Data processing method and device for monitoring activity time of ankylosing spondylitis patients using wearable devices

Abstract

The present application discloses a method and apparatus for processing activity time data of ankylosing spondylitis patients monitored by a wearable device. The method comprises: obtaining initial time data corresponding to various activity behaviors of ankylosing spondylitis users by using a wearable device, wherein the activity behaviors include sedentary behavior, sleeping behavior, low-intensity physical activity behavior, and medium-to-high-intensity physical activity behavior; determining comparative features between the various activity behaviors based on the initial time data; constructing a sleep quality regression classification model based on the comparative features; and determining a target time allocation scheme corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized based on the sleep quality regression classification model. The method can allocate personalized target time data that can improve the sleep quality of ankylosing spondylitis users based on the initial time data of various types of activity behaviors of the ankylosing spondylitis users.

Description

Technical Field

[0001] The present disclosure generally relates to the field of time allocation decision-making, and more particularly to a method and apparatus for processing activity time data of ankylosing spondylitis patients monitored by a wearable device. Background Art

[0002] Ankylosing spondylitis (AS) is a highly disabling disease that can lead to disability due to spinal osteophyte formation and hip joint destruction. Clinical manifestations include chronic back pain, fatigue, and functional limitations, severely impacting patients' quality of life and ability to perform daily activities. In recent years, the increasing incidence of AS has posed numerous challenges to patients. While current treatments have made some progress in alleviating symptoms and slowing disease progression, patients still face challenges with quality of life, such as sleep quality.

[0003] Sleep quality is a key indicator of quality of life for patients with ankylosing spondylitis. Studies have shown that physical activity has a positive impact on improving overall health and reducing inflammation. Therefore, providing patients with ankylosing spondylitis with personalized time allocation for activities to improve their sleep quality has become a pressing issue. Summary of the Invention

[0004] In view of the above-mentioned defects or deficiencies in the prior art, it is desired to provide a method and apparatus for processing activity time data of ankylosing spondylitis patients using wearable devices, which can allocate personalized target time data that can improve sleep quality to ankylosing spondylitis users based on the initial time data of various types of activity behaviors of ankylosing spondylitis users.

[0005] In a first aspect, an embodiment of the present application provides a method for processing activity time data of ankylosing spondylitis patients using a wearable device, comprising:

[0006] Using wearable devices to obtain initial time data corresponding to various activity behaviors of users with ankylosing spondylitis, the activity behaviors include sedentary behavior, sleeping behavior, low-intensity physical activity behavior, and medium-to-high-intensity physical activity behavior;

[0007] Determining comparative features between the activity behaviors based on the initial time data;

[0008] Constructing a sleep quality regression classification model based on the contrast features;

[0009] Based on the sleep quality regression classification model, a target time allocation plan corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized is determined.

[0010] In some embodiments, determining the comparative features between the activity behaviors based on the initial time data includes:

[0011] The isometric logarithmic ratio transformation is used to obtain the contrast characteristics between each activity behavior and other activity behaviors.

[0012] In some embodiments, before determining the comparative features between the activity behaviors based on the initial time data, the method further includes:

[0013] performing standardization processing on the initial time data corresponding to the low-intensity physical activity behavior and the medium-to-high-intensity physical activity behavior acquired by the wearable device respectively; and

[0014] The initial time data corresponding to the sedentary behavior acquired by the wearable device is corrected based on the sedentary correction data input by the ankylosing spondylitis user.

[0015] In some embodiments, determining, based on the sleep quality regression classification model, the target time allocation scheme corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized includes:

[0016] Obtaining at least one time constraint added by the ankylosing spondylitis user to at least one of the activity behaviors;

[0017] Based on at least one of the time constraints, a target time allocation scheme corresponding to each of the activity behaviors is determined when the probability of the ankylosing spondylitis user having good sleep quality is maximized, wherein the target time data of each activity behavior meets the corresponding time constraint.

[0018] In some embodiments, the time constraint includes at least one of a minimum time length and a maximum time length; or, the time constraint includes an association relationship between two types of activity behaviors.

[0019] In some embodiments, further comprising:

[0020] Using the wearable device to collect the duration data of the sedentary behavior of the ankylosing spondylitis user in real time;

[0021] When the duration data exceeds the target time data corresponding to the sedentary behavior, the wearable device is used to send a timeout reminder to the ankylosing spondylitis user.

[0022] In a second aspect, an embodiment of the present application provides a processing device for monitoring activity time data of ankylosing spondylitis patients using a wearable device, comprising:

[0023] an acquisition module for acquiring, using a wearable device, initial time data corresponding to various activity behaviors of an ankylosing spondylitis user, wherein the activity behaviors include sedentary behavior, sleeping behavior, low-intensity physical activity behavior, and high-intensity physical activity behavior;

[0024] a determination module, configured to determine, based on the initial time data, comparison features between the activity behaviors;

[0025] A construction module, configured to construct a sleep quality regression classification model based on the contrast features;

[0026] The decision module is configured to determine, based on the sleep quality regression classification model, a target time allocation plan corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized.

[0027] In a third aspect, an embodiment of the present application provides an electronic device, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor implements the method described in the embodiment of the present application when executing the program.

[0028] In a fourth aspect, an embodiment of the present application provides a computer-readable storage medium on which a computer program is stored, which, when executed by a processor, implements the method described in the embodiment of the present application.

[0029] In a fifth aspect, an embodiment of the present application provides a computer program product, including a computer program, characterized in that when the computer program is executed by a processor, it implements the method described in the embodiment of the present application.

[0030] The application embodiment proposes a processing method and device for monitoring the activity time data of ankylosing spondylitis patients using wearable devices. The initial time data of different activity behavior types of ankylosing spondylitis users are obtained through the wearable device, and a sleep quality regression model is constructed based on the initial time data. The regression classification model is used to determine the target time allocation plan corresponding to each activity behavior when the probability of the ankylosing spondylitis user having good sleep quality is maximized. Based on the initial time data of the actual different activity behavior types of the ankylosing spondylitis users, a personalized target time allocation plan for improving sleep quality is provided to the ankylosing spondylitis users on the basis of their initial time data. The ankylosing spondylitis users can effectively improve their sleep quality after implementing various activity behaviors according to the target time allocation plan, thereby achieving the purpose of improving the quality of life of ankylosing spondylitis users by using the activity behavior time allocation plan.

[0031] Additional aspects and advantages of the present invention will be set forth in part in the description which follows and, in part, will be obvious from the description which follows, or may be learned through practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0032] Other features, objects and advantages of the present application will become more apparent upon reading the detailed description of non-limiting embodiments made with reference to the following drawings:

[0033] Figure 1 A diagram illustrating an implementation environment architecture of a method for processing activity time data of ankylosing spondylitis patients using a wearable device provided in an embodiment of the present application is shown;

[0034] Figure 2 A flowchart of a method for processing activity time data of ankylosing spondylitis patients using a wearable device provided in one embodiment of the present application is shown;

[0035] Figure 3 A schematic diagram of the structure of a processing device for monitoring activity time data of ankylosing spondylitis patients using a wearable device according to an embodiment of the present application is shown;

[0036] Figure 4 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing an embodiment of the present application is shown. DETAILED DESCRIPTION

[0037] The present application will be further described in detail below with reference to the accompanying drawings and examples. It should be understood that the specific embodiments described herein are merely for the purpose of explaining the relevant invention and are not intended to limit the invention. It should also be noted that, for ease of description, only portions relevant to the invention are shown in the accompanying drawings.

[0038] It should be noted that, in the absence of conflict, the embodiments and features of the embodiments in this application can be combined with each other. The present application will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0039] In recent years, the increasing incidence of ankylosing spondylitis (AS) has brought numerous challenges to patients' lives. Physical activity (PA) refers to physical activity generated by skeletal muscle contraction, as well as various occupational, leisure, and other daily activities that increase energy expenditure at the basal metabolic level. AS is a disease characterized by inflammatory pain, and PA can alleviate symptoms to a certain extent and is used as a therapeutic intervention for AS patients. Therefore, studying the correlation between PA and quality of life in AS patients is of great practical significance.

[0040] Physical activity is one of the important factors affecting disease progression and quality of life in patients with ankylosing spondylitis. Physical activity patterns include sleep, prolonged sitting, low-intensity physical activity, moderate-intensity physical activity, and high-intensity physical activity, and these activity patterns are closely related to patients' pain levels, fatigue, and functional status. However, relevant technologies generally focus on the relationship between disease activity and physical activity, while less attention has been paid to the relationship between physical activity and the quality of life of patients with ankylosing spondylitis, particularly their sleep quality.

[0041] Based on this, the present application proposes a method and apparatus for processing activity time data of ankylosing spondylitis patients using wearable devices.

[0042] The specific implementation environment of the method for processing the activity time data of ankylosing spondylitis patients using wearable devices proposed in this application can be found in Figure 1 . Figure 1 The following is a diagram illustrating an implementation environment architecture of a method for processing activity time data of ankylosing spondylitis patients using a wearable device, as provided in an embodiment of the present application.

[0043] like Figure 1 As shown, the implementation environment architecture includes: a wearable device 101 and a server 102.

[0044] Wearable device 101 is used to accurately collect various activity parameters and activity durations of users with ankylosing spondylitis. Wearable device 101 includes at least a triaxial accelerometer. For example, a sports bracelet with a triaxial wrist accelerometer combines motion sensor technology with smart wearable devices. This built-in triaxial accelerometer can monitor and record a user's motion data in real time, supporting health management, exercise tracking, and daily activity analysis. A triaxial accelerometer is a sensor that measures acceleration changes in three orthogonal directions (X, Y, and Z). Its core principle is based on microelectromechanical systems (MEMS) technology. It converts acceleration signals into electrical signals using tiny mechanical structures (such as cantilever beams and masses) and capacitive, piezoresistive, or piezoelectric detection methods. This is prior art and will not be further described. Wearable device 101 can be, but is not limited to, a smartphone, smartwatch, smart glasses, or other devices that integrate with clothing, shoes, etc. to collect activity data from users with ankylosing spondylitis.

[0045] The server 102 is connected to the wearable device 101, and is used to receive activity data collected by the wearable device 101, including but not limited to activity behavior parameters and activity time, and to execute the processing method of using a wearable device to monitor the activity time data of ankylosing spondylitis patients proposed in the embodiment of the present application based on the received activity data.

[0046] Server 102 can be an independent physical server, or a server cluster or distributed system composed of multiple physical servers. It can also be a cloud server that provides basic cloud computing services such as cloud services, cloud databases, cloud computing, cloud functions, cloud storage, network services, cloud communications, middleware services, domain name services, security services, CDN, as well as big data and artificial intelligence platforms.

[0047] The wearable device 101 and the server 102 are connected directly or indirectly via wired or wireless communication. Optionally, the wireless or wired network uses standard communication technologies and / or protocols. The network is typically the Internet, but can also be any network, including but not limited to a local area network (LAN), a metropolitan area network (MAN), a wide area network (WAN), a mobile, wired or wireless network, a private network, or any combination of a virtual private network.

[0048] The processing method for monitoring the activity time data of ankylosing spondylitis patients using a wearable device proposed in this application can be implemented by a processing device for monitoring the activity time data of ankylosing spondylitis patients using a wearable device. The processing device for monitoring the activity time data of ankylosing spondylitis patients using a wearable device can be installed on a wearable device or a server.

[0049] In order to further illustrate the technical solutions provided by the embodiments of the present application, this is described in detail below with reference to the accompanying drawings and specific embodiments. Although the embodiments of the present application provide the method operation instruction steps shown in the following embodiments or drawings, more or fewer operation instruction steps may be included in the method based on conventional or no creative labor. In the steps where there is no necessary causal relationship logically, the execution order of these steps is not limited to the execution order provided by the embodiments of the present application. The method may be executed in the order of the methods shown in the embodiments or drawings or in parallel during the actual processing process or when the device is executed.

[0050] It should be noted that the acquisition or use of data in the embodiments of this application requires the user's consent. The relevant data can only be obtained after the user's authorization and permission, and the acquisition or use of the data complies with the provisions of relevant laws and regulations.

[0051] Please refer to Figure 2 , Figure 2 FIG2 shows a flow chart of a processing method for monitoring activity time data of ankylosing spondylitis patients using a wearable device according to an embodiment of the present application. Figure 2 As shown, the method includes:

[0052] In step 201, the wearable device is used to obtain the initial time corresponding to each activity behavior of the ankylosing spondylitis user, where the activity behaviors include sedentary behavior, sleeping behavior, low-intensity physical activity behavior, and medium-to-high-intensity physical activity behavior.

[0053] It should be noted that wearable devices are used to obtain real-time behavioral data and physical status data of ankylosing spondylitis users, wherein the behavioral data include but are not limited to parameter data such as behavioral amplitude, behavioral frequency, behavioral intensity and behavioral time that can be used to identify the activity behavior type of ankylosing spondylitis users, and the physical status data include but are not limited to parameter data such as heart rate, blood oxygen and blood pressure that can be used to identify the sleep behavior type of ankylosing spondylitis users, which are not specifically limited in this application. Then, by performing type analysis on the real-time behavioral data and physical status data, the activity behavior type to which each behavioral data belongs and the duration corresponding to each activity behavior are obtained, such as sedentary behavior, sleeping behavior, low-intensity physical activity behavior and high-intensity physical activity behavior. Among them, the method for identifying the activity behavior type of ankylosing spondylitis users based on real-time behavioral data and physical status data collected by wearable devices is not specifically limited in this application.

[0054] It should be understood that in the embodiments of the present application, the wearable device can continuously collect real-time behavioral data and physical condition data of the user with ankylosing spondylitis over a period of time, such as 7 days, 15 days, or a month, and these data can be used as the initial time for personalized activity time allocation. In other words, the time data corresponding to the user's activity behavior before the personalized activity time allocation decision can be used as the initial time data corresponding to the user's activity behavior.

[0055] In one specific embodiment, the wearable device can be a fitness tracker containing a three-axis wrist accelerometer, used to monitor 24-hour activity data and physical condition data of a user with ankylosing spondylitis. The user wears the fitness tracker on the wrist of their dominant hand starting at the start of the test day (e.g., after waking up) and continuing to wear it for at least 24 hours. Then, based on the characteristics of the user's activity data, such as intensity, the activity data is classified from weak to strong into sedentary behavior (SED), light physical activity (LPA), moderate physical activity (MPA), vigorous physical activity (VPA), and sleep (SLP). Sleep includes nighttime sleep and naps, and moderate-intensity MPA and vigorous-intensity VPA are combined into moderate-to-high-intensity MVPA.

[0056] In a feasible embodiment, in order to further improve the reliability of data collected by the wearable device, the present application further proposes to correct the time data collected by the wearable device.

[0057] Optionally, the time data of low-intensity physical activity behavior LPA and medium-to-high-intensity physical activity behavior MVPA obtained by the wearable device are standardized respectively. Specifically, the time data collected by the wearable device is standardized to a 24-hour time range, for example, when the actual length of time the user wears the wearable device meets the effective wearing time but is less than 24 hours. Exemplarily, the low-intensity physical activity behavior LPA and medium-to-high-intensity physical activity behavior MVPA are standardized using the following formula:

[0058]

[0059]

[0060] Among them, MVPA is the initial time of medium and high intensity physical activity behavior after standardization, and serves as the initial time data corresponding to medium and high intensity physical activity behavior. is the initial time before standardization of medium and high intensity physical activity behaviors, and LPA is the initial time after standardization of low intensity physical activity behaviors. As the initial time data corresponding to low intensity physical activity behaviors, is the initial time before the standardization of low-intensity physical activity behavior, SLP is the initial time corresponding to the sleep behavior, is the actual wearing time of the wearable device.

[0061] It should also be noted that in the embodiment of the present application, before normalizing the time data, a further determination is made as to whether the user's effective wearing time within 24 hours is greater than or equal to a preset time length. If the effective wearing time is greater than or equal to the effective wearing time length, the time data collected by the user's wearable device is confirmed to be valid, and further time data normalization processing can be performed. If the effective wearing time is less than the effective wearing time length, the time data collected by the user's wearable device on that day is confirmed to be invalid and needs to be deleted and cannot be used for subsequent data analysis. The effective wearing time length can be determined based on the accuracy of the time data. In the embodiment of the present application, the effective wearing time length is preferably 1200 minutes (20 hours). It should be understood that the effective wearing time length can be continuous or intermittent, and this application does not specifically limit it. In one feasible embodiment, the time when the wearable device collects the user's physical status data (e.g., heart rate, blood oxygen, blood pressure) is determined as the effective time the user wears the wearable device.

[0062] It can be seen that in the standardization process of the embodiment of the present application, low-intensity physical activity behavior LPA and medium-to-high-intensity physical activity behavior MVPA are standardized by ignoring sedentary behavior SED. Based on this, the present application also proposes a method for correcting the initial time data of sedentary behavior SED.

[0063] Specifically, the initial time data corresponding to the sedentary behavior acquired by the wearable device is corrected based on the sedentary correction data input by the ankylosing spondylitis user.

[0064] That is, for sedentary behavior (SED), to prevent wearable devices from being unable to effectively and reasonably identify it, data correction is performed by having the ankylosing spondylitis user input sedentary correction data. For example, the sedentary correction data entered by the ankylosing spondylitis user can be obtained through manual input or questionnaire surveys, and used as the initial time data corresponding to the sedentary behavior. Optionally, in an embodiment of the present application, the ankylosing spondylitis user can input the sedentary correction data through a remote dual-terminal ankylosing spondylitis follow-up system, or other ankylosing spondylitis management system, which is not specifically limited in this application.

[0065] It should be understood that in the embodiment of the present application, the initial time data of the sleep behavior collected by the wearable device is directly used as the initial time data corresponding to the sleep behavior.

[0066] Step 202: Determine the comparative features between the various activity behaviors based on the initial time data.

[0067] It should be understood that the initial time data of each activity behavior concerned in the embodiments of the present application are all non-negative data, there is collinearity between the initial time data corresponding to different activity actions, and the initial time data corresponding to multiple activity behaviors also have certain restrictions (closed time constraints). Therefore, when making personalized activity behavior time allocation decisions, increasing the implementation time of one activity behavior will correspond to reducing the implementation time of at least one activity behavior, that is, paying more attention to the relative change relationship between the time data of different activity behaviors, rather than the absolute time data of each activity behavior data. Based on this, the present application determines the comparative characteristics between each activity behavior based on the initial time data, so as to pay attention to the change relationship between different activity behaviors when making time allocation decisions later.

[0068] In a feasible embodiment, isometric logarithmic ratio transformation is used to obtain the contrast features between each activity behavior and other activity behaviors.

[0069] It should be noted that the proportional values ​​between the initial time data of each activity behavior exist in a proportional space (also called a simplex space), so the proportional relationship between the initial time data of each activity behavior needs to be converted to Euclidean space. Generally, isometric log-ratio (ILR), central log-ratio (CLR), additive log-ratio (ALR), etc. can be used. In the embodiment of the present application, the isometric log-ratio ILR is preferably used to obtain the comparative characteristics between the initial time data of the activity behavior that can be used for subsequent calculations.

[0070] For example, the comparative features obtained in the embodiment of the present application based on the initial time data of sedentary behavior SED, sleep behavior SLP, low-intensity physical activity behavior LPA, and medium-to-high-intensity physical activity behavior MVPA are as follows:

[0071]

[0072]

[0073]

[0074] Among them, SED is the initial time data of sedentary behavior, SLP is the initial time data of sleep behavior, LPA is the initial time data of low-intensity physical activity behavior, and MVPA is the initial time data of medium-to-high-intensity physical activity behavior.

[0075] Based on this, the initial time data corresponding to the four activities concerned by the embodiment of the present application can be converted into the three-dimensional coordinate axis in the Euclidean space .

[0076] Step 203: construct a sleep quality regression classification model based on the contrast features.

[0077] It should be noted that this application uses contrast features to construct a sleep quality regression classification model to determine the user's sleep quality under different activity behavior durations through regression classification. In this embodiment of the application, sleep quality can be classified into two categories: good sleep quality and poor sleep quality.

[0078] Optionally, sleep quality can be assessed using the Pittsburgh Sleep Quality Index (PSQI). In some feasible embodiments, the user's sleep quality can be assessed using only Q1-Q5 of the PSQI. In this case, the score values ​​for Q1-Q5 can be directly determined using the data collected by the wearable device without the need for additional user participation. In other feasible embodiments, the user's sleep quality can be assessed using Q1-Q9 of the PSQI. In this case, the user is required to complete the sleep information for at least one day corresponding to the initial time data.

[0079] For example, the sleep quality regression classification model constructed based on the contrast features can be:

[0080]

[0081] in, , , In order to convert the initial time data corresponding to the four activities concerned by the embodiment of the present application into a three-dimensional coordinate axis in the Euclidean space, p is the classification result, when p=1, the user's sleep quality is good, and when p=0, the user's sleep quality is poor. 、 、 、 is the influence coefficient.

[0082] In some feasible embodiments, the influence coefficient in this application may be solved using the maximum likelihood estimation method.

[0083] In a preferred embodiment, after obtaining the regression classification model, the present application further validates the regression classification model. Preferably, the present application validates the model using a modified odds ratio (OR). The odds ratio (OR) is used to characterize the impact of an activity on the probability of a change in sleep quality.

[0084] Specifically, the improved odds ratio is:

[0085]

[0086] in, It is used to normalize the overall effect of the model in the three-dimensional coordinate system, where j is the activity behavior corresponding to the current odds ratio OR, and k is other activity behaviors except j.

[0087] It should be understood that, compared to the traditional odds ratio (OR), the present embodiment of the present application adds a square root adjustment term to the OR, enabling the OR to no longer focus solely on the temporal changes of a single activity, but instead reflect the balanced changes among multiple activities. Specifically, because the time data corresponding to the activities in the present embodiment of the present application are constrained by a closed time constraint of 24 hours (1440 minutes), increasing the duration of one type of activity inevitably reduces the duration of at least one other activity. The adjusted OR can fully account for the combined impact of temporal displacements among multiple activity types.

[0088] Step 204 : Based on the sleep quality regression classification model, determine the target time allocation plan corresponding to each activity behavior when the probability of the ankylosing spondylitis user having good sleep quality is maximized.

[0089] Specifically, an objective function can be constructed based on the sleep quality regression classification model to characterize the probability of ankylosing spondylitis users having good sleep quality. By adjusting the time allocation of various types of activity behaviors, the time allocation plan for each activity behavior when the probability of the user having good sleep quality is maximized, that is, the target time allocation plan, can be obtained.

[0090] For example, the objective function constructed based on the sleep quality regression classification model to characterize the probability of ankylosing spondylitis users having good sleep quality is:

[0091]

[0092] And the constraints are:

[0093]

[0094] in, The target time data for sedentary behavior SED, sleeping behavior SLP, low-intensity physical activity behavior LPA, and medium-to-high-intensity physical activity behavior MVPA, that is, the target time data corresponding to each activity behavior in the target time allocation plan, The minimum time length limit of the target time data, The maximum time limit.

[0095] It should be understood that the above constraints are used to constrain the sum of the target time data corresponding to each activity behavior to meet the closed time constraint, that is, the total time is 1440 minutes corresponding to 24 hours. In addition, the upper and lower limits of the target time data corresponding to the selected activity behavior type j are constrained. For example, based on the health guidelines, the time of high-intensity physical activity behavior MVPA should be greater than or equal to 30 minutes / day, and the sleep time should be greater than or equal to 4 hours. This application does not make specific restrictions.

[0096] Based on this, the embodiment of the present application proposes a processing method for monitoring the activity time data of ankylosing spondylitis patients using wearable devices, obtaining the initial time data of different activity behavior types of ankylosing spondylitis users through wearable devices, and constructing a sleep quality regression model based on the initial time data, and using the regression classification model to determine the target time allocation plan corresponding to each activity behavior when the probability of the ankylosing spondylitis user having good sleep quality is maximized, thereby providing ankylosing spondylitis users with a personalized target time allocation plan for improving sleep quality based on their initial time data according to the actual initial time data of different activity behavior types of ankylosing spondylitis users, so that ankylosing spondylitis users can effectively improve their sleep quality after implementing various activity behaviors according to the target time allocation plan, thereby achieving the purpose of improving the quality of life of ankylosing spondylitis users using the activity behavior time allocation plan.

[0097] In some embodiments, since ankylosing spondylitis patients have limited activities due to pain, such as being unable to perform medium to high intensity physical activities (MVPA) for a long time, based on this, the present application can further configure a more accurate personalized target time allocation plan based on the characteristics of the ankylosing spondylitis user himself.

[0098] Specifically, at least one time constraint added by an ankylosing spondylitis user to at least one activity behavior is obtained. Based on the at least one time constraint, a target time allocation plan corresponding to each activity behavior is determined when the probability of the ankylosing spondylitis user having good sleep quality is maximized, and the target time data of each activity behavior data satisfies the corresponding time constraint.

[0099] Optionally, the time constraint includes at least one of a minimum time length and a maximum time length.

[0100] That is, before making personalized activity time allocation decisions, ankylosing spondylitis users can add time constraints corresponding to various activity behaviors through the interactive interface or questionnaire of the follow-up system or management system, such as the maximum duration of moderate to high-intensity physical activity (MVPA) and the minimum duration of sleep (SLP). The time constraints entered by ankylosing spondylitis users can be determined based on doctor's advice or the ankylosing spondylitis user's own disease condition. For example, if the user is a long sleeper and needs to ensure sufficient sleep time, the minimum duration of sleep (SLP) can be set. Or if the user's muscle condition is severely ill and cannot support long-term moderate to high-intensity physical activity (MVPA), the maximum duration of moderate to high-intensity physical activity (MVPA) can be set.

[0101] Specifically, the time constraint added by ankylosing spondylitis users to at least one activity behavior is used to construct a constraint condition for the objective function. In the process of solving the objective function, the constraint condition is used to limit the solution of the objective function, thereby obtaining the target time data corresponding to each activity behavior that meets the time constraint.

[0102] Therefore, the embodiments of the present application can provide ankylosing spondylitis users with a personalized target time allocation plan that meets their needs when the users clearly define the time limits for their own activities. On the basis of ensuring improved sleep quality, the embodiments of the present application can ensure the time requirements of the users with ankylosing spondylitis for various activities, and on the basis of improving the quality of life of the users with ankylosing spondylitis, improve the user experience of the users with ankylosing spondylitis.

[0103] Optionally, the time constraint includes an association relationship between two types of activity behaviors, including but not limited to target time data of one type of activity behavior being greater than target time data of another type of activity behavior.

[0104] Specifically, when constructing a regression classification model, based on the correlation between the two types of activity behaviors input by ankylosing spondylitis users, interaction terms related to the two types of activity behaviors are generated, and constraints are constructed based on the correlation between the two types of activity behaviors.

[0105] For example, when the user adds more low-intensity physical activity behaviors (LPA) and fewer medium- and high-intensity physical activity behaviors (MVPA), the regression classification model is:

[0106]

[0107] in, is the interaction term between low-intensity physical activity (LPA) and moderate-to-high-intensity physical activity (MVPA). ≠0 indicates the existence of a synergistic effect between low-intensity physical activity behavior LPA and moderate-to-high-intensity physical activity behavior MVPA.

[0108] And the constraints of the new construction are:

[0109]

[0110] Therefore, the embodiments of the present application can provide ankylosing spondylitis users with a personalized target time allocation plan that meets their needs when the users have clear time allocation preferences. On the basis of ensuring improved sleep quality, the time requirements of ankylosing spondylitis users for various activities can be guaranteed. On the basis of improving the quality of life of ankylosing spondylitis users, the user experience of ankylosing spondylitis users can be improved.

[0111] Furthermore, in some embodiments, a wearable device is used to collect the duration data of the sedentary behavior of an ankylosing spondylitis user in real time, and when the duration data exceeds the target time data corresponding to the sedentary behavior, the wearable device is used to send a timeout reminder to the ankylosing spondylitis user.

[0112] The duration data may be the duration of a single sedentary behavior or the sum of the durations of multiple sedentary behaviors, which is not specifically limited in this application.

[0113] That is to say, for sedentary behavior, this application further provides a reminder mechanism to issue a timeout reminder to the user when the duration of the sedentary behavior of ankylosing spondylitis user exceeds the target time data. This can effectively prevent ankylosing spondylitis users from maintaining sedentary behavior for a long time, assist ankylosing spondylitis users in relieving sedentary fatigue, etc., and further avoid unnecessary deterioration of the disease.

[0114] It should be noted that although the operations of the present method are described in a particular order in the drawings, this does not require or imply that the operations must be performed in this particular order, or that all illustrated operations must be performed to achieve desirable results.

[0115] Figure 3 A schematic diagram of the structure of a processing device for monitoring activity time data of ankylosing spondylitis patients using a wearable device provided in one embodiment of the present application is shown.

[0116] like Figure 3 As shown, the processing device 10 for monitoring activity time data of ankylosing spondylitis patients using a wearable device provided in an embodiment of the present application includes:

[0117] An acquisition module 11 is configured to acquire, using a wearable device, initial time data corresponding to various activity behaviors of an ankylosing spondylitis user, wherein the activity behaviors include sedentary behavior, sleeping behavior, low-intensity physical activity behavior, and high-intensity physical activity behavior;

[0118] a determination module 12, configured to determine comparative features between the activity behaviors based on the initial time data;

[0119] A construction module 13 is used to construct a sleep quality regression classification model based on the contrast features;

[0120] The decision module 14 is configured to determine, based on the sleep quality regression classification model, a target time allocation plan corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized.

[0121] In some embodiments, the determination module 12 is further configured to:

[0122] The isometric logarithmic ratio transformation is used to obtain the contrast characteristics between each activity behavior and other activity behaviors.

[0123] In some embodiments, the determination module 12 is further configured to:

[0124] performing standardization processing on the initial time data corresponding to the low-intensity physical activity behavior and the medium-to-high-intensity physical activity behavior acquired by the wearable device respectively; and

[0125] The initial time data corresponding to the sedentary behavior acquired by the wearable device is corrected based on the sedentary correction data input by the ankylosing spondylitis user.

[0126] In some embodiments, the decision module 14 is further configured to:

[0127] Obtaining at least one time constraint added by the ankylosing spondylitis user to at least one of the activity behaviors;

[0128] Based on at least one of the time constraints, a target time allocation scheme corresponding to each of the activity behaviors is determined when the probability of the ankylosing spondylitis user having good sleep quality is maximized, wherein the target time data of each activity behavior meets the corresponding time constraint.

[0129] In some embodiments, the time constraint includes at least one of a minimum time length and a maximum time length; or, the time constraint includes an association relationship between two types of activity behaviors.

[0130] In some embodiments, the decision module 14 is further configured to:

[0131] Using the wearable device to collect the duration data of the sedentary behavior of the ankylosing spondylitis user in real time;

[0132] When the duration data exceeds the target time data corresponding to the sedentary behavior, the wearable device is used to send a timeout reminder to the ankylosing spondylitis user.

[0133] It should be understood that the modules or modules recorded in the processing device 10 for monitoring the activity time data of ankylosing spondylitis patients using wearable devices are the same as those in the reference Figure 2 The various steps in the described method correspond to each other. Therefore, the operations and features described above for the method are also applicable to the processing device 10 for monitoring the activity time data of ankylosing spondylitis patients using wearable devices and the modules contained therein, and will not be repeated here. The processing device 10 for monitoring the activity time data of ankylosing spondylitis patients using wearable devices can be pre-implemented in the browser or other security applications of the electronic device, or can be loaded into the browser or security application of the electronic device by downloading or other means. The corresponding modules in the processing device 10 for monitoring the activity time data of ankylosing spondylitis patients using wearable devices can cooperate with the modules in the electronic device to implement the solution of the embodiment of the present application.

[0134] The several modules or units mentioned in the detailed description above are not necessarily divided into one module or unit. In fact, according to the embodiments of the present disclosure, the features and functions of two or more modules or units described above can be embodied in one module or unit. Conversely, the features and functions of one module or unit described above can be further divided into multiple modules or units to be embodied.

[0135] Reference below Figure 4 , Figure 4 A schematic diagram of the structure of a computer system of an electronic device or server suitable for implementing the embodiments of the present application is shown.

[0136] like Figure 4 As shown, the computer system includes a central processing unit (CPU) 401, which can perform various appropriate actions and processes according to programs stored in a read-only memory (ROM) 402 or programs loaded from a storage unit 408 into a random access memory (RAM) 403. RAM 403 also stores various programs and data required for the system's operating instructions. CPU 401, ROM 402, and RAM 403 are connected to each other via a bus 404. An input / output (I / O) interface 405 is also connected to bus 404.

[0137] The following components are connected to the I / O interface 405: an input section 406 including a keyboard, a mouse, and the like; an output section 407 including devices such as a cathode ray tube (CRT), a liquid crystal display (LCD), and speakers; a storage section 408 including devices such as a hard disk; and a communication section 409 including a network interface card such as a LAN card or a modem. The communication section 409 performs communication processing via a network such as the Internet. A drive 410 is also connected to the I / O interface 405 as needed. Removable media 411, such as a magnetic disk, an optical disk, a magneto-optical disk, or a semiconductor memory, is installed in the drive 410 as needed, so that computer programs read therefrom can be installed in the storage section 408 as needed.

[0138] In particular, according to the embodiment of the present application, the above reference flow chart Figure 2 The described processes can be implemented as a computer software program. For example, an embodiment of the present application includes a computer program product comprising a computer program carried on a computer-readable medium, the computer program containing program code for executing the method illustrated in the flowchart. In such an embodiment, the computer program contains program code for executing the method illustrated in the flowchart. In such an embodiment, the computer program can be downloaded and installed from a network via the communication section 409 and / or installed from removable media 411. When the computer program is executed by the central processing unit (CPU) 401, the aforementioned functions defined in the system of the present application are performed.

[0139] It should be noted that the computer-readable medium described herein may be a computer-readable signal medium or a computer-readable storage medium, or any combination thereof. A computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination thereof. More specific examples of computer-readable storage media may include, but are not limited to, an electrical connection having one or more conductors, a portable computer disk, a hard disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination thereof. In this application, a computer-readable storage medium may be any tangible medium containing or storing a program that can be used by or in conjunction with an instruction execution system, apparatus, or device. In this application, a computer-readable signal medium may include a data signal propagated in baseband or as part of a carrier wave, carrying computer-readable program code. This propagated data signal may take a variety of forms, including, but not limited to, electromagnetic signals, optical signals, or any suitable combination thereof. A computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium that can transmit, propagate, or transfer a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any suitable medium, including but not limited to wireless, wire, optical cable, RF, or any suitable combination thereof.

[0140] The flowcharts and block diagrams in the accompanying drawings illustrate the possible implementation architecture, functions and operating instructions of the systems, methods and computer program products according to various embodiments of the present application. In this regard, each box in the flowchart or block diagram can represent a module, program segment, or a part of code, and the aforementioned module, program segment, or a part of code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the box can also occur in a different order than the order marked in the accompanying drawings. For example, the boxes represented by two connections can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each box in the block diagram and / or flowchart, and the combination of the boxes in the block diagram and / or flowchart, can be implemented using a dedicated hardware-based system that performs the specified function or operating instruction, or can be implemented using a combination of dedicated hardware and computer instructions.

[0141] The units or modules involved in the embodiments described in the present application may be implemented by software or by hardware. The units or modules described may also be provided in a processor. For example, they may be described as: a processor including an acquisition module, a determination module, a construction module, and a decision module. The names of these units or modules do not, in some cases, constitute a limitation on the units or modules themselves. For example, the acquisition module may also be described as "using a wearable device to obtain the initial time data corresponding to each activity behavior of an ankylosing spondylitis user, wherein the activity behavior includes sedentary behavior, sleep behavior, low-intensity physical activity behavior, and high-intensity physical activity behavior."

[0142] As another aspect, the present application further provides a computer-readable storage medium, which may be included in the electronic device described in the above embodiments, or may exist independently and not be incorporated into the electronic device. The computer-readable storage medium stores one or more programs, which, when used by one or more processors, execute the processing method described in the present application for monitoring activity time data of ankylosing spondylitis patients using a wearable device.

[0143] The above description is merely a preferred embodiment of the present application and an illustration of the technical principles employed. Those skilled in the art should understand that the scope of disclosure in this application is not limited to technical solutions formed by a specific combination of the aforementioned technical features. It also encompasses other technical solutions formed by any combination of the aforementioned technical features or their equivalents, without departing from the aforementioned disclosed concepts. For example, a technical solution formed by replacing the aforementioned features with (but not limited to) technical features with similar functions disclosed in this application.

Claims

1. A method for processing activity time data of ankylosing spondylitis patients using a wearable device, characterized in that: include: Using wearable devices to obtain initial time data corresponding to various activity behaviors of users with ankylosing spondylitis, the activity behaviors include sedentary behavior, sleeping behavior, low-intensity physical activity behavior, and medium-to-high-intensity physical activity behavior; Determining comparative features between the activity behaviors based on the initial time data; Constructing a sleep quality regression classification model based on the contrast features; Determining, based on the sleep quality regression classification model, a target time allocation plan corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized; The step of determining the target time allocation scheme corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized based on the sleep quality regression classification model includes: Obtaining at least one time constraint added by the ankylosing spondylitis user to at least one of the activity behaviors; Based on at least one of the time constraints, determining a target time allocation scheme corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized, wherein the target time data of each activity behavior satisfies the corresponding time constraint; The time constraint includes at least one of a minimum time length and a maximum time length, and the method further includes: constructing a constraint condition for the objective function using the time constraint added by the ankylosing spondylitis user to at least one activity behavior; In the process of solving the objective function, the constraint condition is used to restrict the objective function solution to obtain target time data corresponding to each activity behavior that meets the time constraint.

2. The method for processing activity time data of ankylosing spondylitis patients using a wearable device according to claim 1, characterized in that: Determining the comparative features between the activity behaviors based on the initial time data includes: The isometric logarithmic ratio transformation is used to obtain the contrast characteristics between each activity behavior and other activity behaviors.

3. The method for processing activity time data of ankylosing spondylitis patients using a wearable device according to claim 1 or 2, characterized in that: Before determining the comparative features between the activity behaviors based on the initial time data, the method further includes: performing standardization processing on the initial time data corresponding to the low-intensity physical activity behavior and the medium-to-high-intensity physical activity behavior acquired by the wearable device respectively; and The initial time data corresponding to the sedentary behavior acquired by the wearable device is corrected based on the sedentary correction data input by the ankylosing spondylitis user.

4. The method for processing activity time data of ankylosing spondylitis patients using a wearable device according to claim 1, characterized in that: The time constraint includes the association relationship between two types of activity behaviors.

5. The method for processing activity time data of ankylosing spondylitis patients using a wearable device according to claim 4, characterized in that: Also includes: When constructing the regression classification model, based on the correlation between the two types of activity behaviors input by the ankylosing spondylitis user, interaction terms related to the two types of activity behaviors are generated, and constraints are constructed based on the correlation between the two types of activity behaviors.

6. The method for processing activity time data of ankylosing spondylitis patients using a wearable device according to claim 1, characterized in that: Also includes: Using the wearable device to collect the duration data of the sedentary behavior of the ankylosing spondylitis user in real time; When the duration data exceeds the target time data corresponding to the sedentary behavior, the wearable device is used to send a timeout reminder to the ankylosing spondylitis user.

7. A processing device for monitoring activity time data of ankylosing spondylitis patients using a wearable device, characterized in that: include: an acquisition module for acquiring, using a wearable device, initial time data corresponding to various activity behaviors of an ankylosing spondylitis user, wherein the activity behaviors include sedentary behavior, sleeping behavior, low-intensity physical activity behavior, and high-intensity physical activity behavior; a determination module, configured to determine, based on the initial time data, comparison features between the activity behaviors; A construction module, configured to construct a sleep quality regression classification model based on the contrast features; A decision module, configured to determine, based on the sleep quality regression classification model, a target time allocation plan corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized; The decision module is specifically used to: Obtaining at least one time constraint added by the ankylosing spondylitis user to at least one of the activity behaviors; Based on at least one of the time constraints, determining a target time allocation scheme corresponding to each of the activity behaviors when the probability of the ankylosing spondylitis user having good sleep quality is maximized, wherein the target time data of each activity behavior satisfies the corresponding time constraint; The time constraint includes at least one of a minimum time length and a maximum time length, and the decision module is specifically configured to: constructing a constraint condition for the objective function using the time constraint added by the ankylosing spondylitis user to at least one activity behavior; In the process of solving the objective function, the constraint condition is used to restrict the objective function solution to obtain target time data corresponding to each activity behavior that meets the time constraint.

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