Infant autonomous activity time period identification method

Abstract

The invention discloses an infant autonomous activity time period identification method, which comprises the following steps of wearing equipment, setting a time period, calculating the number of effective students, regenerating a student data set, obtaining a new data set by taking a motion state average value, calculating the autonomous activity duration in the time period, and recording the autonomous activity time of an infant in one day through the wearing equipment. Through long-term observation and comparison, real behavior habits and development conditions of each child can be reflected better and more comprehensively, so that teachers and parents can know preference, social contact, athletic ability and character characteristics of each child conveniently.

Description

Technical Field

[0001] The present invention relates to the technical field of early childhood education, and in particular to a method for identifying a time period of independent activities of early childhood. Background Art

[0002] The daily activities of children in kindergarten can be roughly divided into two categories. One is organized group activities, such as lunch, nap, group courses, etc., which are arranged by teachers and children participate in group activities as required; the other is some activities that children have a certain degree of autonomy to carry out, such as autonomous regional activities, autonomous outdoor sports, etc. Children can choose to participate in activities according to their own preferences. In the current kindergarten teaching process, more and more attention is paid to the arrangement of autonomous activities in kindergartens, which can not only improve the autonomy of kindergartens, but also better allow children to participate in various activities. During the autonomous activities of children, teachers can observe the real behavior habits and development status of children. The length and arrangement of children's autonomous activity time are directly related to the improvement of the teaching quality of kindergartens. Through the performance of children's autonomous activity time in kindergartens, we can clearly understand children's preferences, social, motor skills, physical coordination and other abilities and internal development. However, currently, whether the kindergarten has carried out independent activities, the duration of independent activities, related course arrangements, etc. are mostly filled out manually by teachers. This is very cumbersome and not only increases the teachers' daily workload, but also fails to reflect the authenticity of the kindergarten's independent activity arrangements. Summary of the invention

[0003] The present invention provides a method for identifying a child's autonomous activity time period, which automatically identifies a child's autonomous activity time of a day through a wearable device to solve the problems mentioned in the background technology.

[0004] In order to achieve the above-mentioned invention object, the present invention adopts the following technical solutions: A method for identifying a child's autonomous activity time period comprises the following steps: S1: After the child enters the park, he / she puts on the wearable device, which automatically starts working. Each wearable device records the step data of the child bound to it and uploads it to the cloud platform; S2: When the number of children wearing wearable devices in the morning reaches 50% of the total number of students in the class, it is counted as time T1. When the number of children wearing wearable devices in the afternoon is less than 50% of the total number of students in the class, it is counted as time T2. S3: Count the number of valid devices N1, N2…Nn per minute in the time period from T1 to T2, where n is the number of minutes in the time window from T1 to T2, and calculate the number of valid students Dn={N1, N2, N3…Nn} in the time period from T1 to T2; S4: The step counting data of each child's wearable device is a step counting data set STUi={st1,st2,…stn} in units of one minute. The step counting data set of each student is preprocessed to obtain a regenerated step counting data set ST of each student (the newly generated data set is distinguished from the original data set by different letters); S5: Add all students’ step data sets ST, take the average value of the movement state according to the number of valid students Nn per minute, and obtain a new data set Astu={A1, A2, A3…An}; S6: Based on the data set Astu, the upper and lower thresholds are set, and the minimum time reference value Tcan of the effective autonomous activity time period is increased. Based on each data in the Astu data set, data that is within the two threshold ranges and has a continuous length greater than or equal to Tcan is considered to be an effective autonomous activity time period. After accumulating the effective time periods throughout the day, the duration of the children's autonomous activity time for the whole day is obtained.

[0005] Preferably, the wearable device has a wearing detection function, which can identify whether the device is worn and works normally, including a smart bracelet, a smart badge, and a positioning watch.

[0006] Preferably, the data collected by the wearable device in step S1 is not limited to step counting data, and other relevant data that can represent the activity status of the child, such as heart rate, calories and other exercise-related data can be used as the data source of this method.

[0007] As a preference, the order of steps S4 and S5 can be freely adjusted as needed. Preferably, the preprocessing in step S4 includes the following sub-steps: S41: For the part of the step data set STUi generated when the time is greater than T1 and less than T2, zero is added to make the number of step data of all students equal, which is recorded as n; S42: Poll each student's step data set STUi. If the number of steps within this minute is greater than or equal to 50, the activity status value is recorded as 2; if it is greater than or equal to 10 and less than 50, the activity status value is recorded as 1; if it is less than 10, the activity status value is recorded as 0. Based on this rule, regenerate each student's step data set ST.

[0008] Preferably, in step S5, the calculated average value of the motion state retains at least two decimal places.

[0009] Preferably, the relevant parameter values ​​in step S6 can be appropriately adjusted according to the activity types of different schools and the overall status of students to meet the personalized needs of different schools.

[0010] Preferably, in step S6, the obtained time periods of autonomous activity can be recorded separately for further analysis of the performance of other activity states of the child within the autonomous activity time period.

[0011] The present invention provides a method for identifying the time period of children's independent activity. By recording the children's independent activity time of the day through wearable devices, long-term observation and comparison can better and more comprehensively reflect the real behavior habits and development status of each child, thereby making it convenient for teachers and parents to understand the preferences, social interaction, athletic ability and personality characteristics of each child, so as to better arrange various activities, formulate and implement teaching strategies in a targeted manner, improve teaching quality, and also reduce the teacher's daily workload. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] Figure 1 It is a flow chart of the present invention. DETAILED DESCRIPTION

[0013] The present invention will be further described below in conjunction with the accompanying drawings: like Figure 1 As shown, a method for identifying a child's autonomous activity time period comprises the following steps: S1: After the child enters the park, he / she puts on the wearable device, which automatically starts working. Each wearable device records the step data of the child bound to it and uploads it to the cloud platform; S2: When the number of children wearing wearable devices in the morning reaches 50% of the total number of students in the class, it is counted as time T1. When the number of children wearing wearable devices in the afternoon is less than 50% of the total number of students in the class, it is counted as time T2. S3: Count the number of valid devices N1, N2…Nn per minute in the time period from T1 to T2, where n is the number of minutes in the time window from T1 to T2, and calculate the number of valid students Dn={N1, N2, N3…Nn} in the time period from T1 to T2; S4: The step counting data of each child's wearable device is a step counting data set STUi={st1,st2,…stn} in units of one minute. The step counting data set of each student is preprocessed to obtain a regenerated step counting data set ST of each student; The preprocessing in the above steps includes the following sub-steps: S41: For the part of the step data set STUi generated when the time is greater than T1 and less than T2, zero is added to make the number of step data of all students equal, which is recorded as n; S42: Poll each student's step data set STUi. If the number of steps within this minute is greater than or equal to 50, the activity status value is recorded as 2; if it is greater than or equal to 10 and less than 50, the activity status value is recorded as 1; if it is less than 10, the activity status value is recorded as 0. Based on this rule, regenerate each student's step data set ST.

[0014] S5: Add all students’ step data sets ST, take the average value of the movement state according to the number of effective students per minute Dn, and obtain a new data set Astu={A1, A2, A3…An}; S6: Based on the data set Astu, the upper and lower thresholds are set, and the minimum time reference value Tcan of the effective autonomous activity time period is increased. Based on each data in the Astu data set, data that is within the two threshold ranges and has a continuous length greater than or equal to Tcan is considered to be an effective autonomous activity time period. After accumulating the effective time periods throughout the day, the duration of the children's autonomous activity time for the whole day is obtained. Example

[0015] There are 30 children in a class. Starting from 9 am (T1), the number of children wearing bracelets reaches 15, and the algorithm starts. At 16:00 (T2), the number of children wearing bracelets is less than 15, and the algorithm ends. The total time is 7 hours (420 minutes).

[0016] During this 420 minutes, the sequence of the number of students Dn is = {15,15,15,16,16,17,18,20,20,20,24,24,28,28, … 28,26,26,26,25,24 … 15}; where n = 420.

[0017] If the students in the middle start wearing the glasses normally from 9 o'clock, the data will be: STU1={33,19,25,85,112…110,34,0} data length is 420; If the start or end time of the student's wearing time is greater than T1 or less than T2, the insufficient data is filled with 0, so that the total data length is 420, for example: STU2={0,0,0,0,0,0,25,24,…128,86,0,0,0}, the total data length is 420.

[0018] In the STUi data set, the numbers at the corresponding sequence positions are added and divided by the total number of students at the corresponding time point to obtain the average step value of the children's exercise in the class. This value is then preprocessed to obtain the children's activity state sequence: Astu={0.12,0.31,1.5…0.80,0.32} and the dataset length is 420.

[0019] The upper threshold of the reference is set to 1.10, the lower threshold is set to 0.35, and Tcan is set to 5. Based on the number of continuous data that simultaneously meet the relevant parameters in the Astu data set, 25, 48, 37, and 42 are obtained respectively. The duration of independent activities for the children in this class on that day is: 25+48+37+42=152 minutes.

[0020] The above are preferred embodiments of the present invention. It should be pointed out that, for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for identifying a child's autonomous activity time period, characterized in that: The following steps are involved: S1: After the child enters the park, he / she puts on the wearable device, which automatically starts working. Each wearable device records the step data of the child bound to it and uploads it to the cloud platform; S2: When the number of children wearing wearable devices in the morning reaches 50% of the total number of students in the class, it is counted as time T1. When the number of children wearing wearable devices in the afternoon is less than 50% of the total number of students in the class, it is counted as time T2. S3: Count the number of valid devices N1, N2…Nn per minute in the time period from T1 to T2, where n is the number of minutes in the time window from T1 to T2, and calculate the number of valid students Dn in the time period from T1 to T2; S4: The step counting data of each child's wearable device is a step counting data set STUi in units of one minute. The step counting data set of each student is preprocessed to obtain a regenerated step counting data set ST of each student; S5: Add all students’ step data sets ST, take the average of the movement status according to the number of valid students Nn per minute, and obtain a new data set Astu; S6: Based on the data set Astu, the upper and lower thresholds are set, and the minimum time reference value Tcan of the effective autonomous activity time period is increased. Based on each data in the Astu data set, data that is within the two threshold ranges and has a continuous length greater than or equal to Tcan is considered to be an effective autonomous activity time period. After accumulating the effective time periods throughout the day, the duration of the children's autonomous activity time for the whole day is obtained.

2. The method for identifying a child's autonomous activity time period according to claim 1, characterized in that: The wearable device in step S1 has a wearing detection function, including a smart bracelet, a smart badge, and a positioning watch.

3. The method for identifying a child's autonomous activity time period according to claim 1, characterized in that: The valid device in step S3 refers to a device that is worn and works normally.

4. The method for identifying a child's autonomous activity time period according to claim 1, characterized in that: The pre-processing in step S4 includes the following sub-steps: S41: For the part of the step data set STUi generated when the time is greater than T1 and less than T2, zero is added to make the number of step data of all students equal, which is recorded as n; S42: poll each student's step count data set STUi, if the number of steps in the minute is greater than or equal to 50, the activity state value is recorded as 2; if it is greater than or equal to 10 and less than 50, the activity state value is recorded as 1; If it is less than 10, the activity state value is recorded as 0. According to this rule, the step counting data set ST of each student is regenerated.

5. The method for identifying a child's autonomous activity time period according to claim 1, characterized in that: The data collected by the wearable device in step S1 also includes heart rate, calories, and other data that can represent the activity status of the child.

6. The method for identifying a child's autonomous activity time period according to claim 1, characterized in that: The order of steps S4 and S5 can be adjusted as needed.

7. The method for identifying a child's autonomous activity time period according to claim 1, characterized in that: In step S5, the calculated average value of the motion state retains at least two decimal places.

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