Processing module, device and system for glucose concentration before and after exercise

Abstract

The application provides a processing module, device and system for glucose concentration before and after exercise, the processing module is used for detecting glucose concentration of a subject and giving guidance information, the processing module is configured to determine a fluctuation type of a glucose concentration curve between before exercise and after exercise based on glucose data including time-varying glucose concentration between before exercise and after exercise of the subject, the fluctuation type reflects a change trend of the glucose concentration between before exercise and after exercise, and guidance information or suggestions related to exercise behavior are generated according to the fluctuation type. Therefore, the time spent on seeking doctor's advice can be reduced.

Description

[0001] This application is a divisional application of the patent application filed on September 15, 2021, with application number 2021110783351, entitled "A Glucose Monitoring System for Glucose Concentration Levels Before and After Exercise". Technical Field

[0002] This invention specifically relates to a module, device, and system for processing glucose concentrations before and after exercise. Background Technology

[0003] Diabetes and its chronic complications have become one of the most serious diseases affecting human health today. To delay and reduce the chronic complications of diabetes, strict glucose control is necessary. Therefore, continuous glucose monitoring systems (CGMS), which dynamically reflect glucose fluctuations, are widely used. Currently, several CGMS systems have received FDA and / or CE certifications in the United States, allowing their use in Europe and America. Most of these are minimally invasive, using subcutaneous probes to monitor interstitial fluid glucose, while a few are performed on the skin surface. The interstitial fluid glucose concentration measured by CGMS shows a good correlation with venous and fingertip glucose concentrations, and can be used as an auxiliary glucose monitoring method.

[0004] Currently, dynamic glucose monitoring systems generally have high and low glucose warning mechanisms, which can issue warning signals when glucose levels are too low or too high. At the same time, clinicians can use the high and low glucose warning mechanisms to design more individualized treatment plans.

[0005] However, existing technologies do not provide users with analysis of the reasons for exercise and guidance or suggestions based on the dynamic curve of CGMS. They only give some simple suggestions based on the glucose level in the curve (such as whether to see a doctor or other educational guidance). Furthermore, doctors or diabetes educators need to spend 30-60 minutes interpreting the dynamic curve of exercise time in order to output risk level assessment and auxiliary decision-making suggestions. Summary of the Invention

[0006] The present invention was made in view of the above-mentioned state of the prior art, and its purpose is to provide a glucose monitoring system for glucose concentration before and after exercise. It is a glucose monitoring system used to detect the glucose concentration of the subject and provide guidance information. It can analyze the fluctuation characteristics based on the glucose dynamic curve and help the subject and doctor to quickly and accurately read the report and provide suggestions or guidance information related to exercise behavior.

[0007] Therefore, this invention discloses a glucose monitoring system for glucose concentration before and after exercise. This system is used to detect the glucose concentration of a subject and provide guidance information. It is characterized by comprising a sensing module, a communication module, and a processing module. The sensing module is configured to detect glucose data of the subject, including the glucose concentration of the subject changing over time before and after exercise. The communication module is configured to receive the glucose data and send it to the processing module. The processing module is configured to determine the fluctuation type of glucose concentration based on the glucose data, the fluctuation type reflecting the trend of glucose concentration change before and after exercise, and generate guidance information or suggestions related to exercise behavior based on the fluctuation type.

[0008] In this scenario, the sensing module can continuously monitor the glucose concentration of the subject before and after exercise in real time and generate data to send to the communication module, which is more convenient and faster than manual glucose concentration collection. In addition, the communication module can send the real-time glucose data of the subject to the processing module to generate real-time and continuous dynamic glucose data with a short sampling cycle, improving data accuracy. Furthermore, the processing module can receive and determine the fluctuation type of the subject's glucose concentration based on the received glucose data, and then generate guidance information or suggestions related to exercise behavior based on the fluctuation type, which can be provided to the subject or doctor in a timely manner, reducing the time spent on traditional manual glucose concentration collection and seeking doctor's advice.

[0009] Optionally, the glucose monitoring system provided by this invention further includes a data entry module for recording the start time of the exercise of the subject. In this case, the glucose monitoring system can determine the exercise time of the subject in order to provide more accurate guidance or suggestions to the subject or doctor based on the glucose dynamic curve.

[0010] The glucose data includes glucose concentrations at multiple detection points and corresponding detection times. If the recorded exercise time of the subject falls between the detection times of two adjacent detection points, either of those two points is used as the exercise detection point. If the recorded exercise time is not between or between two adjacent detection points, the detection point closest to the recorded exercise time is used as the exercise detection point. In this configuration, the glucose monitoring system can more accurately determine the glucose data of the subject to identify corresponding fluctuation characteristics.

[0011] According to the glucose monitoring system provided by the present invention, optionally, the time before exercise is the detection time corresponding to the exercise detection point, and the time after exercise is 3 to 5 hours after the time before exercise. In this case, the glucose monitoring system can determine the fluctuation type based on the change in the glucose concentration of the subject to provide corresponding guidance or suggestions to the subject or doctor.

[0012] According to the glucose monitoring system provided by the present invention, optionally, the processing module obtains the glucose concentration, first glucose fluctuation amplitude, and second glucose fluctuation amplitude at the exercise detection point based on the glucose data, and obtains the fluctuation type based on the glucose concentration, first glucose fluctuation amplitude, and second glucose fluctuation amplitude at the exercise detection point, wherein the first glucose fluctuation amplitude is the difference between the maximum glucose concentration at the detection point between the time before exercise and the time after exercise and the glucose concentration at the exercise detection point, and the second glucose fluctuation amplitude is the difference between the glucose concentration at the exercise detection point and the minimum glucose concentration at the detection point between the time before exercise and the time after exercise.

[0013] In this case, the glucose monitoring system can determine the corresponding fluctuation type by comparing the glucose concentration, first glucose fluctuation amplitude, and second glucose fluctuation amplitude at the motion detection point of the object under test with the glucose concentration and fluctuation type algorithm configured in the processing module.

[0014] According to the glucose monitoring system provided by the present invention, optionally, the fluctuation type includes increase, decrease, increase followed by decrease, decrease followed by increase, fluctuation within the normal range before exercise, and fluctuation slightly higher than normal before exercise. In this case, the glucose monitoring system can determine, based on the acquired glucose data, whether the subject at the time of exercise belongs to one of the fluctuation types (increase, decrease, increase followed by decrease, decrease followed by increase, fluctuation within the normal range before exercise, and fluctuation slightly higher than normal before exercise) and provide corresponding guidance or suggestions to the subject or doctor based on the determined fluctuation type.

[0015] Optionally, the guidance information or suggestions related to exercise behavior provided by the glucose monitoring system of the present invention may include at least one of the following: suggestions for exercise intensity, suggestions for exercise duration, and suggestions for exercise mode.

[0016] In this context, the glucose monitoring system can provide the subject or physician with at least one of the following recommendations based on the identified fluctuation type: exercise intensity, exercise duration, and exercise type. Such recommendations can improve the subject's quality of life and reduce the time spent by the subject seeking a physician and the physician providing assessment advice.

[0017] Optionally, the processing module performs noise reduction processing on the glucose data according to the glucose monitoring system provided by the present invention. In this case, the glucose monitoring system can eliminate variables that may affect the determination of the fluctuation type in the glucose data to obtain a more accurate fluctuation type.

[0018] Optionally, in the glucose monitoring system provided by the present invention, the processing module obtains a glucose concentration curve based on the glucose data and smooths the glucose concentration curve. In this case, the glucose monitoring system can display a smoother dynamic glucose curve for the test subject or doctor, thereby improving the user experience.

[0019] Optionally, the glucose monitoring system provided by the present invention further includes a display module configured to display at least one of guidance information, a glucose concentration curve, and fluctuation types. The guidance information includes the cause of the fluctuation type and guidance information or suggestions related to exercise behavior. In this case, the subject or doctor can observe the subject's glucose data in real time and obtain the corresponding fluctuation type and guidance information or suggestions without the need for analysis and evaluation by a doctor.

[0020] Optionally, the glucose monitoring system provided by the present invention further includes a storage module configured to store the glucose data. In this case, the glucose monitoring system can store glucose data for more days or exercise periods, and can compare glucose data from multiple days or exercise periods to provide more reference information to the test subject or doctor.

[0021] According to the glucose monitoring system provided by the present invention, optionally, the sensing module is used to acquire the glucose concentration in the interstitial fluid, and the sensing module acquires the glucose concentration at a preset frequency. In this case, the glucose monitoring system can measure the interstitial fluid glucose concentration, which has a good correlation with the intravenous glucose concentration and the finger glucose concentration, and can be used as an auxiliary glucose monitoring method to improve measurement accuracy.

[0022] Optionally, in the glucose monitoring system provided by the present invention, the input module, the processing module, and the display module are integrated into a mobile terminal device. The mobile terminal device has software configured to input exercise time via the input module, obtain fluctuation type and guidance information via the processing module, and display the guidance information via the display module. In this configuration, the subject can conveniently monitor their glucose concentration in real time via the mobile terminal device and obtain corresponding guidance suggestions, thereby improving their quality of life.

[0023] According to the glucose monitoring system provided by the present invention, optionally, the processing module classifies the fluctuation type of glucose concentration based on classification conditions related to glucose concentration. The classification conditions include a first classification condition, a second classification condition, a third classification condition, and a fourth classification condition. The first classification condition is that the fluctuation amplitude of the first glucose is not less than a first preset value; the second classification condition is that the fluctuation amplitude of the second glucose is not less than a second preset value; the third classification condition is that the first peak of the glucose data occurs within a preset time after the exercise time; and the fourth classification condition is that the glucose concentration corresponding to the exercise detection point is not less than a third preset value.

[0024] In this situation, the glucose monitoring system can more quickly obtain the fluctuation type of glucose concentration of the subject at a certain exercise time based on the categorized conditions in the processing module and provide corresponding guidance information or suggestions in a timely manner.

[0025] According to the glucose monitoring system provided by the present invention, optionally, the second preset value is equal to the first preset value, and the third preset value is greater than the second preset value. In this case, the glucose monitoring system can more quickly obtain the fluctuation type of glucose concentration of the subject at a certain exercise time according to the conditions classified in the processing module and provide corresponding guidance information or suggestions in a timely manner.

[0026] According to the glucose monitoring system provided by the present invention, optionally, the first preset value is 1.5 to 2.0 mmol / L, the second preset value is 1.5 to 2.0 mmol / L, and the third preset value is not less than 7.0 mmol / L. In this case, the glucose concentration curve measured by the glucose monitoring system can more accurately classify the fluctuation type.

[0027] In the glucose monitoring system provided by the present invention, optionally, the preset time is 10 minutes to 1 hour. In this case, the glucose monitoring system can accurately monitor the glucose concentration during exercise.

[0028] Optionally, in the glucose monitoring system provided by the present invention, the sensing module detects the glucose concentration in the interstitial fluid of the test subject using a sensor component capable of reacting with glucose. In this case, the glucose monitoring system can acquire the required glucose data of the test subject from the sensing module and analyze and process it, thereby providing corresponding guidance information to the test subject or physician.

[0029] According to the glucose monitoring system provided by the present invention, optionally, the communication module transmits the glucose data to the processing module wirelessly or via a wired connection. In this case, transmitting the glucose data wirelessly to the processing module is more convenient for the test subject and provides a better user experience, while transmitting the glucose data via a wired connection improves the integrity and stability of the data.

[0030] In the glucose monitoring system provided by the present invention, optionally, the wireless method includes at least one of Bluetooth, Wi-Fi, 3G / 4G / 5G, NFC, UWB, and Zig-Bee. In this case, transmitting glucose data wirelessly to the processing module is more convenient for the subject and provides a better user experience. It also allows for remote monitoring of the subject's glucose concentration, facilitating doctors to provide professional guidance, advice, and medical care.

[0031] According to the present invention, a glucose monitoring system for glucose concentration before and after exercise is provided. This system is used to detect the glucose concentration of a subject and provide guidance or suggestions. It can analyze fluctuation characteristics based on the glucose dynamic curve and help the subject and doctor to quickly and accurately read the report, as well as provide guidance or suggestions related to exercise behavior. Attached Figure Description

[0032] Figure 1 This is a schematic diagram illustrating an application scenario of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0033] Figure 2 This is a structural block diagram of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0034] Figure 3a This is a glucose concentration fluctuation curve of an embodiment of the present invention, which is an increase in glucose concentration before and after exercise.

[0035] Figure 3b This is a glucose concentration curve showing a decrease in the fluctuation type of the glucose monitoring system for glucose concentration before and after exercise, as described in the embodiments of the present invention.

[0036] Figure 3c This is a glucose concentration curve diagram showing the fluctuation type of the glucose monitoring system for glucose concentration before and after exercise, as described in the embodiments of the present invention, where the glucose concentration first increases and then decreases.

[0037] Figure 3dThe glucose concentration monitoring system for glucose concentration before and after exercise, as described in the embodiments of the present invention, exhibits a glucose concentration curve that first decreases and then increases.

[0038] Figure 3e-1 The fluctuation type of the glucose monitoring system for glucose concentration before and after exercise involved in the embodiments of the present invention is a glucose concentration curve with small fluctuations and low pre-exercise glucose concentration.

[0039] Figure 3e-2 The glucose monitoring system for glucose concentration before and after exercise, as described in the embodiments of the present invention, exhibits a glucose concentration curve with relatively small fluctuations and a higher concentration before exercise.

[0040] Figure 4a This is a schematic diagram of the interface of a mobile terminal device for real-time monitoring of glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0041] Figure 4b This is a schematic diagram of the motion analysis interface of a mobile terminal device of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0042] Figure 4c This is a schematic diagram of the interface of the guidance information of the mobile terminal device of the glucose monitoring system for glucose concentration before and after exercise, which is involved in the embodiments of the present invention.

[0043] Figure 4d This is a schematic diagram of the exercise recording interface of a mobile terminal device of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0044] Figure 4e This is a schematic diagram of the interface for recording glucose concentration on a mobile terminal device of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention. Detailed Implementation

[0045] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0046] It should be noted that the terms "first," "second," "third," and "fourth," etc., in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, system, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices. In the following description, the same reference numerals are used for the same parts, and repeated descriptions are omitted. Additionally, the accompanying drawings are merely schematic diagrams, and the scale of the dimensions of the parts or the shape of the parts may differ from the actual figures.

[0047] The glucose monitoring system provided by this invention, which monitors glucose concentration before and after exercise, is used to detect the glucose concentration of a subject and provide guidance. It can analyze fluctuation characteristics based on a dynamic glucose curve and help the subject and doctor quickly and accurately interpret reports, as well as provide guidance or suggestions related to exercise behavior. A detailed description is provided below with reference to the accompanying drawings.

[0048] This invention discloses a glucose monitoring system for glucose concentration before and after exercise. In some examples, the glucose monitoring system for glucose concentration before and after exercise may also be simply referred to as a glucose concentration monitoring system or a glucose monitoring system.

[0049] Figure 1 This is a schematic diagram illustrating an application scenario of a glucose monitoring system for pre- and post-exercise glucose concentration, as described in an embodiment of the present invention. Figure 2 This is a structural block diagram of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0050] like Figure 1 , 2 As shown, this invention discloses a glucose monitoring system 1 for glucose concentration before and after exercise. This system is used to detect the glucose concentration of a test subject 2 and provide guidance information. It may include a sensing module 11, a communication module 12, and a processing module 134. The sensing module 11 can be configured to detect glucose data of the test subject 2, which may include the glucose concentration of the test subject 2 changing over time before and after exercise. The communication module 12 can be configured to receive glucose data and send it to the processing module 134. The processing module 134 can be configured to determine the fluctuation type of glucose concentration based on the glucose data. The fluctuation type reflects the trend of glucose concentration change before and after exercise, and can generate guidance information or suggestions related to exercise behavior based on the fluctuation type.

[0051] In this scenario, the sensing module 11 can continuously monitor the glucose concentration of the test subject 2 before and after exercise in real time and generate data to send to the communication module 12, which is more convenient and faster than manual glucose concentration collection. In addition, the communication module 12 can send the real-time glucose data of the test subject 2 to the processing module 134 to generate real-time and continuous dynamic glucose data with a short sampling cycle, thus improving data accuracy. Furthermore, the processing module 134 can receive and determine the fluctuation type of the glucose concentration of the test subject 2 based on the received glucose data, and then generate guidance information or suggestions related to exercise behavior based on the fluctuation type, which can be provided to the test subject 2 or the doctor in a timely manner, reducing the time spent on traditional manual glucose concentration collection and seeking doctor's advice.

[0052] In some examples, glucose data may include glucose concentrations at multiple detection points and detection times matching those points. If the movement time recorded for the subject 2 is at the midpoint between the detection times of two adjacent detection points, then either of those two points is used as the movement detection point. If the movement time recorded for the subject 2 is not between two adjacent detection points or is not at the midpoint between the corresponding detection times of two adjacent detection points, then the detection point closest to the movement time recorded for the subject 2 is used as the movement detection point. In this case, the glucose monitoring system 1 can more accurately grasp the glucose data of the subject 2 to determine the corresponding fluctuation characteristics.

[0053] In some examples, such as Figure 2 As shown, the glucose monitoring system 1 may include a sensing module 11, in which the glucose monitoring system 1 is able to collect glucose concentration information.

[0054] In some examples, the sensing module 11 can be an implantable or semi-implantable glucose detection sensor. Preferably, the sensing module 11 can be an implantable glucose detection sensor. In this case, the implantable or semi-implantable sensor can reduce the physiological pain caused by the traditional blood collection method for the subject 2, and has the advantages of short collection cycle, large amount of sampled data, and continuous sampling. In other examples, the sensing module 11 can also be a non-implantable sensor, in which case the sampled patient needs to have blood collected regularly, and the data accuracy is high.

[0055] In some examples, the sensing module 11 can be used to acquire the glucose concentration in the interstitial fluid; in other words, the sensing module 11 can be a subcutaneously implanted sensor. In this case, since the glucose concentration in the interstitial fluid is equal to or strictly corresponds to plasma glucose under steady-state conditions, and the rate of change in blood glucose concentration precedes that of the interstitial fluid in the short term after ingestion of high-sugar foods or injection of glucose, it can accurately reflect the glucose concentration of the subject being tested. That is, the glucose concentration in the interstitial fluid that the glucose monitoring system 1 can measure has a good correlation with venous glucose concentration and finger glucose concentration, and can be used as an auxiliary glucose monitoring method to improve measurement accuracy.

[0056] In some examples, the sensing module 11 can acquire glucose concentrations at a preset frequency (or a preset acquisition frequency). In this case, multiple glucose concentrations can be obtained, thereby forming an approximately continuous glucose concentration curve.

[0057] In some examples, the sensing module 11 can be adjusted at a preset frequency. For instance, when the glucose concentration of the test object 2 changes slightly, the sensing module 11 can acquire the glucose concentration at a lower preset frequency; when the glucose concentration of the test object 2 changes significantly, the sensing module 11 can acquire the glucose concentration at a higher preset frequency. In this case, the preset frequency of the sensing module 11 can be adjusted according to the actual situation.

[0058] In some examples, the sensing module 11 can also be used to acquire glucose data in other bodily fluids of the test subject 2. For example, the glucose concentration in urine.

[0059] In other examples, the sensing module 11 can detect the glucose concentration in the interstitial fluid of the test subject 2 using a sensor component capable of reacting with glucose. In this case, the glucose monitoring system 1 can acquire the required glucose data of the test subject 2 from the sensing module 11 and analyze and process it, thereby providing corresponding guidance information to the test subject 2 or the doctor.

[0060] In some examples, the sensing module 11 may consist of a bioactive substance and a microelectrode. In this case, the bioactive substance can react with glucose and generate an electrical signal on the microelectrode, thus producing data.

[0061] In some examples, the sensing module 11 can be positioned near the upper arm of the subject 2, thereby reducing the impact of the sensing module 11 on the subject 2's daily life activities.

[0062] In some examples, preferably, the communication module 12 can be a wireless communication device, and the communication method of the wireless communication device can be at least one of Bluetooth, Wi-Fi, 3G / 4G / 5G, NFC, UWB, and Zig-Bee. In other examples, the communication module 12 can be a wired communication device, in which case it can prevent interference such as radiation and noise, and improve the stability and effectiveness of data transmission.

[0063] In some examples, the sensing module 11 and the communication module 12 can be integrated into one unit and implanted into the subject 2 in either an implantable or semi-implantable manner. In this case, after detecting glucose data from the subject 2, the sensing module 11 can directly send it to the communication module 12, achieving real-time detection quickly and efficiently, reducing the inconvenience of the subject 2 needing to carry the communication module 12 at all times.

[0064] In some examples, the communication module 12 can transmit glucose data to the processing module 134 wirelessly or via a wired connection. In this case, transmitting glucose data wirelessly to the processing module 134 is more convenient for the test subject 2 and provides a better user experience, while transmitting glucose data via a wired connection improves data integrity and stability.

[0065] In some examples, preferably, the communication module 12 can wirelessly transmit glucose data to the processing module 134. In this case, the glucose monitoring system 1 can provide a better user experience for the subject 2 or other users.

[0066] In some examples, the wireless method may include at least one of Bluetooth, Wi-Fi, 3G / 4G / 5G, NFC, UWB, and Zig-Bee. In this case, transmitting glucose data wirelessly to the processing module 134 is more convenient for the test subject 2, provides a better user experience, and allows for remote monitoring of the glucose concentration of the test subject 2, facilitating doctors to provide professional guidance and medical care.

[0067] In some examples, the communication module 12 can transmit data via Bluetooth. In this case, the processing module 134 can acquire the monitoring data from the sensing module 11 within a limited range.

[0068] In some examples, such as Figure 2 As shown, the glucose monitoring system 1 may further include a display module 131, which can be configured to display at least one of guidance information, glucose concentration curves, and fluctuation types. The display module 131 can also be integrated into a mobile terminal device; in other words, the display module can be the display interface of the mobile terminal device.

[0069] For example, Figures 4a to 4e This document illustrates the real-time monitoring interface, exercise analysis interface, guidance information interface, exercise recording interface, and glucose concentration recording interface of a mobile terminal device for a glucose monitoring system for pre- and post-exercise glucose concentration, as described in embodiments of the present invention.

[0070] In some examples, the guidance information includes the reasons for the fluctuation type and guidance or suggestions related to the exercise behavior. In this case, the test subject 2 or the doctor can observe the test subject 2's glucose data in real time and obtain the corresponding fluctuation type and guidance suggestions without the need for analysis and evaluation by the doctor.

[0071] In some examples, such as Figure 2 As shown, the glucose monitoring system 1 may also include an input module 132, which can be used to input the start time of exercise for the subject 2. In this case, the glucose monitoring system 1 can determine the exercise time of the subject 2 in order to provide more accurate guidance or suggestions to the subject 2 or the doctor based on the glucose dynamic curve.

[0072] In some examples, preferably, the input module 132 can be integrated with the processing module 134. In this case, the motion information of the object under test 2 can be received by the processing module 134 in real time and used for processing and analysis.

[0073] In some examples, the data entry module 132 can automatically identify exercise time based on glucose concentration. In this case, the number of steps required for the test subject 2 can be reduced, thereby improving the convenience of the glucose monitoring system 1.

[0074] In some examples, the input module 132 can also input at least one of the time, type, and amount of exercise.

[0075] For example, such as Figure 4b , 4e As shown, the subject 2 can input its exercise-related information, such as check-in time and exercise type, into the mobile terminal device of the glucose monitoring system 1 through the input module.

[0076] In some examples, the glucose monitoring system 1 may also include a storage module 133 configured to store glucose data. In this case, the glucose monitoring system 1 can store glucose data for more days or exercise periods, and can compare glucose data from multiple days or exercise periods to provide more information for the subject 2 or the doctor.

[0077] In some examples, storage module 133 may be located within sensing module 11. In this case, glucose data acquired by sensing module 11 can be temporarily stored in storage module 133. In some examples, storage module 133 may be located within processing module 134. In this case, glucose data from sensing module 11 is collected and stored permanently in storage module 133. In other words, storage module 133 may include a first storage module (not shown) and a second storage module (not shown). The first storage module is integrated into sensing module 11, and the second storage module is integrated into mobile terminal device 13. The first storage module can be used to temporarily store glucose data, and when communication module 12 is working normally, the glucose data in the first storage module can be transmitted to the second storage module.

[0078] In some examples, storage module 133 can overwrite old glucose data with new glucose data, where the detection times of the new and old glucose data can differ by more than 14 days. In this case, the storage space of storage module 133 can be fully utilized.

[0079] In some examples, preferably, the processing module 134 is located on a mobile terminal device 13 (described later). For example, a personal mobile phone, laptop computer, computer, custom processor, etc., in which case the subject 2 or the observer such as a doctor can conveniently and quickly obtain the glucose data of the subject 2.

[0080] In some examples, the processing module 134 may also be a cloud-based processing device. In this case, the processing module 134 can simultaneously monitor the glucose concentration of each test object 2.

[0081] In some examples, the typing device of the mobile terminal device 13 can be used to input exercise time information. In other words, the input module 132 can be the typing device of the mobile terminal device 13. In this case, the exercise time information of the test subject 2 can be input using voice input, touch screen input, or keyboard input. In this case, the input information can be conveniently and quickly processed and analyzed by the processing module 134, and corresponding guidance information can be generated based on the glucose data and fed back to the test subject 2 or the doctor.

[0082] In some examples, as described above, the storage module 133 can be integrated with the processing module 134 in the same mobile terminal device 13. In this case, the processing module 134 and the storage module 133 can coordinate to process the glucose data of the test subject 2, generate multiple data types, and store data for multiple days.

[0083] In some examples, the display module 131 can be integrated with the processing module 134 in the same mobile terminal device 13. In this case, after the processing module 134 processes the glucose concentration data of the test subject 2, it can display it to the test subject 2 or the doctor in real time and control the display module 131 to display corresponding guidance information or suggestions in a timely manner.

[0084] In some examples, the input module 132, processing module 134, and display module 131 can be integrated into the mobile terminal device 13. The mobile terminal device 13 has software configured to input exercise time through the input module 132, obtain fluctuation type and guidance information through the processing module 134, and display the guidance information through the display module 131. In this case, the subject 2 can monitor their own glucose concentration in real time through the mobile terminal device 13 and obtain corresponding guidance suggestions to improve their quality of life.

[0085] In some examples, the input module 132, processing module 134, and display module 131 may not be integrated with the mobile terminal device 13; that is, the input module 132, processing module 134, and display module 131 can be set separately. In this case, the functions of the input module 132, processing module 134, and display module 131 can be implemented in different locations.

[0086] In some examples, the exercise time recorded for subject 2 can be determined by using the detection point closest to the exercise time, in addition to two adjacent detection points. In this case, glucose monitoring system 1 can more accurately grasp the glucose data of subject 2 to determine the corresponding fluctuation characteristics.

[0087] In some examples, the time before exercise can be the detection time corresponding to the exercise detection point, and the time after exercise can be 3 to 5 hours after the time before exercise. In this case, the glucose monitoring system 1 can determine the fluctuation type based on the changes in the glucose concentration of the subject 2 to provide the subject 2 or the doctor with corresponding guidance, advice or information.

[0088] In some examples, the time before exercise is the detection time corresponding to the exercise detection point, and the time after exercise is 1 hour, 2 hours, 3 hours, 4 hours, 5 hours, 6 hours, 7 hours, 8 hours, etc., after the time before exercise. In this case, the glucose monitoring system 1 can determine the fluctuation type based on the changes in the glucose concentration of the subject 2 to provide the subject 2 or the doctor with corresponding guidance, advice, or information.

[0089] In some examples, the processing module 134 can obtain the glucose concentration, first glucose fluctuation amplitude and second glucose fluctuation amplitude of the motion detection point based on glucose data, and obtain the fluctuation type based on the glucose concentration, first glucose fluctuation amplitude and second glucose fluctuation amplitude of the motion detection point.

[0090] In some examples, the first glucose fluctuation amplitude can be the difference between the maximum glucose concentration at the detection point between the time before and the time after exercise and the glucose concentration at the exercise detection point.

[0091] In some examples, the second glucose fluctuation amplitude can be the difference between the glucose concentration at the exercise detection point and the minimum glucose concentration among the detection points between the time before and the time after exercise.

[0092] In this case, the glucose monitoring system 1 can determine the corresponding fluctuation type by comparing the glucose concentration, first glucose fluctuation amplitude and second glucose fluctuation amplitude of the motion detection point of the object to be tested 2 with the glucose concentration and fluctuation type algorithm configured in the processing module 134.

[0093] Figure 3a This is a glucose concentration fluctuation curve of an embodiment of the present invention, which is an increase in glucose concentration before and after exercise. Figure 3b This is a glucose concentration curve showing a decrease in the fluctuation type of the glucose monitoring system for glucose concentration before and after exercise, as described in the embodiments of the present invention. Figure 3c This is a glucose concentration curve diagram showing the fluctuation type of the glucose monitoring system for glucose concentration before and after exercise, as described in the embodiments of the present invention, where the glucose concentration first increases and then decreases. Figure 3d The glucose concentration monitoring system for glucose concentration before and after exercise, as described in the embodiments of the present invention, exhibits a glucose concentration curve that first decreases and then increases. Figure 3e-1 The fluctuation type of the glucose monitoring system for glucose concentration before and after exercise involved in the embodiments of the present invention is a glucose concentration curve with small fluctuations and low pre-exercise glucose concentration. Figure 3e-2 This is a glucose monitoring system for glucose concentration before and after exercise, according to an embodiment of the present invention. The fluctuation type is a glucose concentration curve with relatively small fluctuations and a higher concentration before exercise.

[0094] In other examples, the processing module 134 can also obtain the fluctuation type based on the glucose concentration, first glucose fluctuation amplitude, and second glucose fluctuation amplitude at the motion detection point, and can use other mathematical methods or algorithms based on the glucose concentration, first glucose fluctuation amplitude, and second glucose fluctuation amplitude at the motion detection point. In this case, the glucose monitoring system 1 can determine the corresponding fluctuation type by comparing the obtained glucose concentration, first glucose fluctuation amplitude, and second glucose fluctuation amplitude at the motion detection point of the test object 2 with the algorithm configured in the processing module 134 for glucose concentration and fluctuation type.

[0095] In some examples, fluctuation types can include increase, decrease, increase followed by decrease, decrease followed by increase, fluctuations that are normal before exercise, and fluctuations that are slightly higher than normal before exercise. In this case, the glucose monitoring system 1 can determine, based on the acquired glucose data, whether the subject 2 at the time of exercise belongs to one of the fluctuation types: increase, decrease, increase followed by decrease, decrease followed by increase, fluctuations that are normal before exercise, and fluctuations that are slightly higher than normal before exercise. Based on the determined fluctuation type, the system can provide corresponding guidance or suggestions to the subject 2 or the doctor.

[0096] In some examples, guidance or recommendations can be provided, but not limited to one type, based on the number of monitoring days and fluctuation type of the glucose monitoring system 1. For example, the monitoring days of the glucose monitoring system 1 can be set to 15 consecutive days, or longer or shorter periods of time. In this case, based on the corresponding exercise information, fluctuation type, and number of days, the glucose monitoring system 1 can provide guidance or recommendations adapted to the glucose concentration of the subject 2 to help improve their quality of life.

[0097] Figure 4a This is a schematic diagram of the interface of a mobile terminal device for real-time monitoring of glucose concentration before and after exercise, as described in an embodiment of the present invention. Figure 4b This is a schematic diagram of the motion analysis interface of a mobile terminal device of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention. Figure 4c This is a schematic diagram of the interface of the guidance information of a mobile terminal device of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0098] In some examples, a single fluctuation type can correspond to multiple guidelines based on the number of monitoring days of glucose monitoring system 1.

[0099] like Figure 4a , 4bIn some examples, the guidance suggestions, including 4C, may include suggestions for different types of exercise at the same intensity and the number of times to complete the exercise. For example, the guidance suggestions may include those for walking, brisk walking, housework, and ball games. Specifically, for the first check-in, which is a low-intensity walk, the corresponding guidance information or suggestions could be: low intensity, 1 kg of body weight burns 2.625 kcal per hour, 12 minutes of walking is equivalent to 1000 steps; walking helps with food absorption, promotes gastrointestinal motility, and normalizes bowel movements. For the second check-in, which is also a low-intensity walk, the corresponding guidance information or suggestions could be: low intensity, 1 kg of body weight burns 2.625 kcal per hour, 12 minutes of walking is equivalent to 1000 steps; exercise 3-5 times a week, 0.5-1 hour each time. At the same time, it can increase insulin sensitivity by 30%; the first check-in is moderate-intensity brisk walking, and the corresponding guidance or suggestions are: moderate intensity, 1 kg of body weight burns 4.2 kcal per hour, brisk walking for 8 minutes is equivalent to walking 1000 steps of energy. Brisk walking puts a lot of pressure on the knee and ankle joints, so you can use knee and ankle braces to protect them; the second check-in is moderate-intensity brisk walking, and the corresponding guidance or suggestions are: moderate intensity, 1 kg of body weight burns 4.2 kcal per hour, brisk walking for 8 minutes is equivalent to walking 1000 steps of energy. When brisk walking, pay attention to your walking posture, keep your chest up, head up, buttocks up, and abdomen in, and never hunch over.

[0100] In some examples, the guidance may include the number of monitoring days. For example, the guidance may indicate that the day is the first day of using glucose monitoring system 1, the guidance may indicate that the day is the second day of using glucose monitoring system 1, the guidance may indicate that the day is the fourteenth day of using glucose monitoring system 1, and so on.

[0101] In some examples, as mentioned above, guidance may include the reasons for the type of fluctuation. Specifically, if the fluctuation type is upward, there can be several reasons, such as: 1. Did you exercise after a meal? Generally, the calories in a meal are much greater than the calories burned through exercise. After a meal, glucose levels may still rise overall, just by a smaller margin; 2. People often have both blood sugar-lowering and blood sugar-raising factors present. If the blood sugar-lowering factor is strong, glucose levels will decrease, and vice versa. If glucose levels rise after exercise, it is generally because the intensity, duration, and energy expenditure of the exercise are too low to offset the blood sugar-raising factors such as food; 3. If you exercise during the period when blood glucose levels are rising after a meal, the effect of exercise is to consume energy and reduce the blood sugar-raising effect of food. Eating less and exercising more will lower glucose levels, while eating more and exercising less will raise glucose levels. If the fluctuation type is decreasing, there can be several reasons, such as: 1. Exercise is a very good blood sugar lowering agent. Generally, exercise works faster than blood sugar lowering drugs. When glucose is high or rising, exercise often lowers glucose levels; 2. During exercise, glycogen in muscles is consumed, and glucose in the blood is absorbed by the muscles, resulting in a rapid and significant decrease in glucose; 3. The blood sugar lowering effect of exercise lasts for up to 8 hours. Even though there may be many small fluctuations in glucose due to various blood sugar-lowering agents at every moment, the overall glucose level tends to decrease. In this case, it is possible to preliminarily infer the cause of the corresponding fluctuation type in subject 2.

[0102] In some examples, the reasons for the fluctuation type in each guidance suggestion may be different. In this case, it is possible for the test subject 2 to fully understand the multiple reasons for the occurrence of the corresponding fluctuation type.

[0103] Figure 4d This is a schematic diagram of the exercise recording interface of a mobile terminal device of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention. Figure 4e This is a schematic diagram of the interface for recording glucose concentration on a mobile terminal device of a glucose monitoring system for glucose concentration before and after exercise, as described in an embodiment of the present invention.

[0104] In some examples, guidance or advice related to exercise behavior may also include at least one of the following: advice on exercise intensity, advice on exercise duration, and advice on exercise method.

[0105] In this context, the glucose monitoring system 1 can provide the subject 2 or a physician with at least one of the following suggestions based on the identified fluctuation type: suggestions on exercise intensity, suggestions on exercise duration, and suggestions on exercise type. These suggestions can improve the subject 2's quality of life and reduce the time spent by the subject 2 seeking a physician and receiving assessment advice from the physician.

[0106] In some examples, the guidance may look like this (but is not limited to):

[0107] Daily exercise: walking; guidance or suggestions: low intensity, 1 kg of body weight consumes 2.625 kcal per hour, 12 minutes of walking is equivalent to 1000 steps of energy; if you walk less than 4000 steps a day, your physical activity is relatively low.

[0108] Daily exercise: brisk walking; guidance or suggestions: moderate intensity, 1 kg of body weight consumes 4.2 kcal per hour, 8 minutes of brisk walking is equivalent to 1000 steps of energy; those with a good physique can choose moderate-speed walking, 100-120 steps per minute.

[0109] Daily exercise: running; guidance or suggestions: high intensity, 1 kg of body weight consumes 8.4 kcal per hour, 4 minutes of running is equivalent to the energy of walking 1000 steps; the appropriate level of exercise is when you are not out of breath, have no chest tightness, are slightly sweaty but not sweating heavily, and your legs are not sore.

[0110] Household activity: cooking; Guidance or suggestions: low intensity, 1 kg of body weight consumes 2.625 kcal per hour, cooking for 12 minutes is equivalent to the energy of walking 1000 steps; develop a cooking habit and enjoy the process of cooking.

[0111] Household chores: Tidying up the house; Guidance or suggestions: Moderate intensity. One kilogram of body weight burns 3.675 kcal per hour. Tidying up the house for 9 minutes is equivalent to walking 1,000 steps. Although tidying up the house is not a high-intensity activity, it is often repeated and is a good exercise for muscles.

[0112] Exercise: Yoga; Guidance or suggestions: Low intensity, 1 kg of body weight burns 2.625 kcal per hour, 12 minutes of yoga is equivalent to the energy of walking 1000 steps; It is best to practice yoga on an empty stomach, not after a full meal.

[0113] Fitness exercise: aerobics; guidance information or suggestions: moderate intensity, 1 kg of body weight consumes 3.675 kcal per hour, 9 minutes of aerobics is equivalent to the energy of walking 1000 steps; aerobics is both relaxing and entertaining, and also a good exercise.

[0114] Ball sport: Table tennis; Guidance or suggestions: Moderate intensity, 1 kg of body weight burns 4.2 kcal per hour, playing table tennis for 8 minutes is equivalent to walking 1000 steps; Table tennis can improve reaction speed.

[0115] Ball sport: Basketball; Guidance or advice: High intensity, 1 kg of body weight burns 6.3 kcal per hour, playing basketball for 5 minutes is equivalent to walking 1000 steps; playing basketball regularly can cultivate teamwork.

[0116] In some examples, processing module 134 can perform noise reduction on glucose data. In this case, glucose monitoring system 1 can eliminate variables in glucose data that may affect the determination of fluctuation type to obtain a more accurate fluctuation type.

[0117] In some examples, processing module 134 can obtain a glucose concentration curve based on glucose data and smooth the glucose concentration curve. In this case, glucose monitoring system 1 can display a smoother glucose dynamic curve for the subject 2 or the doctor, which makes it easier for the doctor to interpret and classify the glucose dynamic curve, thereby providing more accurate guidance and improving the user experience.

[0118] In some examples, the processing module 134 can classify the fluctuation type of glucose concentration based on classification conditions related to glucose concentration. The classification conditions include a first classification condition, a second classification condition, a third classification condition, and a fourth classification condition. The first classification condition is that the first glucose fluctuation amplitude is not less than a first preset value; the second classification condition is that the second glucose fluctuation amplitude is not less than a second preset value; the third classification condition is that the first peak of glucose data occurs within a preset time after the exercise time; and the fourth classification condition is that the glucose concentration corresponding to the exercise detection point is not less than a third preset value.

[0119] In this situation, the glucose monitoring system 1 can more quickly obtain the fluctuation type of glucose concentration corresponding to a certain exercise time of the test object 2 based on the conditions classified in the processing module 134 and the fluctuation characteristics of the glucose data curve, and provide corresponding guidance and suggestions in a timely manner.

[0120] In some examples, the second preset value can be greater than the first preset value, and the third preset value can be greater than the second preset value. In this case, the glucose monitoring system 1 can be optimized in terms of algorithm and can more quickly obtain the fluctuation type of glucose concentration of the test object 2 at a certain exercise time according to the conditions classified in the processing module 134, and provide corresponding guidance and suggestions in a timely manner.

[0121] In some examples, the first preset value can be between 1.5 and 2.0 mmol / L, for example, the first preset value can be 1.5 mmol / L, 1.6 mmol / L, 1.7 mmol / L, 1.8 mmol / L, 1.9 mmol / L, or 2.0 mmol / L, etc. Preferably, the first preset value can be 1.7 mmol / L or 1.8 mmol / L. In this case, it is possible to determine whether the glucose fluctuation before and after exercise is large.

[0122] In some examples, the second preset value can be between 1.5 and 2.0 mmol / L, for example, the second preset value can be 1.5 mmol / L, 1.6 mmol / L, 1.7 mmol / L, 1.8 mmol / L, 1.9 mmol / L, or 2.0 mmol / L, etc. Preferably, the second preset value can be 1.7 mmol / L or 1.8 mmol / L. In this case, it is possible to determine whether the glucose fluctuation before and after exercise is large.

[0123] In some examples, the third preset value can be no less than 7.0 mmol / L. For example, the third preset value can be 7.0 mmol / L, 7.2 mmol / L, 7.4 mmol / L, 7.6 mmol / L, 7.8 mmol / L, 8.0 mmol / L, 8.2 mmol / L, 8.4 mmol / L, 8.8 mmol / L, 9.0 mmol / L, or 10.0 mmol / L, etc. Preferably, the third preset value can be 7.0 mmol / L, 7.2 mmol / L, or 7.4 mmol / L. In this case, it is possible to determine whether pre-exercise glucose levels are high. Therefore, the glucose concentration curve measured by the glucose monitoring system 1 can more accurately classify the fluctuation type.

[0124] In some examples, the preset time can be from 10 minutes to 1 hour. For example, the preset time can be 0.5h, 0.6h, 0.7h, 0.8h, 0.9h, or 1.0h, preferably 0.8h, 0.9h, or 1.0h. In this case, the glucose monitoring system 1 can determine whether the glucose concentration first decreases and then increases or first increases and then decreases, thereby accurately monitoring the glucose concentration during exercise.

[0125] In some examples, the preset time can be set to a larger or smaller range of 0.5 to 2 hours. In this case, the glucose monitoring system 1 can accurately monitor the glucose concentration during exercise. For example, based on the four classification conditions mentioned above—the first, second, third, and fourth classification conditions—the following fluctuation types can be derived (unless otherwise specified, all units of the data described in the following algorithms are mmol / L for glucose concentration):

[0126] In some examples, such as Figure 3aAs shown: The analysis period for the glucose concentration curve with an increasing fluctuation type is from the exercise time point to 4 hours after exercise. The fluctuation characteristics of the glucose concentration curve with an increasing fluctuation type are: 1. The peak generally rises (partially it may fall); 2. The highest peak value is not lower than the glucose value at the start of exercise (variable value) + a first preset value; 3. The lowest trough value is not lower than the glucose value at the start of exercise (variable value) - a first preset value. The formula algorithm for the glucose concentration curve with an increasing fluctuation type is: 1. The highest peak value - the glucose value at exercise (variable value) >= the first preset value; 2. The glucose value at exercise (variable value) - the lowest trough value < a second preset value.

[0127] In some examples, such as Figure 3b As shown: The analysis period for the glucose concentration curve with a decreasing fluctuation type is from the exercise time point to 4 hours after exercise. The fluctuation characteristics of the glucose concentration curve with a decreasing fluctuation type are: 1. The peak generally decreases (some may rise); 2. The highest peak value is lower than the glucose value at the start of exercise (variable value) + a first preset value; 3. The lowest trough value is lower than the glucose value at the start of exercise (variable value) - a first preset value. The formula algorithm for the glucose concentration curve with a decreasing fluctuation type is: 1. Peak value - glucose value at exercise (variable value) < first preset value; 2. Glucose value at exercise (variable value) - lowest trough value >= second preset value.

[0128] In some examples, such as Figure 3c As shown: The analysis period for the glucose concentration curve with a fluctuation type of first rising and then falling is from the exercise time point to 4 hours after exercise. The fluctuation characteristics of the glucose concentration curve with the first rising and then falling type are: 1. A fluctuation with a predominantly rising trend appears first, followed by a fluctuation with a predominantly falling trend; 2. The highest peak value is not lower than the glucose value at the start of exercise (variable value) + a first preset value; 3. The lowest trough value is not higher than the glucose value at the start of exercise (variable value) - a first preset value; 4. The first peak with a predominantly rising trend begins within 0.5 hours of the exercise time. The formula algorithm for the glucose concentration curve with the first rising and then falling type is: 1. Peak value - glucose value at exercise (variable value) >= first preset value; 2. Glucose value at exercise (variable value) - lowest trough value >= second preset value.

[0129] In some examples, such as Figure 3dAs shown: The analysis period for the glucose concentration curve with a fluctuation type of first decreasing and then increasing is from the exercise time point to 4 hours after exercise. The fluctuation characteristics of the glucose concentration curve with the first decreasing and then increasing fluctuation type are: 1. Initially, there is a fluctuation dominated by decreasing, followed by a fluctuation dominated by increasing; 2. The highest peak value is not lower than the glucose value at the start of exercise (variable value) + a first preset value; 3. The trough value is not higher than the glucose value at the start of exercise (variable value) - a first preset value; 4. The first peak dominated by decreasing occurs within 0.5 hours of the exercise time. The formula algorithm for the glucose concentration curve with the first decreasing and then increasing fluctuation type is: 1. Glucose value at exercise (variable value) - lowest trough value >= first preset value; 2. Peak value - glucose value at exercise (variable value) >= second preset value.

[0130] In some examples, such as Figure 3e-1 As shown: The analysis period for the glucose concentration curve with relatively small fluctuations and low pre-exercise levels is from the exercise time point to 4 hours after exercise. The fluctuation characteristics of the glucose concentration curve with relatively small fluctuations and low pre-exercise levels are: 1. The peaks alternate between rising and falling; 2. The peaks are lower than (glucose value at the start of exercise + first preset value) (variable value); 3. The troughs are higher than (glucose value at the start of exercise - first preset value) (variable value); 4. Glucose at the start of exercise is lower than the third preset value. The formula algorithm for the glucose concentration curve with relatively small fluctuations and low pre-exercise levels is: 1. Peak value - glucose value at exercise (variable value) < first preset value; 2. Glucose value at exercise (variable value) - trough value < second preset value; 3. Pre-exercise glucose < third preset value.

[0131] In some examples, such as Figure 3e-2 As shown: The analysis period for the glucose concentration curve with relatively small fluctuations and a pre-exercise elevation is from the exercise time point to 4 hours after exercise. The fluctuation characteristics of the glucose concentration curve with relatively small fluctuations and a pre-exercise elevation are: 1. The peaks alternate between rising and falling; 2. The peaks are lower than (glucose value at exercise + first preset value) (variable value); 3. The troughs are higher than (glucose value at exercise - first preset value) (variable value); 4. Glucose at the start of exercise is not lower than the third preset value. The formula algorithm for the glucose concentration curve with relatively small fluctuations and a pre-exercise elevation is: 1. Peak value - glucose value at exercise (variable value) < first preset value; 2. Glucose value at exercise (variable value) - trough value < second preset value; 3. Pre-exercise glucose >= third preset value.

[0132] Other types: If the analysis period is the time point of exercise—4 hours after exercise, and the judgment logic or fluctuation characteristics do not meet the above 5 types, then the evaluation result of the fluctuation trend is irregular fluctuation of glucose after exercise (this type needs to be iterated and further classified).

[0133] While the invention has been specifically described above in conjunction with the accompanying drawings and examples, it is to be understood that the above description does not limit the invention in any way. Those skilled in the art can make modifications and variations to the invention as needed without departing from its essential spirit and scope, and all such modifications and variations fall within the scope of the invention.

Claims

1. A processing module for glucose concentration before and after exercise, used to detect the glucose concentration of a subject and provide guidance information, characterized in that, The processing module is configured to classify the fluctuation type of the glucose concentration curve between pre- and post-exercise based on glucose data, including the time-varying glucose concentration of the subject before and after exercise, to determine the fluctuation type of the glucose concentration curve between pre- and post-exercise. The fluctuation type of the glucose concentration curve reflects the trend of glucose concentration change between pre- and post-exercise. Based on the fluctuation type, the module generates guidance information or suggestions related to exercise behavior. The processing module is also configured to perform noise reduction processing on the glucose data. Specifically, based on the glucose data, the glucose concentration, first glucose fluctuation amplitude, and second glucose fluctuation amplitude at the time before exercise are obtained. The fluctuation type is obtained based on the glucose concentration at the time before exercise, the first glucose fluctuation amplitude, and the second glucose fluctuation amplitude. The first glucose fluctuation amplitude is the difference between the maximum glucose concentration at the detection point between the time before exercise and the time after exercise, and the glucose concentration at the time before exercise. The second glucose fluctuation amplitude is the difference between the glucose concentration at the time before exercise and the minimum glucose concentration at the detection point between the time before exercise and the time after exercise. The glucose concentration curve is classified into fluctuation types based on classification conditions related to glucose concentration. These classification conditions include a first classification condition, a second classification condition, a third classification condition, and a fourth classification condition. The first classification condition is that the fluctuation amplitude of the first glucose level is not less than a first preset value; the second classification condition is that the fluctuation amplitude of the second glucose level is not less than a second preset value; the third classification condition is that the first peak of the glucose data occurs within a preset time after exercise; and the fourth classification condition is that the glucose concentration before exercise is not less than a third preset value. The time prior to exercise is the detection time corresponding to the exercise detection point. The glucose data includes glucose concentrations at multiple detection points and detection times matching those points. If the exercise time recorded by the subject falls between the midpoints of the detection times corresponding to two adjacent detection points, then either of those two adjacent detection points is taken as the exercise detection point. If the exercise time recorded by the subject is not between two adjacent detection points or does not fall between the midpoints of the detection times corresponding to two adjacent detection points, then the detection point closest to the exercise time recorded by the subject is taken as the exercise detection point. The time after exercise is 3 to 5 hours after the time before exercise. The fluctuation types include rising, falling, rising first and then falling, falling first and then rising, fluctuations that are not high before normal movement, and fluctuations that are high before normal movement.

2. The processing module according to claim 1, characterized in that, Guidance or advice related to exercise behavior includes at least one of the following: advice on exercise intensity, advice on exercise duration, and advice on exercise type.

3. The processing module according to claim 1, characterized in that, Guidance or advice related to exercise behavior includes the cause of the fluctuation type or the number of monitoring days.

4. A mobile terminal device, characterized in that, The device integrates an input module, a processing module as described in any one of claims 1 to 3, and a display module; the input module is used to input the start time of the movement of the object under test; the processing module is used to obtain the fluctuation type and guidance information; and the display module is used to display at least one of the guidance information, the glucose concentration curve, and the fluctuation type.

5. A glucose monitoring system for pre- and post-exercise glucose concentration, used to detect the glucose concentration of a subject and provide guidance information, characterized in that, The glucose monitoring system includes a sensing module, a communication module, and a processing module as described in claim 1 or 3; the sensing module is configured to detect glucose data of the object to be tested; the communication module is configured to receive the glucose data and send it to the processing module.

Images