CN113081493A - Old and young monitoring system - Google Patents

Old and young monitoring system Download PDF

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CN113081493A
CN113081493A CN202110490766.2A CN202110490766A CN113081493A CN 113081493 A CN113081493 A CN 113081493A CN 202110490766 A CN202110490766 A CN 202110490766A CN 113081493 A CN113081493 A CN 113081493A
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张琦
李佳朋
倪琦
张柯涵
李晓
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Guilin University of Electronic Technology
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    • A—HUMAN NECESSITIES
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    • A61F13/00—Bandages or dressings; Absorbent pads
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    • A—HUMAN NECESSITIES
    • A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
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    • A61F13/00—Bandages or dressings; Absorbent pads
    • A61F13/15—Absorbent pads, e.g. sanitary towels, swabs or tampons for external or internal application to the body; Supporting or fastening means therefor; Tampon applicators
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Abstract

The invention relates to a monitoring system for old and young children, which solves the technical problem that abnormality cannot be found in time, and comprises an intelligent paper diaper capable of detecting a bed and body temperature, wherein the intelligent paper diaper is provided with a control unit which is connected to a mobile communication device through a wireless communication module, and the mobile communication device outputs a monitoring result or an alarm signal; the control unit is used for receiving and judging the technical scheme of the bed-wetting detection result and the body temperature detection result, better solves the problem and can be used for monitoring the old and the children.

Description

Old and young monitoring system
Technical Field
The invention relates to the field of old-age care equipment, in particular to a monitoring system for old and young people.
Background
At present, for the bedwetting situation and the abnormal body temperature situation of old people and children, a guardian needs to regularly patrol and check in a short distance. This results in a heavy work task and failure to timely detect anomalies.
Aiming at the night monitoring problem of the old and the children in life, the invention provides the old and the children monitoring system capable of monitoring the bed wetting and the abnormal body temperature conditions of the old and the children.
Disclosure of Invention
The invention aims to solve the technical problem that the abnormality cannot be found in time in the prior art. The monitoring system for the old and the young is provided and has the characteristics of timely and simple discovery.
In order to solve the technical problems, the technical scheme is as follows:
a monitoring system for old and young children comprises an intelligent paper diaper capable of detecting a bed and body temperature, wherein the intelligent paper diaper is provided with a control unit, the control unit is connected to a mobile communication device through a wireless communication module, and the mobile communication device outputs a monitoring result or an alarm signal; the control unit is used for receiving and judging a bed wetting detection result and a body temperature detection result. The absorption area of the intelligent paper diaper is provided with a first bed wetting monitoring unit for monitoring the bed wetting condition, and one surface, close to the human body, of the intelligent paper diaper is provided with a first temperature detector for detecting the body temperature of the human body; the first bed wetting monitoring unit comprises a second temperature detector which is arranged on the middle layer of the absorption region at intervals and is used for detecting urine, and the second temperature detector is connected with the control unit
The working principle of the invention is as follows: the invention detects the body temperature of human body, monitors the bed-wetting condition, and remotely transmits the body temperature to a mobile communication device, such as a mobile phone, in a wireless communication mode. Therefore, the monitoring result is output by the mobile phone, and an alarm signal is sent out to remind a guardian to check and take care of the abnormal state (such as abnormal body temperature and bed wetting). Bedwetting detection is based on the difference between the absorbent region before and after absorption of urine. Before bed wetting, the absorbent region is not filled with urine, is dry and has a relatively constant internal temperature. After the bed-wetting, the absorption zone is filled with urine, the problem of urine is different from its inside temperature, and the temperature value can change in the twinkling of an eye, at this moment, can judge the bed-wetting condition. Meanwhile, if a relation model of the urine temperature and the human body temperature is established, the human body temperature can be calculated accordingly, so that the human body temperature detection is supplemented favorably, and errors are reduced.
As a simplified version, the first temperature detectors may be distributed so as to function as the second temperature detectors, which may be omitted. When urine flows in from the upper layer (side close to the human body) of the absorption region, a temperature change can be detected, and when the change value is larger than a threshold value, a bedwetting condition can be determined.
Further, the first temperature detector includes a plurality of temperature sensors disposed in a distributed manner.
Further, the second temperature detector includes a plurality of temperature sensors disposed in a distributed manner.
Further, the temperature sensor adopts a sht20 sensor.
Furthermore, the absorption area is provided with a second bed wetting monitoring unit for monitoring the bed wetting condition, the second bed wetting monitoring unit comprises a plurality of electrodes which are arranged on the middle layer of the absorption area at intervals, the electrodes are mutually conducted under the bed wetting condition to output conducting signals to the control unit to realize urine detection, and the first bed wetting monitoring unit and the second bed wetting monitoring unit are connected to the control unit through the AND gate unit.
In order to reduce the false detection of bed wetting detection, the invention is additionally provided with a second bed wetting monitoring unit on the basis of the first bed wetting monitoring unit. The second bed-wetting monitoring unit is based on the characteristic that the main component of urine is water, and the middle layer of the absorption area is provided with spaced electrodes, and before the bed-wetting, the electrodes are not conducted based on the insulation characteristic. After bed wetting, the electrodes are conducted, and the control unit judges that the bed wetting condition occurs after receiving the conduction signal.
Further, the wireless communication module is an HC05 bluetooth communication module.
Further, the abnormal state prediction method in the control unit includes the steps of:
extracting urine-time sample data, and generating a training set and a test set;
step two, establishing a traditional Gaussian regression prediction model, wherein the traditional Gaussian regression prediction model adopts a mean function of m (x) -E [ f (x) ], and an average exponential covariance function of m (x) ]
Figure BDA0003051930980000031
Wherein the content of the first and second substances,
Figure BDA0003051930980000032
is the signal variance, M ═ diag (l)2) L is a variance scale, and x, x' are random arbitrary variables; defining a hyper-parameter as
Figure BDA0003051930980000033
Initializing bees, randomly initializing the positions H, the step length delta H, the neighborhood radius R, the inertia weight omega, the collision avoidance weight s of the same kind, the same speed weight a of the same kind, the closing weight c of the same kind, the appetite weight f and the rejection weight e of natural enemies;
step four, sequentially assigning the information of the individual positions H of the bees to l, sigmafσ n; the first row of the position H matrix stores the value of the parameter l, the second row stores the value of the parameter sigma f, the third row stores the value of the parameter sigma n, and each bee individual corresponds to a group of parameter values;
calculating a fitness function value by using a bee algorithm, judging whether the current fitness function value is the optimal fitness value or not, and if so, judging the corresponding hyper-parameter
Figure BDA0003051930980000034
Storing the value as an optimal hyper-parameter value, otherwise, still storing the original fitness value and the corresponding hyper-parameter value; calculating a fitness function of the bee algorithm:
ΔXt+1＝(sSi+aAi+cCi+fFi+PEi)+ωΔXt；Sias the ith individualIs/are as follows
Figure BDA0003051930980000035
Figure BDA0003051930980000036
AiFor the ith individual
Figure 100002_1
CiFor the ith individual
Figure BDA0003051930980000038
Figure BDA0003051930980000039
FiIs the appetite value of the ith individual ═ X+-X，EiIs the natural enemy rejection value of the ith individual ═ X-+ X; x is the current individual's location, XjIs the position of the adjacent individual j, N is the number of the adjacent individuals, VjIs the speed of the j-th adjacent individual, X+Is the position of the target flower, X-The position of the heaven and earth;
position X of next generation bee when there is adjacent beet+1＝Xt+ΔXt+1(ii) a When no adjacent bees exist, the function set as the random walk behavior is a fitness function, namely an Le' vy function, and the individual positions of the next generation of bees are as follows: xt+1＝Xt+Le′xy(d)×Xt；
Wherein t is the current iteration number, i is the ith bee individual, and XiThe position of the current t generation population individual; Δ Xt+1Updating the step length for the next generation of population position; xt+1The position of the next generation population individual is shown, and d is the dimension of the individual position vector;
the fitness function Le' vy of the bee algorithm is as follows:
Figure BDA0003051930980000041
r1，r2is [0,1 ]]The random number of (2); function(s)Γ is Γ (x) ═ x-1! Beta is a constant;
step six, judging whether the maximum iteration times is reached, if so, outputting an optimal hyper-parameter and creating an optimal Gaussian regression prediction model; otherwise, updating the optimal individual and the worst individual of the bee, updating the neighborhood radius R and updating the individual position in sequence, then returning to the fourth step and continuing iteration;
step seven, inputting the test set into the optimal Gaussian regression prediction model determined according to the step six and the step two, and outputting the mean value and the variance of the predicted value;
and step eight, predicting and calculating a real-time interval prediction result of the urine volume.
Further, the urine-time sample data in the first step is body temperature-time sample data, and the body temperature real-time interval prediction result is calculated in the eighth step.
The invention has the beneficial effects that: the invention realizes remote alarm display through detecting the body temperature and bed wetting condition of the human body and remote communication. The bed-wetting condition is monitored by detecting the body temperature of the human body and is remotely transmitted to a mobile communication device, such as a mobile phone, in a wireless communication mode. Therefore, the monitoring result is output by the mobile phone, and an alarm signal is sent out to remind a guardian to check and take care of the abnormal state (such as abnormal body temperature and bed wetting). Bedwetting detection is based on the difference between the absorbent region before and after absorption of urine. Before bed wetting, the absorbent region is not filled with urine, is dry and has a relatively constant internal temperature. After the bed-wetting, the absorption zone is filled with urine, the problem of urine is different from its inside temperature, and the temperature value can change in the twinkling of an eye, at this moment, can judge the bed-wetting condition. Meanwhile, if a relation model of the urine temperature and the human body temperature is established, the human body temperature can be calculated accordingly, so that the human body temperature detection is supplemented favorably, and errors are reduced. In order to further optimize the scheme, the invention introduces a mechanism of future prediction, and a prediction program of the bed wetting condition and the abnormal body temperature is loaded on the control unit.
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The invention is further illustrated with reference to the following figures and examples.
Fig. 1 is a schematic diagram of a monitoring system for young and old people.
Fig. 2 is a schematic plan view of the first bed wetting monitoring unit.
Fig. 3 is a schematic cross-sectional view of a first bed wetting monitoring unit.
Fig. 4 is a schematic plan view of the second bed wetting monitoring unit.
Fig. 5 is a schematic diagram of the sht20 circuit.
Fig. 6 is a circuit schematic diagram of the HC05 bluetooth communication module.
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is further described in detail with reference to the following embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention.
Example 1
The embodiment provides a monitoring system for old and young children, as shown in fig. 1, the monitoring system for old and young children comprises an intelligent paper diaper capable of detecting a bed and a body temperature, the intelligent paper diaper is provided with a control unit, the control unit is connected to a mobile communication device through a wireless communication module, and the mobile communication device outputs a monitoring result or an alarm signal; the control unit is used for receiving and judging a bed wetting detection result and a body temperature detection result.
The embodiment is used for detecting the body temperature of a human body and monitoring the bed wetting condition, and the bed wetting condition is remotely transmitted to a mobile communication device, such as a mobile phone, in a wireless communication mode. Therefore, the monitoring result is output by the mobile phone, and an alarm signal is sent out to remind a guardian to check and take care of the abnormal state (such as abnormal body temperature and bed wetting).
Specifically, as shown in fig. 2 and fig. 3, the absorption region of the intelligent diaper is provided with a first bed wetting monitoring unit for monitoring a bed wetting situation, and one side of the intelligent diaper close to the human body is provided with a first temperature detector for detecting the body temperature of the human body; the first bed wetting monitoring unit comprises a second temperature detector which is arranged on the middle layer of the absorption area at intervals and used for detecting urine, and the second temperature detector is connected with the control unit.
Bedwetting detection is based on the difference between the absorbent region before and after absorption of urine. Before bed wetting, the absorbent region is not filled with urine, is dry and has a relatively constant internal temperature. After the bed-wetting, the absorption zone is filled with urine, the problem of urine is different from its inside temperature, and the temperature value can change in the twinkling of an eye, at this moment, can judge the bed-wetting condition. Meanwhile, if a relation model of the urine temperature and the human body temperature is established, the human body temperature can be calculated accordingly, so that the human body temperature detection is supplemented favorably, and errors are reduced.
In order to reduce false positives, the first temperature detector includes a plurality of temperature sensors arranged in a distributed manner.
In order to reduce false positives, the second temperature detector includes a plurality of temperature sensors arranged in a distributed manner.
Specifically, as shown in fig. 5, the temperature sensor is a sht20 sensor. The sht20 sensor also has the function of detecting humidity. The sht20 sensor sends a 16-bit data to the singlechip, the singlechip carries out corresponding calculation to the 16-bit data received by judging whether the incoming address is temperature or humidity, and converts the data into temperature or humidity, then the singlechip sends the temperature and the humidity after processing to the mobile phone through the Bluetooth, and the mobile phone displays the received data on the interface by using the APP and detects the data.
The sht20 sensor adopts a standard I2C communication protocol, the SDA is a serial data line, the SCL is a serial clock line, the SDA and the SCL are respectively connected with the IO ports corresponding to the SCM, a program is written according to a time sequence diagram of I2C to realize data transmission, and the sensor can detect ambient temperature and humidity and send data to the SCM. And meanwhile, the outer part of the bag is packaged by waterproof materials, so that the waterproof effect is realized, and the stability is ensured while the accuracy is higher.
As shown in fig. 4, the absorption region is provided with a second bed wetting monitoring unit for monitoring the bed wetting condition, the second bed wetting monitoring unit comprises a plurality of electrodes arranged at intervals on the middle layer of the absorption region, the electrodes are mutually conducted under the bed wetting condition and output conducting signals to the control unit to realize urine detection, and the first bed wetting monitoring unit and the second bed wetting monitoring unit are connected to the control unit through the and gate unit.
In order to reduce the false detection of bed wetting detection, the second bed wetting monitoring unit is additionally arranged on the basis of the first bed wetting monitoring unit. The second bed-wetting monitoring unit is based on the characteristic that the main component of urine is water, an electrode pair is arranged at intervals in the middle layer of the absorption area, and the electrodes are not conducted before the bed-wetting based on the insulation characteristic. After bed wetting, the electrodes are conducted, and the control unit judges that the bed wetting condition occurs after receiving the conduction signal.
As shown in fig. 6, the wireless communication module is a HC05 bluetooth communication module. By utilizing serial port communication, the TXD pin is connected with the RXD pin of the singlechip, and the RXD pin is connected with the TXD pin of the singlechip. The method comprises the steps of firstly entering an AT command mode, configuring HC05 as a slave mode, enabling the Baud rate to be 9600, and then restarting HC05 to perform Bluetooth pairing. The circuit can realize the communication between the singlechip and the mobile phone.
Preferably, the control unit is loaded with an abnormal state prediction program including:
extracting urine-time sample data, and generating a training set and a test set;
step two, establishing a traditional Gaussian regression prediction model, wherein the traditional Gaussian regression prediction model adopts a mean function of m (x) -E [ f (x) ], and an average exponential covariance function of m (x) ]
Figure BDA0003051930980000071
Wherein the content of the first and second substances,
Figure BDA0003051930980000072
is the signal variance, M ═ diag (l)2) L is a variance scale, and x, x' are random arbitrary variables; defining a hyper-parameter as
Figure BDA0003051930980000073
Initializing bees, randomly initializing the positions H, the step length delta H, the neighborhood radius R, the inertia weight omega, the collision avoidance weight s of the same kind, the same speed weight a of the same kind, the closing weight c of the same kind, the appetite weight f and the rejection weight e of natural enemies;
step four, sequentially assigning the information of the individual positions H of the bees to l, sigmafσ n; the first row of the position H matrix stores the value of the parameter l, the second row stores the value of the parameter sigma f, the third row stores the value of the parameter sigma n, and each bee individual corresponds to a group of parameter values;
calculating a fitness function value by using a bee algorithm, judging whether the current fitness function value is the optimal fitness value or not, and if so, judging the corresponding hyper-parameter
Figure BDA0003051930980000081
Storing the value as an optimal hyper-parameter value, otherwise, still storing the original fitness value and the corresponding hyper-parameter value; calculating a fitness function of the bee algorithm:
ΔXt+1＝(sSi+aAi+cCi+fFi+eEi)+ωΔXt；Sifor the ith individual
Figure BDA0003051930980000083
AiFor the ith individual
Figure 2
CiFor the ith individual
Figure BDA0003051930980000086
FiIs the appetite value of the ith individual ═ X+-X，EiIs the natural enemy rejection value of the ith individual ═ X-+ X; x is the current individual's location, XjIs the position of the adjacent individual j, N is the number of the adjacent individuals, VjIs the speed of the j-th adjacent individual, X+Is the position of the target flower, X-The position of the heaven and earth;
position X of next generation bee when there is adjacent beet+1＝Xt+ΔXt+1(ii) a When no adjacent bees exist, the function set as the random walk behavior is a fitness function, namely an Le' vy function, and the individual positions of the next generation of bees are as follows: xt+1＝Xt+Le′xy(d)×Xt；
Wherein t is the current iteration number, i is the ith bee individual, and XiThe position of the current t generation population individual; Δ Xt+1Updating the step length for the next generation of population position; xt+1The position of the next generation population individual is shown, and d is the dimension of the individual position vector;
the fitness function Le' vy of the bee algorithm is as follows:
Figure BDA0003051930980000087
r1，r2is [0,1 ]]The random number of (2); the function Γ is Γ (x) ═ x-1! Beta is a constant;
step six, judging whether the maximum iteration times is reached, if so, outputting an optimal hyper-parameter and creating an optimal Gaussian regression prediction model; otherwise, updating the optimal individual and the worst individual of the bee, updating the neighborhood radius R and updating the individual position in sequence, then returning to the fourth step and continuing iteration;
step seven, inputting the test set into the optimal Gaussian regression prediction model determined according to the step six and the step two, and outputting the mean value and the variance of the predicted value;
and step eight, predicting and calculating a real-time interval prediction result of the urine volume.
Specifically, the urine-time sample data in the first step is body temperature-time sample data, and the body temperature real-time interval prediction result is calculated in the eighth step.
The abnormal states of the old and the children can be predicted in advance through an abnormal state prediction algorithm.
Although the illustrative embodiments of the present invention have been described above to enable those skilled in the art to understand the present invention, the present invention is not limited to the scope of the embodiments, and it is apparent to those skilled in the art that all the inventive concepts using the present invention are protected as long as they can be changed within the spirit and scope of the present invention as defined and defined by the appended claims.

Claims (8)

1. A monitoring system for old and young children comprises an intelligent paper diaper capable of detecting a bed and body temperature, wherein the intelligent paper diaper is provided with a control unit, the control unit is connected to a mobile communication device through a wireless communication module, and the mobile communication device outputs a monitoring result or an alarm signal; the control unit is used for receiving and judging a bed wetting detection result and a body temperature detection result, and is characterized in that:
the absorption area of the intelligent paper diaper is provided with a first bed wetting monitoring unit for monitoring the bed wetting condition, and one surface, close to the human body, of the intelligent paper diaper is provided with a first temperature detector for detecting the body temperature of the human body;
the first bed wetting monitoring unit comprises a second temperature detector which is arranged on the middle layer of the absorption area at intervals and used for detecting urine, and the second temperature detector is connected with the control unit.
2. The monitoring system for young and old people as claimed in claim 1, wherein: the first temperature detector includes a plurality of temperature sensors arranged in a distributed manner.
3. The monitoring system for young and old people as claimed in claim 1, wherein: the second temperature detector includes a plurality of temperature sensors arranged in a distributed manner.
4. The monitoring system for young and old people as claimed in claim 2 or 3, wherein: the temperature sensor adopts a sht20 sensor.
5. The monitoring system for young and old people as claimed in claim 1, wherein: the absorption region is further provided with a second bed wetting monitoring unit for monitoring the bed wetting condition, the second bed wetting monitoring unit comprises a plurality of electrodes which are arranged on the middle layer of the absorption region at intervals, the electrodes are mutually conducted under the bed wetting condition and output conducting signals to the control unit to realize urine detection, and the first bed wetting monitoring unit and the second bed wetting monitoring unit are connected to the control unit through the AND gate unit.
6. The monitoring system for young and old people as claimed in claim 1, wherein: the wireless communication module is an HC05 Bluetooth communication module.
7. The monitoring system for young and old people as claimed in claim 2, wherein: the abnormal state prediction method in the control unit comprises the following steps:
extracting urine-time sample data, and generating a training set and a test set;
step two, establishing a traditional Gaussian regression prediction model, wherein the traditional Gaussian regression prediction model adopts a mean function of m (x) -E [ f (x) ], and an average exponential covariance function of m (x) ]
Figure FDA0003051930970000021
Wherein the content of the first and second substances,
Figure FDA0003051930970000022
is the signal variance, M ═ diag (l)2) L is a variance scale, and x, x' are random arbitrary variables; defining a hyper-parameter as
Figure FDA0003051930970000023
Initializing bees, randomly initializing the positions H, the step length delta H, the neighborhood radius R, the inertia weight omega, the collision avoidance weight s of the same kind, the same speed weight a of the same kind, the closing weight c of the same kind, the appetite weight f and the rejection weight e of natural enemies;
step four, sequentially assigning the information of the individual positions H of the bees to l, sigmafσ n; the first row of the position H matrix stores the value of the parameter l, the second row stores the value of the parameter sigma f, the third row stores the value of the parameter sigma n, and each bee individual corresponds to a group of parameter values;
calculating a fitness function value by using a bee algorithm, judging whether the current fitness function value is the optimal fitness value or not, and if so, judging the corresponding hyper-parameter
Figure FDA0003051930970000024
Storing the value as an optimal hyper-parameter value, otherwise, still storing the original fitness value and the corresponding hyper-parameter value; calculating a fitness function of the bee algorithm:
ΔXt+1＝(sSi+aAi+cCi+fFi+eEi)+ωΔXt；Sifor the ith individual
Figure FDA0003051930970000025
Figure FDA0003051930970000028
AiFor the ith individual
Figure 3
CiFor the ith individual
Figure FDA00030519309700000210
Figure 1
FiIs the appetite value of the ith individual ═ X+-X，EiIs the natural enemy rejection value of the ith individual ═ X-+ X; x is the current individual's location, XjIs the position of the adjacent individual j, N is the number of the adjacent individuals, VjIs the speed of the j-th adjacent individual, X+Is the position of the target flower, X-The position of the heaven and earth;
position X of next generation bee when there is adjacent beet+1＝Xt+ΔXt+1(ii) a When no adjacent bees exist, the function set as the random walk behavior is a fitness function, namely an Le' vy function, and the individual position of the next generation of bees is Xt+1＝Xt+Le′xy(d)×Xt；
Wherein t is the current iteration number, i is the ith bee individual, and XiThe position of the current t generation population individual; Δ Xt+1Updating the step length for the next generation of population position; xt+1For next generation population individualsPosition, d is the dimension of the individual position vector;
the fitness function Le' vy of the bee algorithm is as follows:
Figure FDA0003051930970000031
r1，r2is [0,1 ]]The random number of (2); the function Γ is Γ (x) ═ x-1! Beta is a constant;
step six, judging whether the maximum iteration times is reached, if so, outputting an optimal hyper-parameter and creating an optimal Gaussian regression prediction model; otherwise, updating the optimal individual and the worst individual of the bee, updating the neighborhood radius R and updating the individual position in sequence, then returning to the fourth step and continuing iteration;
step seven, inputting the test set into the optimal Gaussian regression prediction model determined according to the step six and the step two, and outputting the mean value and the variance of the predicted value;
and step eight, predicting and calculating a real-time interval prediction result of the urine volume.
8. The monitoring system for young and old people as claimed in claim 7, wherein: the urine-time sample data in the first step is body temperature-time sample data, and the body temperature real-time interval prediction result is calculated in the eighth step.
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Cited By (1)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117315885A (en) * 2023-09-04 2023-12-29 中国人民解放军总医院第四医学中心 Remote sharing alarm system for monitoring urine volume of urine bag and electrocardiograph monitor

Cited By (2)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN117315885A (en) * 2023-09-04 2023-12-29 中国人民解放军总医院第四医学中心 Remote sharing alarm system for monitoring urine volume of urine bag and electrocardiograph monitor
CN117315885B (en) * 2023-09-04 2024-05-28 中国人民解放军总医院第四医学中心 Remote sharing alarm system for monitoring urine volume of urine bag and electrocardiograph monitor

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