CN111214211A - Sleep monitoring method and device and intelligent bed - Google Patents
Sleep monitoring method and device and intelligent bed Download PDFInfo
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- CN111214211A CN111214211A CN202010049567.3A CN202010049567A CN111214211A CN 111214211 A CN111214211 A CN 111214211A CN 202010049567 A CN202010049567 A CN 202010049567A CN 111214211 A CN111214211 A CN 111214211A
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47C—CHAIRS; SOFAS; BEDS
- A47C31/00—Details or accessories for chairs, beds, or the like, not provided for in other groups of this subclass, e.g. upholstery fasteners, mattress protectors, stretching devices for mattress nets
- A47C31/12—Means, e.g. measuring means for adapting chairs, beds or mattresses to the shape or weight of persons
- A47C31/123—Means, e.g. measuring means for adapting chairs, beds or mattresses to the shape or weight of persons for beds or mattresses
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- A—HUMAN NECESSITIES
- A47—FURNITURE; DOMESTIC ARTICLES OR APPLIANCES; COFFEE MILLS; SPICE MILLS; SUCTION CLEANERS IN GENERAL
- A47D—FURNITURE SPECIALLY ADAPTED FOR CHILDREN
- A47D7/00—Children's beds
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/48—Other medical applications
- A61B5/4806—Sleep evaluation
- A61B5/4812—Detecting sleep stages or cycles
-
- 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
- A61B5/4815—Sleep quality
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61B—DIAGNOSIS; SURGERY; IDENTIFICATION
- A61B5/00—Measuring for diagnostic purposes; Identification of persons
- A61B5/72—Signal processing specially adapted for physiological signals or for diagnostic purposes
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
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- A—HUMAN NECESSITIES
- A61—MEDICAL OR VETERINARY SCIENCE; HYGIENE
- A61M—DEVICES FOR INTRODUCING MEDIA INTO, OR ONTO, THE BODY; DEVICES FOR TRANSDUCING BODY MEDIA OR FOR TAKING MEDIA FROM THE BODY; DEVICES FOR PRODUCING OR ENDING SLEEP OR STUPOR
- A61M21/00—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis
- A61M2021/0005—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus
- A61M2021/0083—Other devices or methods to cause a change in the state of consciousness; Devices for producing or ending sleep by mechanical, optical, or acoustical means, e.g. for hypnosis by the use of a particular sense, or stimulus especially for waking up
Abstract
The invention provides a sleep monitoring method, a sleep monitoring device and an intelligent bed, which are applied to the intelligent bed, wherein the method comprises the following steps: acquiring original sleep parameters of a person to be detected in a current time period; preprocessing the original sleep parameters to obtain target data; inputting the target data into an SVM training model of a person to be detected for judgment; and acquiring the sleep state of the person to be detected in the current time period. According to the sleep monitoring method, the sleep states of the person to be detected in different time periods are acquired through the sleep parameters of the person to be detected in different time periods and the SVM training model, so that when the sleep monitoring method is applied to the intelligent bed, the intelligent bed can monitor the sleep state of the user in real time when the user sleeps, then the user can accurately know the sleep quality of the user, the management of body health is enhanced, the intelligence and the practicability of the intelligent bed are improved, and the requirements of modern users are met.
Description
Technical Field
The application relates to the technical field of smart home, in particular to a sleep monitoring method and device and a smart bed.
Background
With the rapid development of the internet of things and internet technology, smart homes are influencing and changing the life style of people, and smart beds are used by more and more families.
The intelligent bed in the prior art is added with functions of music playing, monitoring and the like on the basis of a traditional bed, but still has the problems of low intelligent degree and poor practicability, and cannot monitor the sleep state of a user.
Therefore, the intelligent bed in the prior art has low intelligent degree, can not monitor the sleep state of a user, and can not meet the requirements of modern users.
Disclosure of Invention
In order to solve the technical problems, the application provides a sleep monitoring method and device and an intelligent bed, and solves the problems that the intelligent degree of the intelligent bed in the prior art is low, the sleep state of a user cannot be monitored, and the requirements of modern users cannot be met.
In a first aspect, the present invention provides a sleep monitoring method applied in an intelligent bed, the method including: acquiring original sleep parameters of a person to be detected in a current time period; preprocessing the original sleep parameters to obtain target data; inputting the target data into an SVM training model of a person to be detected for judgment; and acquiring the sleep state of the person to be detected in the current time period.
Optionally, the SVM training model of the person to be detected includes: acquiring historical sleep parameters of the person to be detected within a first preset time period; FCM clustering is carried out on the historical sleep parameters to obtain historical sample parameters; and inputting the historical sample parameters into the SVM model to obtain an SVM training model of the person to be detected.
Optionally, after FCM clustering is performed on the historical sleep parameters to obtain historical sample parameters, before the historical sample parameters are input into the SVM model, the method further includes: obtaining an optimized parameter set of the SVM model according to the historical sample parameters and the GA algorithm; inputting the historical sample parameters into the SVM model to obtain an SVM training model of the person to be detected, wherein the SVM training model comprises the following steps: and inputting the historical sample parameters and the optimized parameter set into the SVM model to obtain an SVM training model of the person to be detected.
Optionally, preprocessing the original sleep parameters to obtain target data, including: carrying out high-pass filtering on the original sleep parameters to obtain filtered sleep parameters; and carrying out normalization processing on the sleep filtering parameters to obtain target data.
Optionally, after the obtaining of the sleep state of the person to be detected in the current time period, the method further includes: acquiring a wake-up instruction for the person to be detected; and starting a wake-up function matched with the sleep state of the current time period according to the wake-up instruction.
Optionally, the method further comprises: acquiring all sleep states of the person to be detected within a second preset time period; and sending all the sleep states to a terminal.
In a second aspect, the present invention provides a sleep monitoring device, for use in an intelligent bed, the device comprising: the first acquisition module is used for acquiring the original sleep parameters of the person to be detected in the current time period; the preprocessing module is used for preprocessing the sleep parameters to obtain target data; the input module is used for inputting the target data into an SVM training model of a person to be detected for judgment; and the second acquisition module is used for acquiring the sleep state of the person to be detected in the current time period.
In a third aspect, the present invention provides a smart bed, comprising: the system comprises a signal acquisition device, a sleep detection device and a wake-up device, wherein the signal acquisition device is used for acquiring original sleep parameters of a person to be detected, the sleep detection device is used for acquiring the sleep state of the person to be detected according to the original sleep parameters, and the wake-up device is used for starting a corresponding wake-up function according to the sleep state of the person to be detected; the sleep detection apparatus includes: the system comprises a first acquisition module, a preprocessing module and a second acquisition module, wherein the first acquisition module is used for acquiring original sleep parameters of a person to be detected in the current time period, the preprocessing module is used for preprocessing the sleep parameters to obtain target data, the input module is used for inputting the target data into an SVM training model of the person to be detected for judgment, and the second acquisition module is used for acquiring the sleep state of the person to be detected in the current time period.
Optionally, the wake-up apparatus includes: a receiving module, configured to receive the sleep state sent by the sleep detection apparatus; the judging module is used for judging the current wake-up mode according to the sleep state; and the starting module is used for starting the corresponding awakening function according to the awakening mode.
Optionally, the wake-up apparatus further includes: the sound sensor is used for detecting the sound of the person to be detected and sending the sound to the judging module; the judging module is also used for judging the current starting mode according to the sound; the starting module is also used for starting a corresponding shaking function according to the starting mode.
The invention provides a sleep monitoring method, a sleep monitoring device and an intelligent bed, which are applied to the intelligent bed, wherein the method comprises the following steps: acquiring original sleep parameters of a person to be detected in a current time period; preprocessing the original sleep parameters to obtain target data; inputting the target data into an SVM training model of a person to be detected for judgment; and acquiring the sleep state of the person to be detected in the current time period. According to the sleep monitoring method, the sleep states of the person to be detected in different time periods are acquired through the sleep parameters of the person to be detected in different time periods and the SVM training model, so that when the sleep monitoring method is applied to the intelligent bed, the intelligent bed can monitor the sleep state of the user in real time when the user sleeps, then the user can accurately know the sleep quality of the user, the management of body health is enhanced, the intelligence and the practicability of the intelligent bed are improved, and the requirements of modern users are met.
Drawings
The accompanying drawings, which are incorporated in and constitute a part of this specification, illustrate embodiments consistent with the invention and together with the description, serve to explain the principles of the invention.
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below, and it is obvious for those skilled in the art that other drawings can be obtained according to the drawings without inventive exercise.
Fig. 1 is a flowchart of a sleep monitoring method according to an embodiment of the present invention;
fig. 2 is a flowchart of a sleep monitoring method according to an embodiment of the present invention;
fig. 3 is a flowchart of a sleep monitoring method according to an embodiment of the present invention;
fig. 4 is a block diagram of a sleep monitoring apparatus according to an embodiment of the present invention;
fig. 5 is a block diagram of an intelligent bed according to an embodiment of the present invention;
fig. 6 is a block diagram of a wake-up apparatus according to an embodiment of the present invention;
fig. 7 is an application scene diagram of an intelligent crib according to an embodiment of the invention.
Detailed Description
In order to make the objects, technical solutions and advantages of the embodiments of the present application clearer, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is obvious that the described embodiments are some embodiments of the present application, but not all embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present application.
Fig. 1 is a flowchart of a sleep monitoring method according to an embodiment of the present invention; as shown in fig. 1, the sleep monitoring method in the embodiment of the present invention, which is applied to an intelligent bed, specifically includes the following steps:
step S101, obtaining the original sleep parameters of the person to be detected in the current time period.
And step S102, preprocessing the original sleep parameters to obtain target data.
And S103, inputting the target data into an SVM training model of the person to be detected for judgment.
And step S104, acquiring the sleep state of the person to be detected in the current time period.
Specifically, the intelligent bed in the invention can be a baby bed, a common user bed and a medical bed; a signal acquisition device is arranged in a mattress or a pillow of the intelligent bed and is used for acquiring original sleep parameters of a person to be detected sleeping on the intelligent bed for a preset time, wherein the original sleep parameters include but are not limited to respiratory signals and electrocardiosignals; because the acquired original sleep parameters include interferences such as noise and the like, the original sleep parameters need to be preprocessed such as amplification, filtering, noise reduction and the like to obtain target data for analysis; and inputting the target data into a trained SVM model of the person to be detected to judge the sleep state, and acquiring the current sleep state according to the current sleep parameters, wherein the sleep state comprises a waking period, a light sleep period, a deep sleep period and a rapid eye movement period.
The sleep monitoring method provided by the embodiment of the invention is applied to an intelligent bed, and comprises the following steps: acquiring original sleep parameters of a person to be detected in a current time period; preprocessing the original sleep parameters to obtain target data; inputting the target data into an SVM training model of a person to be detected for judgment; and acquiring the sleep state of the person to be detected in the current time period. According to the sleep monitoring method, the sleep states of the person to be detected in different time periods are acquired through the sleep parameters of the person to be detected in different time periods and the SVM training model, so that when the sleep monitoring method is applied to the intelligent bed, the intelligent bed can monitor the sleep state of the user in real time when the user sleeps, then the user can accurately know the sleep quality of the user, the management of body health is enhanced, the intelligence and the practicability of the intelligent bed are improved, and the requirements of modern users are met.
In the embodiment of the present invention, the preprocessing the original sleep parameters to obtain target data includes: carrying out high-pass filtering on the original sleep parameters to obtain filtered sleep parameters; and carrying out normalization processing on the sleep filtering parameters to obtain target data.
In this embodiment of the present invention, after acquiring the sleep state of the person to be detected in the current time period, the method further includes: acquiring a wake-up instruction for the person to be detected; and starting a wake-up function matched with the sleep state of the current time period according to the wake-up instruction.
In an embodiment of the present invention, the method further comprises: acquiring all sleep states of the person to be detected within a second preset time period; and sending all the sleep states to a terminal.
Fig. 2 is a flowchart of a sleep monitoring method according to an embodiment of the present invention; fig. 2 is a detailed flowchart of the SVM training model involved in step S103 of fig. 1, where the SVM training model includes:
step S201, obtaining a historical sleep parameter of the person to be detected within a first preset time period.
Step S202, FCM clustering is carried out on the historical sleep parameters to obtain historical sample parameters.
And S203, obtaining an optimized parameter set of the SVM model according to the historical sample parameters and the GA algorithm.
Step S204, inputting the historical sample parameters and the optimized parameter set into the SVM model to obtain the SVM training model of the person to be detected.
Specifically, in the embodiment, an SVM model is trained by acquiring historical sleep data of a person to be detected, so as to obtain the SVM model of the person to be detected; FCM is a clustering algorithm that aims to classify feature vectors extracted from historical sleep parameters into a class, for example, respiratory signals are classified into a class, and heartbeat signals are classified into a class; and obtaining an optimization parameter set of the SVM model after the classified historical sample parameters are processed by a GA algorithm, wherein the optimization parameter set of the SVM model comprises an SVM kernel function parameter g and a penalty factor c, and inputting the historical sample parameters and the optimization parameter set into the SVM model for training to obtain the SVM training model of the person to be detected.
Fig. 3 is a flowchart of a sleep monitoring method according to an embodiment of the present invention; as shown in fig. 3, the sleep detection method in the embodiment of the present invention specifically includes the following steps:
step S301, obtaining the original sleep parameters of the person to be detected in the current time period.
Step S302, preprocessing the original sleep parameters to obtain target data.
Step S303, inputting the target data into an SVM training model of the person to be detected for judgment.
Step S304, acquiring the sleep state of the person to be detected in the current time period.
In step S305, it is determined whether a wake-up command is acquired, and when the wake-up command is acquired, step S306 is executed, and when the wake-up command is not acquired, step S301 is executed.
Step S306, determining whether the current sleep state is a deep sleep state, if the current sleep state is the deep sleep state, executing step S307, and if the current sleep state is not the deep sleep state, executing step S308.
Step S307 is executed to perform the pre-wake-up operation, and after the pre-wake-up operation is performed, step S301 is continuously executed.
In step S308, a wakeup operation is performed.
Specifically, in this embodiment, after monitoring the current sleep state of the user, a corresponding wake-up function may be started to serve as a function of an alarm clock, where the execution processes from step S301 to step S304 are the same as those described above, and are not described herein again; after the current sleep state of a user is acquired, if an instruction for waking up the user is received, whether the current sleep state is deep sleep is judged, if the current sleep state is the deep sleep state, pre-waking operation is needed to enable the user to enter a non-deep sleep state, if the current sleep state is the non-deep sleep state, the user directly wakes up the user directly, and because the user directly wakes up the user in the deep sleep state and possibly has adverse effects on the user, the user needs to be pre-wakened up from the deep sleep state to the non-deep sleep state and then wakes up, wherein the pre-waking up operation is different from the waking up operation, the pre-waking up can be the shaking of a pillow or a mattress, the waking up operation can be music or voice reminding, and switching is performed according to the age or the preference of.
Fig. 4 is a block diagram of a sleep monitoring apparatus according to an embodiment of the present invention; as shown in fig. 4, a sleep monitoring device provided in an embodiment of the present invention includes:
a first obtaining module 410, configured to obtain an original sleep parameter of a person to be detected in a current time period;
a preprocessing module 420, configured to preprocess the sleep parameter to obtain target data;
an input module 430, configured to input the target data into an SVM training model of a person to be detected for judgment;
a second obtaining module 440, configured to obtain a sleep state of the person to be detected in the current time period.
In one embodiment of the invention, the input module comprises: the historical sleep parameter acquisition module is used for acquiring the historical sleep parameters of the person to be detected within a first preset time period; the FCM clustering module is used for carrying out FCM clustering on the historical sleep parameters to obtain historical sample parameters; and the historical sample parameter input module is used for inputting the historical sample parameters into the SVM model to obtain the SVM training model of the person to be detected.
In one embodiment of the present invention, the input module further comprises: the optimized parameter set module is used for obtaining an optimized parameter set of the SVM model according to the historical sample parameters and the GA algorithm;
in one embodiment of the present invention, the preprocessing module further comprises: the filtering module is used for carrying out high-pass filtering on the original sleep parameters to obtain filtered sleep parameters; and the normalization module is used for performing normalization processing on the sleep filtering parameters to obtain target data.
In one embodiment of the present invention, the sleep monitoring apparatus further comprises: a third obtaining module, configured to obtain a wake-up instruction for the person to be detected; and the awakening module is used for starting an awakening function matched with the sleep state of the current time period according to the awakening instruction.
In one embodiment of the present invention, the preprocessing module further comprises: the fourth acquisition module is used for acquiring all sleep states of the person to be detected within a second preset time period; and the sending module is used for sending all the sleep states to the terminal.
Fig. 5 is a structural block diagram of an intelligent bed according to an embodiment of the present invention, and as shown in fig. 5, the intelligent bed according to the embodiment includes:
the signal acquisition device 510 is used for acquiring the original sleep parameters of a person to be detected;
a sleep detection device 520, configured to obtain a sleep state of the person to be detected according to the original sleep parameters;
the awakening device 530 is used for starting a corresponding awakening function according to the sleep state of the person to be detected;
in an embodiment of the present invention, the sleep detection apparatus 520 includes: the first acquisition module is used for acquiring the original sleep parameters of the person to be detected in the current time period; the preprocessing module is used for preprocessing the sleep parameters to obtain target data; the input module is used for inputting the target data into an SVM training model of a person to be detected for judgment; and the second acquisition module is used for acquiring the sleep state of the person to be detected in the current time period.
In an embodiment of the present invention, the wake-up device 530 includes: a receiving module, configured to receive the sleep state sent by the sleep detection apparatus; the judging module is used for judging the current wake-up mode according to the sleep state; and the starting module is used for starting the corresponding awakening function according to the awakening mode.
In an embodiment of the present invention, the wake-up device 530 further includes: the sound sensor is used for detecting the sound of the person to be detected and sending the sound to the judging module; the judging module is also used for judging the current starting mode according to the sound; the starting module is also used for starting a corresponding shaking function according to the starting mode.
Fig. 6 is a structural block diagram of a waking device provided in an embodiment of the present invention, in which a gyroscope sensor is disposed at the bottom of a bed body of an intelligent bed, and the gyroscope adjusts the shaking amplitude of the bed body according to the detected angle variation of the bed body, so as to wake up a person to be detected or sooth crying.
Fig. 7 is an application scene diagram of an intelligent crib according to an embodiment of the present invention, where the intelligent crib according to the embodiment of the present invention has the following functions: 1. the intelligent mattress realizes various detections of body temperature, respiration, sleep, heart rate and the like and generates reports, and helps parents to know the body condition of the baby every day; 2. voice setting: contains sleep music, stories and songs to help sleep; 3. the automatic cradle realizes that parents can bring babies easily. In appearance: 1. is suitable for the babies within 3 years old; 2. the periphery of the head part adopts sound insulation materials to achieve the purpose of noise reduction; 3. the head fixing device protects the cervical vertebra of a child within 1 year old; 4. the wide space allows the baby to move fully; 4. the shell is made of soft materials and is in an arc shape, so that the baby is not easily cut; 5. the two sides are hollowed out, so that a baby within 1 year of age can see the outside world, and the visual field is widened; 5. shaping the pillow to protect the healthy development of the skull and prevent head deviation; 6. the side pillows are protected, the position of the baby is stabilized, the left side and the right side are prevented from moving, and the width of the side pillows is freely adjusted; 7. the latex mattress is soft, high in elasticity, high in air permeability and good in supporting performance; intelligent shaking table: the user opens the bed body mode of gently shaking through cell-phone APP, and bed body shaking amplitude is adjusted by oneself to bed body bottom gyroscope sensor according to angle variation, and the baby then stops sleeping, and bed body is inside to be inlayed sound sensor, if detect the baby and cry, then starts the mode of shaking by oneself. And (3) intelligent awakening: a user presets the sleep time of the baby through APP, judges the sleep state of the baby according to data collected by a sensor arranged in the bed body, realizes switching the baby to a light sleep state by adjusting the bed body, and then realizes awakening; for example, a baby needs to be awakened at 12 points, a user inputs 12 points at a mobile phone end, the baby starts to enter a pre-awakening mode after automatically judging 11.30 in the future, the baby enters the pre-awakening mode, a mattress collects data such as respiratory frequency, heartbeat, sleeping time and the like, whether the baby is in a deep sleep state or not is judged, if not, the baby is awakened slowly by light music until 11.50, if the baby is in the deep sleep state, a crib starts to shake, the baby is changed from the deep sleep state to a light sleep state, and then the baby is awakened slowly by light music; since wake-up from a light sleep state is the healthiest way for an infant. Intelligently soothing sleep: the bed both sides have the pronunciation hole, and the user selects the music through APP, begins the broadcast, but the automatic set time.
The invention provides a sleep monitoring method, a sleep monitoring device and an intelligent bed, which are applied to the intelligent bed, wherein the method comprises the following steps: acquiring original sleep parameters of a person to be detected in a current time period; preprocessing the original sleep parameters to obtain target data; inputting the target data into an SVM training model of a person to be detected for judgment; and acquiring the sleep state of the person to be detected in the current time period. According to the sleep monitoring method, the sleep states of the person to be detected in different time periods are acquired through the sleep parameters of the person to be detected in different time periods and the SVM training model, so that when the sleep monitoring method is applied to the intelligent bed, the intelligent bed can monitor the sleep state of the user in real time when the user sleeps, then the user can accurately know the sleep quality of the user, the management of body health is enhanced, the intelligence and the practicability of the intelligent bed are improved, and the requirements of modern users are met.
It is noted that, in this document, relational terms such as "first" and "second," and the like, may be used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Also, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising an … …" does not exclude the presence of other identical elements in a process, method, article, or apparatus that comprises the element.
The foregoing are merely exemplary embodiments of the present invention, which enable those skilled in the art to understand or practice the present invention. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the invention. Thus, the present invention is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (10)
1. A sleep monitoring method is applied to an intelligent bed, and the method comprises the following steps:
acquiring original sleep parameters of a person to be detected in a current time period;
preprocessing the original sleep parameters to obtain target data;
inputting the target data into an SVM training model of a person to be detected for judgment;
and acquiring the sleep state of the person to be detected in the current time period.
2. The method of claim 1, wherein the training model of the SVM of the person to be detected comprises:
acquiring historical sleep parameters of the person to be detected within a first preset time period;
FCM clustering is carried out on the historical sleep parameters to obtain historical sample parameters;
and inputting the historical sample parameters into the SVM model to obtain an SVM training model of the person to be detected.
3. The method of claim 2, wherein after FCM clustering the historical sleep parameters to obtain historical sample parameters, prior to inputting the historical sample parameters into the SVM model, the method further comprises:
obtaining an optimized parameter set of the SVM model according to the historical sample parameters and the GA algorithm;
inputting the historical sample parameters into the SVM model to obtain an SVM training model of the person to be detected, wherein the SVM training model comprises the following steps:
and inputting the historical sample parameters and the optimized parameter set into the SVM model to obtain an SVM training model of the person to be detected.
4. The method of claim 1, wherein preprocessing the raw sleep parameters to obtain target data comprises:
carrying out high-pass filtering on the original sleep parameters to obtain filtered sleep parameters;
and carrying out normalization processing on the sleep filtering parameters to obtain target data.
5. The method according to claim 1, wherein after the obtaining of the sleep state of the person to be detected in the current time period, the method further comprises:
acquiring a wake-up instruction for the person to be detected;
and starting a wake-up function matched with the sleep state of the current time period according to the wake-up instruction.
6. The method of claim 5, further comprising:
acquiring all sleep states of the person to be detected within a second preset time period;
and sending all the sleep states to a terminal.
7. A sleep monitoring device for use in an intelligent bed, the device comprising:
the first acquisition module is used for acquiring the original sleep parameters of the person to be detected in the current time period;
the preprocessing module is used for preprocessing the sleep parameters to obtain target data;
the input module is used for inputting the target data into an SVM training model of a person to be detected for judgment;
and the second acquisition module is used for acquiring the sleep state of the person to be detected in the current time period.
8. An intelligent bed, comprising:
the signal acquisition device is used for acquiring the original sleep parameters of a person to be detected;
the sleep detection device is used for acquiring the sleep state of the person to be detected according to the original sleep parameters;
the awakening device is used for starting a corresponding awakening function according to the sleep state of the person to be detected;
the sleep detection apparatus includes:
the first acquisition module is used for acquiring the original sleep parameters of the person to be detected in the current time period;
the preprocessing module is used for preprocessing the sleep parameters to obtain target data;
the input module is used for inputting the target data into an SVM training model of a person to be detected for judgment;
and the second acquisition module is used for acquiring the sleep state of the person to be detected in the current time period.
9. The smart bed of claim 8, wherein the wake-up unit comprises:
a receiving module, configured to receive the sleep state sent by the sleep detection apparatus;
the judging module is used for judging the current wake-up mode according to the sleep state;
and the starting module is used for starting the corresponding awakening function according to the awakening mode.
10. The smart bed of claim 9, wherein the wake-up unit further comprises:
the sound sensor is used for detecting the sound of the person to be detected and sending the sound to the judging module;
the judging module is also used for judging the current starting mode according to the sound;
the starting module is also used for starting a corresponding shaking function according to the starting mode.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202010049567.3A CN111214211A (en) | 2020-01-16 | 2020-01-16 | Sleep monitoring method and device and intelligent bed |
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| Application Number | Priority Date | Filing Date | Title |
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| CN202010049567.3A CN111214211A (en) | 2020-01-16 | 2020-01-16 | Sleep monitoring method and device and intelligent bed |
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| CN112487235A (en) * | 2020-11-27 | 2021-03-12 | 珠海格力电器股份有限公司 | Audio resource playing method and device, intelligent terminal and storage medium |
| CN113208353A (en) * | 2021-02-26 | 2021-08-06 | 好孩子儿童用品有限公司 | Liftable crib based on respiratory frequency |
| WO2022099953A1 (en) * | 2020-11-11 | 2022-05-19 | 麒盛科技股份有限公司 | Wakeup method for smart bed and device thereof |
| CN114947458A (en) * | 2022-06-27 | 2022-08-30 | 慕思健康睡眠股份有限公司 | Hotel room recommendation method, device, equipment and medium based on sleep mattress |
| CN115192850A (en) * | 2022-08-15 | 2022-10-18 | 福建工程学院 | Wake-up system based on heart rate variability |
| CN115952449A (en) * | 2023-03-14 | 2023-04-11 | 首都医科大学附属北京同仁医院 | Sleep stage monitoring method and device, computer and storage medium |
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111657890A (en) * | 2020-06-23 | 2020-09-15 | 深圳市联奕实业有限公司 | Sleep state monitoring method and device, intelligent mattress and medium |
| CN112206395A (en) * | 2020-09-11 | 2021-01-12 | 深圳数联天下智能科技有限公司 | Setting method and device of linkage scene |
| CN112206395B (en) * | 2020-09-11 | 2022-07-15 | 深圳数联天下智能科技有限公司 | Setting method and device of linkage scene |
| WO2022099953A1 (en) * | 2020-11-11 | 2022-05-19 | 麒盛科技股份有限公司 | Wakeup method for smart bed and device thereof |
| CN112487235A (en) * | 2020-11-27 | 2021-03-12 | 珠海格力电器股份有限公司 | Audio resource playing method and device, intelligent terminal and storage medium |
| CN113208353A (en) * | 2021-02-26 | 2021-08-06 | 好孩子儿童用品有限公司 | Liftable crib based on respiratory frequency |
| CN114947458A (en) * | 2022-06-27 | 2022-08-30 | 慕思健康睡眠股份有限公司 | Hotel room recommendation method, device, equipment and medium based on sleep mattress |
| CN114947458B (en) * | 2022-06-27 | 2023-09-05 | 慕思健康睡眠股份有限公司 | Hotel room recommending method, device, equipment and medium based on sleeping mattress |
| CN115192850A (en) * | 2022-08-15 | 2022-10-18 | 福建工程学院 | Wake-up system based on heart rate variability |
| CN115192850B (en) * | 2022-08-15 | 2023-12-15 | 福建工程学院 | Wake-up system based on heart rate variability |
| CN115952449A (en) * | 2023-03-14 | 2023-04-11 | 首都医科大学附属北京同仁医院 | Sleep stage monitoring method and device, computer and storage medium |
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