Intelligent systems and methods for event tracking

Abstract

This invention provides an intelligent system and method for event tracking, which can assist a user in predicting the event type and occurrence time of an upcoming event that the user needs to handle. The intelligent event tracking system includes a cloud server and a tracking device. The cloud server is configured to determine the event type and occurrence time of a current event, and based on the determined event type and occurrence time of the current event, as well as statistical data related to the occurrence time and frequency of the determined event type and / or at least one related event type, predict the event type and occurrence time of the next event.

Description

Technical Field

[0001] This invention generally relates to an intelligent event tracking system and method, and more specifically to an event tracking system and method capable of predicting the type and timing of future events. Background Technology

[0002] Caring for infants, the elderly, or those with chronic illnesses can be a demanding and exhausting task. For example, inexperienced parents may face numerous challenges when dealing with the many infant care events, such as feeding, soothing to sleep, urination, and defecation. Babies typically cry when they are hungry, tired, or uncomfortable due to urination or defecation, and their crying patterns can vary depending on their needs. New parents need time to familiarize themselves with their baby's crying patterns and develop the ability to immediately identify their baby's needs from these patterns. When a baby cries, parents often rush to find each need individually. The more time parents spend figuring out the exact need, the more intense the crying becomes, further increasing parental anxiety. If parents could have reminders about the type and timing of upcoming infant care events, they could address the baby's needs promptly, reducing or even preventing crying. This, in turn, reduces parental anxiety.

[0003] Older adults often experience a decline in short-term memory. Daily routines can be challenging for them. They are aware of their needs but easily forget what they are supposed to do in their schedules, especially medical tasks that are crucial to their health. Having reminders about the types and times of upcoming events that older adults need to pay attention to can reduce the risk of missing scheduled activities. Summary of the Invention

[0004] One objective of this invention is to provide an intelligent event tracking system and method to assist users in predicting the type and timing of upcoming events that users need to handle.

[0005] According to one aspect of the present invention, an intelligent event tracking system includes: a cloud server including an event database; a tracking device including: at least one receiving unit for receiving event data; at least one presentation unit for presenting event data; and a communication module connected to the receiving unit and the presentation unit and configured to receive event data from the cloud server and transmit event data to the cloud server.

[0006] The cloud server is configured to acquire event data from a receiving unit via a communication module; determine the event type and occurrence time of the current event from the received event data; retrieve statistical data from the event database related to the occurrence time, frequency, and distribution of event attributes associated with the determined event type and / or at least one related event type; predict the event type and occurrence time of the next event based on the determined event type and occurrence time of the current event and the retrieved statistical data related to the occurrence time and frequency of the determined event type and / or at least one related event type; update the event database with the predicted event type and occurrence time of the next event and the event data acquired from the receiving unit; and transmit the predicted event type and occurrence time of the next event to a presentation unit via the communication module; and the presentation unit is configured to present the predicted event type at the predicted occurrence time of the next event.

[0007] According to another aspect of the present invention, an intelligent event tracking method includes: receiving event data via a receiving unit; transmitting the event data from the receiving unit to a cloud server via a communication module; determining the event type and occurrence time of the current event from the acquired event data via the cloud server; retrieving, via the cloud server, statistical data related to the occurrence time and frequency associated with the determined event type and / or at least one related event type from an event database; predicting the event type and occurrence time of the next event via the cloud server based on the determined event type and occurrence time of the current event and the retrieved statistical data related to the occurrence time and frequency associated with the determined event type and / or at least one related event type; updating the event database via the cloud server with the predicted event type and occurrence time of the next event and the event data acquired from the receiving unit; transmitting, via the communication module, the predicted event type and occurrence time of the next event to a presentation unit; and presenting, via the presentation unit, the predicted event type at the predicted occurrence time of the next event for the user. Attached Figure Description

[0008] Embodiments of the present invention will be described in more detail below with reference to the accompanying drawings.

[0009] Figure 1 A block diagram depicting an intelligent event tracking system according to an exemplary embodiment of the present invention.

[0010] Figure 2 A capacitive touch slider and display panel according to an exemplary embodiment of the present invention are depicted.

[0011] Figure 3 One or more physical buttons and a display panel are depicted according to an exemplary embodiment of the present invention. Detailed Implementation

[0012] In the following description, specific details are set forth for purposes of explanation in order to provide a thorough understanding of embodiments of the present disclosure. However, it will be apparent to those skilled in the art that the present disclosure may be practiced without these specific details or with equivalent arrangements.

[0013] See Figure 1 According to the present invention, an intelligent event tracking system 100 is provided. The intelligent event tracking system 100 may include: a cloud server 110, which includes an event database; at least one tracking device 120, which includes: at least one receiving unit 121 for receiving event data; at least one presentation unit 122 for presenting event data; and a communication module 123 connected to the receiving unit 121 and the presentation unit 122 and configured to receive event data from the cloud server 110 and transmit event data to the cloud server 110.

[0014] The cloud server 110 can be configured to acquire event data from the receiving unit 121 via the communication module 123; determine the event type and occurrence time of the current event from the received event data; retrieve statistical data from the event database related to the occurrence time, frequency, and distribution of event attributes associated with the determined event type and / or at least one related event type; predict the event type and occurrence time of the next event based on the determined event type and occurrence time of the current event and the retrieved statistical data related to the occurrence time and frequency of the determined event type and / or at least one related event type; update the event database with the predicted event type and occurrence time of the next event and the event data acquired from the receiving unit 121; and transmit the predicted event type and occurrence time of the next event to the presentation unit 122 via the communication module 123. The presentation unit 122 is configured to present the predicted event type at the predicted occurrence time of the next event.

[0015] For example, in infant care, diaper changing is a routine daytime event. The frequency and amount of urination and defecation are highly correlated with the amount of milk consumed by the infant. Depending on the infant's digestive system, the time lag between feeding and diaper changing follows a normal distribution to some extent. This distribution can be extracted from the event history of diaper changing and other related events. For instance, predictions of diaper changing are related to the timing, frequency, and amount of milk consumed during feeding. Here, milk consumption is an event attribute of feeding. Therefore, the more event types and their associated attributes recorded using tracking devices, the more statistical event data accumulates, and the more accurate the event predictions can be.

[0016] Furthermore, the prediction of upcoming events in cloud server 110 can take into account both long-term trends and short-term variations in event data. For example, infant behavior can change from time to time. Therefore, predictions should be adapted to infant behavior as closely as possible. Thus, the statistical event data for infants will reflect both long-term and short-term event trends. In contrast, for specific event types such as medical intake, the timing of upcoming events is fixed based on doctor's prescriptions.

[0017] The presentation unit 122 can be a user's mobile device. It presents the prediction of an upcoming event to the user by pushing a notification at the time the predicted event is expected. The user can open the mobile application and view the predicted event, historical trends of the predicted event type, or other relevant event types. The user can then decide how to react to the prediction. Typically, the user follows the reminders for the predicted event and takes appropriate action.

[0018] Optionally, the cloud server 110 may be further configured to train a deep learning model with an event database and use the trained deep learning model to predict the event type and occurrence time of the next event.

[0019] Optionally, the cloud server 110 may be further configured to determine the duration of the current event from the received event data; update the event database with the determined duration of the current event; and adjust the predicted occurrence time and predicted event type of the next event based on the determined duration of the current event.

[0020] In one embodiment, the cloud server 110 may be further configured to convert the predicted event type and occurrence time of the next event into an audio signal. The presentation unit 122 may include a speaker configured to receive the audio signal from the cloud server 110 from the communication module 123 and convert the received audio signal into sound.

[0021] In another embodiment, the cloud server 110 may be further configured to convert the predicted event type and occurrence time of the next event into a display signal; and the presentation unit 122 may include a display configured to receive the display signal from the cloud server 110 via the communication module 123 and convert the received display signal into an image or video.

[0022] Preferably, the cloud server 110 may be further configured to convert historical event data and statistical event data into display signals; and the display may be configured to receive display signals from the cloud server 110 via the communication module 123 and convert the received display signals into graphs or motion images.

[0023] In such Figure 2In one embodiment shown, the receiving unit 121 may include a capacitive touch slider 210, which includes one or more touch areas 211. The tracking device 120 may further include a display panel 220. The capacitive touch slider is positioned near the display panel 220; and the display panel 220 is configured to display one or more reconfigurable information markers 221 corresponding to one or more touch areas 211 of the capacitive touch slider at locations adjacent to the touch areas of the slider.

[0024] The cloud server 110 can be further configured to determine the event type and time of occurrence of the current event when one of the touch areas 211 is triggered.

[0025] The duration of the current event can be the time difference between the event start time when one of the touch areas 211 is triggered for input event type and the event end time when the touch area triggered at the event start time is triggered again.

[0026] Preferably, the display panel 220 may be further configured to display a user interface for reconfiguring the trigger patterns of one or more touch areas. Each of the touch areas 211 is divided into one or more sub-areas to facilitate user input of one or more event attributes of the current event.

[0027] In such Figure 3 In one embodiment shown, the receiving unit 121 may include one or more physical buttons 311. The tracking device 120 may further include a display panel 320. The one or more physical buttons 311 are located near the display panel 320; and the display panel 320 is configured to display one or more reconfigurable information markers 321 corresponding to the one or more physical buttons 311 at locations adjacent to the one or more physical buttons.

[0028] The cloud server 110 can be further configured to determine the event type and occurrence time of the current event when one of the physical buttons 311 is triggered.

[0029] The duration of the current event can be the time difference between the event start time when one of the physical buttons 311 is triggered to input the event type and the event end time when the physical button that was triggered at the event start time is triggered again.

[0030] Optionally, each of the physical buttons 311 may include a sensor configured to sense one or more directions of a force applied thereto to assist the user in inputting one or more event attributes of the current event.

[0031] One or more physical buttons 311 can be configured to have additional button trigger modes for inputting more information about event attributes. The display panel 320 can be further configured to display a user interface for defining an event template corresponding to each of the trigger modes of the one or more physical buttons 311. The event template can contain a set of event attributes for a specific event type. A basic trigger mode can simply be a single click, where the button is pressed for a short period and released. If the user wants to store events with more information, additional trigger modes can be used, such as double-click, long press, super long press, triple click, and click followed by long press. Once a trigger mode is executed, its defined corresponding event template will be used as event data for event prediction.

[0032] One or more physical buttons 311 may be designed with identifiable features, including but not limited to specific patterns, touchable points, or specific shapes, to facilitate touch or visual sensing. Patterns or shapes should be chosen for ease of identification. When physical buttons 311 are configured via a user interface of a tracking device or mobile application, buttons with identifiable features will help select the correct button for inputting event types or event attributes without requiring visual identification to find the button. Even with markers in the display panel indicating the event type for each of the physical buttons 311, button designs with identifiable features are easier to remember than markers.

[0033] In one embodiment, the receiving unit 121 may include a touch display that includes one or more touchscreen buttons. The cloud server 110 may be further configured to determine the event type and time of occurrence of the current event when one of the touchscreen buttons is triggered.

[0034] The duration of the current event can be the time difference between the start time of the event when one of the touchscreen buttons is triggered for input event type and the end time of the event when the touchscreen button that was triggered at the start time is triggered again. Optionally, touchscreen buttons can be categorized and organized in single-level or multi-level menus.

[0035] In one embodiment, the receiving unit 121 may include a microphone for receiving one or more audio recordings. The cloud server 110 may be further configured to determine the event type and occurrence time of the current event from one or more received audio recordings. Preferably, the cloud server 110 may be further configured to determine one or more event attributes of the current event from the received audio recordings.

[0036] In one embodiment, the receiving unit 121 may include a camera for capturing one or more videos. The cloud server 110 may be further configured to determine the event type and occurrence time of a current event from the one or more captured videos. Preferably, the cloud server 110 may be further configured to determine one or more event attributes of the current event from the captured videos.

[0037] In cases where automatic identification of received audio recordings and captured video is ineffective or inaccurate, the microphone and display can be configured to allow the user to listen to the recorded audio or view the captured video to recall memories of event attributes and edit the event log. Furthermore, the user interface of the tracking device or mobile application can be configured to help the user change event attributes regardless of whether event-related media is present in the recording. If the user misses some event details or even the entire event log when it occurs, the user interface of the tracking device or mobile application can help the user fill in the missing parts.

[0038] In one embodiment, the communication module 123 may be further configured to detect the relative signal strength indicator (RSSI) signal pattern of the current event. The cloud server 110 may be further configured to determine the event type and occurrence time of the current event from the detected RSSI signal pattern by matching the detected RSSI signal pattern with historical RSSI signal patterns.

[0039] The receiving unit 121 may further include a motion sensor for sensing the motion pattern of the current event. The cloud server 110 may be further configured to determine the event type and occurrence time of the current event from the sensed motion pattern by matching the sensed motion pattern with historical motion patterns.

[0040] The cloud server 110 can be further configured to identify the individual to whom the current event belongs by matching the detected current motion pattern and RSSI signal pattern with historical motion patterns and RSSI signal patterns.

[0041] In one embodiment, the tracking device 120 may include a display panel configured to display a user interface to facilitate the user inputting physical attributes of the person requiring care (e.g., a young child, an elderly person, or a patient). The cloud server 110 is configured to construct a personal profile containing the input physical attributes or general data collected from social media platforms.

[0042] In another embodiment, the tracking device 120 may include a display panel configured to display a user interface to assist a user in entering the inventory quantity of one or more consumer goods related to an event type. The cloud server 110 may be further configured to calculate the inventory quantity of one or more consumer goods based on the frequency of occurrence associated with the event type stored in the cloud server 110 and the entered inventory quantity. Preferably, the user interface may be further configured to assist a user in entering the brand name, model, and other shopping information of one or more consumer goods related to the event type, as well as a threshold inventory quantity. The cloud server 110 may be further configured to automatically order one or more consumer goods related to the event type online when the recorded inventory quantity of the consumer goods related to the event type is lower than the entered threshold inventory quantity.

[0043] In one embodiment, cloud server 110 may be further configured to compare an individual’s statistical event data with global statistical event data; and to send the comparison results and corresponding recommendations to the user’s mobile device, the mobile device of another authorized user, or a tracking device 120 with a display panel, for review and preparatory action when the individual’s statistical event data deviates significantly from the standard of global statistical event data.

[0044] Global statistical event data can be derived from other event tracking systems used by other users and connected to the cloud server. When personal event data is sent to the cloud server 110 via communication module 123, the cloud server 110 can collect all such event data from different users and prepare anonymized statistical event data for individual comparison. Global statistical event data can be categorized based on the physical attributes of the personal profiles provided by different users. When comparing personal event data with statistical event data in a category rather than overall statistics, individual comparisons can focus on specific behaviors or characteristics of individuals within a particular category of interest. Finally, global statistical event data can be considered when predicting an individual's upcoming events. This is particularly useful when a user is just starting out with the event tracking system and has only generated a few event data points to create reasonable statistical event data for prediction. The cloud server 110 can be further configured to use global statistical event data as a primary data source for training deep learning models and using the trained deep learning models to predict the event type and timing of the next event.

[0045] In one embodiment, the tracking device 120 may be a monitoring unit of an infant monitor, wherein the communication module 123 is a WiFi module connected to a router; the receiving unit 121 is a capacitive touch slider on the monitoring unit; the presentation unit 122 is a display panel of the tracking device 120; and the event type is an infant care event.

[0046] In one embodiment, the tracking device 120 may be the phone case of a user's mobile device, wherein the communication module 123 is a Bluetooth Low Energy (BLE) module connected to the user's mobile device; the receiving unit 121 is a physical button on the phone case; and the event type is a medical event.

[0047] In one embodiment, the tracking device 120 may be a desktop clock, wherein the communication module 123 is a GSM module connected to a cellular network; the receiving unit 121 is a physical button on the desktop clock; the presentation unit 122 is a mobile application running on the mobile device of an elderly family member; and the event type is elderly care event.

[0048] According to the present invention, an intelligent event tracking method 400 is provided. The intelligent event tracking method may include: receiving event data through a receiving unit; transmitting the event data from the receiving unit to a cloud server via a communication module; determining the event type and occurrence time of the current event from the acquired event data through the cloud server; retrieving statistical data related to the occurrence time and frequency associated with the determined event type and / or at least one related event type from an event database through the cloud server; predicting the event type and occurrence time of the next event through the cloud server based on the determined event type and occurrence time of the current event and the retrieved statistical data related to the occurrence time and frequency associated with the determined event type and / or at least one related event type; updating the event database through the cloud server with the predicted event type and occurrence time of the next event and the event data acquired from the receiving unit; transmitting the predicted event type and occurrence time of the next event to a presentation unit via the communication module; and presenting the predicted event type to a user at the predicted occurrence time of the next event through the presentation unit.

[0049] Optionally, the intelligent event tracking method may further include training a deep learning model with an event database and using the trained deep learning model to predict the event type and timing of the next event.

[0050] Preferably, the intelligent event tracking method may further include converting the predicted event type and occurrence time of the next event into an audio signal; and converting the received audio signal into sound.

[0051] Preferably, the intelligent event tracking method may further include converting the predicted event type and occurrence time of the next event into a display signal; and converting the received display signal into an image or video.

[0052] Optionally, the intelligent event tracking method may further include determining the duration of the current event from the received event data; updating the event database with the determined duration of the current event; and adjusting the predicted occurrence time and predicted event type of the next event based on the determined duration of the current event.

[0053] Optionally, the intelligent event tracking method may further include: receiving one or more audio recordings; determining the event type and occurrence time of the current event from the one or more received audio recordings; and determining one or more event attributes of the current event from the one or more received audio recordings.

[0054] Optionally, the intelligent event tracking method may further include: capturing one or more videos; determining the event type and occurrence time of the current event from the one or more captured videos; and determining one or more event attributes of the current event from the one or more captured videos.

[0055] Optionally, the intelligent event tracking method may further include detecting the relative signal strength indicator (RSSI) signal pattern of the current event; and determining the event type and occurrence time of the current event from the detected RSSI signal pattern by matching the detected RSSI signal pattern with historical RSSI signal patterns.

[0056] Optionally, the intelligent event tracking method may further include sensing the motion pattern of the current event; and determining the event type and occurrence time of the current event from the sensed motion pattern by matching the sensed motion pattern with historical motion patterns.

[0057] Optionally, the intelligent event tracking method may further include detecting the relative signal strength indicator (RSSI) signal pattern of the current event; sensing the motion pattern of the current event; and identifying the individual to whom the current event belongs by matching the detected current motion pattern and RSSI signal pattern with historical motion patterns and RSSI signal patterns.

[0058] Optionally, the intelligent event tracking method may further include constructing a personal profile containing physical attributes of the individual entered by the user or general data collected from social media platforms.

[0059] Optionally, the intelligent event tracking method may further include calculating the inventory of one or more consumer products based on the frequency of occurrence associated with event types stored in a cloud server and the input inventory of one or more consumer products associated with the event type input by the user.

[0060] Optionally, the intelligent event tracking method may further include ordering one or more consumer products of the relevant event type online when the recorded inventory level of the consumer product of the relevant event type is lower than a threshold inventory level entered by the user.

[0061] Optionally, the intelligent event tracking method may further include comparing an individual's statistical event data with global statistical event data; and sending the comparison results and corresponding recommendations to the user's mobile device, the mobile device of another authorized user, or a tracking device with a display panel for review and preparatory action when the individual's statistical event data deviates significantly from the standard of global statistical event data.

[0062] The embodiments disclosed herein may be implemented using computing devices, computer processors, or electronic circuit systems, including but not limited to application-specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), and other programmable logic devices configured or programmed according to the teachings of this disclosure. Computer instructions or software code that run in a computing device, computer processor, or programmable logic device can be readily prepared by those skilled in the art based on the teachings of this disclosure.

[0063] The embodiments include computer storage media in which computer instructions or software code are stored, which can be used to program a computer or microprocessor to perform any of the processes of the present invention. The storage media may include, but is not limited to, floppy disks, optical disks, Blu-ray discs, DVDs, CD-ROMs and magneto-optical disks, ROMs, RAMs, flash memory devices, or any type of media or device suitable for storing instructions, code, and / or data.

[0064] The foregoing description of the invention has been provided for purposes of illustration and description. It is not intended to be exhaustive or to limit the invention to the precise forms disclosed. Many modifications and variations will be apparent to those skilled in the art.

Claims

1. A system for tracking care-related events, characterized in that, include: Cloud servers, which include event databases; Tracking device, comprising: At least one receiving unit for receiving event data; At least one presentation unit for presenting event data; and A communication module, which is connected to the receiving unit and the presentation unit and is configured to receive event data from the cloud server and transmit event data to the cloud server; The cloud server is configured for Event data is obtained from the receiving unit via the communication module; The event type and occurrence time of the current event are determined from the acquired event data; Retrieve statistical event data from the event database related to the occurrence time, frequency, and distribution of event attributes associated with the determined event type and / or at least one related event type; The event type and occurrence time of the next event are predicted based on the determined event type and occurrence time of the current event, and the retrieved statistical event data related to the occurrence time, frequency, and distribution of event attributes associated with the determined event type and / or at least one related event type. The event database is updated with the predicted event type and occurrence time of the next event and the event data obtained from the receiving unit; and The predicted event type and occurrence time of the next event are transmitted to the presentation unit via the communication module; and The presentation unit is configured to present the predicted event type at the predicted occurrence time of the next event; The receiving unit includes one or more physical buttons; and The cloud server is further configured as follows: When one of the physical buttons is triggered, the event type and the time of occurrence of the current event are determined; The duration of the current event is determined from the acquired event data; Update the event database with the determined duration of the current event; and The predicted occurrence time and predicted event type of the next event are adjusted based on the determined duration of the current event; and The duration of the current event is the time difference between the event start time when one of the physical buttons is triggered to input the event type and the event end time when the physical button that was triggered at the event start time is triggered again.

2. The system according to claim 1, characterized in that, The cloud server is further configured to: Train a deep learning model using the event database; and The trained deep learning model is used to predict the event type and timing of the next event.

3. The system according to claim 1, characterized in that, The cloud server is further configured to convert the predicted event type and occurrence time of the next event into an audio signal; and The presentation unit includes a speaker configured to receive the audio signal from the cloud server via the communication module and convert the received audio signal into sound.

4. The system according to claim 1, characterized in that, The cloud server is further configured to convert the predicted event type and occurrence time of the next event into a display signal; and The presentation unit includes a display configured to receive the display signal from the cloud server via the communication module and convert the received display signal into an image or video.

5. The system according to claim 1, characterized in that, The cloud server is further configured to convert historical statistical event data into display signals; and The presentation unit includes a display configured to receive the display signal from the cloud server via the communication module and convert the received display signal into a graph or motion image.

6. The system according to claim 1, characterized in that, The tracking device further includes a display panel; and wherein The one or more physical buttons are located near the display panel; and The display panel is configured to display one or more reconfigurable information markers corresponding to the one or more physical buttons at locations adjacent to the one or more physical buttons.

7. The system according to claim 6, characterized in that, The display panel is further configured to display a user interface for reconfiguring the trigger modes of the one or more physical buttons.

8. The system according to claim 1, characterized in that, The receiving unit further includes a touch display, which includes one or more touch screen buttons; and The cloud server is further configured to determine the event type and the time of occurrence of the current event when one of the touchscreen buttons is triggered.

9. The system according to claim 8, characterized in that... The cloud server is further configured to: The duration of the current event is determined from the acquired event data; Update the event database with the determined duration of the current event; and The predicted occurrence time and predicted event type of the next event are adjusted based on the determined duration of the current event; and The duration of the current event is the time difference between the event start time when one of the touchscreen buttons is triggered for input of the event type and the event end time when the touchscreen button that was triggered at the event start time is triggered again.

10. The system according to claim 1, characterized in that, The receiving unit further includes a microphone for receiving one or more sound recordings; and The cloud server is further configured to determine the event type and the time of occurrence of the current event from the one or more received audio recordings.

11. The system according to claim 10, characterized in that, The cloud server is further configured for use Determine one or more event attributes of the current event from the one or more received audio recordings; and The predicted occurrence time and predicted event type of the next event are adjusted based on the determined event attributes of the current event.

12. The system according to claim 1, characterized in that, The receiving unit also includes a camera for capturing one or more videos; and The cloud server is further configured to determine the event type and the time of occurrence of the current event from the one or more captured videos.

13. The system according to claim 12, characterized in that, The cloud server is further configured for use Determine one or more event attributes of the current event from the one or more captured videos; and The predicted occurrence time and predicted event type of the next event are adjusted based on the determined event attributes of the current event.

14. The system according to claim 1, characterized in that: The communication module is further configured to detect the relative signal strength indicator (RSSI) signal pattern of the current event; and The cloud server is further configured to determine the event type and occurrence time of the current event from the detected RSSI signal pattern by matching the detected RSSI signal pattern with historical RSSI signal patterns.

15. The system according to claim 1, characterized in that: The receiving unit further includes a motion sensor for sensing the motion pattern of the current event; and The cloud server is further configured to determine the event type and the time of occurrence of the current event from the sensed motion pattern.

16. The system according to claim 1, characterized in that: The communication module is further configured to detect the relative signal strength indicator (RSSI) signal pattern of the current event; The receiving unit further includes a motion sensor for sensing the motion pattern of the current event; and The cloud server is further configured to identify the individual to whom the current event belongs by matching the detected current motion pattern and RSSI signal pattern with historical motion patterns and RSSI signal patterns.

17. The system according to claim 1, characterized in that, It further includes a display panel for displaying the user interface; and wherein: The user interface is configured to facilitate the user's input of personal physical attributes; and The cloud server is configured to construct a personal profile containing the input physical attributes or general data collected from social media platforms.

18. The system according to claim 17, characterized in that: The user interface is further configured to facilitate the user's input of the inventory levels of one or more consumer goods related to the event type; and The cloud server is further configured to calculate the inventory of the one or more consumer products based on the frequency of occurrence associated with the event type stored in the cloud server and the input inventory quantity.

19. The system according to claim 18, characterized in that... The user interface is further configured to facilitate the user's input of the brand name, model, and other shopping information of the one or more consumer products related to the event type, as well as the threshold inventory level; and The cloud server is further configured to automatically order one or more consumer products of the event type online when the calculated inventory level of the consumer products of the event type is lower than the input threshold inventory level.

20. The system according to claim 1, characterized in that... The cloud server is further configured to: Compare the individual's statistical event data with global statistical event data; and The comparison results and corresponding recommendations are sent to the user's mobile device, the mobile devices of other authorized users, or tracking devices with display panels for review and preparatory action when an individual's statistical event data deviates significantly from the standard of the global statistical event data.

21. A method for tracking care-related events, characterized in that, include: Event data is obtained through the physical button on the receiving unit; The acquired event data is transmitted from the receiving unit to the cloud server via the communication module; The event type and occurrence time of the current event are determined from the acquired event data using the cloud server. The cloud server retrieves statistical data related to the occurrence time and frequency of the determined event type and / or at least one related event type associated with the current event from the event database. The cloud server predicts the event type and occurrence time of the next event based on the determined event type and occurrence time of the current event, as well as the retrieved statistical data related to the occurrence time and frequency of the determined event type and / or at least one related event type. The cloud server determines the event type and occurrence time of the current event when one of the physical buttons is triggered. The duration of the current event is determined by the cloud server from the acquired event data; The cloud server updates the event database with the determined duration of the current event; as well as The cloud server adjusts the predicted occurrence time and predicted event type of the next event based on the determined duration of the current event; The cloud server updates the event database with the predicted event type and occurrence time of the next event and the event data obtained from the receiving unit. The predicted event type and occurrence time of the next event are transmitted to the presentation unit via the communication module; as well as At the predicted time of occurrence of the next event, the predicted event type is presented to the user through the presentation unit; and The duration of the current event is the time difference between the event start time when one of the physical buttons is triggered to input the event type and the event end time when the physical button that was triggered at the event start time is triggered again.

22. The method according to claim 21, characterized in that, Further includes: Train a deep learning model using the event database; as well as The trained deep learning model is used to predict the event type and timing of the next event.

23. The method according to claim 21, characterized in that, Further includes: The predicted event type and occurrence time of the next event are converted into an audio signal; as well as The converted audio signal is then converted into sound.

24. The method according to claim 21, characterized in that, Further includes: The predicted event type and occurrence time of the next event are converted into a display signal; as well as The converted display signal is then converted into an image or video.

25. The method according to claim 21, characterized in that, Further includes: Receive one or more audio records; The event type and the time of occurrence of the current event are determined from the one or more received audio recordings; Determine one or more event attributes of the current event from the one or more received sound records; as well as The predicted occurrence time and predicted event type of the next event are adjusted based on the determined event attributes of the current event.

26. The method according to claim 21, characterized in that, Further includes: Capture one or more videos; The event type and the time of occurrence of the current event are determined from the one or more captured videos; Determine one or more event attributes of the current event from the one or more captured videos; as well as The predicted occurrence time and predicted event type of the next event are adjusted based on the determined event attributes of the current event.

27. The method according to claim 21, characterized in that, Further includes: Detect the relative signal strength indicator (RSSI) signal pattern of the current event; The event type and occurrence time of the current event are determined from the detected RSSI signal pattern by matching the detected RSSI signal pattern with historical RSSI signal patterns.

28. The method according to claim 21, characterized in that, Further includes: Sensing the motion pattern of the current event; The event type and the time of occurrence of the current event are determined from the sensed motion pattern.

29. The method according to claim 21, characterized in that, Further includes: Detect the relative signal strength indicator (RSSI) signal pattern of the current event; Sensing the motion pattern of the current event; as well as The individual to whom the current event belongs is identified by matching the detected current motion pattern and RSSI signal pattern with historical motion patterns and RSSI signal patterns.

30. The method according to claim 21, characterized in that, Further, it includes constructing a personal profile containing physical attributes of the individual entered by the user or general data collected from social media platforms.

31. The method according to claim 21, characterized in that, It further includes calculating the inventory of the one or more consumer products based on the frequency of occurrence associated with the event type stored in the cloud server and the input inventory of one or more consumer products related to the event type entered by the user.

32. The method according to claim 21, characterized in that, This further includes online ordering of one or more consumer products related to the event type when the recorded inventory level of the consumer products related to the event type is lower than a threshold inventory level input by the user.

33. The method according to claim 21, characterized in that, Further includes: Compare individual statistical event data with global statistical event data; as well as The comparison results and corresponding recommendations are sent to the user's mobile device, the mobile devices of other authorized users, or tracking devices with display panels for review and preparatory action when an individual's statistical event data deviates significantly from the standard of the global statistical event data.

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