CN113974568A - Method for calculating metabolic rate of sleep process based on slope interference removal - Google Patents
Method for calculating metabolic rate of sleep process based on slope interference removal Download PDFInfo
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- 230000007958 sleep Effects 0.000 title claims abstract description 127
- 238000000034 method Methods 0.000 title claims abstract description 76
- 230000037323 metabolic rate Effects 0.000 title claims abstract description 50
- 230000008569 process Effects 0.000 title claims abstract description 29
- 238000005259 measurement Methods 0.000 claims abstract description 52
- 230000004580 weight loss Effects 0.000 claims abstract description 29
- 230000037396 body weight Effects 0.000 claims abstract description 26
- 238000012216 screening Methods 0.000 claims abstract description 8
- 230000013632 homeostatic process Effects 0.000 claims description 18
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000008030 elimination Effects 0.000 claims description 7
- 238000003379 elimination reaction Methods 0.000 claims description 7
- 239000000126 substance Substances 0.000 claims description 6
- 208000022249 Sleep-Wake Transition disease Diseases 0.000 claims description 2
- 230000002503 metabolic effect Effects 0.000 abstract description 6
- 239000013585 weight reducing agent Substances 0.000 abstract description 5
- 230000009467 reduction Effects 0.000 abstract description 4
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- 238000005303 weighing Methods 0.000 description 8
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- 238000012544 monitoring process Methods 0.000 description 2
- 238000012935 Averaging Methods 0.000 description 1
- 208000031648 Body Weight Changes Diseases 0.000 description 1
- 230000005856 abnormality Effects 0.000 description 1
- 230000004579 body weight change Effects 0.000 description 1
- 238000006243 chemical reaction Methods 0.000 description 1
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- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4866—Evaluating metabolism
Abstract
The invention relates to a slope interference-removal-based method for calculating the metabolic rate of a sleep process, which comprises the steps of measuring the weight of a bed once at intervals of first preset time, identifying a steady state according to the measured value, and screening out an effective sleep steady state according to the slope and the duration of each steady state in the sleep period; calculating the sum of the weight loss values in each effective sleep steady state period as an absolute weight loss value in the sleep period; the sleep metabolic rate was calculated using the absolute weight loss value during sleep. In the invention, the absolute reduction of the body weight of a person in the sleeping period is measured, and the metabolic capacity is calculated according to the body weight reduction in the sleeping process, and the metabolic condition of the person can be reflected more truly as the measurement of the body weight is more direct than the measurement of the heat; by adopting the method for calculating the weight loss value of each steady-state duration, various interference factors influencing the weight in the sleeping process can be eliminated, and the steady state with interference can be eliminated by setting the slope threshold to eliminate the steady state with larger slope deviation.
Description
Technical Field
The invention belongs to the technical field of sleep process metabolic rate monitoring, and relates to a sleep process metabolic rate calculation method based on slope interference elimination.
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 the metabolism of heat, but the monitoring of the heat is difficult, and the conversion can be carried out only by other indexes, so that a calculation result has large errors.
Because people all consume energy constantly, under the condition of not drinking water, not eating, the weight must be in the gradual reduction, therefore metabolic rate also can be calculated through the change of weight, but this individual weight change numerical value is very little, and traditional weight measurement means can't accurately monitor this reduced numerical value, and people also can produce great fluctuation because the different metabolic rate of the amount of exercise of each period in the activity process. However, the metabolic rate of a person is basically stable in the sleeping process, and if the weight of the person in the sleeping process can be monitored at higher precision, various interference factors influencing the weight in the sleeping process are eliminated, and the absolute reduction of the weight of the person in the sleeping period in bed is measured, the metabolic rate can be calculated through the weight reduction in the sleeping process.
Disclosure of Invention
In view of the above, the present invention provides a method for calculating a metabolic rate of a sleep process based on slope interference elimination.
In order to achieve the purpose, the invention provides the following technical scheme:
a method for calculating the metabolic rate of a sleep process based on slope interference elimination comprises the following steps:
step S1, every first preset time T0Measuring the weight of the primary bed;
step S2, defining the length as uT0The time period of (2) is a long time window, whether the long time window taking the current measurement time as the end time is in a stable state or not 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 long time window is judged to be in the stable state; identifying the starting time and the ending time of sleep according to the steady state, and calculating the duration and the slope of each steady state in the sleep period;
step S3, screening effective sleep homeostasis according to the slope and duration of each homeostasis in the sleep period, and calculating the sum of weight loss values in each effective sleep homeostasis period as an absolute weight loss value D in the sleep period;
step S4, calculating the sleep metabolic rate using the value of the absolute weight loss during sleep.
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 T0Measuring the weight of the primary bed; definition ofThe total weight S is the sum of the measured values of all the pressure sensors, the empty bed weight B is defined as the sum of the measured values of all the pressure sensors when the bed is empty, and the instantaneous weight W is defined as S-B;
in the step S2, the method of identifying the sleep start time includes: judging whether a bed getting event occurs or not according to the measured total weight S; and whether the sleep mode enters the steady state after the bed entering event occurs, if the sleep mode enters the steady state after the bed entering event, the sleep mode is judged to enter, and the starting time of the steady state is taken as the starting time of the sleep.
Further, the step S2 includes the following sub-steps:
step S201, judging whether a bed getting event occurs according to the measurement value of the pressure sensor; if the bed getting event happens, executing step S202;
step S202, detecting whether to enter a steady state and whether to have a getting-off event, and if so, judging to enter a sleep state; step S203 is executed; if the bed getting-off event occurs, returning to execute the step S201;
step S203, detecting whether the steady state is finished, and executing step S204 if the steady state is finished;
step S204, calculating the duration and the slope of the steady state;
step S205, detecting whether to re-enter the steady state or whether the getting-off event occurs, if so, returning to execute the step S204; if the bed exit event occurs, executing step S206;
step S206, detecting whether a bed getting-on event occurs within the time threshold values of bed getting-on and bed getting-off and entering a steady state, and if the bed getting-on event occurs within the time threshold values of bed getting-on and bed getting-off and entering the steady state, returning to execute the step S204; otherwise, go to step S207;
step S207 determines that the sleep is completed, and step S3 is executed.
Further, the method for determining whether the long time window is in a stable state includes: the calculation takes the current measurement time as the end time and the length as 2uT0Average number S of all total weights S in the time period of (1)TWBy mean number STWCalculating the value of all total weights S recorded in the long time window for the mean value of the total weightsStandard deviation, defined as σTWCSetting a long steady state standard deviation threshold delta1When a long time window ends, if σ of the long time windowTWC≤δ1If yes, determining that the long time window is in a stable state; if σTWC＞δ1Then the long time window is determined to be in an unstable state.
Further, the method for judging whether the getting-on event and the getting-off event occur comprises the following steps: if a certain measurement moment of the sensor is changed from an empty bed state to a bed state, judging that a bed getting event occurs at the measurement moment; if a certain measuring time of the sensor changes from the in-bed state to the empty-bed state, the occurrence of a bed-off event at the measuring time is judged.
Further, the calculation method of the slope of the steady state is to perform linear fitting on the values of all the instantaneous body weights W in the steady state by using a least square method and calculate the slope.
Further, the weight loss value Δ W during steady state was calculated using the following formulaa：
ΔWa＝-K×ΔTW
Wherein, Delta TWIndicating the duration of the steady state.
Further, the method for screening effective sleep homeostasis comprises the following steps:
defining a slope threshold and a steady-state duration threshold, firstly calculating the average value of all steady-state slopes during sleep, and then removing the steady state with the slope and the steady state with the positive slope, wherein the absolute value of the difference between the slope and the average value is greater than the slope threshold; and then removing the steady state with the steady state duration being less than the steady state duration threshold, and taking the remaining steady state as the effective sleep steady state.
Further, the method for calculating the sleep metabolic rate using the absolute weight loss value D during sleep includes:
the sleep metabolic rate m is calculated according to the following formula:
wherein, Ws1Indicating the onset of sleepBody weight.
Further, the method for calculating the sleep metabolic rate using the absolute weight loss value during sleep further includes:
the 8-hour daily chemical sleep metabolic rate m (8h) was calculated according to the following formula:
where Δ T represents the duration of sleep.
In the invention, the sleep metabolic rate can be calculated according to the weight reduction during the sleep period by measuring the absolute weight reduction of the person in the sleeping period, so that the metabolic level of the person can be reflected through long-term statistics as well as the caloric metabolic rate. Since the measurement of body weight is more direct than the measurement of calories, the metabolic condition of a person can be reflected more realistically. By introducing the steady state and calculating the weight loss value of each steady state duration, various interference factors influencing the weight in the sleeping process can be eliminated, and the weight measurement is more accurate. By setting the slope threshold value to remove a steady state in which the slope deviates from a large value, a steady state including interference can be removed.
Drawings
For the purposes of promoting a better understanding of the objects, aspects and advantages of the invention, reference will now be made to the following detailed description taken in conjunction with the accompanying drawings in which:
FIG. 1 is a flow chart of a preferred embodiment of the method for calculating the metabolic rate of a slope based interference-free sleep process according to the present invention.
Fig. 2 is a flowchart for identifying the start time and the end time of sleep from the steady state and calculating the duration and slope of each steady state during sleep.
Fig. 3 is a graph showing the change in weight reading over 8 hours.
Fig. 4 is a schematic diagram showing the details of the change in the weighing readings for 8 hours.
Fig. 5 is a graph showing the steady state body weight change over 8 hours.
Detailed Description
The embodiments of the present invention are described below with reference to specific embodiments, and other advantages and effects of the present invention will be easily understood by those skilled in the art from the disclosure of the present specification. The invention is capable of other and different embodiments and of being practiced or of being carried out in various ways, and its several details are capable of modification in various respects, all without departing from the spirit and scope of the present invention. It should be noted that the drawings provided in the following embodiments are only for illustrating the basic idea of the present invention in a schematic way, and the features in the following embodiments and examples may be combined with each other without conflict.
As shown in FIG. 1, a preferred embodiment of the method for calculating the metabolic rate of the slope based interference-free sleep process of the present invention comprises the following steps:
step S1, arranging a plurality of pressure sensors under the bed, wherein each pressure sensor is spaced for a first preset time T0The weight of the primary bed was measured.
Because the weight change generated by metabolism in the sleeping process is very weak, the weight change is difficult to measure, 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 arranged under four bed feet, of course, the pressure sensors can also be arranged 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 1 g. Every interval of every pressure sensor is first preset time T0(T0Generally set to 0.1 to 2s, preferably 1s) is measured. 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 called as total weight; the weight output values measured by the pressure sensors when the bed is unloaded are set as B1, B2, … … and Bn, and the sum of the weight output values is B, which is called the empty bed weight; (the value of the empty bed weight can be updated at regular intervals, so that the bed taken off is generally not taken up or taken down frequently. The instantaneous body weight W ═ S-B is defined.
When the accuracy of the sensor is high, the measurement value of the sensor is continuously changed even in a static state, and the measurement result does not stay at a value and is not changed, so that a stable reading cannot be obtained; therefore, the present embodiment introduces the concept of steady state to determine whether the measured value of the weight of the bed is stable, and introduces the concepts of steady state and steady state body weight to measure the body weight value.
And step S2, identifying a steady state according to the measurement value of the pressure sensor, identifying the start time and the end time of sleep according to the steady state, and calculating the duration and the slope of each steady state in the sleep period. When the steady state is identified, the judgment can be directly carried out according to the value of the total weight S, or the instantaneous weight W can be calculated according to the total weight S and then the judgment can be carried out according to the value of the instantaneous weight W.
It should be noted that the steady state and the steady state in this embodiment are completely different, and the steady state refers to a state in which the 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 steady state determination method comprises the following steps:
define length as uT0The time period of (a) is a long time window, wherein u is a natural number, and the value of u is preferably equal to 30 (i.e. uT)0Half a minute, of course u may take other value such as 60) to determine whether the long time window having 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 long time window is in a steady state.
The following method can be used to determine whether the long time window is in a steady state: the calculation takes the current measurement time as the end time and the length as 2uT0The average number S of all total weights S in the time period of (i.e. two times the long time window, in this example one minute)TWBy mean number STWCalculating the standard deviation of the values of all the total weights S recorded in the long time window for the mean value of the total weights, defining the standard deviation as σTWCSetting a long steady state standard deviation threshold delta1(δ1The value range of (a) is generally 5 to 1000, preferably 200 in the present embodiment), when a long time window ends, if σ of the long time window isTWC≤δ1If yes, determining that the long time window is in a stable state; if σTWC＞δ1Then, thenThe long time window is determined to be in an unstable state. The standard deviation of this embodiment adopts twice long time window to calculate the mean value, can avoid appearing just that the reading value changes greatly but the standard deviation changes little when long time window is alternative thereby the condition of inaccurate discernment.
In this embodiment, to avoid the situation that the reading value changes greatly but the standard deviation does not change much just when the time windows alternate, a long time window of 2 times, that is, 60s, is used instead of 30s when calculating the average value. And the present embodiment appropriately increases the standard deviation threshold δ1The steady state can be prevented from being interrupted by small disturbance, and the continuity of the steady state is improved, so that the measurement precision is improved, and the increase and decrease events of the object can be better identified; for example: delta1When the number is 200, the mobile phones added or reduced on the bed can be accurately identified. Of course, if the mean value can be calculated by using a double time window, the long steady-state standard deviation threshold delta needs to be reduced1Thereby reducing the ability to identify an increase or decrease event of the object.
In specific combination with the example, the length of the long time window is set to 30 seconds, and the interval between each measurement is set to 1 second. When the current time is 8 hours and 01 minutes and 00 seconds (namely 08:01:00), the long time window taking the current time as the ending time is 08:00: 31-8: 01: 00; since the long time window contains 30 measurement instants, there are 30 total weight S values; then, calculating the average number of 60 total weight S in a 2-time long time window (08: 00: 01-8: 01:00), and calculating the standard deviation of the values of 30 total weight S in 08:00: 31-8: 01:00 by taking the average number of 60 total weight S in 08:00: 01-8: 01:00 as a mean value, wherein if the standard deviation is not more than 200, the long time window of 08:00: 31-08: 01:00 is in a stable state, and the time of 8 hours 01 minutes and 00 seconds is in a stable state (namely the time in the stable state). Otherwise, the long time window of 08:00:31 to 08:01:00 is in an unstable state, and the time of 8 hours, 01 minutes and 00 seconds is not in a stable state (namely is not the time in the stable state).
Each time of measuring the time, whether the current measuring time is a stable state or not needs to be judged according to the above example, which is specifically combined with the example:
assume the first measurement instant after going to bed: and 8:01:00, judging whether the 8:00:00 is a steady state or not by taking 8:01:00 as the end time of the long time window, advancing for 30 seconds (the length of the long time window) and taking the average of the previous 60 seconds as a mean value, namely judging whether the total weight standard deviation obtained within 30 seconds exceeds 200 or not from 08:00:31 to 8:01:00, and if so, judging that the 8:01:00 is in an unsteady state.
And (3) second measurement: and 8:01:01, and determining whether 8:01:01 is in a steady state or not by pushing forward for 30 seconds by taking 8:01:01 as the end time of the long time window, namely determining whether the total weight standard deviation obtained within 30 seconds exceeds 200 or not from 08:00:32 to 8:01:01, and if not, determining that 8:01:01 is in the steady state.
The third measurement: 8:01:02, and determining whether 8:01:02 is in a steady state by pushing forward for 30 seconds by taking 8:01:02 as the end time of the long time window, namely determining whether the total weight standard deviation obtained within 30 seconds exceeds 200 or not from 08:00:33 to 8:01:02, and if not, determining that 8:01:02 is in the steady state.
The fourth measurement: and 8:01:03, judging whether 8:01:03 is in a steady state or not by pushing forward for 30 seconds by taking 8:01:03 as the end time of the long time window, namely judging whether the total weight standard deviation obtained within 30 seconds exceeds 200 or not from 08:00:34 to 8:01:03, and if not, judging that 8:01:03 is in the steady state.
The fifth measurement, 8:01:04, is at steady state.
The sixth measurement, 8:01:05, was at steady state.
… … (in steady state)
8:31:59, at steady state.
8:32:00, in unsteady state.
… … (in an unstable state and no occurrence of a bed exit event is detected).
8:35:00, in non-steady state and no occurrence of a bed exit event was detected.
8:35:01, at steady state.
… … (in steady state)
11:00:00, at steady state.
11:00:01, in an unstable state.
… … (in unsteady state)
11:01:00, occurrence of a bed exit event is detected.
… … (in the empty bed state)
11:03:00, occurrence of a bed event is detected.
… … (in unsteady state)
11:03:10, in unsteady state.
11:03:11, in steady state.
… … (in steady state)
13:09:00, at steady state.
13:09:01, in an unstable state.
… … (in unsteady state)
13:10:00, occurrence of a bed exit event is detected.
… … (in the empty bed state)
13:40:00, no occurrence of a bed event was detected.
The steady state is defined as: if the long time window of the stable state is included between two adjacent long time windows of the unstable state, the duration of the long time window of the stable state between the two adjacent long time windows of the unstable state is defined as a stable state.
In this embodiment, the time of the ith measurement is defined as tiI is a natural number; defining the starting and ending time as tiHas a long time window TWiDefining the end time as ti+1Has a long time window TWi+1If the long time window TWiFor non-steady state, the long time window TWi+1In steady state, the slave long time window TW is consideredi+1End time t ofi+1Begins to enter a steady state, ti+1A start time defined as a steady state; if long time window TWi～TWi+kAll are in steady state, k is a natural number, and the long time window TWi+k+1If the state is unstable, the long time window TW is consideredi+kEnd time t ofi+kThe steady state is ended, ti+kDefined as the end time of the steady state.
For example: in the above example, the long time window ending at time 08:01:00 is in an unstable state, and the long time window ending at time 08:01:01 is in a stable state, so time 08:01:01 is the beginning of the first stable state after getting to 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 windows with the end time of 08:32:00 are in an unstable state, the time period of time 08:01:01 to 08:31:59 is considered to be a stable state, and time 08:31:59 is considered to be the end time of the stable state. The long time windows ending at time 8:32:00 to 8:35:00 are all in an unstable state, and the long time windows ending at time 8:35:01 are in a stable state again, so that time 8:35:01 is the starting time of the next stable state.
As shown in fig. 2, in the present embodiment, step S2 is split into the following sub-steps:
step S201, judging whether a bed getting event occurs according to the measurement value of the pressure sensor; if the bed getting event happens, executing step S202; in this embodiment, the instant weight W is preferably used to determine the getting-on event and the getting-off event, but may be directly determined by the total weight S.
In order to eliminate errors of the sensors due to disturbance factors such as temperature changes during the empty bed, the pressure sensors may be calibrated once at intervals of a certain time, for example, at intervals of 30 minutes, during the steady state of the empty bed state. The calibration method is to use the value of the currently measured total weight S (i.e., the average or median of the total weights at the respective measurement times within a short time window in which the current measurement time is the end time) as the value of the empty bed weight B, so that the value of the instantaneous body weight W at the measurement time is 0. The influence of temperature change on the pressure sensor can be reduced through calibration, the measurement accuracy is improved, and the interference generated after articles are added or reduced on the bed can be eliminated.
The method for judging whether the getting-on event and the getting-off event occur comprises the following steps: if a certain measurement moment of the sensor is changed from an empty bed state to a bed state, judging that a bed getting event occurs at the measurement moment; if a certain measuring time of the sensor changes from the in-bed state to the empty-bed state, the occurrence of a bed-off event at the measuring time is judged.
The following method can be adopted to judge whether the empty bed state is present: defining a length of vT0The time period of (2) is a short time window, wherein V is a natural number (V is generally not more than 10, for example, V can be set to be 3, 5, etc.), whether the short time window with the current measurement time as the end time 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 number of the body weight W at each moment in the short time window is compared with an empty bed threshold value, and if the short time window is less than the empty bed threshold value, the measurement time is judged to be in an empty bed state.
The method for judging whether the short time window is in a stable state comprises the following steps: defining the standard deviation of all instantaneous weight W values recorded within a short time window as σTWDSetting a short steady state standard deviation threshold delta0When a short time window ends, if σ of the short time windowTWD≤δ0If yes, judging that the short time window is in a stable state; if σTWD＞δ0Then the short time window is determined to be 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 of the time window is different from the standard deviation threshold.
The following method can be adopted to judge whether the bed is in a state: setting the reference weight W of the user during the measurement periodr(ii) a And setting a weight difference threshold value of (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, then the in-bed status is determined. At the time of initial use, the user's weight should be set to the reference weight WrCan be used for measuring the reference body weight W according to the measurement value in the measurement processrIs adaptively adjusted.
Step S202, whether a steady state is entered and whether an out-of-bed event occurs are detected, if the steady state is entered, the user enters a sleep state, and step S203 is executed; if the event of getting out of bed occurs, it indicates that the user is not going to bed, and the process returns to step S201. For example: in the above example, after getting in bed, the long time window with time 08:01:00 as the end time is in the unstable state, and the long time window with time 08:01:01 as the end time is in the stable state, so time 08:01:01 is the start time of the first stable state after getting in bed, and is also the start time of sleep. Step S202 is preferably divided into step S2021 and step S2022:
step S2021, judging whether a bed getting-off event occurs, and if the bed getting-off event occurs, returning to execute the step S201; otherwise, step S2022 is performed.
Step S2022, detecting whether to enter a steady state, and executing step S203 if the steady state is entered; otherwise, the process returns to step S2021.
Of course, in the implementation, step S2021 may be executed first, or step S2022 may be executed first.
Step S203, whether the steady state is finished or not is detected, if the steady state is finished, step S204 is executed, and the measured value in the steady state period is counted and calculated. Continuing with the above example: the measurement result at the time 08:01:01 is that a long time window of a stable state is measured for the first time after getting out of bed, and then the sleep state is entered. 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 windows with the end time of 08:32:00 are in an unstable state, the time period of time 08:01:01 to 08:31:59 is considered to be a steady state, and time 08:31:59 is considered to be the end time of the steady state.
Step S204, calculating the duration, slope K and weight loss value Δ W of the steady statea. Of course, the weight loss Δ W may not be calculated in this stepaAfter the effective sleep homeostasis is screened in step S3, only the steady state weight loss Δ W for effective sleep is calculateda。
In this embodiment, it is preferable to perform linear fitting on all the instantaneous weight W values in the steady state by using a least square method and calculate the slope; the calculation formula is as follows:
where N represents the number of values of the instantaneous body weight involved in the steady state, xiA time value corresponding to a value representing the body weight at each instant in a steady state;
represents the average of the time values in steady state; y isiA value representing the instantaneous body weight at each time of steady state;
mean of the values representing the instantaneous body weight at steady state.
Of course, the slope of the steady state may be calculated by other linear fitting methods, as long as the slope can be calculated from all the instantaneous values of the body weight W in the steady state.
The method of calculating the weight loss value during steady state was: the body weight difference is calculated from the duration and slope of the steady state, i.e.:
ΔWa＝-K×ΔTW
wherein, Delta TWIndicates the duration of the steady state; therefore, the interference of fluctuation at the start or the end of the steady state can be eliminated, and the calculation result is more reliable.
Of course, the weight loss Δ W during steady state can also be calculated directly from the steady state body weightaThe calculation formula is as follows:
ΔWa＝Wa+-Wa-
wherein, Wa+Steady state body weight representing the onset of effective sleep homeostasis; wa-Indicating steady state body weight at the end of effective sleep homeostasis. Since the empty bed weight B value is not recalculated during the steady state duration, the empty bed weight B value must be equal during the same steady state, and therefore the steady state reading can also be used directly to calculate the weight loss value AW during the steady statea：
ΔWa＝Saw+-Saw-
Wherein S isaw+A steady state reading representing the onset of effective sleep homeostasis; saw-A steady state reading indicating the end of effective sleep homeostasis.
As shown in fig. 3 and 4, in order to show the change of the weighing reading in 8 hours when the instantaneous weight is taken as the weighing reading, fig. 4 shows that the straight weighing reading curve appears in the range of 0-100 kg of the ordinate, and after the straight weighing reading curve is amplified, the straight weighing reading curve always has very large jump, the large jump exists in the reading in the unsteady state, but the stable reading does not exist in the steady state, and the instantaneous weight W at a single moment cannot be directly used for calculation. Therefore, two things are done before the calculation: 1. removing readings at unsteady state; 2. and performing average calculation on the readings at the steady state.
Newly defining the value of the total weight S in the unsteady state period as a null value; defining a steady state reading SaCalculating the length rT with the current measurement time as the end time0The average of all the total weights S over the time period of (2) is taken as the steady state reading SaWhere r is a natural number and r ≧ u, preferably r ═ 2u, i.e., the mean one minute of the total weight S is taken as the steady-state reading Sa. It should be noted that the null value is no value, and is not "0", because "0" is a value, and participates in the calculation of averaging; for example, for the first time of steady state, since the previous 59 times are non-steady state and the total weight is null, there is only one total weight value, and the average S at that time is calculatedTWThen, 59 null values are removed, and only the average value of one value is calculated; if the total weight during the non-steady state is defined as "0", the total weight at that time and the average of 59 "0" s are calculated, and thus an error may occur. Defining the Steady State body weight Wa＝Sa-B; outputting a steady state weight (one-minute average) every 30s as a weighing reading, wherein the weighing reading change is schematically shown in fig. 5, wherein a part outputting a null value is regarded as a non-steady state and is equivalent to an abnormal event, a part outputting a non-null value is regarded as a steady state and is equivalent to effective sleep, and interference of the abnormal event can be effectively eliminated by calculating only the weight reduction at the steady state.
Step S205, detecting whether to re-enter the steady state or whether the getting-off event occurs, if so, returning to execute the step S204; step S206 is performed if a bed exit event occurs.
Continuing with the above example: after the first steady state at time 08:31:59 ends, the long time windows ending at times 8:32:00 to 8:35:00 are all in the unstable state, and the long time windows ending at times 8:35:01 are in the stable state again, so that time 8:35:01 is the starting time of the next steady state. Since the bed getting-out event does not occur after the first steady state is completed and the steady state is entered, it is determined that the sleep is not completed, and the process does not proceed to step S206 to determine the sleep completion and the bed getting-out event. After the steady state is ended at time 11:00:00, the occurrence of the getting-out-of-bed event is detected at 11:01:00, and therefore step S206 is executed to determine the end of sleep and the getting-out-of-bed event. Similarly, after the steady state is finished at time 13:09:00, the occurrence of the getting-out-of-bed event is detected at time 13:10:00, and step S206 is executed to determine the sleep completion and the getting-out-of-bed event.
Step S206, detecting whether a bed getting-on event occurs within the time threshold values of bed getting-on and bed getting-off and entering a steady state, and if the bed getting-on event occurs within the time threshold values of bed getting-on and bed getting-off and entering the steady state, returning to execute the step S204; otherwise, step S207 is executed.
Continuing with the above example: after detecting the occurrence of a bed exit event at 11:01:00, the occurrence of a bed entry event is detected at 11:03:00 and is again at steady state at 11:03: 11. Since the time interval from getting out of bed (11:01:00) to getting into bed again to enter steady state (11:03:11) is less than the threshold value of getting in and out of bed (30 minutes), it is judged that the event of getting in and out of bed occurs and sleep is not finished. After the occurrence of the going-out event is detected at the time 13:10:00, the time interval exceeds the going-in/out threshold value until the going-out event is not detected at the time 11:40:00, so that the end of sleep is judged; time 13:09:00 is the end of the steady state before the occurrence of the bed exit event, and is also taken as the end of sleep.
Step S207 determines that the sleep is completed, and step S3 is executed.
And step S3, screening effective sleep homeostasis according to the slope and duration of each homeostasis in the sleep period, and calculating the sum of the weight loss values in each effective sleep homeostasis period as the absolute weight loss value D in the sleep period.
The method for screening effective sleep homeostasis comprises the following steps:
defining a slope threshold and a steady-state duration threshold, firstly calculating the average value of all steady-state slopes during sleep, and then removing the steady state with the slope and the steady state with the positive slope, wherein the absolute value of the difference between the slope and the average value is greater than the slope threshold; and then removing the steady state with the steady state duration being less than the steady state duration threshold, and taking the remaining steady state as the effective sleep steady state. The slope threshold may be adjusted according to an actual measurement condition to screen out a stable state in which an abnormality obviously exists, the steady state duration threshold is generally selected from 5min to 15min, and in this embodiment, 10min is preferred.
As shown in table 1, a slope and duration statistical table for the steady states in fig. 5 identifies 23 steady states altogether.
TABLE 1
| Steady state sequence number | 1 | 2 | 3 | 4 | 5 | 6 | 7 | 8 |
| Slope of | -1.01143 | 0.00414 | -0.01466 | -0.00938 | -0.01547 | -0.02469 | -0.12304 | -0.01671 |
| Hours of duration | 0.04167 | 0.31667 | 0.90000 | 0.27500 | 0.10833 | 0.05833 | 0.23333 | 0.08333 |
| Steady state sequence number | 9 | 10 | 11 | 12 | 13 | 14 | 15 | 16 |
| Slope of | -0.02102 | 0.04059 | -0.02770 | 0.03900 | -0.04728 | -0.04366 | -0.01842 | -0.07339 |
| Hours of duration | 1.0000 | 0.11667 | 0.48333 | 0.06667 | 0.25000 | 0.64167 | 0.10000 | 0.15000 |
| Steady state sequence number | 17 | 18 | 19 | 20 | 21 | 22 | 23 | |
| Slope of | -0.01483 | -0.00897 | -0.05706 | -0.14544 | -0.03605 | -1.12640 | -0.39966 | |
| Hours of duration | 0.09166 | 0.04167 | 0.12500 | 0.15833 | 0.47500 | 0.02500 | 0.12500 |
Calculating the slope average value of 23 steady states in the table, removing the steady states with larger slope deviation from the average value, positive slope and steady state duration less than 10min, and screening the steady states 3, 4, 7, 9, 11, 13, 14 and 21 as effective sleep steady states. The slope threshold is generally set to 0.1-0.2, and in this embodiment, the slope threshold is 0.15.
The formula for calculating the sum of the weight loss values during each effective sleep steady state as the absolute weight loss value D during sleep is as follows:
D＝∑ΔW
step S4, calculating the sleep metabolic rate by using the weight absolute reduction value D during the sleep period; preferably, the sleep metabolic rate is calculated by defining a sleep metabolic rate m and an 8-hour daily chemical sleep metabolic rate m (8 h); of course, other indicators (e.g., daily sleep metabolic rate of 12 hours, etc.) may be used to calculate the sleep metabolic rate.
The sleep metabolic rate m is calculated as follows:
wherein, Ws1Indicating the body weight at the beginning of sleep.
The calculation formula of the 8-hour daily chemical sleep metabolic rate m (8h) is as follows:
where Δ T represents the duration of sleep, in this embodiment the total duration of each steady state during sleep. The sleep metabolic rate m and the 8-hour daily chemical sleep metabolic rate m (8h) are preferably expressed in thousands; this index is used to normalize sleep metabolic rate at different sleep times.
As shown in Table 2, the effective sleep homeostasis selected from Table 1, and the calculated absolute weight loss D during sleep, sleep metabolic rate m, and 8-hour daily chemical sleep metabolic rate m (8h) were used.
TABLE 2
In this embodiment, the sleep metabolic rate can be calculated from the decrease in body weight during sleep by measuring the absolute decrease in body weight during the period in which a person sleeps in bed, and the metabolic level of a person can be reflected by long-term statistics as well as the caloric metabolic rate. Since the measurement of body weight is more direct than the measurement of calories, the metabolic condition of a person can be reflected more realistically. By introducing the steady state and calculating the weight loss value in each steady state duration, various interference factors (such as toilet, drinking, clothes adding and the like) influencing the weight in the sleeping process can be eliminated, and the weight measurement is more accurate. By setting a larger standard deviation threshold delta1The method can avoid the interruption of the steady state by fine disturbance, and increase the continuity of the steady state, thereby improving the measurement precision and better identifying the increase and decrease events of the object. By setting the slope threshold value to remove the steady state with a large deviation of the slope, the steady state with interference can be removed, and the threshold value delta caused by the increase of the standard deviation can be eliminated1Resulting in unrecognized interference.
Finally, the above embodiments are only intended to illustrate the technical solutions of the present invention and not to limit the present invention, and although the present invention has been described in detail with reference to the preferred embodiments, it will be understood by those skilled in the art that modifications or equivalent substitutions may be made on the technical solutions of the present invention without departing from the spirit and scope of the technical solutions, and all of them should be covered by the claims of the present invention.
Claims (10)
1. A method for calculating the metabolic rate of a sleep process based on slope interference elimination is characterized by comprising the following steps:
step S1, every first preset time T0Measuring the weight of the primary bed;
step S2, defining the length as uT0The time period of (2) is a long time window, whether the long time window taking the current measurement time as the end time is in a stable state or not 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 long time window is judged to be in the stable state; identifying the starting time and the ending time of sleep according to the steady state, and calculating the duration and the slope of each steady state in the sleep period;
step S3, screening effective sleep homeostasis according to the slope and duration of each homeostasis in the sleep period, and calculating the sum of weight loss values in each effective sleep homeostasis period as an absolute weight loss value D in the sleep period;
step S4, calculating the sleep metabolic rate using the value of the absolute weight loss during sleep.
2. The method for calculating the metabolic rate of sleep process based on slope de-interference as claimed in claim 1, wherein in the step of S1, a plurality of pressure sensors are disposed under a bed, each pressure sensor being spaced apart by a first preset time T0Measuring the weight of the primary bed; defining total weight S as the sum of the measured values of all pressure sensors, defining empty bed weight B as the sum of the measured values of all pressure sensors when the bed is empty, and defining instant weight W as S-B;
in the step S2, the method of identifying the sleep start time includes: judging whether a bed getting event occurs or not according to the measured total weight S; and whether the sleep mode enters the steady state after the bed entering event occurs, if the sleep mode enters the steady state after the bed entering event, the sleep mode is judged to enter, and the starting time of the steady state is taken as the starting time of the sleep.
3. The method for calculating the metabolic rate of sleep process based on slope interference elimination as claimed in claim 2, wherein the step S2 comprises the following sub-steps:
step S201, judging whether a bed getting event occurs according to the measurement value of the pressure sensor; if the bed getting event happens, executing step S202;
step S202, detecting whether to enter a steady state and whether to have a getting-off event, and if so, judging to enter a sleep state; step S203 is executed; if the bed getting-off event occurs, returning to execute the step S201;
step S203, detecting whether the steady state is finished, and executing step S204 if the steady state is finished;
step S204, calculating the duration and the slope of the steady state;
step S205, detecting whether to re-enter the steady state or whether the getting-off event occurs, if so, returning to execute the step S204; if the bed exit event occurs, executing step S206;
step S206, detecting whether a bed getting-on event occurs within the time threshold values of bed getting-on and bed getting-off and entering a steady state, and if the bed getting-on event occurs within the time threshold values of bed getting-on and bed getting-off and entering the steady state, returning to execute the step S204; otherwise, go to step S207;
step S207 determines that the sleep is completed, and step S3 is executed.
4. The method for calculating the metabolic rate of sleep process based on slope interference cancellation according to claim 2, wherein the method for determining whether the long time window is in a stable state comprises: the calculation takes the current measurement time as the end time and the length as 2uT0Average number S of all total weights S in the time period of (1)TWBy mean number STWCalculating the standard deviation of the values of all the total weights S recorded in the long time window for the mean value of the total weights, defining the standard deviation as σTWCSetting a long steady state standard deviation threshold delta1When a long time window ends, if σ of the long time windowTWC≤δ1If yes, determining that the long time window is in a stable state; if σTWC＞δ1Then the long time window is determined to be in an unstable state.
5. The method for calculating the metabolic rate of sleep process based on slope interference elimination as claimed in claim 3, wherein the method for determining whether the going-to-bed event and the going-out-of-bed event occur is as follows: if a certain measurement moment of the sensor is changed from an empty bed state to a bed state, judging that a bed getting event occurs at the measurement moment; if a certain measuring time of the sensor changes from the in-bed state to the empty-bed state, the occurrence of a bed-off event at the measuring time is judged.
6. The method of claim 3, wherein the slope of the steady state is calculated by fitting the values of all instantaneous body weights W in the steady state linearly using a least square method and calculating the slope.
7. The method of claim 6, wherein the weight loss Δ WV during steady state is calculated using the following formulaa：
ΔWa＝-K×ΔTW
Wherein, Delta TWIndicating the duration of the steady state.
8. The method of claim 1, wherein the method of screening for effective sleep homeostasis comprises:
defining a slope threshold and a steady-state duration threshold, firstly calculating the average value of all steady-state slopes during sleep, and then removing the steady state with the slope and the steady state with the positive slope, wherein the absolute value of the difference between the slope and the average value is greater than the slope threshold; and then removing the steady state with the steady state duration being less than the steady state duration threshold, and taking the remaining steady state as the effective sleep steady state.
9. The method for calculating sleep metabolic rate based on slope interference elimination as claimed in claim 1, wherein the method for calculating sleep metabolic rate using the absolute weight loss value D during sleep comprises:
the sleep metabolic rate m is calculated according to the following formula:
wherein, Ws1Indicating the body weight at the beginning of sleep.
10. The method for calculating sleep metabolic rate based on slope de-interference according to claim 9, wherein the method for calculating sleep metabolic rate using the absolute weight loss value during sleep further comprises:
the 8-hour daily chemical sleep metabolic rate m (8h) was calculated according to the following formula:
where Δ T represents the duration of sleep.
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