Method and device for monitoring sleepy child in class and child watch

Abstract

The invention relates to a method and device for monitoring sleepy children in class and a children watch, and belongs to the technical field of intelligent monitoring, and the method comprises the steps: obtaining motion data and physiological data of a user in a preset time period, determining a scene where the user is located according to the motion data, and if the scene where the user is located is in class, determining a user state according to the physiological data, determining reminding intensity according to the user state; according to the method, when the scene is determined to be in class through the motion data, the user state is monitored according to the physiological data, reminding of different intensities is used according to different user states, the scene is automatically recognized, so that the monitoring function is started, comprehensive and accurate judgment of user sleepiness is achieved based on the physiological data, and the user experience is improved. And the use experience of the user can be effectively improved.

Description

Technical Field

[0001] The present invention belongs to the technical field of intelligent monitoring, and in particular relates to a method, a device and a children's watch for monitoring children's sleepiness during class. Background Art

[0002] Most of the existing smart wearable devices for children have basic positioning, communication and motion monitoring functions, such as step counting and heart rate monitoring, etc. During class, children often miss out on the knowledge taught by the teacher due to sleepiness, which leads to problems such as poor academic performance. Therefore, it is particularly important to use smart wearable devices for children to monitor and remind them of their sleepiness during class.

[0003] Existing drowsiness monitoring generally directly uses facial recognition technology to determine whether the child is drowsy by identifying features such as closed eyes on the child's face. Children may need to rest when going out or riding in a car. Existing smart children's wearable devices do not have scene recognition functions, and cannot distinguish when to turn on drowsiness monitoring and drowsiness reminder functions. The switches need to be set manually. Moreover, using facial recognition alone to monitor whether the child is drowsy is not comprehensive enough. Sometimes, misjudgments may occur due to individual and environmental differences, resulting in poor user experience. Summary of the invention

[0004] To this end, the present invention provides a method, device and children's watch for monitoring children's drowsiness in class, so as to solve the problem that the existing drowsiness monitoring cannot distinguish when to turn on the drowsiness monitoring, and the single facial recognition monitoring is prone to misjudgment, thus causing a poor user experience.

[0005] To achieve the above objectives, the present invention adopts the following technical solutions:

[0006] In a first aspect, the present invention provides a method for monitoring a child's sleepiness during class, comprising:

[0007] Obtain the user's motion data and physiological data within a preset time period;

[0008] Determine the scene where the user is located according to the motion data; the scene includes class and others;

[0009] If the user is in a class, the user status is determined based on the physiological data; the user status includes normal, mild fatigue, moderate fatigue and severe fatigue;

[0010] The reminder intensity is determined according to the user status.

[0011] Further, the physiological data includes heart rate data and facial recognition data; and determining the user status based on the physiological data includes:

[0012] obtaining a heart rate deviation according to the heart rate data;

[0013] Obtaining the eye closing duration and the number of yawns according to the facial recognition data;

[0014] Obtaining a status score based on the heart rate deviation, eye closing time, and yawning frequency;

[0015] The user status is determined according to the status points.

[0016] Furthermore, obtaining the status score according to the heart rate deviation, eye closing time and yawning times includes:

[0017] The state score is obtained by a state formula according to the heart rate deviation, the eye closing time and the number of yawns. The state formula is:

[0018] Z=α·HR 偏 +β·T 闭眼 +γ·N;

[0019] Among them, Z is the status score; HR 偏 T is the heart rate deviation within the preset time period; 闭眼 is the eye closing time within the preset time; N is the number of yawns within the preset time; α, β and γ are the heart rate deviation weight coefficient, the eye closing time weight coefficient and the yawn number weight coefficient respectively.

[0020] Further, obtaining the heart rate deviation according to the heart rate data includes:

[0021] The heart rate deviation is obtained by the heart rate deviation formula, which is:

[0022] HR 偏 =HR 实时 / HR 最大 ;

[0023] Among them, HR 偏 is the heart rate deviation within the preset time period; HR 实时 The average heart rate of the user within the preset time period; HR 最大 It is the user's maximum heart rate value within the preset time period.

[0024] Further, determining the user status according to the status score includes:

[0025] If the status score is equal to 0, the user status is normal;

[0026] If the status score is greater than 0 and less than the first preset score, the user status is a mild fatigue state, and low-frequency vibration and low-amplitude vibration are used to provide fatigue reminders to the user;

[0027] If the status score is greater than or equal to the first preset score and the status score is less than or equal to the second preset score, the user status is moderate fatigue, and medium-frequency vibration and medium-amplitude vibration are used to provide fatigue reminders to the user;

[0028] If the status score is greater than the second preset score, the user status is severe fatigue, and high-frequency vibration and large-amplitude vibration are used to provide fatigue reminders for the user.

[0029] Furthermore, before determining the user status according to the physiological data, the method further includes:

[0030] The heart rate threshold is obtained by a threshold formula according to the heart rate data, and the threshold formula is:

[0031] RH 阈值 =RH 基础 -d×RH 基础 ;

[0032] Among them, RH 阈值 is the heart rate threshold; RH 基础 is the user's basic resting heart rate; d is a constant, usually between 0.05 and 0.1, indicating the set threshold offset;

[0033] If the real-time heart rate in the heart rate data is less than the heart rate threshold, the real-time status tag of the user is marked as a sleepy state so as to adjust the monitoring frequency.

[0034] Further, the motion data includes speed, real-time acceleration and positioning data; and determining the scene where the user is located based on the motion data includes:

[0035] Determining whether the user is in school based on the positioning data;

[0036] If the user is in a school, and the speed and the acceleration are respectively smaller than a preset speed and a preset acceleration, it is determined that the user is in a class.

[0037] Furthermore, the method further comprises:

[0038] If the real-time body temperature in the physiological data is greater than the preset body temperature, the user's real-time status tag is marked as a fever state so as to adjust the monitoring frequency.

[0039] In a second aspect, the present invention provides a device for monitoring children's sleepiness in class, comprising:

[0040] An acquisition module is used to acquire the user's motion data and physiological data within a preset time period;

[0041] A scene module, used to determine the scene where the user is located according to the motion data; the scene includes class and others;

[0042] A state module, for determining a user state based on the physiological data if the user is in a class; the user state includes normal, mild fatigue, moderate fatigue and severe fatigue;

[0043] The reminder module is used to determine the reminder intensity according to the user status.

[0044] In a third aspect, the present invention provides a children's watch, which applies any of the above methods for monitoring children's drowsiness in class.

[0045] The present invention adopts the above technical solution and has at least the following beneficial effects:

[0046] The present invention provides a method, device and children's watch for monitoring children's drowsiness in class, acquiring the user's motion data and physiological data within a preset time period, determining the user's current scene based on the motion data, and if the user is in a class, determining the user's state based on the physiological data, and determining the reminder intensity based on the user's state; in the present application, when the scene is determined to be a class through motion data, the user's state is monitored based on the physiological data, and reminders of different intensities are used according to the user's state, thereby realizing automatic scene recognition to complete the activation of the monitoring function, and realizing a comprehensive and accurate judgment of the user's drowsiness based on the physiological data, which can effectively improve the user's experience.

[0047] It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings required for use in the embodiments or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0049] Figure 1 is a flow chart of a method for monitoring children's sleepiness in class, shown in an exemplary embodiment of the present invention;

[0050] Figure 2 It is a schematic block diagram of a device for monitoring children's drowsiness in class, shown in an exemplary embodiment of the present invention.

[0051] The present invention will be further described below in conjunction with the accompanying drawings and specific embodiments. DETAILED DESCRIPTION

[0052] In order to make the purpose, technical solution and advantages of the present invention clearer, the technical solution of the present invention will be described in detail below. Obviously, the described embodiments are only part of the embodiments of the present invention, rather than all the embodiments. Based on the embodiments of the present invention, all other implementation methods obtained by ordinary technicians in this field without making creative work belong to the scope of protection of the present invention.

[0053] Most existing smart children's watches have basic positioning, communication and motion monitoring functions, such as step counting and heart rate monitoring. However, in certain scenarios, especially during class, the device lacks a timely reminder mechanism for children's sleepiness. At the same time, children may need to rest when going out or riding in a car. Existing smart watches do not have scene recognition functions and cannot distinguish when to turn on the reminder function. In addition, children are prone to abnormal body temperature or fever in places such as schools. Existing watches also fail to provide body temperature monitoring and fever reminder functions, and cannot help parents keep track of their children's health status in a timely manner. Therefore, it is of great practical significance to provide a smart children's watch that can detect and remind children of their sleepiness during class within a set time period, and has abnormal body temperature monitoring and reminder functions.

[0054] The embodiments of the present invention provide a method, device and children's watch for monitoring children's drowsiness in class. Parents can set the time period for the function to be enabled (such as during class). During the set time period, the watch monitors the child's heart rate and activity level through physiological signals to determine whether there is drowsiness; if the child is drowsy, the watch reminds the child to stay awake through continuous vibration or strong vibration. When the child is out playing or riding in a car, the watch automatically turns off the alert reminder function to ensure the child's normal rest. At the same time, the watch integrates a body temperature detection module that can monitor the child's body temperature in real time. When the child's body temperature is detected to be abnormal (such as fever), the watch reminds the child to inform the teacher or parents through slight vibration, and sends the information to the parent's mobile phone, prompting the parent to take timely countermeasures.

[0055] The method and device of the present invention are described below through specific embodiments.

[0056] See also Figure 1 , Figure 1 is a flow chart of a method for monitoring children's sleepiness in class according to an exemplary embodiment of the present invention, see Figure 1 , the method comprising:

[0057] Step S11, obtaining the user's exercise data and physiological data within a preset time period;

[0058] Step S12, determining the scene where the user is located based on the motion data; the scene includes class and others;

[0059] Step S13: If the user is in a class, determine the user status based on the physiological data; the user status includes normal, mild fatigue, moderate fatigue and severe fatigue;

[0060] Step S14: Determine reminder intensity according to user status.

[0061] It should be noted that the technical solution provided in this embodiment can be loaded in the existing smart wear system or application in the form of a small program or a plug-in in practice, or can be used in the form of a separate application through an external interface to realize monitoring and reminder functions. Applicable scenarios include but are not limited to: children's smart watches and children's smartphones.

[0062] Specifically, data acquisition starts once every preset time period, and the acquired data includes motion data and physiological data between the current time node and the last acquired time node; the preset time period is set according to the monitoring frequency.

[0063] It can be understood that the method provided in this embodiment determines that the scene is a class time through motion data, starts to monitor the user status based on physiological data, uses reminders of different intensities according to different user statuses, realizes automatic recognition of scenes to complete the activation of the monitoring function, and realizes comprehensive and accurate judgment of user drowsiness based on physiological data, which can effectively improve the user experience.

[0064] In specific practice, in step S11, "obtaining the user's motion data and physiological data within a preset time period", the motion data includes the user's speed, acceleration and GPS positioning data.

[0065] It should be noted that the preset duration is set according to the monitoring frequency, and the monitoring frequency is determined according to specific business needs or the user's real-time status tag.

[0066] In specific practice, "determining the user's scene based on motion data" in step S12 includes: determining whether the user is in school based on positioning data; if the user is in school, and the speed and acceleration are respectively less than the preset speed and preset acceleration, then determining that the user is in class.

[0067] It should be noted that by inputting the user's GPS positioning information into an existing real-time updated map system (where the map system contains schools marked in advance by the user), the user's specific location on the map can be obtained, and then it can be determined whether the user is in school.

[0068] It should be noted that the preset speed and preset acceleration are set according to specific business needs. When the user's speed and acceleration are respectively less than the preset speed and preset acceleration, the user is deemed to be in a stationary state, that is, in class; when the user's speed and acceleration are respectively greater than or equal to the preset speed and preset acceleration, the user is deemed to be in a non-stationary state, that is, in other states.

[0069] It can be understood that the technical solution provided in this embodiment determines that the scene is a class time through motion data, starts monitoring the user status according to physiological data, and realizes automatic recognition of the scene to complete the activation of the monitoring function.

[0070] In specific practice, "determining the user status based on physiological data" in step S13 includes: obtaining heart rate deviation based on heart rate data; obtaining eye closure duration and yawning frequency based on facial recognition data; obtaining status score based on heart rate deviation, eye closure duration and yawning frequency; and determining the user status based on the status score.

[0071] It should be noted that both heart rate data and facial recognition data are obtained through existing technologies. For example, heart rate data can be obtained through a heart rate monitoring component set on the wrist, which is an existing component available on the market; facial recognition data: first, the wearer's facial image can be collected through a camera set on the wearable device, or the wearer's facial image can be collected using a camera in the classroom that is pre-wired to the wearable device, and the real-time collected facial image is used as facial recognition data; the eye closure duration and the number of yawns obtained based on the facial recognition data are also obtained through existing algorithms. For example, the facial recognition data is identified through an existing face AI algorithm to identify eye closure and yawning actions, and the number of yawns within a preset time is accumulated, and the eye closure duration within the preset time is accumulated; the preset time can be the duration from the start to the end of the class, or it can be a separately set duration.

[0072] Specifically, the state score is obtained according to the heart rate deviation, the eye closing time and the number of yawns, including: the state score is obtained according to the heart rate deviation, the eye closing time and the number of yawns through the state formula, and the state formula is: Z = α·HR 偏 +β·T 闭眼 +γ·N; where Z is the status score; HR 偏 T is the heart rate deviation within the preset time period; 闭眼 is the eye closing time within the preset time; N is the number of yawns within the preset time; α, β and γ are the heart rate deviation weight coefficient, eye closing time weight coefficient and yawn number weight coefficient respectively.

[0073] It should be noted that α, β and γ are obtained after multiple experiments, and can generally be: α = 0.5, β = 0.3, γ = 0.2.

[0074] Specifically, the heart rate deviation is obtained according to the heart rate data, including: obtaining the heart rate deviation through a heart rate deviation formula, the heart rate deviation formula is: HR 偏 =HR 实时 / HR 最大 ; Among them, HR 偏 It is the heart rate deviation within the preset time period; HR 实时 The average heart rate of the user within the preset time period; HR 最大 It is the user's maximum heart rate value within the preset time period.

[0075] Specifically, the user status is determined based on the status score, including: if the status score is equal to 0, the user status is normal; if the status score is greater than 0 and the status score is less than the first preset score, the user status is mild fatigue, and low-frequency vibration and low-amplitude vibration are used to provide fatigue reminders for the user; if the status score is greater than or equal to the first preset score and the status score is less than or equal to the second preset score, the user status is moderate fatigue, and medium-frequency vibration and medium-amplitude vibration are used to provide fatigue reminders for the user; if the status score is greater than the second preset score, the user status is severe fatigue, and high-frequency vibration and large-amplitude vibration are used to provide fatigue reminders for the user.

[0076] It should be noted that the first preset score and the second preset score can be set according to specific business needs. The first preset score can generally be 30, and the second preset score can generally be 60; the low-frequency vibration can be 1-5Hz, the medium-frequency vibration can be 5-10Hz, and the high-frequency vibration can be 10-20Hz; the low-amplitude vibration can be 0.1g, the medium-amplitude vibration can be 1g, and the high-amplitude vibration can be 2g.

[0077] It can be understood that the technical solution provided in this embodiment effectively improves the accuracy of monitoring by combining multiple physiological data to determine whether the user is sleepy.

[0078] In specific practice, before determining the user state based on the physiological data, the method further includes: obtaining a heart rate threshold value through a threshold formula based on the heart rate data, and the threshold formula is: RH 阈值 =RH 基础 -d×RH 基础 ; Among them, RH 阈值 is the heart rate threshold; RH 基础 is the user's basic resting heart rate; d is a constant, usually between 0.05-0.1, indicating the set threshold offset; if the real-time heart rate in the heart rate data is less than the heart rate threshold, the user's real-time status label is marked as drowsy in order to adjust the monitoring frequency.

[0079] It should be noted that the real-time status tag is marked as drowsy, and the monitoring frequency needs to be increased.

[0080] In specific practice, the method also includes: if the real-time body temperature in the physiological data is greater than the preset body temperature, the user's real-time status tag is marked as a fever state in order to adjust the monitoring frequency.

[0081] It should be noted that people are more likely to feel sleepy when they have a fever, so the monitoring frequency needs to be increased.

[0082] See also Figure 2 , Figure 2 is a schematic block diagram of a device for monitoring children's sleepiness in class according to an exemplary embodiment of the present invention, see Figure 2 , the device 100 for monitoring children's sleepiness in class includes:

[0083] The acquisition module 101 is used to acquire the user's motion data and physiological data within a preset time period;

[0084] A scene module 102 is used to determine the scene where the user is located based on the motion data; the scene includes class and others;

[0085] The state module 103 is used to determine the user state based on the physiological data if the user is in a class; the user state includes normal, mild fatigue, moderate fatigue and severe fatigue;

[0086] The reminder module 104 is used to determine the reminder intensity according to the user status.

[0087] It should be noted that the device provided in this embodiment is applicable to scenarios including but not limited to: children's smart watches and children's smartphones.

[0088] Specifically, data acquisition starts once every preset time period, and the acquired data includes motion data and physiological data between the current time node and the last acquired time node; the preset time period is set according to the monitoring frequency.

[0089] It can be understood that the device provided in this embodiment determines that the scene is a class time through motion data, starts to monitor the user status based on physiological data, uses reminders of different intensities according to different user statuses, realizes automatic recognition of scenes to complete the activation of the monitoring function, and realizes comprehensive and accurate judgment of user drowsiness based on physiological data, which can effectively improve the user experience.

[0090] In a specific embodiment, the present application also provides a children's watch, which applies any of the above-mentioned methods for monitoring children's drowsiness in class.

[0091] In specific practice, the structure of children's watches includes: physiological signal detection module: used to monitor the heart rate, breathing rate and activity level of children to determine whether the children are sleepy. Vibration reminder module: when the child is detected to be sleepy, the watch reminds the child to stay awake through continuous vibration or strong vibration. Time setting module: parents can set the time period for the refreshing function to be turned on (such as class time) through the mobile phone application. Scene recognition module: the watch determines whether the child is out playing or riding through the acceleration sensor or GPS module. If the child is detected to be resting or drowsy, the refreshing reminder function is automatically turned off. Body temperature detection module: used to detect the child's body temperature in real time to determine whether it exceeds the fever threshold. Abnormal body temperature reminder module: when the abnormal body temperature is detected, the watch reminds the child through slight vibration, and sends the abnormal body temperature information to the parent's mobile phone through the mobile network. Intelligent control module: combined with time setting, scene recognition and physiological signal detection results, control the opening and closing of the refreshing reminder and abnormal body temperature reminder functions.

[0092] In specific practice, the control methods of children's watches include: Refreshing function: During the time period set by parents (such as during class), the watch monitors the child's heart rate through the physiological signal detection module to determine whether the child is drowsy. If the child is detected to be drowsy, the vibration reminder module is activated, and continuous vibration or strong vibration is selected to remind the child to stay awake. Scene recognition function: The watch determines whether the child is out playing or riding in a car through the scene recognition module. If the child is detected to be resting, the refreshing reminder function is automatically turned off to avoid affecting the child's rest. Body temperature detection and reminder function: The body temperature detection module of the watch monitors the child's body temperature in real time. If the body temperature exceeds the fever threshold, the abnormal body temperature reminder module will activate a slight vibration reminder so that the child can inform the teacher or parents in time. At the same time, this function will also notify the parents through the mobile network.

[0093] Those skilled in the art can understand that all or part of the processes in the above-mentioned embodiment methods can be completed by instructing the relevant hardware through a computer program, and the computer program can be stored in a non-volatile computer-readable storage medium. When the computer program is executed, it can include the processes of the embodiments of the above-mentioned methods. Among them, any reference to memory, storage, database or other media used in the embodiments provided in this application can include non-volatile and / or volatile memory. Non-volatile memory can include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM) or flash memory. Volatile memory can include random access memory (RAM) or external cache memory. As an illustration and not limitation, RAM is available in many forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDRSDRAM), enhanced SDRAM (ESDRAM), synchronous link (Synchlink) DRAM (SLDRAM), memory bus (Rambus) direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and memory bus dynamic RAM (RDRAM).

[0094] It should be noted that, in this article, relational terms such as first and second, etc. are only used to distinguish one entity or operation from another entity or operation, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Moreover, the terms "include", "comprise" or any other variants thereof are intended to cover non-exclusive inclusion, so that a process, method, article or device including a series of elements includes not only those elements, but also other elements not explicitly listed, or also includes elements inherent to such process, method, article or device. In the absence of further restrictions, the elements defined by the sentence "comprise a ..." do not exclude the existence of other identical elements in the process, method, article or device including the elements.

[0095] It should also be noted that the user information (including but not limited to user device information, user personal information, etc.) and data (including but not limited to data used for display, data for analysis, etc.) involved in this application are all information and data authorized by the user or fully authorized by all parties.

[0096] Each embodiment in this specification is described in a related manner, and the same or similar parts between the embodiments can be referred to each other, and each embodiment focuses on the differences from other embodiments. In particular, for the system embodiment, since it is basically similar to the method embodiment, the description is relatively simple, and the relevant parts can be referred to the partial description of the method embodiment.

[0097] The technical features of the above embodiments may be combined arbitrarily. To make the description concise, not all possible combinations of the technical features in the above embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0098] The above-mentioned embodiments only express several implementation methods of the present application, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for a person of ordinary skill in the art, several variations and improvements can be made without departing from the concept of the present application, and these all belong to the protection scope of the present application. Therefore, the protection scope of the patent of the present application shall be subject to the attached claims.

Claims

1. A method for monitoring children's sleepiness in class, characterized in that: include: Obtain the user's motion data and physiological data within a preset time period; Determining a scene where the user is located based on the motion data; The scenarios described include classes and others; If the user is in a class, the user status is determined based on the physiological data; the user status includes normal, mild fatigue, moderate fatigue and severe fatigue; The reminder intensity is determined according to the user status.

2. The method according to claim 1, characterized in that The physiological data includes heart rate data and facial recognition data; and determining the user status based on the physiological data includes: obtaining a heart rate deviation according to the heart rate data; Obtaining the eye closing duration and the number of yawns according to the facial recognition data; Obtaining a status score based on the heart rate deviation, eye closing time, and yawning frequency; The user status is determined according to the status points.

3. The method according to claim 2, characterized in that The state score is obtained according to the heart rate deviation, eye closing time and yawning times, including: The state score is obtained by a state formula according to the heart rate deviation, the eye closing time and the number of yawns. The state formula is: Z=α·HR 偏 +β·T 闭眼 +γ·N; Among them, Z is the status score; HR 偏 T is the heart rate deviation within the preset time period; 闭眼 is the eye closing time within the preset time; N is the number of yawns within the preset time; α, β and γ are the heart rate deviation weight coefficient, the eye closing time weight coefficient and the yawn number weight coefficient respectively.

4. The method according to claim 2, characterized in that: The obtaining of the heart rate deviation according to the heart rate data comprises: The heart rate deviation is obtained by the heart rate deviation formula, which is: HR 偏 =HR 实时 / HR 最大 ; Among them, HR 偏 is the heart rate deviation within the preset time period; HR 实时 The average heart rate of the user within the preset time period; HR 最大 It is the user's maximum heart rate value within the preset time period.

5. The method according to claim 2, characterized in that: The determining the user status according to the status points includes: If the status score is equal to 0, the user status is normal; If the status score is greater than 0 and less than the first preset score, the user status is a mild fatigue state, and low-frequency vibration and low-amplitude vibration are used to provide fatigue reminders to the user; If the status score is greater than or equal to the first preset score and the status score is less than or equal to the second preset score, the user status is moderate fatigue, and medium-frequency vibration and medium-amplitude vibration are used to provide fatigue reminders to the user; If the status score is greater than the second preset score, the user status is severe fatigue, and high-frequency vibration and large-amplitude vibration are used to provide fatigue reminders for the user.

6. The method according to claim 2, characterized in that Before determining the user status according to the physiological data, the method further includes: The heart rate threshold is obtained by a threshold formula according to the heart rate data, and the threshold formula is: RH 阈值 =RH 基础 -d×RH 基础 ; Among them, RH 阈值 is the heart rate threshold; RH 基础 is the user's basic resting heart rate; d is a constant, usually between 0.05 and 0.1, indicating the set threshold offset; If the real-time heart rate in the heart rate data is less than the heart rate threshold, the real-time status tag of the user is marked as a sleepy state so as to adjust the monitoring frequency.

7. The method according to claim 1, characterized in that The motion data includes speed, real-time acceleration and positioning data; and determining the scene where the user is located based on the motion data includes: Determining whether the user is in school based on the positioning data; If the user is in a school, and the speed and the acceleration are respectively smaller than a preset speed and a preset acceleration, it is determined that the user is in a class.

8. The method according to claim 1, characterized in that: The method further comprises: If the real-time body temperature in the physiological data is greater than the preset body temperature, the user's real-time status tag is marked as a fever state so as to adjust the monitoring frequency.

9. A device for monitoring children's sleepiness in class, characterized in that: The device comprises: An acquisition module is used to acquire the user's motion data and physiological data within a preset time period; A scene module, used to determine the scene where the user is located according to the motion data; the scene includes class and others; A state module, for determining a user state based on the physiological data if the user is in a class; the user state includes normal, mild fatigue, moderate fatigue and severe fatigue; The reminder module is used to determine the reminder intensity according to the user status.

10. A children's watch, characterized in that: The children's watch applies the method for monitoring children's drowsiness in class as described in any one of claims 1-8.

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