CN114027792B - Metabolic rate detection method for sleep process based on linear correlation coefficient interference elimination - Google Patents
Metabolic rate detection method for sleep process based on linear correlation coefficient interference elimination Download PDFInfo
- Publication number
- CN114027792B CN114027792B CN202111321449.4A CN202111321449A CN114027792B CN 114027792 B CN114027792 B CN 114027792B CN 202111321449 A CN202111321449 A CN 202111321449A CN 114027792 B CN114027792 B CN 114027792B
- Authority
- CN
- China
- Prior art keywords
- steady state
- sleep
- weight
- state
- correlation coefficient
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000000034 method Methods 0.000 title claims abstract description 74
- 230000037323 metabolic rate Effects 0.000 title claims abstract description 46
- 230000008569 process Effects 0.000 title claims abstract description 25
- 238000001514 detection method Methods 0.000 title claims abstract description 7
- 230000008030 elimination Effects 0.000 title abstract description 5
- 238000003379 elimination reaction Methods 0.000 title abstract description 5
- 238000005259 measurement Methods 0.000 claims abstract description 45
- 230000037396 body weight Effects 0.000 claims abstract description 27
- 238000012417 linear regression Methods 0.000 claims abstract description 24
- 238000004458 analytical method Methods 0.000 claims abstract description 23
- 239000013585 weight reducing agent Substances 0.000 claims abstract description 22
- 238000012216 screening Methods 0.000 claims abstract description 18
- 238000004364 calculation method Methods 0.000 claims description 14
- 230000004060 metabolic process Effects 0.000 claims description 6
- 230000002503 metabolic effect Effects 0.000 abstract description 6
- 230000013632 homeostatic process Effects 0.000 abstract description 3
- 230000009467 reduction Effects 0.000 abstract description 3
- 230000008859 change Effects 0.000 description 10
- 238000005303 weighing Methods 0.000 description 9
- 230000004580 weight loss Effects 0.000 description 8
- 230000002159 abnormal effect Effects 0.000 description 3
- 208000022249 Sleep-Wake Transition disease Diseases 0.000 description 2
- 230000002596 correlated effect Effects 0.000 description 2
- 230000000875 corresponding effect Effects 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 230000007774 longterm Effects 0.000 description 2
- 238000012544 monitoring process Methods 0.000 description 2
- 230000002688 persistence Effects 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
- 230000003321 amplification Effects 0.000 description 1
- 230000004579 body weight change Effects 0.000 description 1
- 230000003247 decreasing effect Effects 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
- 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
- 230000003068 static effect Effects 0.000 description 1
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 1
Classifications
-
- 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
-
- 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
- A61B5/4806—Sleep evaluation
- A61B5/4809—Sleep detection, i.e. determining whether a subject is asleep or not
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/68—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient
- A61B5/6887—Arrangements of detecting, measuring or recording means, e.g. sensors, in relation to patient mounted on external non-worn devices, e.g. non-medical devices
- A61B5/6891—Furniture
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
-
- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
- A61B5/7203—Signal processing specially adapted for physiological signals or for diagnostic purposes for noise prevention, reduction or removal
Abstract
The invention relates to a metabolic rate detection method for a sleep process based on linear correlation coefficient interference elimination, which comprises the steps of measuring the weight of a bed once every first preset time interval, identifying a steady state according to the measured value, and screening out an effective sleep steady state according to the correlation coefficient of linear regression analysis of each steady state; calculating the sum of weight reduction values during each effective sleep homeostasis period as an absolute weight reduction value during sleep; sleep metabolic rate was calculated using the absolute decrease in body weight 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 adopting the method for calculating the weight reduction value of each stable state duration, each interference factor affecting the weight in the sleeping process can be eliminated, and the stable state containing interference can be removed by calculating the correlation coefficient of linear regression analysis.
Description
Technical Field
The invention belongs to the technical field of sleep process metabolic rate monitoring, and relates to a metabolic rate detection method for a sleep process based on linear correlation coefficient 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 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, the present invention is directed to a method for detecting a metabolic rate of a sleep process based on linear correlation coefficient interference.
In order to achieve the above purpose, the present invention provides the following technical solutions:
a metabolic rate detection method for a sleep process based on linear correlation coefficient interference elimination comprises the following steps:
step S1, a first preset time T is set every interval 0 Measuring the weight of the primary bed;
step 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, and calculating the correlation coefficient of the linear regression analysis of the steady state;
step S3, screening effective sleep stable states according to correlation coefficients of linear regression analysis of each stable state in the sleep period, and calculating the sum of weight reduction values in each effective sleep stable state period as an absolute weight reduction value D in the sleep period;
and S4, calculating the sleep metabolism rate by using the value of the absolute weight reduction during the sleep period.
Further, in the step S1, 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;
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, 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 judging that the sleeping state is entered if the steady state is entered; step S203 is performed; returning to execute step S201 if a get-off event occurs;
step S203, detecting whether the steady state is ended, if so, executing step S204;
step S204, calculating a correlation coefficient of the steady-state linear regression analysis;
step S205, whether a steady state is re-entered or whether a getting-out event occurs is detected, and if the steady state is re-entered, the step S204 is executed again; executing step S206 if a get-off event occurs;
step S206, detecting whether a loading event occurs within the threshold of the loading and unloading time and entering a steady state, and if the loading event occurs within the threshold of the loading and unloading time and entering the steady state, returning to execute step S204; otherwise, step S207 is performed;
step S207, determining that sleep is completed, and executing step S3.
Further, the method for judging whether the long time window is in a stable state is as follows: calculating the current measurement time as the end time and the length of 2uT 0 Average S of all total weights S over a period of time TW By means of average number S TW Calculating a standard deviation of all total weight S values recorded over the long time window for the total weight mean, the standard deviation being defined as σ TWC Setting a long steady-state standard deviation threshold delta 1 When a long time window ends, if sigma of the long time window TWC ≤δ 1 Judging that the 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.
Further, the method for calculating the correlation coefficient of the steady-state linear regression analysis comprises the following steps: performing linear fitting on the values of all the instant weights W in the steady state by adopting a least square method and calculating a correlation coefficient K'; the calculation formula is as follows:
wherein N represents the number of values of the instantaneous body weight involved in the steady state, x i A time value corresponding to the value of the body weight at each moment in the steady state;representing an average of time values in steady state; y is i A value representing the instantaneous weight at each moment in time of steady state; />Mean of values representing instantaneous body weight in steady state.
Further, the method comprises the steps of performing linear fitting on the values of the weight W at all moments in the steady state by using a least square method, calculating a slope, and calculating a weight difference DeltaW according to the duration of the steady state and the slope a The method comprises the steps of carrying out a first treatment on the surface of the The calculation formula is as follows:
ΔW a ＝-K×ΔT W
wherein K represents a slope; delta T W Indicating the duration of the steady state.
Further, the method for screening the effective sleep stable state comprises the following steps: and defining a correlation coefficient threshold, taking a steady state of which the correlation coefficient is smaller than the correlation coefficient threshold in linear regression analysis as an interference steady state, removing the interference steady state, and taking the rest steady state as an effective sleep steady state.
Further, a steady state duration threshold is set, the slope of the steady state is calculated, and the steady state with the duration smaller than the steady state duration threshold and the positive slope is also taken as the interference steady state to be removed.
Further, the method of 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 W is s1 Indicating the body weight at the beginning of sleep.
Further, the method of calculating the sleep metabolic rate using the absolute weight loss value 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 present invention, 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 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 steady state and calculating the weight reduction value of each steady state duration, each interference factor affecting the weight in the sleeping process can be eliminated, and the weight measurement is more accurate. By calculating the correlation coefficient of the linear regression analysis, the steady state with disturbances can be removed.
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 the method for detecting metabolic rate during sleep based on linear correlation coefficient interference removal according to the present invention.
Fig. 2 is a flowchart of identifying the start and end times of sleep from a steady state and the correlation coefficients of the linear regression analysis of the 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 metabolic rate detection method for sleep process based on linear correlation coefficient interference removal of 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; bed airThe weight output value measured by each pressure sensor during loading is set as B1, B2, … … and Bn, and the sum of the weight output values is B, and the weight output value is 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.
When the accuracy of the sensor is high, the measured value of the sensor is not changed even in a static state, and the measured result is not remained at a constant value, so that a stable reading cannot be obtained; thus, the present embodiment introduces the concept of steady state to determine whether the measured value of the weight of the bed is stable, and the concept of steady state and steady state weight to measure the weight value.
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, and calculating the correlation coefficient of the linear regression analysis of the steady state. 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.
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: calculating the current measurement time as the end time and the length of 2uT 0 The average S of all total weights S over a period of time (i.e. twice as long window, in this example one minute) TW By means of average number S TW Calculate the total weight average valueStandard deviation of all total weight S values recorded over a long time window, defined as σ TWC Setting a long steady-state standard deviation threshold delta 1 (δ 1 The value of (a) is generally in the range of 5 to 1000, preferably 200 in this embodiment), when a long window ends, if sigma of the long window TWC ≤δ 1 Judging that the 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. The standard deviation of the embodiment adopts a twice long time window to calculate the mean value, so that the situation that the reading value changes greatly but the standard deviation changes little when the long time window is alternated, and thus inaccurate identification can be avoided.
In this embodiment, in order to avoid a situation where the change in the reading value is large but the change in the standard deviation is not large just when the time windows are alternated, a 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 value delta 1 The method can avoid the interruption of the steady state by tiny disturbance, and increase the persistence of the steady state, thereby improving the measurement precision and better identifying the increase and decrease events of the object; for example: delta 1 When the number is 200, the number of mobile phones on the bed can be accurately identified. Of course, the mean value can also be calculated by adopting a time window, and the long steady-state standard deviation threshold delta needs to be reduced 1 And thus reduce the ability to identify increasing or decreasing events of the object.
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 instants, there are 30 total weight S values; then calculate the average number of 60 total weights S in a 2-time window (i.e. 08:00:01-8:01:00), and calculate the standard deviation of the values of 30 total weights S in 08:00:31-8:01:00 with the average number of 60 total weights S in 08:00:01-8:01:00 as the average value, if not greater than 200, it is indicated 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) and the average of the previous 60 seconds is taken as the average value to judge whether 8:00:00 is steady state or not, namely, whether the total weight standard deviation of 30 times obtained in the 30 seconds exceeds 200 or not is judged from 08:00:31-8:01:00, if yes, the 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 the total weight standard deviation of 30 times obtained in the 30 seconds is more than 200 is judged from 08:00:32-8:01:01, and if the total weight standard deviation is not more than 200, the time is 8:01:01 in the 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 the total weight standard deviation of 30 times obtained in the 30 seconds is more than 200 is judged from 08:00:33-8:01:02, and if the total weight standard deviation is not more than 200, the time is 8:01:02 in a 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 the total 30 total weight standard deviation obtained from 08:00:34-8:01:03 in 30 seconds exceeds 200, if not, the total weight standard deviation is in the steady state 8:01:03.
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 selfHowever, a 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.
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 measured value of the pressure sensor; executing step S202 if a get-in event occurs; 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.
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.
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.
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. At the time of initial use, the weight of the user should be set as the reference weight W r The reference weight W can be compared with the measured value in the measuring process r Is adaptively adjusted.
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. 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. 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, whether the steady state is ended is detected, if the steady state is ended, step S204 is executed, and statistics and calculation are carried out on the measured value during the steady state. 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.
Step S204, calculating the correlation coefficient and the weight reduction value DeltaW in the linear regression analysis of the steady state a The method comprises the steps of carrying out a first treatment on the surface of the Of course, the duration of the steady state and the slope K may also be calculated simultaneously. Of course, this step is also possible. Not calculate the weight loss value DeltaW a After screening the effective sleep steady state in step S3, only calculating the steady state weight reduction value DeltaW of the effective sleep a 。
The correlation coefficient in the linear regression analysis is measured as a weighing reading curve in a steady state which is recognized by us, the linear correlation between the weighing reading curve and a straight line obtained by linear regression is obtained, the maximum value is 1, the closer to 1 is the more positive correlation, the minimum value is-1, and the closer to-1 is the more negative correlation; when the absolute value of the correlation coefficient is greater than 0.8, the correlation coefficient is considered to be highly correlated, significant correlation is considered to be between 0.5 and 0.8, low correlation is considered to be between 0.3 and 0.5, and no correlation is considered to be less than 0.3. In this embodiment, the least square method is preferably adopted to linearly fit the values of all the instant weights W in the steady state and calculate the correlation coefficient K'; the calculation formula is as follows:
wherein N represents the number of values of the instantaneous body weight involved in the steady state, x i A time value corresponding to the value of the body weight at each moment in the steady state;representing an average of time values in steady state; y is i A value representing the instantaneous weight at each moment in time of steady state; />Mean of values representing instantaneous body weight in steady state.
Of course, other methods of calculating the correlation coefficient for linear regression analysis may be used.
The method for calculating the weight loss value during the steady state is as follows: the weight difference was calculated from the duration and slope of the steady state, namely:
ΔW a ＝-K×ΔT W
wherein K represents a slope; delta T W Indicating the duration of steady state; this eliminates the disturbance of the fluctuation at the start or end of the steady state, and the calculation result is more reliable. When calculating the slope K, adopting a least square method to linearly fit the values of all the instant weight W in the steady state and calculating the slope; the calculation formula is as follows:
of course, other linear fitting methods may be used to calculate the slope of the steady state, as long as the slope can be calculated from the values of all instantaneous body weights W in the steady state.
In addition, the difference between the end times of the steady state start time may be directly calculated as the weight reduction value during the steady state. Definition of steady state body weight W a ＝S a -B; the weight loss value Δw during steady state a The calculation formula of (2) is as follows:
ΔW a ＝W a+ -W a-
wherein W is a+ A steady state body weight representing the onset of effective sleep steady state; w (W) a- Indicating steady state body weight at the end of effective sleep steady state. Since the value of the empty bed weight B is not recalculated during the steady state duration, the empty bed weight B value must be equal during the same steady state, and thus the steady state reading can also be directly used to calculate the weight reduction value aw during steady state a ：
ΔW a ＝S aw+ -S aw-
Wherein S is aw+ A steady state reading representing an effective sleep steady state onset time; s is S aw- A steady state reading representing the end of active sleep steady state.
As shown in fig. 3 and 4, when the instantaneous weight is taken as the weighing reading, the change of the weighing reading for 8 hours is shown schematically, and it can be seen from fig. 4 that the weighing reading curve looks flat in the ordinate range of 0-100 kg, and has very large jump all the time after amplification, so that the reading has huge jump in unsteady state, and even in steady state, no stable reading exists, and the instantaneous weight W at a single moment cannot be directly used for calculation. 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 the value of the total weight S during the unsteady state as a null value; definition of steady state reading S a Calculating the current measurement time as the end time and the length rT 0 Time of (1)The average of all total weights S over the interval is taken as steady state reading S a Where r is a natural number and r.gtoreq.u, preferably r=2u, i.e. the steady state reading S is taken as the one minute average of the total weight S a . It should be noted that a null value is no value, and is not "0", because "0" is a valued value, and participates in the calculation at the time of averaging; for example, for the first moment of steady state, since the previous 59 moments are unsteady, the total weight is null, so there is only one total weight value, and the average S at that moment is calculated TW 59 null values are removed, and only the average value of one value is calculated; if the total weight during the unsteady state is defined as "0", the average of the total weight at that time and 59 "0" s is calculated, so that an error may occur. Definition of steady state body weight W a ＝S a -B; a steady-state body weight (one minute mean) is output every 30s as a weighing reading, and a change of the weighing reading at this time is shown in fig. 5, at this time, we consider the part outputting the null value as unsteady 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 the disturbance of the abnormal event can be effectively eliminated by calculating only the body weight reduction at steady-state.
Step S205, whether a steady state is re-entered or whether a getting-out event occurs is detected, and if the steady state is re-entered, the step S204 is executed again; step S206 is performed if a get-off event occurs.
Continuing with the description of the above example: after the first steady state of time 08:31:59 ends, the long time window with time 8:32:00 to 8:35:00 as the end time is in an unsteady state, and the long time window with time 8:35:01 as the end time is in a steady state again, so time 8:35:01 is the starting time of the next steady state. Since the first steady state is ended and the getting-out event does not occur, the sleep is judged not to be ended, and the step S206 is not performed to judge the sleep end and the getting-out event. After the steady state is completed at time 11:00:00, the occurrence of the getting-out event is detected at 11:01:00, so that step S206 is executed to determine the sleep completion and the getting-in/out event. Similarly, after the steady state at the time 13:09:00 is ended, the occurrence of the getting-out event is detected at the time 13:10:00, and step S206 is also executed to determine that the sleep is ended and the getting-in/out event is performed.
Step S206, detecting whether a loading event occurs within the threshold of the loading and unloading time and entering a steady state, and if the loading event occurs within the threshold of the loading and unloading time and entering the steady state, returning to execute step S204; otherwise, step S207 is performed.
Continuing with the description of the above example: after detecting the occurrence of a bedevent at 11:01:00, detecting the occurrence of a bedevent at 11:03:00, and again being in steady state at 11:03:11. 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 (30 minutes), it is judged that the event of getting in and out of bed occurs, and sleep is not ended. After detecting the occurrence of the getting-out event at the time 13:10:00, until the getting-in event is not detected at the time 11:40:00, the time interval exceeds the getting-in and getting-out 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.
Step S207, determining that sleep is completed, and executing step S3.
And S3, screening out effective sleep stable states according to correlation coefficients of linear regression analysis of each stable state in the sleep period, and calculating the sum of weight reduction values in each effective sleep stable state period as an absolute weight reduction value D in the sleep period.
Of course, in order to further screen out a reliable steady state, the screening is also performed together with the slope and duration of the steady state, and it should be noted that the three screening methods do not have a fixed sequence, i.e. the screening can be performed by first screening the correlation coefficients of the linear regression analysis, and then screening by adopting two other modes; it is also possible to first filter by the slope of the steady state or first filter by the duration of the steady state. In this embodiment, the correlation coefficient screening by the linear regression analysis is described by taking the duration screening of the steady state, the slope screening of the steady state, and the final screening of the correlation coefficient.
As shown in table 1, 23 steady states were identified for the slope and duration statistics of the steady states in fig. 5.
TABLE 1
A steady state duration threshold is defined first, and the steady state duration threshold is generally selected to be 5min to 15min, preferably 10min in this embodiment. Removing the steady state with the steady state duration threshold less than 10min (namely 0.16667 h), and obtaining the steady states of 2, 3, 4, 7, 9, 11, 13, 14 and 21 after screening.
The steady state 2 is further eliminated by removing the steady state with positive slope, leaving the steady states 3, 4, 7, 9, 11, 13, 14, 21. It can be seen from fig. 5 that there is clearly an abnormality in the steady state 7 (the portion indicated by a dotted frame in the figure), but this cannot be removed by the steady state duration and slope method.
Finally, the correlation coefficient of the linear regression analysis of the remaining steady state is calculated as shown in Table 2:
TABLE 2
| Steady state sequence number | 3 | 4 | 7 | 9 | 11 | 13 | 14 | 21 |
| Correlation coefficient | 0.8690 | 0.6584 | 0.6437 | 0.9787 | 0.9410 | 0.9642 | 0.9696 | 0.8965 |
Since a closer correlation coefficient to 1 indicates a more positive correlation and a higher absolute value than 0.8 is considered to be highly correlated, a correlation coefficient threshold value of not less than 0.8 is generally set, and preferably 0.85 in this embodiment. As can be seen from table 2, steady state 7 with obvious anomalies has a significantly worse correlation and can be removed; in addition, the correlation of steady state 4, which is not clearly abnormal, is also poor and thus is removed altogether, as is evident from fig. 5. Thereby screening the steady state 3, 9, 11, 13, 14, 21 for effective sleep steady state.
The formula for calculating the sum of the weight loss values during each effective sleep homeostasis period as the absolute weight loss value D during sleep is as follows:
D＝∑ΔW
step S7, calculating a sleep metabolic rate by using the absolute weight reduction value D in the sleep period; 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.
As shown in table 3, the effective sleep homeostasis selected from table 1 was obtained, and the absolute weight loss value D, the sleep metabolic rate m, and the 8-hour daily sleep metabolic rate m (8 h) during sleep were calculated.
TABLE 3 Table 3
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 steady state and calculating the weight reduction value of each steady state duration, various interference factors (such as toilet, water drinking, clothes adding and the like) affecting the weight in the sleeping process can be eliminated, and the weight measurement is more accurate. By setting a larger standard deviation threshold delta 1 The method can avoid the interruption of the steady state by tiny disturbance, and increase the persistence of the steady state, thereby improving the measurement precision and better identifying the increase and decrease events of the object. By calculating the correlation coefficient of the linear regression analysis, the steady state containing the interference can be effectively removed and can be dischargedExcept for increasing the standard deviation threshold delta 1 Resulting in unrecognized interference.
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 (7)
1. The metabolic rate detection method for the sleep process based on the linear correlation coefficient interference removal is characterized by comprising the following steps of:
step S1, a first preset time T is set every interval 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;
step 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, and calculating the correlation coefficient of the linear regression analysis of the 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, judging to enter a sleep state, and taking the starting time of the steady state as the starting time of sleep;
the method for calculating the correlation coefficient of the steady-state linear regression analysis comprises the following steps: performing linear fitting on the values of all the instant weights W in the steady state by adopting a least square method and calculating a correlation coefficient K'; the calculation formula is as follows:
wherein N represents the number of values of the instantaneous body weight involved in the steady state, x i A time value corresponding to the value of the body weight at each moment in the steady state;representing an average of time values in steady state; y is i A value representing the instantaneous weight at each moment in time of steady state; />Average number of values representing instantaneous body weight in steady state;
step S3, screening effective sleep stable states according to correlation coefficients of linear regression analysis of each stable state in the sleep period, and calculating the sum of weight reduction values in each effective sleep stable state period as an absolute weight reduction value D in the sleep period;
the method for screening the effective sleep stable state comprises the following steps: defining a correlation coefficient threshold, taking a steady state of which the correlation coefficient is smaller than the correlation coefficient threshold in linear regression analysis as an interference steady state, removing the interference steady state, and taking the rest steady state as an effective sleep steady state;
and S4, calculating the sleep metabolism rate by using the value of the absolute weight reduction during the sleep period.
2. The method for detecting the metabolic rate of a sleep process based on linear correlation coefficient interference removal according to claim 1, wherein the step S2 comprises the following sub-steps:
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 judging that the sleeping state is entered if the steady state is entered; step S203 is performed; returning to execute step S201 if a get-off event occurs;
step S203, detecting whether the steady state is ended, if so, executing step S204;
step S204, calculating a correlation coefficient of the steady-state linear regression analysis;
step S205, whether a steady state is re-entered or whether a getting-out event occurs is detected, and if the steady state is re-entered, the step S204 is executed again; executing step S206 if a get-off event occurs;
step S206, detecting whether a loading event occurs within the threshold of the loading and unloading time and entering a steady state, and if the loading event occurs within the threshold of the loading and unloading time and entering the steady state, returning to execute step S204; otherwise, step S207 is performed;
step S207, determining that sleep is completed, and executing step S3.
3. The method for detecting the metabolic rate of a sleep process based on linear correlation coefficient interference according to claim 1, wherein the method for judging whether the long time window is in a steady state is as follows: calculating the current measurement time as the end time and the length of 2uT 0 Average S of all total weights S over a period of time TW By means of average number S TW Calculating a standard deviation of all total weight S values recorded over the long time window for the total weight mean, the standard deviation being defined as σ TWC Setting a long steady-state standard deviation threshold delta 1 When a long time window ends, if sigma of the long time window TWC ≤δ 1 Judging that the 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.
4. The method for detecting the metabolic rate in a sleep process based on linear correlation coefficient interference as claimed in claim 1, wherein the values of the weight W at all moments in the steady state are linearly fitted by a least square method and the slope is calculated, and the weight difference DeltaW is calculated based on the duration of the steady state and the slope a The method comprises the steps of carrying out a first treatment on the surface of the The calculation formula is as follows:
ΔW a ＝-K×ΔT W
wherein K represents a slope; delta T W Indicating the duration of the steady state.
5. The method for detecting the metabolic rate of a sleep process based on linear correlation coefficient interference according to claim 4, wherein a steady state duration threshold is further set, a slope of the steady state is calculated, and steady states with duration less than the steady state duration threshold and positive values of the slope are also removed as interference steady states.
6. The method for detecting a metabolic rate of a sleep process based on linear correlation coefficient interference according to claim 1, wherein the method for calculating a sleep metabolic rate using an absolute weight reduction value D during sleep comprises:
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.
7. The method for detecting metabolic rate in a sleep process based on linear correlation coefficient interference according to claim 6, wherein the method for calculating a sleep metabolic rate using an absolute weight reduction value 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.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111321449.4A CN114027792B (en) | 2021-11-09 | 2021-11-09 | Metabolic rate detection method for sleep process based on linear correlation coefficient interference elimination |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202111321449.4A CN114027792B (en) | 2021-11-09 | 2021-11-09 | Metabolic rate detection method for sleep process based on linear correlation coefficient interference elimination |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114027792A CN114027792A (en) | 2022-02-11 |
| CN114027792B true CN114027792B (en) | 2024-04-09 |
Family
ID=80137038
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202111321449.4A Active CN114027792B (en) | 2021-11-09 | 2021-11-09 | Metabolic rate detection method for sleep process based on linear correlation coefficient interference elimination |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114027792B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| WO2024025768A1 (en) * | 2022-07-26 | 2024-02-01 | Sleep Number Corporation | Bed system with features to track changes in body weight within a sleep session |
Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3914251B1 (en) * | 2006-04-12 | 2007-05-16 | 株式会社クロスウェル | Basal metabolic rate measuring device and basal metabolic rate measuring system |
| JP3967365B1 (en) * | 2006-12-12 | 2007-08-29 | 株式会社クロスウェル | Biological information management system, control method thereof, and program |
| CN102946800A (en) * | 2010-04-05 | 2013-02-27 | 科文迪斯有限公司 | Method and apparatus for personalized physiologic parameters |
| WO2013046810A1 (en) * | 2011-09-26 | 2013-04-04 | オムロンヘルスケア株式会社 | Weight management device |
| JP2013198574A (en) * | 2012-03-23 | 2013-10-03 | Tanita Corp | Biometric apparatus and biometric system |
| WO2017048622A1 (en) * | 2015-09-17 | 2017-03-23 | Susan Roberts | Method and system for improving weight management interventions by assessing variability in serial weight measurements |
| CN109065113A (en) * | 2018-07-12 | 2018-12-21 | 山东师范大学 | Human body loss of weight proposal recommending method, system and medium based on machine learning |
| JP2020014696A (en) * | 2018-07-25 | 2020-01-30 | オムロンヘルスケア株式会社 | Biometric information management device, biometric information management method and program |
| US10595776B1 (en) * | 2014-09-09 | 2020-03-24 | Vital Connect, Inc. | Determining energy expenditure using a wearable device |
| CN112472052A (en) * | 2020-12-21 | 2021-03-12 | 安徽华米智能科技有限公司 | Weight prediction method, device and equipment based on personal motor function index (PAI) |
| CN112914884A (en) * | 2021-01-19 | 2021-06-08 | 重庆火后草科技有限公司 | Method for measuring weight value in sleep state through steady-state duration confidence |
| CN112924007A (en) * | 2021-01-19 | 2021-06-08 | 重庆火后草科技有限公司 | Weight measurement method based on target sleep |
| CN113576423A (en) * | 2020-04-15 | 2021-11-02 | 广东小天才科技有限公司 | Method, device, equipment and storage medium for determining core body temperature |
-
2021
- 2021-11-09 CN CN202111321449.4A patent/CN114027792B/en active Active
Patent Citations (13)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| JP3914251B1 (en) * | 2006-04-12 | 2007-05-16 | 株式会社クロスウェル | Basal metabolic rate measuring device and basal metabolic rate measuring system |
| JP3967365B1 (en) * | 2006-12-12 | 2007-08-29 | 株式会社クロスウェル | Biological information management system, control method thereof, and program |
| CN102946800A (en) * | 2010-04-05 | 2013-02-27 | 科文迪斯有限公司 | Method and apparatus for personalized physiologic parameters |
| WO2013046810A1 (en) * | 2011-09-26 | 2013-04-04 | オムロンヘルスケア株式会社 | Weight management device |
| JP2013198574A (en) * | 2012-03-23 | 2013-10-03 | Tanita Corp | Biometric apparatus and biometric system |
| US10595776B1 (en) * | 2014-09-09 | 2020-03-24 | Vital Connect, Inc. | Determining energy expenditure using a wearable device |
| WO2017048622A1 (en) * | 2015-09-17 | 2017-03-23 | Susan Roberts | Method and system for improving weight management interventions by assessing variability in serial weight measurements |
| CN109065113A (en) * | 2018-07-12 | 2018-12-21 | 山东师范大学 | Human body loss of weight proposal recommending method, system and medium based on machine learning |
| JP2020014696A (en) * | 2018-07-25 | 2020-01-30 | オムロンヘルスケア株式会社 | Biometric information management device, biometric information management method and program |
| CN113576423A (en) * | 2020-04-15 | 2021-11-02 | 广东小天才科技有限公司 | Method, device, equipment and storage medium for determining core body temperature |
| CN112472052A (en) * | 2020-12-21 | 2021-03-12 | 安徽华米智能科技有限公司 | Weight prediction method, device and equipment based on personal motor function index (PAI) |
| CN112914884A (en) * | 2021-01-19 | 2021-06-08 | 重庆火后草科技有限公司 | Method for measuring weight value in sleep state through steady-state duration confidence |
| CN112924007A (en) * | 2021-01-19 | 2021-06-08 | 重庆火后草科技有限公司 | Weight measurement method based on target sleep |
Non-Patent Citations (3)
| Title |
|---|
| 中国科学技术大学学报2012年第42卷总目次;中国科学技术大学学报;20121215(第12期);全文 * |
| 絮凝—吸附去除微污染水中THMFP;丁英锋等;《中国给水排水》;20020731(第7期);第45-47页 * |
| 风湿性心脏瓣膜置换术后患者体温近日节律的变化;王聪等;《中华护理杂志》;20131230;第48卷(第12期);第1083-1085页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114027792A (en) | 2022-02-11 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN104169680B (en) | Use the Level Change of baroceptor monitoring device | |
| US8622921B2 (en) | Sleep evaluation device and sleep evaluation method therefor | |
| US10448928B2 (en) | Method and device for detecting physiological index | |
| US7357776B2 (en) | Activity-induced energy expenditure estimating instrument | |
| CN114027792B (en) | Metabolic rate detection method for sleep process based on linear correlation coefficient interference elimination | |
| CN104921736B (en) | A kind of continuous blood sugar monitoring device for including parameter Estimation function filtration module | |
| CN112924007B (en) | Weight measurement method based on target sleep | |
| CN112284556B (en) | Calibration system and method with temperature compensation | |
| Staal et al. | Kalman smoothing for objective and automatic preprocessing of glucose data | |
| US20070168230A1 (en) | Method for conditioning body weight and device for implementing this method | |
| CN103961066A (en) | Temperature measurement method and temperature measurement device | |
| CN112891098B (en) | Body weight measuring method for health monitor | |
| CN113974568B (en) | Slope interference-free method for calculating metabolic rate of sleep process | |
| CN112914884B (en) | Method for measuring weight value in sleep state through steady-state duration confidence | |
| CN104931892B (en) | A kind of battery of mobile terminal capacity calculation methods and system | |
| CN112914883B (en) | Method for measuring weight value in sleep state through eccentricity confidence | |
| CN110179444A (en) | A kind of infant's temperature check foot loop system and its detection method | |
| CN113974567B (en) | Method for calculating metabolic rate in sleeping process | |
| US11759123B2 (en) | Ketone body concentration estimation device, method, and computer-readable storage medium | |
| CN107576710B (en) | Electrochemical gas sensor and its calibration method, air conditioner | |
| CN112284469B (en) | Zero drift processing method of ultrasonic water meter | |
| WO2016014620A1 (en) | Systems and methods for fluid testing | |
| US11589775B2 (en) | Alarm limits update system for a medical monitoring device | |
| US20220192544A1 (en) | Method for determining a current glucose value in a transported fluid | |
| CN116680446B (en) | Intelligent acquisition method and system for fermentation production data of ecological fertilizer containing bacteria |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |