns to step S201. Step S202 is preferably divided into step S2021 and step S2022:
step S2021, judging whether a launch event occurs, if so, returning to execute step S201; otherwise, step S2022 is performed.
Step S2022, detecting whether a steady state is entered, if so, executing step S203; otherwise, step S2021 is executed back.
Of course, step S2021 may be performed first or step S2022 may be performed first in the implementation.
Step S203, determining to enter a sleep state, and recording a start time of steady state start as a sleep start time.
Step S204, whether the steady state is ended is detected, if the steady state is ended, the abnormal event or the sleep is ended is indicated, and step S205 is executed to further judge the event which occurs after the steady state is ended.
Step S205, detecting whether a steady state is re-entered or whether an out-of-bed event occurs, if the steady state is re-entered, indicating that other abnormal events occur, and executing step S206 to calculate the weight difference generated by the event; if the getting-out event occurs, it is still necessary to continue detecting whether the getting-in/out event has occurred or sleep is completed, and step S207 is performed. Step S205 is preferably divided into step S2051 and step S2052:
step S2051, detecting whether to re-enter the steady state, if yes, executing step S206; otherwise, step S2052 is executed.
Step S2052, detecting whether a getting-out event occurs, if yes, executing step S207; otherwise, the process returns to step S2051 to continue the detection.
Of course, step S2051 may be performed first or step S2052 may be performed first in the specific implementation.
Step S206, judging that other abnormal events are generated, calculating the weight difference generated during the event, and returning to step S204. For example: in the above example, after the first steady state at time 08:31:59 ends, no out-of-bed event occurs between times 8:32:00 and 8:35:00, and steady state is again entered at time 8:35:01, so it is determined that other abnormal events occur between times 8:32:00 and 8:35:00.
The calculation formula of the weight difference generated during other abnormal events is:
W E- -W E+ ＝(S E- -B)-(S E+ -B)
＝S E- -S E+
wherein subscript E represents other abnormal events during sleep; the "-" sign in the subscript indicates before the event; the "+" sign indicates after the event; w (W) E- Indicating body weight before other abnormal events occur; w (W) E+ Indicating the body weight after the end of other abnormal events; s is S E- Representing the total weight before other abnormal events occur; s is S E+ Indicating the total weight after the end of other abnormal events. Since the empty bed state is not generated when other abnormal events occur, the empty bed weight is not changed. For example: in the above example, W E- Weight at time 08:31:59, W E+ Body weight at time 8:35:01.
Step S207, detecting whether a loading event occurs within the threshold of the loading and unloading time and entering a steady state, and executing step S208 if the loading event occurs within the threshold of the loading and unloading time and entering the steady state; otherwise, step S209 is performed. The time threshold for getting on/off the bed is preferably 30min, namely after the user getting off the bed is detected, if the user getting on the bed is detected within 30min and enters a steady state, the user is identified as a getting on/off event; if the user does not get on bed within 30 minutes after getting off, or gets on bed within 30 minutes but does not enter a steady state within 30 minutes, the sleep is identified as ending. For example: in the above example, the occurrence of a bedevent is detected at time 11:01:00, followed by the occurrence of a bedevent at 11:03:00, and again at 11:03:11 in steady state. Less than the get-on/get-off threshold and thus identified as a get-on/get-off event. The occurrence of the getting-out event is detected at the time 13:10:00, and then the getting-in event is not detected until the time 11:40:00, and the time interval exceeds the getting-in and getting-out threshold value, so that the sleep is judged to be ended. Of course, the threshold of the time for getting in and out of bed can be set to other values according to the situation of different users, for example: 10min, 60min, etc.
Step S208, determining that the patient is getting on or off the bed, calculating the weight difference generated during the time of the getting on or off the bed, and returning to step S204. The calculation formula of the weight difference generated during the bed-on and bed-off event is:
W d- -W u+ ＝(S d- -B d+ )-(S u+ -B u- )＝S d- -B d+ -S u+ +B u-
wherein subscript u represents a get-on event; subscript d represents a beddown event; w (W) d- Representing the weight before the bedtime event in the bedtime event; w (W) u+ Representing the weight after a get-on event in a get-off event; s is S d- Representing the total weight in bed state prior to the bedtime event; b (B) d+ Represents the weight of the bed after the bed-off event, and is denoted as B because the bed-off event is empty d+ ；B u- The weight of the bed before the loading event is represented as B because the bed is empty before loading u+ ；S u+ Indicating the total weight after the bedevent. For example: in the above example, the occurrence of the get-on/off event occurs at the time 11:01:00 to 11:03:00, and the time 11:00:00 is the end time of the steady state before the occurrence of the get-off event, thus W d- Body weight at time 11:00:00; time 11:03:11 is the start time of steady state after getting on bed, therefore W u+ Body weight at time 11:03:11.
Step S209, determining that sleep is ended, and recording a steady-state end time before sleep is ended as a sleep end time. Step S3 is performed.
And step S3, obtaining an absolute weight reduction value D in the sleeping period according to the measured values of the pressure sensors at the sleeping starting time and the sleeping ending time and the weight difference value generated by the abnormal event. The calculation formula is as follows:
D＝(W s1 -W s2 )-∑(W d- -W u+ )-∑(W E- -W E+ )
wherein W is s1 Indicating the body weight at the beginning of sleep; w (W) s2 Indicating the body weight at the end of sleep.
As shown in fig. 3, when the instantaneous weight is taken as the weighing reading, the change of the weighing reading for 8 hours is shown, and it can be seen from fig. 3 that the user gets on the bed less than 7 points, gets off the bed after 13 points, the weight at the moment of getting on the bed and getting off the bed is not changed significantly, and the weight difference generated by metabolism during sleeping is very tiny, so that it is necessary to use a high-precision pressure sensor to measure the weight difference. However, when the accuracy of the sensor is high, the measured value thereof is not changed even in a stationary state, and the measured result is not remained at a constant value, so that a stable reading cannot be obtained; as shown in fig. 4, which is a schematic diagram showing the details of the change of the weighing readings in fig. 3, it can be seen from fig. 4 that the weighing reading curve looks flat in the ordinate range of 0-100 kg, and there is always a very large fluctuation after amplification, so that not only is there a huge jump in the reading in an unsteady state, but even in a steady state, there is no stable reading, and the calculation cannot be performed directly using the instantaneous weight W at a single moment. Thus, two things are done before computation: 1. removing readings at non-steady state; 2. and (5) calculating the average value of the readings at the steady state.
Newly defining a steady-state body weight, defining the steady-state body weight at each moment in a steady-state period as an average value of all instant body weights W of a long time window taking the moment as a starting moment, and defining the steady-state body weight at each moment in a non-steady-state period as a null value; as shown in fig. 5, i.e., u=30, δ 1 At=15, the change in the weighing readings of the steady-state body weight at 8 hours after converting the instantaneous body weight in fig. 4 to the steady-state body weight was represented by the steady-state body weight as the weighing reading. At this time, we regard the part outputting the null value as unsteady state and equating it to an abnormal event, consider the part outputting the non-null value as steady state and equating it to effective sleep, and easily pick out all points before and after the event, although the curves on the graph still have fluctuation, the value of each point is already an average value, can reflect the average characteristic of 30s, and the fluctuation itself is small, so that the steady state body weight is already available for calculation as a body weight measurement value. It should be noted that: the null value is no value and is not "0", because "0" is a valued, and will participate as a number in the calculation of the average, while the null value will not participate in the calculation of the average; for example: when the average value of 20 measured values and 10 null values is calculated, only 20 measured values are extracted to calculate the average value of 20 numbers, and if 10 null values are replaced by 10 '0's, 20 measured values and 10 '0's are extracted to calculate the average value of 30 numbers. Of course, we can also average the total weight S by the same method, and calculate the absolute weight reduction D during sleep from the average total weight instead of the steady-state weight. The following formula is shown:
wherein, the subscript i represents the sequence number of the times of getting on or getting off, and K represents the total times of other abnormal events during sleeping; the index j indicates the number of times other abnormal events occur. For convenience of description, the event of getting into bed before the start of sleep and the event of getting out of bed after the end of sleep are recorded as the times of getting into bed and getting out of bed during the sleep, the event of getting into bed before the start of sleep is defined as the first event of getting into bed, and the event of getting out of bed after the end of sleep is defined as the nth event of getting out of bed; namely S u1+ Representing the total weight after the loading event before the start of sleep, i.e. the average of the total weight S of the first long time window of the first steady state of sleep; b (B) u1- Indicating the empty bed weight prior to the bedtime event prior to the onset of sleep; s is S dN- Representing the total weight before the bedtime event after the end of sleep, i.e., the average of the total weight S of the last long time window of the last steady state of sleep; b (B) dN+ Indicating the empty bed weight after the bedtime event after sleep was completed.
S4, calculating a sleep metabolic rate by using the absolute weight reduction value during sleep; preferably, the sleep metabolic rate m and the 8-hour daily sleep metabolic rate m (8 h) are defined to calculate the sleep metabolic rate; of course, other indicators (e.g., 12 hour daily sleep metabolic rate, etc.) may be used to calculate the sleep metabolic rate.
The calculation formula of the sleep metabolic rate m is as follows:
wherein W is s1 Indicating the body weight at the beginning of sleep.
The calculation formula of the 8-hour daily sleep metabolic rate m (8 h) is as follows:
where Δt represents the duration of sleep, and in this embodiment represents the total duration of each steady state during sleep. Sleep metabolic rate m and 8 hours daily sleep metabolic rate m (8 hours) are preferably expressed in thousands of points; this index is used to normalize the sleep metabolic rate at different sleep times.
In this embodiment, by measuring the absolute decrease in weight of a person during a period of sleeping in a bed, the sleep metabolic rate can be calculated from the decrease in weight during sleep, and thus the metabolic level of the person can be reflected by long-term statistics, as with the caloric metabolic rate. Since the measurement of body weight is more direct than the measurement of calories, the metabolic situation of a person can be reflected more realistically. By introducing the event of getting on or off the bed and other abnormal events, various interference factors affecting the weight in the sleeping process can be eliminated, and the weight measurement is more accurate. By introducing steady state and steady state weight, the event of getting on and off the bed and other abnormal events can be accurately identified, the influence that the measured value of the high-precision pressure sensor is always in dynamic change can be overcome, the fluctuation amplitude of the weight in the measuring period is reduced, and the accurate measurement of the weight in the sleeping period is realized.
Finally, it is noted that the above embodiments are only for illustrating the technical solution of the present invention and not for limiting the same, and although the present invention has been described in detail with reference to the preferred embodiments, it should be understood by those skilled in the art that modifications and equivalents may be made thereto without departing from the spirit and scope of the present invention, which is intended to be covered by the claims of the present invention.
Claims (6)
1. A method for calculating a metabolic rate during sleep, comprising the steps of:
s1, every interval of a first preset time T 0 Measuring the weight of the primary bed; the method comprises the following steps:
a plurality of pressure sensors are arranged under the bed, and each pressure sensor is arranged at intervals of a first preset time T 0 Measuring the weight of the primary bed; defining total weight S as the sum of the measurements of the pressure sensors, defining empty bed weight B as the sum of the measurements of the pressure sensors when the bed is empty, defining instantaneous weight w=s-B;
s2, defining the length as uT 0 The time period of (2) isA long time window, judging whether the long time window taking the current measurement time as the end time is in a stable state according to the measured value of the weight in the long time window, and judging that the long time window is in a stable state currently if the long time window is in the stable state; recognizing the starting time and the ending time of sleep according to the steady state, recognizing an abnormal event and calculating a weight difference value generated during the abnormal event; the method for identifying the starting time of sleeping comprises the following steps:
judging whether a loading event occurs according to the measured total weight S; and judging whether to enter a steady state after the occurrence of the loading event, if so, judging to enter a sleep state, and taking the starting time of the steady state as the starting time of sleep;
the method for identifying the abnormal event and the end time of sleep comprises the following steps: dividing the abnormal event into an event of getting on/off the bed and other abnormal events; after entering a sleep state, if the steady state is ended and the off-bed event does not occur, entering the steady state again, and identifying other abnormal events; if the getting-out event occurs after the steady state is ended, and the getting-in event occurs within the getting-out threshold value and enters the steady state, the getting-in event is identified; if the off-bed event occurs after the steady state is ended and the on-bed event does not occur within the on-bed and off-bed threshold, or if the on-bed event does not occur within the on-bed and off-bed threshold but does not enter the steady state, judging that the sleep is ended, and taking the ending time of the steady state before the off-bed event occurs as the ending time of the sleep;
s3, obtaining an absolute weight reduction value D in the sleeping period according to the measured values of the pressure sensor at the sleeping starting time and the sleeping ending time and the weight difference value generated by the abnormal event; the formula is as follows:
D＝(W s1 -W s2 )-∑(W d- -W u+ )-∑(W E- -W E+ )
wherein W is s1 Indicating the body weight at the beginning of sleep; w (W) s2 Indicating body weight at the end of sleep; w (W) d- Representing the weight before the bedtime event in the bedtime event; w (W) u+ Representing the weight after a get-on event in a get-off event; w (W) E- Indicating body weight before other abnormal events occur；W E+ Indicating the body weight after the end of other abnormal events;
s4, calculating a sleep metabolic rate m by using a value of absolute weight reduction during sleep; the formula is as follows:
wherein W is s1 Indicating the body weight at the beginning of sleep.
2. The method for calculating the metabolic rate of a sleep process according to claim 1, wherein said step S2 comprises the sub-steps of:
step S201, judging whether a loading event occurs according to the measured value of the pressure sensor; executing step S202 if a get-in event occurs;
step S202, detecting whether a steady state is entered and whether a getting-out event occurs, and executing step S203 if the steady state is entered; returning to execute step S201 if a get-off event occurs;
step S203, judging to enter a sleep state, and recording the weight at the beginning of a steady state as the weight at the beginning of the sleep;
step S204, detecting whether the steady state is ended, and executing step S205 if the steady state is ended;
step S205, detecting whether to reenter a steady state or whether to have an out-of-bed event, and executing step S206 if the steady state is reentered; executing step S207 if a get-off event occurs;
step S206, judging that other abnormal events are generated, calculating weight differences generated during the abnormal events, and returning to the step S204;
step S207, detecting whether a loading event occurs within the threshold of the loading and unloading time and entering a steady state, and executing step S208 if the loading event occurs within the threshold of the loading and unloading time and entering the steady state; otherwise, step S209 is performed;
step S208, judging that the patient gets on or off the bed, calculating the weight difference generated during the patient gets on or off the bed, and returning to the step S204;
step S209, determining that sleep is completed, and step S3 is executed.
3. The method for calculating a metabolic rate during sleep according to claim 1, wherein the method for judging whether the get-in event and the get-out event occur is as follows: if a certain measuring moment of the pressure sensor is changed from an empty bed state to a bed state, judging that a loading event occurs at the measuring moment; if a certain measuring moment of the sensor is changed from the in-bed state to the empty-bed state, the occurrence of an out-of-bed event at the measuring moment is judged.
4. The method for calculating a metabolic rate during sleep according to claim 3, wherein the method for determining whether the sleep state is an empty bed state is as follows: define a length of vT 0 The time period of the moment W is a short time window, whether the short time window taking the current measuring moment as the ending moment is in a stable state or not is judged, if the short time window is in a stable state, the average value or the median of the weight W at each moment in the short time window is compared with an empty bed threshold value, and if the weight W is smaller than the empty bed threshold value, the measuring moment is judged to be in an empty bed state.
5. The method for calculating a metabolic rate during sleep according to claim 3, wherein the method for determining whether the sleep state is in bed is as follows: setting the reference weight W of the user at the measurement time r The method comprises the steps of carrying out a first treatment on the surface of the And sets a weight difference threshold value, and sets (W r -W) is compared with a weight difference threshold value if (W) r -W) is less than or equal to the weight difference threshold value.
6. The method for calculating a metabolic rate of a sleep process according to claim 1, wherein the method for calculating a sleep metabolic rate using a value of an absolute decrease in body weight during sleep further comprises:
the 8-hour daily sleep metabolic rate m (8 h) was calculated according to the following:
where Δt represents the duration of sleep.
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