Myopia prevention and control method and equipment based on action monitoring

Abstract

The invention relates to the technical field of action monitoring, and discloses a myopia prevention and control method and equipment based on action monitoring. Aiming at an indoor scene, the method abandons conspicuous eye using distance and eye using posture detection in the traditional technology, innovatively converts detection of a close-range eye using state of a user into detection of feature actions of the user, and judges whether a healthy eye using prompt needs to be sent to the user and when to send a healthy eye using prompt to the user according to feature action data collected by calculation. And after the determination, prompt information is generated to remind the user to carry out telescopic activities and pay attention to eye health. Based on the detection thought, the equipment can be small, exquisite and hidden, does not interfere others or attract attention, can improve the use comfort, can eliminate the psychological burden of the user during use, and is beneficial for the user to insist in use for a long time, so that the purpose of continuously preventing myopia or delaying myopia development is achieved.

Description

Technical Field

[0001] The present invention relates to the field of motion monitoring technology, and in particular to a myopia prevention and control method and device based on motion monitoring. Background Art

[0002] Myopia is a common eye disease. It refers to a state in which parallel light rays are focused in front of the retina after passing through the eye's refractive system when the eyes are in a relaxed state, resulting in blurred vision of distant objects. In recent years, the incidence of myopia among Chinese teenagers has remained high and continued to rise, and the average age of onset of myopia has also been getting lower and lower. The causes of myopia mainly involve factors such as genetics and bad eye habits. As far as genetic factors are concerned, there is currently no effective intervention method, while bad eye habits include many factors, such as viewing objects too close, improper eye posture, and using the eyes in a low-light environment. Accordingly, the idea of ​​myopia prevention and control can mainly start from monitoring and correcting various bad eye habits.

[0003] There are already many myopia prevention products on the market, including products that monitor the user's eye distance, eye posture, eye environment and remind and correct them, as well as products that intervene directly through physical means. However, these products have some common shortcomings: on the one hand, their appearance is too eye-catching and their shapes are peculiar, which can easily attract the attention of others, such as sitting posture correction equipment, smart glasses, smart glasses clips, etc.; on the other hand, the wearing experience is poor, such as smart glasses, smart glasses clips, etc., which are usually heavy in weight or increase the weight of glasses after being attached to glasses. In addition, some products have poor accuracy in detecting eye distance, posture, etc., resulting in a high misjudgment rate; some products have high power consumption or small battery capacity, and need to be charged frequently, affecting the user experience. These psychological and physical obstacles usually make it difficult for users to wear or use products for a long time, resulting in a significant reduction in the effect of preventing myopia. Although such products generally use accelerometers, they are mostly used to detect eye postures rather than to achieve myopia prevention and control based on motion monitoring.

[0004] In the 1990s, American optometrist Dr. Jeffrey Anshel proposed the "20-20-20" eye protection rule, which means that for every 20 minutes of close-up use of the eyes, one needs to look at an object 20 feet (about 6 meters) away for at least 20 seconds, in order to help the eyes rest and relieve eye fatigue, thereby preventing myopia or delaying the development of myopia. The "20-20-20" eye protection rule has been jointly recommended by the American Optometric Association and the American Academy of Ophthalmology. Its effectiveness has been scientifically verified and widely recognized by many ophthalmologists at home and abroad. This eye protection rule reveals another idea for preventing myopia, that is, there is no need to specifically detect the distance, posture and environment of eye use. Instead, when it is detected that the user has been using the eyes at close range for a long time, it only needs to remind the user to look into the distance every 20 minutes, which can effectively prevent myopia or delay the development of myopia.

[0005] Utility model patent CN201020273807.X discloses a device that uses timed vibrations to remind users to instantly switch from near vision to far vision. Although the patent does not explicitly mention the "20-20-20" eye protection rule, its idea coincides with it. However, the device only sends out vibration reminders at regular intervals and does not perform any detection on the user's current eye status or activity status. Its biggest drawback is that it will disturb the user at inappropriate times, such as in a student nap scenario (when the user is asleep), which may still trigger a reminder.

[0006] There is also a type of sedentary reminder product on the market, which is mainly used in the field of personal health management. In view of the health hazards caused by long-term sitting, when the user is detected to be in a sedentary state, the user is reminded in time. This type of product usually also uses an accelerometer, and comes with a certain function of reminding users to pay attention to eye hygiene, but it is not specifically designed for myopia prevention. The duration of its detection of long-term sitting is usually longer than 20 minutes, resulting in too long intervals between eye hygiene reminders. There may also be scenarios where the sedentary reminder and eye hygiene reminder functions are inconsistent: the user may be in a sedentary state when sitting, but may not be using the eyes for a long time at close range (such as a student nap scene); the user may be judged as not being in a sedentary state when walking a little, but may actually still be using the eyes for a long time at close range (for example, students may occasionally stand and walk during breaks, but most of the time they are still doing homework). Summary of the invention

[0007] Through observation, it is found that for certain groups of people, such as primary and secondary school students studying indoors, when they are in a state of close eye use, their own movements usually have significant characteristics: the body remains relatively still for a long time but not completely still, the amplitude and frequency of movements of various parts of the body are kept at a low level, and there are few large movements (such as walking, running, jumping, etc.). Based on the above observations and combined with the "20-20-20" eye protection rule, the present invention makes innovative improvements to the various defects of the above-mentioned traditional detection methods, and uses motion monitoring sensors to detect and identify the user's characteristic movements to determine whether and when to issue healthy eye reminders to the user.

[0008] The present invention provides a method for myopia prevention and control based on motion monitoring, and its overall process can be found in the attached Figure 8 , including the following steps:

[0009] S1. Start timing: set the monitoring cycle and detection interval, and start timing; define the current monitoring duration as the cumulative duration from the start of timing to the current moment;

[0010] S2. Detection frequency control: When the detection interval is greater than 0 minutes, wait for the corresponding interval length; controlling the detection interval can effectively control the detection frequency; a detection interval of 0 minutes is equivalent to continuous detection;

[0011] S3. Feature action detection: detecting and identifying the user's feature action through the action monitoring sensor, and calculating and collecting at least one feature action data; wherein:

[0012] The motion monitoring sensor includes at least one of an accelerometer, a gyroscope, and a magnetometer; in practical applications, a sensor that supports a low-power working mode can be selected to improve the battery life of the product;

[0013] The characteristic action refers to an action performed by a user that meets preset conditions and is detected and identified by an action monitoring sensor;

[0014] The characteristic action data refers to the data related to the characteristic actions detected and identified during the monitoring period, as well as the derived data calculated based on these data by a preset algorithm;

[0015] S4 monitoring cycle control: If the current monitoring duration is less than the monitoring cycle, return to S2 to continue testing;

[0016] S5. Reminder decision: Check the reminder decision conditions based on the characteristic action data to determine whether the user needs to be reminded or needs to wait for the conditions to be met; if the user does not need to be reminded, the current process is terminated; if the conditions need to be met, return to S3 to continue testing; wherein:

[0017] (1) The reminder decision condition is pre-set based on the calculated and collected user characteristic action data, and is used to perform conditional judgment on the characteristic action data, including at least one of an intervention condition and a non-intervention condition;

[0018] (2) The inspection reminder decision conditions include:

[0019] When only intervention conditions are included, if at least one intervention condition is met, it is determined that the user needs to be reminded; otherwise, it can be determined that the user does not need to be reminded, or it is determined that the condition needs to be met.

[0020] When only the no-intervention conditions are included, if at least one of the no-intervention conditions is met, it is determined that there is no need to remind the user; otherwise, it is determined that the user needs to be reminded;

[0021] When both intervention conditions and non-intervention conditions are included, if at least one non-intervention condition is met, it is determined that there is no need to remind the user; if all non-intervention conditions are not met, and at least one intervention condition is met, it is determined that the user needs to be reminded; if all intervention conditions and non-intervention conditions are not met, it can be determined that there is no need to remind the user, or it can be determined that the waiting condition needs to be met;

[0022] S6. Reminder execution: Generate a reminder message to remind the user to perform telescopic activities and pay attention to eye hygiene.

[0023] Further,

[0024] (1) The characteristic movement includes at least one of an awake state movement and a large movement state movement;

[0025] The awake state action is defined as: an action performed by any part of the user's body within a preset time interval, the amplitude of which exceeds a preset threshold;

[0026] The large motion state action is defined as: an action performed by the user within a preset interval time, in which the vertical fluctuation amplitude of the body exceeds a preset threshold;

[0027] (2) The characteristic action data includes at least one of the following:

[0028] Detecting the recognized feature actions;

[0029] The number of times characteristic actions occur during the user monitoring period;

[0030] The frequency of characteristic actions during the user monitoring period;

[0031] Time domain distribution characteristics of characteristic actions within the user monitoring period;

[0032] User activity status during the monitoring period;

[0033] The duration of close-up eye use during the user monitoring period;

[0034] (3) The intervention conditions include:

[0035] The number of wake-up and activation actions during the user monitoring period is not less than the preset threshold;

[0036] A wake-up state action or a large movement state action is identified in the current feature action detection; the current feature action detection refers to the feature action detection performed within a preset time period before the current moment; if no feature action detection occurs within the time period, it is determined that no feature action is detected and identified in the current feature action detection;

[0037] (4) The conditions for exemption from intervention include:

[0038] The number of wake-up and activation actions within the user monitoring period is less than the preset threshold;

[0039] The number of large motion state actions within the user monitoring period is not less than the preset threshold;

[0040] Further,

[0041] (1) The temporal distribution characteristics of the characteristic actions in the user monitoring period are obtained by dividing the monitoring period into multiple equal-length time periods and counting the number of occurrences of the characteristic actions in each time period;

[0042] (2) The activity status of the user during the monitoring period includes the awake state, the large movement state, the resting state and the close-up eye use state; the determination method includes:

[0043] (a) Whether a user is in an active state during a monitoring period may be determined by any of the following methods:

[0044] Judging based on the number of occurrences of the awake state action in the user monitoring period: if the number of occurrences of the awake state action in the monitoring period is not less than the first threshold, it is determined that the user is in the awake state in the monitoring period; otherwise, it is determined that the user is not in the awake state in the monitoring period;

[0045] Judging according to the time domain distribution characteristics of the wake-up state action in the user monitoring period: if the number of wake-up state actions in more than 50% of the time periods is not less than the second threshold, and the number of wake-up state actions in the last time period is not less than the second threshold, and the total number of wake-up state actions in all time periods is not less than the first threshold, then it is determined that the user is in the wake-up state in the monitoring period; otherwise, it is determined that the user is not in the wake-up state in the monitoring period;

[0046] The first threshold and the second threshold are both preset by the system, or set by the user through an interactive interface;

[0047] (b) Whether the user is in a state of large movement during the monitoring period may be determined by any of the following methods:

[0048] Judging based on the number of occurrences of large motion state actions in the user monitoring period: if the number of occurrences of large motion state actions in the user monitoring period is not less than the third threshold, it is determined that the user is in a large motion state in the user monitoring period; otherwise, it is determined that the user is not in a large motion state in the user monitoring period;

[0049] Judging according to the temporal distribution characteristics of large motion state actions in the user monitoring period: if the number of large motion state actions in more than 50% of the time periods is not less than the fourth threshold, or the sum of the number of large motion state actions in all time periods is not less than the third threshold, then it is determined that the user is in a large motion state in the monitoring period; otherwise, it is determined that the user is not in a large motion state in the monitoring period;

[0050] The third threshold and the fourth threshold are both preset by the system, or set by the user through an interactive interface;

[0051] (c) Whether the user is in a resting state during the monitoring period may be determined by any of the following methods:

[0052] Judging based on whether the user is in an awake state and a state of heavy movement during the monitoring period: if the user is not in an awake state and is not in a state of heavy movement during the monitoring period, the user is judged to be in a resting state during the monitoring period; otherwise, the user is judged to be not in a resting state during the monitoring period;

[0053] Only judge based on whether the user is in the awake state during the monitoring period: if the user is not in the awake state during the monitoring period, it is judged that the user is in the resting state during the monitoring period; otherwise, it is judged that the user is not in the resting state during the monitoring period;

[0054] (d) Whether the user is in a state of close-up eye use during the monitoring period may be determined by any of the following methods:

[0055] Judging based on whether the user is in an awake state and a state of heavy movement during the monitoring period: if the user is in an awake state and not in a state of heavy movement during the monitoring period, it is judged that the user is in a state of close-up eye use during the monitoring period; otherwise, it is judged that the user is not in a state of close-up eye use during the monitoring period;

[0056] Only judge based on whether the user is in the awake state during the monitoring period: if the user is in the awake state during the monitoring period, it is determined that the user is in the close-up eye state during the monitoring period; otherwise, it is determined that the user is not in the close-up eye state during the monitoring period;

[0057] (3) The method for calculating the time of close-range eye use during the user monitoring period includes any of the following:

[0058] It is assumed that the user is continuously using their eyes at close range, and the duration is accumulated directly;

[0059] When the detection recognizes the awake state action and does not recognize the large movement state action, the duration is accumulated;

[0060] If the detection recognizes the awake state action and does not recognize the large movement state action, the duration is accumulated; otherwise, the duration is reset to zero;

[0061] When the detection recognizes the awake state action, the duration is accumulated;

[0062] If the detection recognizes the awake state action, the duration is accumulated; otherwise, the duration is reset to zero;

[0063] (4) The intervention conditions also include:

[0064] The user is in the awake state during the monitoring period;

[0065] The duration of close-range eye use during the user monitoring period is not less than the preset threshold;

[0066] The user is using the eyes at close range during the monitoring period;

[0067] (5) The above-mentioned non-intervention conditions also include:

[0068] The user is in a resting state during the monitoring period;

[0069] The user is in a state of heavy movement during the monitoring period;

[0070] The duration of close-range eye use during the user monitoring period is less than the preset threshold.

[0071] Further,

[0072] The monitoring period is pre-set or set by the user through the interactive interface, with a default duration of 20 minutes and a range of 10-45 minutes;

[0073] The detection interval is a pre-set time interval, the default duration is 0 minutes, and the value range is 0 minutes to the monitoring period.

[0074] Further,

[0075] The detection and identification method of the wake-up state action includes at least one of the following:

[0076] Identify wake-up and wake-up actions through accelerometer detection;

[0077] Identify wake-up and wake-up actions through gyroscope detection;

[0078] Identify wake-up and wake-up actions through magnetometer detection;

[0079] The detection and recognition method of the large motion state action includes:

[0080] Accelerometer detection to identify large motion states.

[0081] The present invention also provides a myopia prevention and control device based on motion monitoring, comprising:

[0082] A data processing unit, comprising a microcontroller with a built-in storage unit, or a microprocessor plus an external storage unit; the storage unit stores a code program for implementing the myopia prevention and control method described above; the microcontroller or microprocessor is configured to run the code program in the storage unit to implement the myopia prevention and control method described above;

[0083] A motion monitoring unit, comprising at least one of an accelerometer, a gyroscope, and a magnetometer; the motion monitoring unit is equivalent to the motion monitoring sensor in the myopia prevention and control method described above, and cooperates with the data processing unit to detect and identify characteristic motions of the user;

[0084] A timing unit, including an RTC real-time clock circuit, is used to provide a real-time clock signal to assist the data processing unit in timing;

[0085] A feedback unit, including a vibration motor, for generating vibrations to remind the user to look far away and pay attention to eye hygiene;

[0086] A power supply unit, including a rechargeable battery, for supplying power to the entire device;

[0087] The shell is used to arrange the units inside.

[0088] Furthermore, the myopia prevention and control device based on motion monitoring includes:

[0089] Charging port and sealing rubber;

[0090] The charging port is arranged on the back of the housing;

[0091] The sealing rubber is embedded in the back of the shell, usually covers the charging interface, and is opened during charging.

[0092] Furthermore, the myopia prevention and control device based on motion monitoring includes:

[0093] Display unit and input unit;

[0094] The display unit is used for information display;

[0095] The input unit is used for user interaction operations.

[0096] Furthermore, the myopia prevention and control device based on motion monitoring includes:

[0097] External connection components;

[0098] The external connection component is detachable;

[0099] The housing and the external connection component together constitute a wearable device.

[0100] The method and device disclosed in the present invention are mainly for people who need to use their eyes indoors for a long time every day, such as primary and secondary school students. In indoor scenes, the method directly converts the detection of close-range eye state into the detection of user actions. This conversion not only simplifies the complexity of technical implementation, but also significantly improves the accuracy and stability of detection; it not only avoids disturbing users during lunch breaks or sleep, but also reduces user troubles caused by misjudgment or missed judgments, and optimizes the user experience. Compared with the method of judging the eye state mainly by detecting the eye distance, eye posture, etc. in the prior art, the method disclosed in the present invention does not need to place the device carrier in a conspicuous position (such as glasses worn on the bridge of the nose and ears), but adopts a more concealed product form (such as an electronic watch that can be conveniently worn on the wrist), thereby enhancing the convenience and concealment of the device. This design effectively reduces the psychological burden and resistance of the user, and is conducive to long-term wear to achieve the goal of continuously preventing myopia or delaying the development of myopia. In addition, compared with smart glasses or smart glasses clip products, the device disclosed in the present invention can accommodate a larger space and weight, so that a larger capacity battery can be carried to further improve the endurance. BRIEF DESCRIPTION OF THE DRAWINGS

[0101] The accompanying drawings are used to provide a further understanding of the present invention and are an integral part of the specification. Together with the embodiments of the present invention, they are used to explain the present invention and do not constitute a limitation of the present invention. In the accompanying drawings:

[0102] Figure 1 It is an internal structure diagram of a preferred embodiment of a myopia prevention and control device based on motion monitoring of the present invention;

[0103] Figure 2 It is the external shape and structure diagram of the front side of the housing of the preferred embodiment of the device;

[0104] Figure 3 The external shape and structure diagram of the back side of the housing of the preferred embodiment and the secondary embodiment of the device;

[0105] Figure 4 It is an external structure diagram of the preferred embodiment and the secondary embodiment of the device after the components are disassembled from the back of the housing;

[0106] Figure 5 is an internal structural diagram of a second preferred embodiment of the device;

[0107] Figure 6 It is the external shape and structure diagram of the front side of the housing of the second preferred embodiment of the device;

[0108] Figure 7It is the external shape and structure diagram of the second preferred embodiment of the device after adding external connection components;

[0109] Figure 8 It is an overall flow chart of the myopia prevention and control method based on motion monitoring of the present invention;

[0110] Fig. 9 is a flowchart of a typical embodiment of the method based on its typical configuration;

[0111] Fig.10 is a flowchart of another embodiment of the method based on its typical configuration;

[0112] The numbers in the figure are: 1. housing, 2. motion monitoring unit, 3. passive crystal, 4. microcontroller, 5. feedback unit, 6. power supply unit, 7. circuit board, 8. display unit, 9. touch input chip, 10. touch input button, 11. cover, 12. charging port, 13. sealing rubber, 14. external connection components. DETAILED DESCRIPTION

[0113] The embodiments of the present invention are described below in conjunction with the accompanying drawings. It should be particularly pointed out that the embodiments described here are only used to explain the present invention, and are not used to limit the protection scope of the present invention.

[0114] Example 1

[0115] Embodiment 1 is a typical embodiment of the myopia prevention and control method based on motion monitoring of the present invention, which is applicable to a device worn or carried around. The specific steps of this embodiment will be described in detail below.

[0116] Step 1. Set the monitoring period and detection interval, divide the monitoring period into N (N≥1) equal-length time periods, and start timing; define the current monitoring duration as the cumulative duration from the start of timing to the current moment.

[0117] The monitoring period is pre-set or set by the user through the interactive interface. The default value is 20 minutes and the value range is [10,45] minutes. The monitoring period can also be a floating range based on a fixed value, for example, fluctuating between 19 and 21 minutes. If the monitoring period is less than 10 minutes, frequent reminders may distract the user's attention; if it is greater than 45 minutes, the user's eye fatigue may have occurred, resulting in reduced prevention and control effects.

[0118] When N=1, the monitoring period is not segmented, that is, the monitoring period is the entire time period, and the judgment logic is simpler.

[0119] When N>1, the time domain distribution details of the characteristic action can be distinguished (such as whether the characteristic action is approximately evenly distributed within the monitoring period or concentrated in a certain period), helping to more accurately determine whether the reminder decision condition is met within the current period. It should be noted that the value of N should not be too large, and the duration of each period should be no less than 1 minute to avoid interference from occasional factors. In practical applications, the value of N needs to be calibrated in combination with sensor characteristics and user characteristic action data.

[0120] Step 2. When the detection interval is greater than 0 minutes, wait for the corresponding interval length. The default detection interval length is 0 minutes, and the value range is from 0 minutes to the monitoring period; a value of 0 minutes is equivalent to continuous detection. Controlling the detection interval can effectively control the detection frequency. Reducing the detection frequency can reduce the power consumption of the system; increasing the detection frequency can improve the accuracy of motion monitoring. The system can achieve an optimal balance between monitoring accuracy and power consumption by adjusting the detection interval.

[0121] Step 3. Detect and identify the user's characteristic actions in the current period through the action monitoring sensor, and calculate and collect at least one characteristic action data.

[0122] The motion monitoring sensor may be any combination of an accelerometer, a gyroscope, and a magnetometer.

[0123] The characteristic action includes at least one of an awake state action and a large movement state action;

[0124] The wake-up state action is defined as: an action performed by any part of the user's body within a preset time interval with an amplitude exceeding a preset threshold, including movement, tilting, rotation of body parts, and grasping, picking up, putting down, moving, shaking, and other operations on the device, as well as actions of directly touching the device, indirectly touching the device, and related actions that cause the device to vibrate.

[0125] The large motion state action is defined as an action performed by the user within a preset time interval in which the vertical fluctuation amplitude of the body exceeds a preset threshold, including walking, running, jumping, etc.

[0126] The detection and identification of characteristic actions by the motion monitoring sensor can be performed by using a preset algorithm based on basic data acquired by the motion monitoring sensor, or the motion monitoring sensor with a built-in relevant detection algorithm can independently complete the detection and identification of characteristic actions within the sensor.

[0127] The detection and identification of large motion state actions depends on the accelerometer, so when the accelerometer is not selected, the detected and identified characteristic actions do not include large motion state actions, and the parts related to large motion state actions in the corresponding intervention conditions and non-intervention conditions should also be deleted.

[0128] Any combination of motion monitoring sensors can be selected to detect and identify wake-up state actions. This method embodiment is applicable to devices worn or carried with you, and the wake-up state actions detected and identified are mainly the movement, tilting, rotation, etc. of the body part or the whole body wearing or carrying the device, as well as the action of directly touching the device.

[0129] Regardless of the combination of motion monitoring sensors selected, the feature motion data collected by calculation can include any combination of the following basic data:

[0130] Whether the feature action detection detects and recognizes the wake-up state action;

[0131] The number of times the wake-up state action occurs during the monitoring period;

[0132] The frequency of awakening and active state actions during the monitoring period;

[0133] The temporal distribution characteristics of the awake and active state actions during the monitoring period.

[0134] When the motion monitoring sensor includes an accelerometer, the characteristic motion data collected by the calculation may also include any combination of the following basic data:

[0135] Whether the feature action detection detects and recognizes large motion state actions;

[0136] The number of occurrences of large motion state actions during the monitoring period;

[0137] The frequency of large motion state actions during the monitoring period;

[0138] The temporal distribution characteristics of large motion state actions within the monitoring period.

[0139] Furthermore, the calculated and collected characteristic action data may also include the activity status of the user during the monitoring period, which is obtained by calculating the basic data of the above characteristic actions;

[0140] The user's activity status during the monitoring period includes the awake state, the large-scale movement state, the resting state and the close-up eye use state; the judgment method includes:

[0141] (1) Whether the user is in an active state during the monitoring period may be determined by any of the following methods:

[0142] When N=1, whether the user is in the awake state during the monitoring period is determined according to the number of occurrences of the awake state action during the user monitoring period: if the number of occurrences of the awake state action during the monitoring period is not less than the first threshold, it is determined that the user is in the awake state during the monitoring period; otherwise, it is determined that the user is not in the awake state during the monitoring period;

[0143] When N>1, whether the user is in the awake state during the monitoring period is determined according to the time domain distribution characteristics of the awake state action during the user monitoring period: if the number of awake state actions in more than 50% of the time periods is not less than the second threshold, and the number of awake state actions in the last time period is not less than the second threshold, and the number of awake state actions in the monitoring period is not less than the first threshold, then it is determined that the user is in the awake state during the monitoring period; otherwise, it is determined that the user is not in the awake state during the monitoring period.

[0144] The first threshold can be set in proportion to the length of the monitoring period (in minutes). For example, when the monitoring period is T minutes, the threshold range can be set to [T×k1, T×k2], where k1 and k2 are empirical coefficients (such as 0.5 and 2.0), which need to be calibrated in combination with sensor characteristics and user motion data. The second threshold can be set in proportion to the number of minutes in the time period. For example, when the time period is t minutes, the threshold range can be set to [t×k3, t×k4], where k3 and k4 are empirical coefficients (such as 0.5 and 2.0), which need to be calibrated in combination with sensor characteristics and user motion data. The selection of these two thresholds is intended to eliminate interference caused by occasional awakening and active movements of the user in a resting state.

[0145] (2) Whether the user is in a state of large movement during the monitoring period may be determined by any of the following methods:

[0146] When N=1, whether the user is in a large motion state during the monitoring period is determined according to the number of large motion state actions in the user monitoring period: if the number of large motion state actions in the user monitoring period is not less than the third threshold, it is determined that the user is in a large motion state during the monitoring period, otherwise it is determined that the user is not in a large motion state during the monitoring period;

[0147] When N>1, whether the user is in a large motion state during the monitoring period is judged based on the time domain distribution characteristics of the large motion state actions during the user monitoring period: if the number of large motion state actions in more than 50% of the time periods is not less than the fourth threshold, or the total number of large motion state actions in all time periods is not less than the third threshold, then it is determined that the user is in a large motion state during the monitoring period; otherwise, it is determined that the user is not in a large motion state during the monitoring period.

[0148] The third threshold can be set in proportion to the length of the monitoring period (in minutes). For example, when the monitoring period is T minutes, the threshold range can be set to [T×k5, T×k6], where k5 and k6 are empirical coefficients (such as 10 and 50), which need to be calibrated in combination with sensor characteristics and user motion data. The fourth threshold can be set in proportion to the number of minutes in the time period. For example, when the time period is t minutes, the threshold range can be set to [t×k7, t×k8], where k7 and k8 are empirical coefficients (such as 10 and 50), which need to be calibrated in combination with sensor characteristics and user motion data. The selection of these two thresholds is intended to eliminate interference caused by occasional large movements of users when using their eyes at close range.

[0149] (3) Whether the user is in a resting state during the monitoring period may be determined by any of the following methods:

[0150] Judging based on whether the user is in an awake state and a large movement state during the monitoring period: if the user is not in an awake state and not in a large movement state during the monitoring period, the user is judged to be in a resting state during the monitoring period, otherwise the user is judged to be not in a resting state during the monitoring period; when the user is in a large movement state during the monitoring period, it is highly likely that the user will also be in an awake state; but this is not absolute, and it depends on the selection of the first to fourth thresholds; judging whether the user is in a resting state based on the large movement state and the awake state will be more accurate;

[0151] Judging only based on whether the user is in an active state during the monitoring period: if the user is not in an active state during the monitoring period, then the user is judged to be in a resting state during the monitoring period; otherwise, the user is judged not to be in a resting state during the monitoring period; because when the user is in a state of large movement during the monitoring period, it is highly likely that the user will also be in an active state, so the judgment of the resting state can be simplified to judging only based on whether the user is in an active state; in addition, when the motion monitoring sensor does not include an accelerometer, or the calculated and collected characteristic motion data does not include large motion state motion-related data, whether the user is in a resting state can only be judged based on whether the user is in an active state.

[0152] (4) Whether the user is in a state of close-up eye use during the monitoring period may be determined by any of the following methods:

[0153] Judging based on whether the user is in the awake state and the state of large movements during the monitoring period: If the user is in the awake state and not in the state of large movements during the monitoring period, it is judged that the user is in the state of close-up eye use during the monitoring period; otherwise, it is judged that the user is not in the state of close-up eye use during the monitoring period; because when the user is in the awake state during the monitoring period, he may not be in the state of large movements, so judging whether the user is in the state of close-up eye use based on the state of large movements and the awake state at the same time will have a greater improvement in accuracy compared to judging based on the awake state alone;

[0154] Judging only based on whether the user is in an active state during the monitoring period: if the user is in an active state during the monitoring period, then it is judged that the user is in a close-up eye-using state during the monitoring period; otherwise, it is judged that the user is not in a close-up eye-using state during the monitoring period; when the motion monitoring sensor does not include an accelerometer, or the calculated and collected characteristic motion data does not include data related to large motion states, whether the user is in a close-up eye-using state can only be judged based on whether the user is in an active state.

[0155] Furthermore, the calculated and collected characteristic action data may also include the user's close-range eye use time during the monitoring period, which is obtained by calculating the basic data of the above characteristic actions; the calculation method of the user's close-range eye use time during the monitoring period includes any of the following:

[0156] (1) It is assumed that the user is constantly using their eyes at close range, and the duration is directly accumulated. In indoor scenarios (such as primary and secondary school students studying indoors), users spend most of their time using their eyes at close range, so directly accumulating the duration is also reasonable. However, when issuing eye reminders to users, it is necessary to detect the user's current activity status and try to avoid disturbing the user when he is resting or sleeping.

[0157] (2) When the wake-up state action is detected and recognized but no large movement state action is detected and recognized, the duration is accumulated; in indoor scenarios (such as primary and secondary school students studying indoors), when the user has an awake state action (such as moving, tilting, turning any part of the body, and directly touching the device, etc.) and no large movement state action (such as walking, running, jumping, etc.) occurs, the user is likely to be in a close-up eye state;

[0158] (3) If the awake state action is detected and no large movement state action is detected, the duration is accumulated; otherwise, the duration is reset to zero. This method is similar to (2), except that (2) calculates the user's cumulative close-up eye use time, while this method calculates the user's continuous close-up eye use time.

[0159] (4) When the wake-up state action is detected and recognized, the duration is accumulated. Compared with methods (2) and (3), this method only detects whether the user currently has an awake state action, but does not detect whether the user currently has a large movement state action. First, the judgment logic is simpler. Second, when the motion monitoring sensor does not include an accelerometer, or the calculated and collected characteristic motion data does not include large movement state action related data, it can only be compromised to choose to judge whether the user is currently in a close-up eye state based only on the wake-up state action;

[0160] (5) If the awake state action is detected, the duration is accumulated; otherwise, the duration is reset to zero. This method is similar to (4), except that (4) calculates the user's cumulative close-up eye usage time, while this method calculates the user's continuous close-up eye usage time.

[0161] Step 4. Determine whether the current monitoring duration is less than the monitoring cycle. If so, return to step 2 to continue monitoring the next period. This step can control the user's eye reminders to be less frequent, avoiding frequent interference to the user.

[0162] Step 5. Check the reminder decision conditions based on the characteristic action data to determine whether it is necessary to remind the user or wait for the conditions to be met; if it is not necessary to remind the user, terminate the current process; if it is necessary to wait for the conditions to be met, return to step 3 to continue testing.

[0163] If the current process is terminated, the user will not be reminded during the current monitoring cycle; if you need to wait for the condition to be met, return to step 3 to continue testing until the specific condition is met. Here, return to step 3 instead of step 2 because the reminder interval has been met. If you need to wait for the specific condition to be met, you should test whether the condition is met as soon as possible instead of waiting for the test interval.

[0164] The reminder decision condition is pre-set based on the type of user characteristic action data collected by calculation, and is used to perform conditional judgment on the characteristic action data to determine whether to issue an eye reminder to the user; the reminder decision condition includes at least one of an intervention condition and a non-intervention condition;

[0165] The inspection reminder decision conditions include:

[0166] When only intervention conditions are included, if at least one intervention condition is met, it is determined that the user needs to be reminded; otherwise, it can be determined that the user does not need to be reminded, or it is determined that the condition needs to be met.

[0167] When only the no-intervention conditions are included, if at least one of the no-intervention conditions is met, it is determined that there is no need to remind the user; otherwise, it is determined that the user needs to be reminded;

[0168] When both intervention conditions and no-intervention conditions are included, if at least one no-intervention condition is met, it is determined that there is no need to remind the user; if all no-intervention conditions are not met and at least one intervention condition is met, it is determined that the user needs to be reminded; if all intervention conditions and no-intervention conditions are not met, you can choose to decide that there is no need to remind the user, or decide that it is necessary to wait for the conditions to be met.

[0169] The intervention conditions include:

[0170] (1) The number of times the user's awake state action occurs during the user's monitoring period is not less than the first threshold. If this condition is met, it can be determined that the user is in the awake state during the current monitoring period. When the action monitoring sensor does not include an accelerometer, or the calculated and collected characteristic action data does not include large motion state data, it can also be used as a basis for determining the close-up eye state.

[0171] (2) A wake-up state action or a large movement state action is identified in the current feature action detection. The current feature action detection refers to the feature action detection performed within a preset time period before the current moment. The preset time period defaults to 30 seconds, and the value range is [5,60] seconds. The specific value needs to be calibrated in combination with the characteristics of the motion monitoring sensor and the user's feature action data. If no feature action detection occurs within this time period, it is determined that no feature action is detected and identified in the current feature action detection. Meeting this condition can confirm that the user is not currently in a resting state, and an eye reminder can be issued to the user without worrying about disturbing the user's rest and sleep.

[0172] (3) The user is in the active state during the monitoring period. There are two ways to determine whether the user is in the active state during the monitoring period: one is based on the number of occurrences of the active state action during the monitoring period, and the other is based on the time domain distribution characteristics of the active state action during the monitoring period. The latter can provide richer details and the judgment is more accurate.

[0173] (4) The duration of close-up eye use during the user's monitoring cycle is not less than a preset threshold. The preset threshold defaults to 90% of the monitoring cycle, and the value range is 50% to 100% of the monitoring cycle. The specific value needs to be calibrated in combination with the characteristics of the motion monitoring sensor and the user's characteristic motion data. The user's close-up eye use duration can be calculated in a variety of ways to meet the needs of different scenarios. If this condition is met, it can be determined that the user is in a state of long-term close-up eye use during the current monitoring cycle, and an eye use reminder should be issued to the user.

[0174] (5) The user is using their eyes at close range during the monitoring period. This condition does not provide more details about the duration of the user's close-range eye use, but takes into account the user's wakefulness, resting state, and large-scale movement state, and can avoid issuing eye use reminders to the user at inappropriate times, thereby avoiding disturbing the user.

[0175] The non-intervention conditions include:

[0176] (1) The number of times the user's wake-up state actions occur during the user's monitoring period is less than the first threshold. If this condition is met, it can be determined that the user is in a resting sleep state during the current monitoring period, and interference with the user should be avoided.

[0177] (2) The number of occurrences of large-scale motion state actions during the user monitoring period is not less than the third threshold. The user has naturally achieved telescopic adjustment of the line of sight during the large-scale motion process, so there is no need to remind the user again.

[0178] (3) The user is in a resting state during the monitoring period. There are two ways to judge whether the user is in a resting state during the monitoring period: one is to judge based on whether the user is in an awake state and a large movement state during the monitoring period, and the other is to judge only based on whether the user is in an awake state during the monitoring period. The former can provide richer details and the judgment is more accurate.

[0179] (4) The user is in a state of large movement during the monitoring period. There are two ways to judge whether the user is in a state of large movement during the monitoring period: one is based on the number of occurrences of large movement state actions during the monitoring period, and the other is based on the time domain distribution characteristics of large movement state actions during the monitoring period. The latter can provide richer details and the judgment is more accurate.

[0180] (5) The duration of close-up eye use during the user's monitoring period is less than the preset threshold. If this condition is met, it can be determined that the user is not using their eyes for a long time at close range during the current monitoring period, so there is no need to remind the user.

[0181] Step 6: Generate a prompt message to remind the user to perform telescopic activities and pay attention to eye hygiene.

[0182] All strategy combinations in this embodiment are aimed at indoor scenarios (such as primary and secondary school students studying indoors). In several typical strategy combinations in this embodiment, the detection interval in step 2 is set to 0 minutes to achieve continuous detection; the specific contents are as follows:

[0183] (1) Detect and identify the user's activity status during the monitoring period, and determine whether the user is in a resting state or a large movement state. If so, terminate the current process. Otherwise, assume that the user is in a close-up eye state during the monitoring period, and generate a prompt message to remind the user to look far away and pay attention to eye hygiene. The specific configuration is as follows:

[0184] In step 1, the monitoring period is set to 20 minutes; N is set to 10, which means that the monitoring period is divided into 10 equal-length time periods.

[0185] In step 3, an accelerometer is selected as a motion monitoring sensor. The monitored user characteristic motions include wake-up state motions and large motion state motions. The characteristic motion data collected and calculated include the time domain distribution characteristics of wake-up state motions within the user monitoring period, the time domain distribution characteristics of large motion state motions within the user monitoring period, and the activity state within the user monitoring period. Among them, the activity state within the user monitoring period is determined based on the time domain distribution characteristics of the characteristic motions within the user monitoring period.

[0186] In step 5, the reminder decision condition includes a no-intervention condition. The no-intervention condition includes that the user is in a resting state or a large movement state during the monitoring period. The reminder decision condition is checked by selecting any one of the no-intervention conditions.

[0187] The corresponding flowchart of this embodiment under this configuration is shown in the attached Fig. 9 .

[0188] (2) Detect and identify the user's activity status during the monitoring period, and determine whether the user is in a resting state or a large movement state. If so, terminate the current process. Otherwise, assume that the user is in a close-up eye state during the monitoring period, and continue to detect the current feature action until an awake state action or a large movement state action is identified, and generate a prompt message to remind the user to look far away and pay attention to eye hygiene. Compared with (1), (2) can ensure that when reminding the user, the user is not in a resting sleep state or a large movement state, so as to avoid disturbing the user. The specific configuration is as follows:

[0189] In step 1, the monitoring period is set to 20 minutes; N is set to 10, which means that the monitoring period is divided into 10 equal-length time periods.

[0190] In step 3, an accelerometer is selected as a motion monitoring sensor. The monitored user characteristic motions include wake-up state motions and large motion state motions. The characteristic motion data collected by calculation include the time domain distribution characteristics of wake-up state motions within the user monitoring period, the time domain distribution characteristics of large motion state motions within the user monitoring period, the activity state within the user monitoring period, and the characteristic motion identified in the current characteristic motion detection. Among them, the activity state within the user monitoring period is judged based on the time domain distribution characteristics of the characteristic motion within the user monitoring period.

[0191] In step 5, the reminder decision conditions include non-intervention conditions and intervention conditions. Non-intervention conditions include the user being in a resting state or a large movement state during the monitoring period. Intervention conditions include the recognition of awake state actions or large movement state actions in the current feature action detection. The inspection of the reminder decision conditions includes: if any non-intervention condition is met, it is determined that there is no need to remind the user; if all non-intervention conditions are not met, and any intervention condition is met, it is determined that the user needs to be reminded; if all intervention conditions and non-intervention conditions are not met, the selection judgment needs to wait for the conditions to be met.

[0192] (3) Detect and identify the user's activity status during the monitoring period, and determine whether the user is in a resting state or a large movement state. If so, terminate the current process; otherwise, determine whether the user's close-range eye use time during the monitoring period is not less than the preset threshold. If not, generate a prompt message to remind the user to look far away and pay attention to eye hygiene. Otherwise, terminate the current process. The specific configuration is as follows:

[0193] In step 1, the monitoring period is set to 20 minutes; N is set to 10, which means that the monitoring period is divided into 10 equal-length time periods.

[0194] In step 3, an accelerometer is selected as a motion monitoring sensor. The monitored user characteristic motions include wake-up state motions and large motion state motions. The characteristic motion data collected and calculated include the time domain distribution characteristics of wake-up state motions during the user monitoring period, the time domain distribution characteristics of large motion state motions during the user monitoring period, the activity state during the user monitoring period, and the close-up eye use duration during the user monitoring period. Among them, the activity state during the user monitoring period is determined based on the time domain distribution characteristics of the characteristic motions during the user monitoring period; the calculation method of the close-up eye use duration during the user monitoring period selects (2) (accumulated duration) or (3) (continuous duration).

[0195] In step 5, the reminder decision conditions include non-intervention conditions and intervention conditions. The non-intervention conditions include that the user is in a resting state or a large movement state during the monitoring period. The intervention conditions include that the length of time of close-range eye use during the user monitoring period is not less than a preset threshold. The inspection of the reminder decision conditions includes: if any non-intervention condition is met, it is determined that there is no need to remind the user; if all non-intervention conditions are not met, and any intervention condition is met, it is determined that the user needs to be reminded; if all intervention conditions and non-intervention conditions are not met, it is selected to determine that there is no need to remind the user.

[0196] (4) Check the user's close-up eye use time during the monitoring period to determine whether it is less than the preset threshold. If so, terminate the current process; otherwise, continue to detect the current feature action until the awake state action or large movement state action is identified, and generate a prompt message to remind the user to look far away and pay attention to eye hygiene. The specific configuration is as follows:

[0197] In step 1, the monitoring period is set to 20 minutes; N is set to 10, which means that the monitoring period is divided into 10 equal-length time periods.

[0198] In step 3, an accelerometer is selected as a motion monitoring sensor. The monitored user characteristic motions include wake-up state motions and large motion state motions. The characteristic motion data collected and calculated include the time domain distribution characteristics of wake-up state motions during the user monitoring period, the time domain distribution characteristics of large motion state motions during the user monitoring period, the close-up eye use time during the user monitoring period, and the characteristic motions identified in the current characteristic motion detection. Among them, the calculation method of the close-up eye use time during the user monitoring period is selected from (2) (accumulated time) or (3) (continuous time).

[0199] In step 5, the reminder decision conditions include non-intervention conditions and intervention conditions. The non-intervention condition includes that the length of time of close-range eye use during the user monitoring period is less than a preset threshold. The intervention condition includes that the awake state action or large movement state action is recognized in the current feature action detection. The inspection of the reminder decision conditions includes: if any non-intervention condition is met, it is determined that there is no need to remind the user; if all non-intervention conditions are not met, and any intervention condition is met, it is determined that the user needs to be reminded; if all intervention conditions and non-intervention conditions are not met, the selection judgment needs to wait for the conditions to be met.

[0200] (5) Check whether the user's close-range eye use time during the monitoring period is not less than the preset threshold, or whether the number of awakening state actions during the monitoring period is not less than the preset threshold. If so, generate a prompt message to remind the user to look far away and pay attention to eye hygiene; otherwise, terminate the current process. The specific configuration is as follows:

[0201] In step 1, the monitoring period is set to 20 minutes; N is set to 1, which means that the monitoring period is regarded as the entire time period.

[0202] In step 3, any combination of an accelerometer, a gyroscope, and a magnetometer can be selected as a motion monitoring sensor. The monitored user characteristic motions include wake-up state motions. The characteristic motion data collected and calculated include the number of occurrences of wake-up state motions during the user monitoring period and the length of time the user uses the eyes at close range during the user monitoring period. The length of time the user uses the eyes at close range during the user monitoring period is calculated in the manner of (4) (cumulative length of time) or (5) (duration of time).

[0203] In step 5, the reminder decision condition includes an intervention condition. The intervention condition includes that the duration of close-up eye use during the user monitoring period is not less than a preset threshold, and the number of awake state actions during the user monitoring period is not less than a preset threshold. The reminder decision condition check is to select any intervention condition to check. If all intervention conditions are not met, it is determined that there is no need to remind the user.

[0204] (6) Check whether the user's close-up eye use time during the monitoring period is less than the preset threshold, or whether the number of large motion state actions during the user monitoring period is not less than the preset threshold. If so, terminate the current process; otherwise, continue to detect the current feature action until the awake state action or large motion state action is identified, and generate a prompt message to remind the user to look far away and pay attention to eye hygiene. The specific configuration is as follows:

[0205] In step 1, the monitoring period is set to 20 minutes; N is set to 1, which means that the monitoring period is regarded as the entire time period.

[0206] In step 3, an accelerometer is selected as a motion monitoring sensor. The monitored user characteristic motions include wake-up state motions and large motion state motions. The calculated and collected characteristic motion data include the number of occurrences of wake-up state motions in the user monitoring period, the number of occurrences of large motion state motions in the user monitoring period, and the characteristic motions identified in the current characteristic motion detection.

[0207] In step 5, the reminder decision conditions include non-intervention conditions and intervention conditions. The non-intervention conditions include that the number of occurrences of awake state actions in the user monitoring period is less than a preset threshold, and the number of occurrences of large motion state actions in the user monitoring period is not less than a preset threshold. The intervention conditions include that the awake state action or large motion state action is identified in the current feature action detection. The inspection of the reminder decision conditions includes: if any non-intervention condition is met, it is determined that there is no need to remind the user; if all non-intervention conditions are not met, and any intervention condition is met, it is determined that the user needs to be reminded; if all intervention conditions and non-intervention conditions are not met, the selection judgment needs to wait for the conditions to be met.

[0208] Further, when the motion monitoring sensor includes an accelerometer, a method for detecting and identifying the wake-up state motion by the accelerometer is as follows:

[0209] Set the accelerometer range to ±2g or the closest available range (such as ±1.5g, ±3g);

[0210] Select an axis of the accelerometer and read the acceleration value of the axis continuously every t milliseconds for a total of n times;

[0211] Calculate the absolute value of the difference between every two adjacent readings;

[0212] If the absolute values ​​of the differences between all two adjacent reading values ​​are greater than the threshold value th, it is determined that an awake state associated action is detected and recognized.

[0213] The value range of t is [10, 100] milliseconds, the value range of th is [50, 1000] mg, and the value range of n is [1, 5]. The specific values ​​need to be calibrated in combination with the characteristics of the accelerometer and the user's characteristic motion data.

[0214] Further, when the motion monitoring sensor includes a gyroscope, detecting and identifying the wake-up state-associated action through the gyroscope includes:

[0215] Set the gyroscope range to ±250dps or the closest available range (such as ±245dps, ±500dps);

[0216] Select one axis of the gyroscope and read the angular velocity of the axis continuously every t milliseconds for a total of n times;

[0217] If each read value is greater than the threshold value th, it is determined that the detection recognizes an awake state associated action.

[0218] The value range of t is [20, 200] milliseconds, the value range of th is [10, 100] dps, and the value range of n is [1, 3]. The specific values ​​need to be calibrated in combination with the sensor characteristics and user characteristic action data.

[0219] Furthermore, when the action monitoring sensor includes a magnetometer, detecting and identifying the wake-up state-associated action through the magnetometer includes:

[0220] Set the range of the magnetometer to ±4 Gauss or the closest available range (such as ±8 Gauss);

[0221] Select one axis of the magnetometer and read the magnetic field strength value of the axis continuously every t milliseconds for a total of n times;

[0222] Calculate the absolute value of the difference between every two adjacent readings;

[0223] If the absolute values ​​of the differences between all two adjacent reading values ​​are greater than the threshold value th, it is determined that an awake state associated action is detected and recognized.

[0224] The value range of t is [50, 200] milliseconds, the value range of th is [20, 200] milli-Gauss, and the value range of n is [1, 3]. The specific values ​​need to be calibrated in combination with the sensor characteristics and user characteristic action data.

[0225] Further, when the motion monitoring sensor includes an accelerometer, detecting and identifying the large motion state-associated motion by the accelerometer includes:

[0226] Set the accelerometer range to ±2g or the closest available range (such as ±1.5g, ±3g);

[0227] Select one axis of the accelerometer and continuously collect the acceleration value of the axis at a sampling frequency of H Hz for a total of N times;

[0228] For the N acceleration values ​​collected , ,..., The data are analyzed in sequence. If they show a "big → small → big" pattern or a "small → big → small" pattern, it is determined that a significant motion state-associated action has been detected.

[0229] To filter out occasional user action interference, the time interval can be limited If more than M significant motion-related actions are detected continuously within a second, the action will be counted; if it is less than M times, it will not be counted; The value range is [2, 10] seconds, and the value range of M is [2, 10]. The specific value needs to be calibrated in combination with the accelerometer characteristics and user motion feature data.

[0230] The judgment of the "big → small → big" rule includes:

[0231] Traverse the sequence from i=2 to i=N-1, if < and > ,but The first local maximum , stop traversal;

[0232] Traverse the sequence from j=i+1 to j=N-1, if > and < ,but is a local minimum , stop traversal;

[0233] Traverse the sequence from k=j+1 to k=N-1, if < and > ,but The second local maximum , stop traversal;

[0234] If exists , and ,calculate =| - | and =| - |, if > and > , then it is determined that the rule is satisfied.

[0235] The judgment of the "small → large → small" rule includes:

[0236] Traverse the sequence from i=2 to i=N-1, if > and < ,but The first local minimum , stop traversal;

[0237] Traverse the sequence from j=i+1 to j=N-1, if < and > ,but is a local maximum , stop traversal;

[0238] Traverse the sequence from k=j+1 to k=N-1, if > and < ,but The second local minimum , stop traversal;

[0239] If exists , and ,calculate =| - | and =| - |, if > and > , then it is determined that the rule is satisfied.

[0240] In the judgment process of the above "big → small → big" and "small → big → small" rules, the value range of H is [2,8], and the value range of N is [1H,3H]. The value range is [300,800]mg, The value range is [500,1000]mg. The specific value needs to be calibrated in combination with the accelerometer characteristics and user characteristic action data.

[0241] Example 2

[0242] Embodiment 2 is another embodiment of the myopia prevention and control method based on motion monitoring of the present invention, which is applicable to devices that are not worn or carried on the body. This embodiment is aimed at indoor scenes (such as primary and secondary school students studying indoors), and it is assumed that the user is constantly using the eyes at close range, and it is necessary to periodically send eye reminders to the user; the time interval for sending eye reminders is controlled by waiting for the monitoring cycle length; and characteristic motion detection is continuously performed until the awake state motion is identified to avoid disturbing the user when the user is resting or sleeping. For the flowchart corresponding to this embodiment, please refer to the attached Fig.10 , the specific steps are described as follows.

[0243] Step 1. Set the monitoring period to 20 minutes, set the detection interval to the monitoring period duration, and start timing; define the current monitoring duration as the cumulative duration from the start of timing to the current moment.

[0244] Step 2: Wait for the detection interval, that is, wait for the monitoring cycle.

[0245] Step 3: Detect and identify the user's current characteristic action through the action monitoring sensor.

[0246] The motion monitoring sensor includes at least one of an accelerometer, a gyroscope, and a magnetometer.

[0247] The characteristic actions include wake-up state actions. The embodiment of the method is applicable to devices that are not worn or carried on the body. The wake-up state actions detected and identified are mainly operations such as grasping, picking up, putting down, moving, and shaking the device. They also include actions of directly touching the device, indirectly touching the device, and related actions that cause the device to vibrate.

[0248] Step 4. Step 2 has ensured that the current monitoring duration is not less than the monitoring period.

[0249] Step 5. Check whether the wake-up state action is recognized in the current feature action detection; if not, return to step 3 to continue detection.

[0250] The reminder decision conditions include intervention conditions; the intervention conditions include: the awake state action is recognized in the current feature action detection.

[0251] The reminder decision condition check is to select any intervention condition to check. If all intervention conditions are not met, select the judgment to wait for the conditions to be met.

[0252] For the specific definition of the current characteristic action detection and the specific detection method of the characteristic action, please refer to Example 1.

[0253] Step 6: Generate a prompt message to remind the user to perform telescopic activities and pay attention to eye hygiene.

[0254] Example 3

[0255] like Figures 1 to 4 As shown, the preferred embodiment of the myopia prevention and control device based on motion monitoring of the present invention adopts a smart watch design, including a shell 1, a charging interface 12, a sealing rubber 13, an external connection component 14, and a motion monitoring unit 2, a passive crystal 3, a microcontroller 4, a feedback unit 5, a power supply unit 6, a circuit board 7, a display unit 8, a touch input chip 9 and a touch input button 10 arranged inside the shell 1.

[0256] The motion monitoring unit 2 , the passive crystal 3 , the microcontroller 4 and the touch input chip 9 are soldered on the circuit board 7 .

[0257] The feedback unit 5 , the power supply unit 6 , the display unit 8 and the touch input key 10 are soldered to the circuit board 7 through wires.

[0258] The display unit 8 adopts an organic light emitting display (OLED), and the control interface is an SPI interface.

[0259] The input unit is composed of a touch input chip 9 and a touch input key 10, and the control interface of the touch input chip 9 is an I2C interface.

[0260] The motion monitoring unit 2 includes at least one of an accelerometer, a gyroscope, and a magnetometer, and the control interface is an I2C interface.

[0261] The power supply unit 6 is composed of a rechargeable lithium battery.

[0262] The timing unit is composed of an RTC real-time clock circuit integrated inside a microcontroller 4 and an external passive crystal 3 .

[0263] The data processing unit is composed of a microcontroller 4, which integrates a lithium battery charging control circuit, an RTC real-time clock circuit, an I2C master mode interface, an SPI master mode interface and a GPIO interface, and controls the motion monitoring unit 2 and the touch input chip 9 through the I2C interface, and controls the display unit 8 through the SPI interface. The storage unit inside the microcontroller 4 stores the code program for implementing the myopia prevention and control method described in Example 1.

[0264] The motion monitoring unit 2 is equivalent to the motion monitoring sensor in the myopia prevention and control method described in Example 1.

[0265] The feedback unit 5 is composed of a vibration motor and is controlled via the GPIO interface of the microcontroller 4 .

[0266] The external connection component 14 is composed of a detachable strap and can be worn on a wrist.

[0267] The display unit 8 and the input unit cooperate with the timing unit to realize conventional watch functions such as clock display, stopwatch, countdown, alarm clock, etc., enriching the practicality of the product.

[0268] After the smart watch is charged for the first time, the internal battery is enabled and the watch is started. The microcontroller 4 cyclically executes the myopia prevention and control method process described in Example 1; wherein:

[0269] The microcontroller 4 detects and identifies the characteristic actions of the user through the action monitoring unit 2;

[0270] The microcontroller 4 realizes the timing function through the timing unit;

[0271] The microcontroller 4 generates prompt information (such as generating vibration) through the feedback unit 5 to remind the user to perform telescopic activities and pay attention to eye hygiene.

[0272] Example 4

[0273] Figures 3 to 7The invention shows a secondary embodiment of a myopia prevention and control device based on motion monitoring, including a housing 1, a cover plate 11, a charging interface 12, a sealing rubber 13, and a motion monitoring unit 2, a passive crystal 3, a microcontroller 4, a feedback unit 5, a power supply unit 6, and a circuit board 7 arranged inside the housing 1 and the cover plate 11. The motion monitoring unit 2, the passive crystal 3, and the microcontroller 4 are welded on the circuit board 7. The feedback unit 5 and the power supply unit 6 are connected to the circuit board 7 by wires. The motion monitoring unit 2 includes at least one of an accelerometer, a gyroscope, and a magnetometer, and the control interface is an I2C interface. The power supply unit 6 is composed of a rechargeable lithium battery. The timing unit is composed of an RTC real-time clock circuit integrated in the microcontroller 4 and an external passive crystal 3. The data processing unit is composed of a microcontroller 4, which has an internal integrated lithium battery charging control circuit, an RTC real-time clock circuit, an I2C master mode interface, and a GPIO interface. The storage unit inside the microcontroller 4 stores a code program for implementing the myopia prevention and control method described in Example 1, and controls the motion monitoring unit 2 through the I2C interface. The motion monitoring unit 2 is equivalent to the motion monitoring sensor in the myopia prevention and control method described in Example 1. The feedback unit 5 is composed of a vibration motor and is controlled by the GPIO interface of the microcontroller 4. The difference between this embodiment and Example 3 is that it does not include a display unit and an input unit, does not support conventional watch functions, has a single function but a lower cost, and can avoid attracting user attention to a greater extent. This embodiment can adopt two forms: one without external connection components (such as Figure 6 As shown in FIG. 1 ), it can be placed in a pocket, trouser pocket or other portable position when in use; another type with an external connection component 14 (such as Figure 7 As shown), it can be worn on the wrist, arm, ankle, etc. according to the type of connecting parts. In this embodiment, the internal battery is activated after the first charge, the system is started, and the microcontroller 4 starts to cyclically execute the same myopia prevention and control method process as in embodiment 3.

[0274] Example 5

[0275] Another embodiment of the myopia prevention and control device based on motion monitoring of the present invention includes a housing and a motion monitoring unit, a passive crystal, a microcontroller, a feedback unit, a power supply unit and a circuit board arranged inside the housing. The motion monitoring unit, the passive crystal and the microcontroller are soldered on the circuit board. The feedback unit and the power supply unit are connected to the circuit board through a wire. The motion monitoring unit includes at least one of an accelerometer, a gyroscope and a magnetometer, and the control interface is an I2C interface. The power supply unit is composed of a rechargeable lithium battery. The timing unit is composed of an RTC real-time clock circuit integrated inside the microcontroller and an external passive crystal. The data processing unit is composed of a microcontroller, which has an internal integrated lithium battery charging control circuit, an RTC real-time clock circuit, an I2C master mode interface and a GPIO interface. The storage unit inside the microcontroller stores a code program for implementing the myopia prevention and control method described in Example 2, and controls the motion monitoring unit through the I2C interface. The motion monitoring unit is equivalent to the motion monitoring sensor in the myopia prevention and control method described in Example 2. The feedback unit is composed of a vibration motor and is controlled through the GPIO interface of the microcontroller. This embodiment may also optionally include a display unit and an input unit to implement conventional clock functions such as clock display, stopwatch, countdown, alarm clock, etc., to enrich the practicality of the product.

[0276] This embodiment is designed not to be worn or carried close to the body; it can be presented in various product forms:

[0277] For example, an electronic clock is placed on a study table; the motion monitoring unit used is mainly an accelerometer;

[0278] Such as small ornaments or small accessories, placed on the desktop or in a pencil case; the motion monitoring unit used is mainly an accelerometer;

[0279] Such as small pendants, hanging on pencil cases; the motion monitoring unit used is mainly an accelerometer;

[0280] For example, special function pens integrate the myopia prevention and control function into an ordinary writing pen, which can be used for writing and preventing myopia at the same time; the motion monitoring unit selected can be any combination of accelerometers, gyroscopes and magnetometers.

[0281] In this embodiment, the internal battery is also enabled after the first charge, the system is started, and the microcontroller begins to cyclically execute the myopia prevention and control method process described in Example 2; wherein:

[0282] The microcontroller detects and recognizes the user's characteristic movements through the movement monitoring unit;

[0283] The microcontroller realizes the timing function through the timing unit;

[0284] The microcontroller generates prompt information (such as generating vibration) through the feedback unit to remind the user to perform telescopic activities and pay attention to eye hygiene.

[0285] When Example 4 is presented in a form without external connection components, it can also exist as a device that is not worn or carried close to the body. In this case, it is equivalent to a small ornament placed on a desk or in a pencil case.

Claims

1. A myopia prevention and control method based on motion monitoring, characterized in that: The following steps are involved: S1. Start timing: set the monitoring cycle and detection interval, and start timing; define the current monitoring duration as the cumulative duration from the start of timing to the current moment; S2. Detection frequency control: When the detection interval is greater than 0 minutes, wait for the corresponding interval length; S3. Feature action detection: detecting and identifying the user's feature action through a motion monitoring sensor, and collecting at least one feature action data; wherein: The motion monitoring sensor includes at least one of an accelerometer, a gyroscope, and a magnetometer; The characteristic action refers to an action performed by a user that meets preset conditions and is detected and identified by an action monitoring sensor; The characteristic action data refers to the data related to the characteristic actions detected and identified during the monitoring period, as well as the derived data calculated based on these data by a preset algorithm; S4 monitoring cycle control: If the current monitoring duration is less than the monitoring cycle, return to S2 to continue testing; S5. Reminder decision: Check the reminder decision conditions based on the characteristic action data to determine whether the user needs to be reminded or needs to wait for the conditions to be met; if the user does not need to be reminded, the current process is terminated; if the conditions need to be met, return to S3 to continue testing; wherein: (1) The reminder decision condition is pre-set based on the calculated and collected user characteristic action data, and is used to perform conditional judgment on the characteristic action data, including at least one of an intervention condition and a non-intervention condition; (2) The inspection reminder decision conditions include: When only intervention conditions are included, if at least one intervention condition is met, it is determined that the user needs to be reminded; otherwise, it can be determined that the user does not need to be reminded, or it is determined that the condition needs to be met. When only the no-intervention conditions are included, if at least one of the no-intervention conditions is met, it is determined that there is no need to remind the user; otherwise, it is determined that the user needs to be reminded; When both intervention conditions and non-intervention conditions are included, if at least one non-intervention condition is met, it is determined that there is no need to remind the user; if all non-intervention conditions are not met, and at least one intervention condition is met, it is determined that the user needs to be reminded; if all intervention conditions and non-intervention conditions are not met, it can be determined that there is no need to remind the user, or it can be determined that the waiting condition needs to be met; S6. Reminder execution: Generate a reminder message to remind the user to perform telescopic activities and pay attention to eye hygiene.

2. The myopia prevention and control method based on motion monitoring according to claim 1, characterized in that , (1) The characteristic action includes at least one of an awake state action and a large movement state action; The awake state action is defined as: an action performed by any part of the user's body within a preset time interval, the amplitude of which exceeds a preset threshold; The large motion state action is defined as: an action performed by the user within a preset interval time, in which the vertical fluctuation amplitude of the body exceeds a preset threshold; (2) The characteristic action data includes at least one of the following: Detecting the recognized feature actions; The number of times characteristic actions occur during the user monitoring period; The frequency of characteristic actions during the user monitoring period; Time domain distribution characteristics of characteristic actions within the user monitoring period; User activity status during the monitoring period; The duration of close-up eye use during the user monitoring period; (3) The intervention conditions include: The number of occurrences of the wake-up state action within the user monitoring period is not less than the first threshold; A wake-up state action or a large movement state action is identified in the current feature action detection; the current feature action detection refers to the feature action detection performed within a preset time period before the current moment; if no feature action detection occurs within the time period, it is determined that no feature action is detected and identified in the current feature action detection; (4) The conditions for exemption from intervention include: The number of wake-up and activation actions within the user monitoring period is less than the preset threshold; The number of large motion state actions during the user monitoring period is not less than the preset threshold.

3. The myopia prevention and control method based on motion monitoring according to claim 2, characterized in that: (1) The temporal distribution characteristics of the characteristic actions in the user monitoring period are obtained by dividing the monitoring period into multiple equal-length time periods and counting the number of occurrences of the characteristic actions in each time period; (2) The user's activity status during the monitoring period includes the awake state, the large-scale movement state, the resting state and the close-up eye use state; The judgment methods include: (a) Whether a user is in an active state during a monitoring period may be determined by any of the following methods: Judging based on the number of occurrences of the awake state action in the user monitoring period: if the number of occurrences of the awake state action in the monitoring period exceeds the first threshold, it is determined that the user is in the awake state in the monitoring period; otherwise, it is determined that the user is not in the awake state in the monitoring period; Judging according to the time domain distribution characteristics of the wake-up state action in the user monitoring period: if the number of wake-up state actions in more than 50% of the time periods is not less than the second threshold, and the number of wake-up state actions in the last time period is not less than the second threshold, and the total number of wake-up state actions in all time periods is not less than the first threshold, then it is determined that the user is in the wake-up state in the monitoring period; otherwise, it is determined that the user is not in the wake-up state in the monitoring period; (b) Whether the user is in a state of large movement during the monitoring period may be determined by any of the following methods: Judging based on the number of occurrences of large motion state actions in the user monitoring period: if the number of occurrences of large motion state actions in the user monitoring period is not less than the third threshold, it is determined that the user is in a large motion state in the user monitoring period; otherwise, it is determined that the user is not in a large motion state in the user monitoring period; Judging according to the temporal distribution characteristics of large motion state actions in the user monitoring period: if the number of large motion state actions in more than 50% of the time periods is not less than the fourth threshold, or the sum of the number of large motion state actions in all time periods is not less than the third threshold, then it is determined that the user is in a large motion state in the monitoring period; otherwise, it is determined that the user is not in a large motion state in the monitoring period; (c) Whether the user is in a resting state during the monitoring period may be determined by any of the following methods: Judging based on whether the user is in an awake state and a state of heavy movement during the monitoring period: if the user is not in an awake state and is not in a state of heavy movement during the monitoring period, the user is judged to be in a resting state during the monitoring period; otherwise, the user is judged to be not in a resting state during the monitoring period; Only judge based on whether the user is in the awake state during the monitoring period: if the user is not in the awake state during the monitoring period, it is judged that the user is in the resting state during the monitoring period; otherwise, it is judged that the user is not in the resting state during the monitoring period; (d) Whether the user is in a state of close-up eye use during the monitoring period may be determined by any of the following methods: Judging based on whether the user is in an awake state and a state of heavy movement during the monitoring period: if the user is in an awake state and not in a state of heavy movement during the monitoring period, it is judged that the user is in a state of close-up eye use during the monitoring period; otherwise, it is judged that the user is not in a state of close-up eye use during the monitoring period; Only judge based on whether the user is in the awake state during the monitoring period: if the user is in the awake state during the monitoring period, it is determined that the user is in the close-up eye state during the monitoring period; otherwise, it is determined that the user is not in the close-up eye state during the monitoring period; (3) The method for calculating the time of close-range eye use during the user monitoring period includes any of the following: It is assumed that the user is continuously using their eyes at close range, and the duration is accumulated directly; When the detection recognizes the awake state action and does not recognize the large movement state action, the duration is accumulated; If the detection recognizes the awake state action and does not recognize the large movement state action, the duration is accumulated; otherwise, the duration is reset to zero; When the detection recognizes the awake state action, the duration is accumulated; If the detection recognizes the awake state action, the duration is accumulated; otherwise, the duration is reset to zero; (4) The intervention conditions also include: The user is in the awake state during the monitoring period; The duration of close-range eye use during the user monitoring period is not less than the preset threshold; The user is using the eyes at close range during the monitoring period; (5) The above-mentioned non-intervention conditions also include: The user is in a resting state during the monitoring period; The user is in a state of heavy movement during the monitoring period; The duration of close-range eye use during the user monitoring period is less than the preset threshold.

4. The myopia prevention and control method based on motion monitoring according to any one of claims 1 to 3, characterized in that: The monitoring period is pre-set or set by the user through the interactive interface, with a default duration of 20 minutes and a range of 10-45 minutes; The detection interval is a pre-set time interval, the default duration is 0 minutes, and the value range is 0 minutes to the monitoring period.

5. A myopia prevention and control device based on motion monitoring, characterized in that: include: a data processing unit, comprising a microcontroller with a built-in storage unit, or a microprocessor plus an external storage unit; The storage unit stores a code program for implementing the myopia prevention and control method according to any one of claims 1 to 4; the microcontroller or microprocessor is configured to run the code program in the storage unit to implement the myopia prevention and control method according to any one of claims 1 to 4; An action monitoring unit, comprising at least one of an accelerometer, a gyroscope, and a magnetometer; cooperating with the data processing unit to detect and identify various characteristic actions of the user; A timing unit, including an RTC real-time clock circuit, for providing a real-time clock signal to assist the data processing unit in timing; A feedback unit, including a vibration motor, for generating vibrations to remind the user to look far away and pay attention to eye hygiene; A power supply unit, including a rechargeable battery, for supplying power to the entire device; The shell is used to arrange the units inside.

6. The myopia prevention and control device based on motion monitoring according to claim 5, characterized in that: include: Charging port and sealing rubber; The charging port is arranged on the back of the housing; The sealing rubber is embedded in the back of the shell, usually covers the charging interface, and is opened during charging.

7. The myopia prevention and control device based on motion monitoring according to claim 5, characterized in that: include: Display unit and input unit; The display unit is used for information display; The input unit is used for user interaction operations.

8. The myopia prevention and control device based on motion monitoring according to claim 5, characterized in that: include: External connection components; The external connection component is detachable; The housing and the external connection component together constitute a wearable device.

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