User health monitoring method and system based on intelligent ring and multistage early warning

Abstract

The invention discloses a user health monitoring method and system based on an intelligent ring and multi-level early warning. The method comprises the following steps: acquiring a plurality of sensing data of a target user through the intelligent ring; performing preprocessing and feature extraction on each piece of sensing data to obtain corresponding physiological feature parameters; according to the physiological feature parameters and a multi-level early warning judgment rule, the early warning level of the target user is judged; and according to the early warning level, determining early warning information and early warning terminals corresponding to the target user, and sending the early warning information to all the early warning terminals. Therefore, accurate health early warning based on real-time sensing and grading rules can be realized, the timeliness and response efficiency of user health monitoring are improved, and the health risk caused by the fact that abnormity is not found in time is reduced.

Description

Technical Field

[0001] This invention relates to the field of data processing technology, and in particular to a user health monitoring method and system based on a smart ring and multi-level early warning. Background Technology

[0002] With the rapid popularization of wearable devices in the field of health monitoring, users and medical institutions are increasingly emphasizing the use of physiological sensor data for accurate health early warning. A key technical challenge is how to promptly detect abnormalities and improve response efficiency to reduce health risks. Current technologies typically collect single or limited sensor data from users through smart rings, using simple threshold analysis or fixed warning rules to determine health status, and then sending warning information to terminals in a standardized manner to support health management. However, existing solutions lack preprocessing of multiple sensor data, feature extraction, and dynamic application of tiered warning rules, making it difficult to accurately determine warning levels and achieve timely responses. Commonly used static warning strategies cannot adapt to individual physiological differences and complex health scenarios, resulting in insufficient timeliness of health monitoring. This can easily lead to health risks due to undetected abnormalities, limiting the response efficiency and reliability of user health management. Therefore, existing technologies have shortcomings that urgently need to be addressed. Summary of the Invention

[0003] The technical problem to be solved by the present invention is to provide a user health monitoring method and system based on a smart ring and multi-level early warning, which can realize accurate health early warning based on real-time sensing and hierarchical rules, improve the timeliness and response efficiency of user health monitoring, and reduce the health risks caused by the failure to detect abnormalities in time.

[0004] To address the aforementioned technical problems, the first aspect of this invention discloses a user health monitoring method based on a smart ring and multi-level early warning, the method comprising: Acquire multiple sensor data from the target user through a smart ring; Each of the sensor data is preprocessed and feature extracted to obtain the corresponding physiological feature parameters; Based on the physiological characteristic parameters and the multi-level early warning judgment rules, the early warning level of the target user is determined; Based on the warning level, the warning information and warning terminal corresponding to the target user are determined, and the warning information is sent to all the warning terminals.

[0005] As an optional implementation, in a first aspect of the invention, the smart ring includes a ring body, a controller disposed within the ring body, and a plurality of sensors; the ring body is made of biocompatible material and is manufactured using a waterproof and dustproof process; the sensors are PPG sensors, bioimpedance sensors, temperature sensors, or motion sensors.

[0006] As an optional implementation, in the first aspect of the invention, the sensing data is photoplethysmography (PPG) data, skin impedance data, fingertip temperature data, or body activity data.

[0007] As an optional implementation, in the first aspect of the invention, the controller of the smart ring is further provided with computer code to perform the following steps: Based on preset threshold rules and / or duration rules, determine whether the sensing data belongs to an abnormal situation, and obtain the judgment result; When the judgment result is negative, the aggregated sensor data is uploaded to the cloud device according to a preset first time interval. When the determination result is yes, the sensor data is uploaded to the cloud device at a second time interval less than the first time interval; the cloud device is used to execute the user health monitoring method.

[0008] As an optional implementation, in the first aspect of the present invention, the physiological characteristic parameter is an instantaneous heart rate parameter, a blood oxygen saturation parameter, a motion amplitude parameter, or a motion posture angle parameter.

[0009] As an optional implementation, in the first aspect of the present invention, determining the warning level of the target user based on the physiological characteristic parameters and multi-level warning judgment rules includes: Based on a parameter rule judgment model corresponding to multiple preset severity levels, all the physiological characteristic parameters are judged to obtain at least one preset severity level in which the parameter rules are satisfied. The highest severity level among at least one of the preset severity levels is determined as the warning level for the target user.

[0010] As an optional implementation, in the first aspect of the present invention, the preset severity level is a reminder level, a warning level, or an emergency level; the parameter rule judgment model is used to determine whether at least one of the physiological characteristic parameters satisfies the corresponding parameter threshold rule and / or parameter duration rule.

[0011] As an optional implementation, in the first aspect of the present invention, determining the warning information and warning terminal corresponding to the target user based on the warning level includes: From the multiple contact terminals corresponding to the target user, terminals matching the warning level are selected to obtain the warning terminals; An early warning message is generated based on the user parameters corresponding to the target user, the sensor data, and the early warning level.

[0012] A second aspect of this invention discloses a user health monitoring system based on a smart ring and multi-level early warning, the system comprising: The acquisition module is used to acquire multiple sensor data of the target user through the smart ring; The processing module is used to preprocess and extract features from each of the sensor data to obtain the corresponding physiological feature parameters; The judgment module is used to determine the warning level of the target user based on the physiological characteristic parameters and the multi-level warning judgment rules; The early warning module is used to determine the early warning information and early warning terminal corresponding to the target user according to the early warning level, and to send the early warning information to all the early warning terminals.

[0013] As an optional implementation, in a second aspect of the invention, the smart ring includes a ring body, a controller disposed within the ring body, and a plurality of sensors; the ring body is made of biocompatible material and is manufactured using a waterproof and dustproof process; the sensors are PPG sensors, bioimpedance sensors, temperature sensors, or motion sensors.

[0014] As an optional implementation, in a second aspect of the invention, the sensing data is photoplethysmography (PPG) data, skin impedance data, fingertip temperature data, or body activity data.

[0015] As an optional implementation, in a second aspect of the invention, the controller of the smart ring further includes computer code to perform the following steps: Based on preset threshold rules and / or duration rules, determine whether the sensing data belongs to an abnormal situation, and obtain the judgment result; When the judgment result is negative, the aggregated sensor data is uploaded to the cloud device according to a preset first time interval. When the determination result is yes, the sensor data is uploaded to the cloud device at a second time interval less than the first time interval; the cloud device is used to execute the user health monitoring method.

[0016] As an optional implementation, in the second aspect of the present invention, the physiological characteristic parameter is an instantaneous heart rate parameter, a blood oxygen saturation parameter, an exercise amplitude parameter, or an exercise posture angle parameter.

[0017] As an optional implementation, in a second aspect of the invention, the specific method by which the judgment module determines the warning level of the target user based on the physiological characteristic parameters and multi-level warning judgment rules includes: Based on a parameter rule judgment model corresponding to multiple preset severity levels, all the physiological characteristic parameters are judged to obtain at least one preset severity level in which the parameter rules are satisfied. The highest severity level among at least one of the preset severity levels is determined as the warning level for the target user.

[0018] As an optional implementation, in the second aspect of the present invention, the preset severity level is a reminder level, a warning level, or an emergency level; the parameter rule judgment model is used to determine whether at least one of the physiological characteristic parameters satisfies the corresponding parameter threshold rule and / or parameter duration rule.

[0019] As an optional implementation, in a second aspect of the invention, the method by which the early warning module determines the specific early warning information and early warning terminal corresponding to the target user based on the early warning level includes: From the multiple contact terminals corresponding to the target user, terminals matching the warning level are selected to obtain the warning terminals; An early warning message is generated based on the user parameters corresponding to the target user, the sensor data, and the early warning level.

[0020] A third aspect of this invention discloses another user health monitoring system based on a smart ring and multi-level early warning, the system comprising: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute some or all of the steps in the user health monitoring method based on smart rings and multi-level early warning disclosed in the first aspect of the present invention.

[0021] The fourth aspect of the present invention discloses a computer storage medium storing computer instructions, which, when invoked, are used to execute some or all of the steps in the user health monitoring method based on a smart ring and multi-level early warning disclosed in the first aspect of the present invention.

[0022] Compared with the prior art, the embodiments of the present invention have the following beneficial effects: This invention acquires multiple sensor data from a target user through a smart ring, performs preprocessing and feature extraction to obtain physiological characteristic parameters, determines the warning level based on multi-level warning judgment rules, and generates warning information to send to the matching terminal. This enables accurate health warnings based on real-time sensing and hierarchical rules, improves the timeliness and response efficiency of user health monitoring, and reduces health risks caused by the failure to detect abnormalities in a timely manner. Attached Figure Description

[0023] To more clearly illustrate the technical solutions in the embodiments of the present invention, the accompanying drawings used in the description of the embodiments will be briefly introduced below. Obviously, the accompanying drawings described below are only some embodiments of the present invention. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0024] Figure 1 This is a flowchart illustrating a user health monitoring method based on a smart ring and multi-level early warning system disclosed in an embodiment of the present invention.

[0025] Figure 2 This is a schematic diagram of a user health monitoring system based on a smart ring and multi-level early warning, as disclosed in an embodiment of the present invention.

[0026] Figure 3 This is a schematic diagram of another user health monitoring system based on a smart ring and multi-level early warning disclosed in an embodiment of the present invention. Detailed Implementation

[0027] To enable those skilled in the art to better understand the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0028] The terms "first," "second," etc., used in the specification, claims, and accompanying drawings of this invention are used to distinguish different objects, not to describe a specific order. Furthermore, the terms "comprising" and "having," and any variations thereof, are intended to cover non-exclusive inclusion. For example, a process, method, apparatus, product, or device that includes a series of steps or units is not limited to the listed steps or units, but may optionally include steps or units not listed, or may optionally include other steps or units inherent to these processes, methods, products, or devices.

[0029] In this document, the term "embodiment" means that a particular feature, structure, or characteristic described in connection with an embodiment may be included in at least one embodiment of the invention. The appearance of this phrase in various places throughout the specification does not necessarily refer to the same embodiment, nor is it a separate or alternative embodiment mutually exclusive with other embodiments. It will be explicitly and implicitly understood by those skilled in the art that the embodiments described herein can be combined with other embodiments.

[0030] This invention discloses a user health monitoring method and system based on a smart ring and multi-level early warning. The method uses a smart ring to acquire multiple sensor data from a target user, performs preprocessing and feature extraction to obtain physiological characteristic parameters, determines the early warning level based on multi-level early warning judgment rules, and generates early warning information which is sent to a matching terminal. This enables accurate health early warning based on real-time sensing and hierarchical rules, improving the timeliness and response efficiency of user health monitoring and reducing health risks caused by undetected anomalies. Detailed explanations follow.

[0031] Example 1 Please see Figure 1 , Figure 1 This is a flowchart illustrating a user health monitoring method based on a smart ring and multi-level early warning system, as disclosed in an embodiment of the present invention. Figure 1 The described user health monitoring method based on smart rings and multi-level early warning can be applied to data processing systems / data processing devices / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 1 As shown, this user health monitoring method based on a smart ring and multi-level alerts may include the following operations: 101. Acquire multiple sensor data of the target user through a smart ring.

[0032] Optionally, the smart ring includes a ring body and a controller and multiple sensors set within the ring body.

[0033] Optionally, the ring body is made of biocompatible materials and based on a waterproof and dustproof process.

[0034] Optionally, the sensor can be a PPG sensor, a bioimpedance sensor, a temperature sensor, or a motion sensor.

[0035] Optionally, the sensing data can be photoplethysmography (PPG) data, skin impedance data, fingertip temperature data, or physical activity data.

[0036] 102. Preprocess and extract features from each sensor data to obtain the corresponding physiological feature parameters.

[0037] Optional physiological characteristic parameters include instantaneous heart rate, blood oxygen saturation, exercise amplitude, or exercise posture angle.

[0038] 103. Determine the warning level for the target user based on physiological characteristic parameters and multi-level warning judgment rules.

[0039] 104. Based on the warning level, determine the warning information and warning terminal corresponding to the target user, and send the warning information to all warning terminals.

[0040] As can be seen, the above-mentioned embodiments of the invention acquire multiple sensor data of the target user through a smart ring, perform preprocessing and feature extraction to obtain physiological feature parameters, determine the warning level based on multi-level warning judgment rules, and generate warning information to send to the matching terminal. This enables accurate health warnings based on real-time sensing and hierarchical rules, improves the timeliness and response efficiency of user health monitoring, and reduces health risks caused by failure to detect abnormalities in a timely manner.

[0041] As an optional embodiment, in the above steps, the controller of the smart ring also contains computer code to perform the following steps: Based on preset threshold rules and / or duration rules, determine whether the sensor data belongs to an abnormal situation and obtain the judgment result; If the result is negative, the aggregated sensor data is uploaded to the cloud device according to the preset first time interval. If the judgment result is yes, the sensor data is uploaded to the cloud device at a second time interval that is less than the first time interval.

[0042] Specifically, cloud-based devices are used to perform user health monitoring methods.

[0043] In one specific implementation, a smart ring is provided to implement the solution in the embodiments of the present invention. Specifically, the smart ring includes a ring body and a built-in hardware module. The ring body is made of biocompatible materials (such as titanium alloy, ceramic, or medical-grade resin) and has a sealed cavity inside, providing waterproof and dustproof functions. Specifically, the hardware module includes: Main control chip (MCU): As the core processing unit, it is responsible for controlling all sensors, performing preliminary data processing, power consumption management, and communication scheduling.

[0044] Wireless communication module: Uses Bluetooth Low Energy (BLE) for data interaction with smartphone apps, gateway devices, or cloud servers.

[0045] Power management module and micro battery: Built-in rechargeable micro battery (such as steel-cased button cell battery) and wireless charging coil to support wireless charging and ensure long battery life.

[0046] Miniature vibration motors: used to provide tactile feedback to users, such as warnings of high heart rate and reminders of prolonged sitting.

[0047] Indicator LEDs: Used to display the ring's working status (such as charging, connecting, low battery alarm).

[0048] A multimodal sensor array, including: Photoelectric sensor (PPG): Used to collect photoplethysmography (PPG) signals to measure heart rate, blood oxygen saturation (SpO2), and heart rate variability (HRV).

[0049] Bioimpedance Sensor (BIA): Used to measure skin electrical impedance, assist in monitoring respiratory rate, skin conductance response (pressure level), and can be extended to estimate fluid balance.

[0050] Three-axis accelerometer & gyroscope: used to monitor the user's activity level, steps, sleep stages (body motion recording), and is crucial for detecting abnormal postures (such as sudden falls or immobility).

[0051] Temperature sensor: Used to continuously monitor the surface temperature of the fingertips.

[0052] As can be seen, through the above optional embodiments, the smart ring controller judges sensor data anomalies based on threshold rules and duration rules and dynamically adjusts the upload interval to the cloud device. Thus, on the basis of accurate health early warning, the real-time performance and energy efficiency of data transmission are improved through local anomaly detection and adaptive upload optimization, providing high-quality and timely data for cloud-based multi-level early warning judgment and reducing the risk of early warning delays caused by data latency.

[0053] As an optional embodiment, the step of determining the warning level of the target user based on physiological characteristic parameters and multi-level warning judgment rules includes: Based on the parameter rule judgment model corresponding to multiple preset severity levels, all physiological characteristic parameters are judged to obtain at least one preset severity level in which the parameter rules are satisfied. The highest severity level among at least one preset severity level is determined as the warning level for the target user.

[0054] Optionally, the default severity level can be set to alert, warning, or emergency.

[0055] Optionally, the parameter rule judgment model is used to determine whether at least one physiological feature parameter satisfies the corresponding parameter threshold rule and / or parameter duration rule.

[0056] Specifically, the early warning level system and overall process can be as follows: Optional warning levels include: L = 1: Alert Level Warning L = 2: Warning level alert L = 3: Emergency Level Warning Optionally, based on real-time monitoring data of multiple physiological parameters, the following four types of rules can be executed respectively: Single-parameter threshold rule; Rules governing the duration of abnormal parameters; Rules governing parameter change trends; Multi-indicator integrated scoring rules.

[0057] The system combines user profiles with adaptive threshold calculations, and finally determines the warning level based on the comprehensive judgment results.

[0058] Optionally, the single-parameter threshold determination rule is as follows: set up: Physiological parameters are recorded as follows: ; The upper threshold values ​​for the three warning levels are as follows: ,in ; The lower threshold values ​​for the three warning levels are as follows: ,in ; Define a single-parameter threshold trigger function: ; Specifically, this rule is used to identify transient anomalies.

[0059] Optionally, the rule for determining the duration of parameter anomalies is as follows: To avoid false alarms caused by transient noise, the duration of the anomaly is constrained: set up: Abnormal start time: ; Current time: ; Duration: ; Minimum duration threshold for the corresponding level: ; Define a persistent anomaly detection function: ; Example: Heart rate (>130 bpm) for 15 seconds → can be classified as a warning level; Blood oxygen saturation (<85%) for 5 seconds → can be classified as an emergency. Optionally, the parameter trend determination rule is as follows: To identify rapidly deteriorating physiological states, a rate of change (trend) determination mechanism is introduced: Let the sampling period be (For example, 1 second), define the first-order difference: ; To reduce noise interference, a window size of [size missing] is introduced. The trend of the moving average: ; Preset thresholds for different levels of rate of change: ; Trend determination rules: ; Example: Blood oxygen levels drop by ≥5% within 20 seconds → at least warning level; A rapid increase in heart rate in a short period of time → Emergency level.

[0060] Optionally, the process for multi-indicator fusion scoring (comprehensive health risk score) is as follows: To comprehensively assess multiple physiological parameters, a weighted health risk scoring model is proposed: 1. Standardization of indicators Let the mean and standard deviation of a certain indicator be: Mean: ; Standard deviation: ; Define the degree of anomaly in the indicator: ; and normalized to Interval: ; 2. Risk weight calculation: Weight satisfy: ; Weights can be obtained through: Medical experience settings; Or it can be calculated based on the correlation of historical data; Or it may be dynamically updated based on online learning.

[0061] 3. Comprehensive Risk Score: ; 4. Scoring thresholds corresponding to warning levels: Set a scoring threshold: Alert Level: ; Warning level: ; Emergency Level: ; satisfy: ; Define the scoring result: ; Optionally, the adaptive threshold adjustment mechanism based on user profiles is as follows: To accommodate the different physical differences among users, the system uses an exponentially weighted moving average (EMA) model to dynamically update the thresholds. set up: Historical average: ; Historical standard deviation: ; Update method: ; in It is used to smooth out long-term health trends.

[0062] The adaptive threshold is: ; This method can automatically adapt to the different physical conditions of different users, thus improving the accuracy of early warnings.

[0063] Optionally, the final warning level (anti-shake processing) is determined as follows: The system integrates single indicator thresholds, durations, trends, and fusion scoring results: ; The final level for all indicators is: ; And jitter suppression is performed in conjunction with the level of the previous time step: ; in This is the time decay factor (used to avoid frequent skipping of levels).

[0064] when: ; An emergency-level warning event was immediately triggered.

[0065] Specifically, the technical advantages of this solution are: Furthermore, by combining thresholds, duration, trends, and scores into a multi-layered assessment, the early warning system becomes more accurate.

[0066] The adaptive threshold can be adjusted based on long-term user data to achieve personalized monitoring.

[0067] Trend rules can identify rapidly deteriorating situations in advance, improving early warning capabilities.

[0068] The jitter-resistant decision model avoids false alarms caused by instantaneous fluctuations.

[0069] Multi-indicator fusion models improve the stability and robustness of early warning systems.

[0070] As can be seen, through the above optional embodiments, the physiological characteristic parameters are judged by multiple preset severity level parameter rule judgment models and the highest severity level is selected as the warning level. Thus, on the basis of accurate warning level determination, the comprehensiveness and accuracy of level assessment are improved by parallel judgment of multiple models, providing a reliable level basis for warning information generation and terminal push, and reducing the risk of level misjudgment caused by a single rule.

[0071] As an optional embodiment, the step above, determining the warning information and warning terminal corresponding to the target user based on the warning level, includes: From the multiple contact terminals corresponding to the target user, terminals that match the warning level are selected to obtain the warning terminals; The warning information is generated based on the user parameters, sensor data, and warning level corresponding to the target user.

[0072] As can be seen, through the above optional embodiments, by selecting terminals that match the warning level from the target user's contact terminals and generating warning information based on user parameters, sensor data and warning level, the pertinence and practicality of warning push are improved on the basis of accurate health warning, through terminal matching and personalized information generation, providing efficient intervention support for users and relevant parties, and reducing the risk of ineffective warnings due to improper push.

[0073] In one specific implementation, a smart ring system for carrying out the solutions in the embodiments of the present invention is implemented, the system comprising: Smart ring: As mentioned above, it is responsible for data collection and uploading.

[0074] User terminal APP: Installed on a smartphone, it is used to receive ring data, perform preliminary analysis and visualization, and serve as a node for receiving and forwarding first-level early warning notifications.

[0075] Cloud server: Receives aggregated data from the app or gateway, and utilizes more powerful computing capabilities and algorithmic models (such as machine learning models) for in-depth analysis, trend prediction, and user profile building. Stores all historical data.

[0076] Early Warning Service Center: (Optional but important) It can be integrated with third-party platforms or emergency contact networks to activate higher-level alerts when an emergency is detected in the cloud, such as making emergency calls or sending text messages / emails to preset emergency contacts.

[0077] The system implements methods for user data processing and security alerts, and the steps may include: S1: Data Acquisition: The multimodal sensor array on the ring continuously collects the user's raw physiological and motion signals.

[0078] S2: Local preprocessing and feature extraction: The main control chip filters and denoises the raw data and extracts key feature values ​​(such as instantaneous heart rate, SpO2 value, motion amplitude, and attitude angle).

[0079] S3: Anomaly Detection and Data Transmission: The chip has a built-in simple threshold rule (e.g., heart rate consistently >150 bpm or <40 bpm). If triggered, high-frequency data upload is immediately initiated. Under normal circumstances, aggregated feature data is uploaded to the APP / cloud at regular intervals.

[0080] S4: Cloud-based in-depth analysis and multi-level early warning judgment: Level 1 (Reminder Level): Triggers ring vibration or APP push notification when a single indicator shows a slight abnormality (such as low battery or prolonged sitting).

[0081] Level 2 (Warning Level): For multiple abnormal indicators or a single severely abnormal indicator (such as persistent abnormal heart rate + detected fall and stillness), the cloud algorithm will determine the cause and send a warning message to the user's APP and the APPs of all emergency contacts.

[0082] Level 3 (Emergency): When the risk is determined to be extremely high (such as cardiac arrest - no pulse signal + no movement response) and the user does not respond to confirm, the cloud-based early warning center will automatically call the nearest emergency center and provide the user's location information, while notifying all emergency contacts.

[0083] S5: Feedback and Learning: The system records every warning event and user feedback, which is used to optimize and personalize the thresholds and rules of the algorithm model.

[0084] Specifically, taking an elderly user living alone as an example, the working process of this embodiment of the invention will be explained as follows: 1. Users wear the smart ring without removing it during daily activities and sleep. The ring continuously monitors their vital signs.

[0085] 2. At night, the ring's accelerometer detected the violent impact of the user rolling off the bed, and subsequent posture data showed that the user maintained an unnatural lying position for a long time without significant movement.

[0086] 3. The ring's main control chip initially determined that the incident was a "possible fall and unconsciousness," and immediately uploaded the current high-frequency data to the mobile app on the bedside table via Bluetooth.

[0087] 4. The app forwards the data to the cloud server. The cloud algorithm makes a comprehensive judgment: the heart rate data suddenly rises and then weakens, the respiratory rate is abnormal, the posture data confirms a fall, and the user does not respond to the confirmation inquiries (ring + vibration) sent by the app.

[0088] 5. If the event is determined to be an emergency (Level 3) by the cloud, the emergency response procedure will be activated immediately: a. Send an alert to the early warning service center, including the user ID and real-time GPS location (from the mobile phone).

[0089] b. The early warning service center automatically calls the 120 emergency medical center and reports using voice synthesis technology: "A user with ID XXX may have fallen and lost consciousness. The location is XXX. Please dispatch the police immediately." c. Simultaneously, the cloud sends a red alert message to the mobile apps and SMS messages of the three pre-set emergency contacts: "Warning! Your family member [user name] may have suffered a serious fall. 120 has been called. Latest location: [map link]". 6. The entire process was completed within tens of seconds, gaining crucial time for the rescue.

[0090] In summary, the solutions disclosed in the embodiments of the present invention have the following advantages: 1. Seamless continuous monitoring: The ring is small and comfortable to wear, enabling true 24 / 7 uninterrupted monitoring and capturing data from nighttime or sudden abnormalities.

[0091] 2. Comprehensive and accurate monitoring: Employing multimodal sensor fusion technology, combining PPG, bioimpedance, and motion data, and cross-calibrating them, the anti-interference capability and accuracy of heart rate, respiration, blood oxygen, and other measurements are improved.

[0092] 3. Timely and effective early warning: A multi-level, collaborative early warning mechanism has been established, from local to cloud, from users to emergency contacts and professional rescue forces, which greatly shortens the response time from the discovery of anomalies to the implementation of rescue.

[0093] 4. Balancing privacy and convenience: Rings are more private than devices such as watches, making them less likely to attract attention from others, while also avoiding the problems of forgetting to bring your phone or the monitoring being interrupted when the bracelet is charging.

[0094] Example 2 Please see Figure 2 , Figure 2 This is a schematic diagram of a user health monitoring system based on a smart ring and multi-level early warning, as disclosed in an embodiment of the present invention. Figure 2 The described user health monitoring system based on smart rings and multi-level early warning can be applied to data processing systems / data processing devices / data processing servers (wherein, the server includes local processing servers or cloud processing servers). For example... Figure 2 As shown, this user health monitoring system based on a smart ring and multi-level early warning may include: The acquisition module 201 is used to acquire multiple sensor data of the target user through the smart ring.

[0095] The processing module 202 is used to preprocess and extract features from each sensor data to obtain the corresponding physiological feature parameters.

[0096] The judgment module 203 is used to determine the warning level of the target user based on physiological characteristic parameters and multi-level warning judgment rules.

[0097] The early warning module 204 is used to determine the corresponding early warning information and early warning terminal for the target user based on the early warning level, and to send the early warning information to all early warning terminals.

[0098] As can be seen, the above-mentioned embodiments of the invention acquire multiple sensor data of the target user through a smart ring, perform preprocessing and feature extraction to obtain physiological feature parameters, determine the warning level based on multi-level warning judgment rules, and generate warning information to send to the matching terminal. This enables accurate health warnings based on real-time sensing and hierarchical rules, improves the timeliness and response efficiency of user health monitoring, and reduces health risks caused by failure to detect abnormalities in a timely manner.

[0099] As an optional embodiment, the smart ring includes a ring body and a controller and multiple sensors disposed within the ring body; the ring body is made of biocompatible materials and is manufactured using a waterproof and dustproof process; the sensors are PPG sensors, bioimpedance sensors, temperature sensors, or motion sensors.

[0100] As can be seen, the above optional embodiments define the device technology details of the smart ring, enabling the smart ring to have a more comfortable user experience and acquire a variety of sensor data that comprehensively characterize the user's health, thereby assisting in achieving accurate health warnings, improving the timeliness and response efficiency of user health monitoring, and reducing health risks caused by the failure to detect abnormalities in a timely manner.

[0101] As an optional embodiment, the sensing data may be photoplethysmography (PPG) data, skin impedance data, fingertip temperature data, or physical activity data.

[0102] As can be seen, the content of the sensor data is defined through the above optional embodiments to comprehensively characterize the user's health-related sensor features, assist in achieving accurate health early warning, improve the timeliness and response efficiency of user health monitoring, and reduce health risks caused by the failure to detect abnormalities in a timely manner.

[0103] As an optional embodiment, the smart ring's controller also contains computer code to perform the following steps: Based on preset threshold rules and / or duration rules, determine whether the sensor data belongs to an abnormal situation and obtain the judgment result; If the result is negative, the aggregated sensor data is uploaded to the cloud device according to the preset first time interval. If the judgment result is yes, the sensor data is uploaded to the cloud device at a second time interval less than the first time interval; the cloud device is used to execute the user health monitoring method.

[0104] As can be seen, through the above optional embodiments, the smart ring controller judges sensor data anomalies based on threshold rules and duration rules and dynamically adjusts the upload interval to the cloud device. Thus, on the basis of accurate health early warning, the real-time performance and energy efficiency of data transmission are improved through local anomaly detection and adaptive upload optimization, providing high-quality and timely data for cloud-based multi-level early warning judgment and reducing the risk of early warning delays caused by data latency.

[0105] As an optional embodiment, the physiological characteristic parameters are instantaneous heart rate parameters, blood oxygen saturation parameters, exercise amplitude parameters, or exercise posture angle parameters.

[0106] As can be seen, the content of physiological characteristic parameters is defined through the above optional embodiments to accurately characterize the user's health relevance, assist in achieving accurate health early warning, improve the timeliness and response efficiency of user health monitoring, and reduce health risks caused by the failure to detect abnormalities in a timely manner.

[0107] As an optional embodiment, the specific method by which the judgment module determines the warning level of the target user based on physiological characteristic parameters and multi-level warning judgment rules includes: Based on the parameter rule judgment model corresponding to multiple preset severity levels, all physiological characteristic parameters are judged to obtain at least one preset severity level in which the parameter rules are satisfied. The highest severity level among at least one preset severity level is determined as the warning level for the target user.

[0108] As can be seen, through the above optional embodiments, the physiological characteristic parameters are judged by multiple preset severity level parameter rule judgment models and the highest severity level is selected as the warning level. Thus, on the basis of accurate warning level determination, the comprehensiveness and accuracy of level assessment are improved by parallel judgment of multiple models, providing a reliable level basis for warning information generation and terminal push, and reducing the risk of level misjudgment caused by a single rule.

[0109] As an optional embodiment, the preset severity level is reminder level, warning level, or emergency level; the parameter rule judgment model is used to determine whether at least one physiological characteristic parameter meets the corresponding parameter threshold rule and / or parameter duration rule.

[0110] As can be seen, the above optional embodiments define the technical details of the preset severity level type and parameter judgment model, so as to accurately judge the severity level of the user's warning, assist in realizing accurate health warning, improve the timeliness and response efficiency of user health monitoring, and reduce the health risks caused by the failure to detect abnormalities in time.

[0111] As an optional embodiment, the early warning module determines the specific method of the early warning information and early warning terminal corresponding to the target user based on the early warning level, including: From the multiple contact terminals corresponding to the target user, terminals that match the warning level are selected to obtain the warning terminals; The warning information is generated based on the user parameters, sensor data, and warning level corresponding to the target user.

[0112] As can be seen, through the above optional embodiments, by selecting terminals that match the warning level from the target user's contact terminals and generating warning information based on user parameters, sensor data and warning level, the pertinence and practicality of warning push are improved on the basis of accurate health warning, through terminal matching and personalized information generation, providing efficient intervention support for users and relevant parties, and reducing the risk of ineffective warnings due to improper push.

[0113] Example 3 Please see Figure 3 , Figure 3 This is yet another user health monitoring system based on a smart ring and multi-level early warning disclosed in the embodiments of the present invention. Figure 3 The described user health monitoring system based on smart rings and multi-level early warning is applied in a data processing system / data processing equipment / data processing server (wherein, the server includes a local processing server or a cloud processing server). For example... Figure 3 As shown, this user health monitoring system based on a smart ring and multi-level early warning may include: Memory 301 storing executable program code; Processor 302 coupled to memory 301; The processor 302 calls the executable program code stored in the memory 301 to execute the steps of the user health monitoring method based on smart ring and multi-level early warning described in Embodiment 1.

[0114] Example 4 This invention discloses a computer read storage medium that stores a computer program for electronic data interchange, wherein the computer program causes a computer to execute the steps of the user health monitoring method based on a smart ring and multi-level early warning described in Embodiment 1.

[0115] Example 5 This invention discloses a computer program product, which includes a non-transitory computer-readable storage medium storing a computer program, and the computer program is operable to cause a computer to perform the steps of the user health monitoring method based on a smart ring and multi-level early warning described in Embodiment 1.

[0116] The foregoing has described specific embodiments of this specification; other embodiments are within the scope of the appended claims. In some cases, the actions or steps described in the claims may be performed in a different order than those shown in the embodiments and may still achieve the desired result. Furthermore, the processes depicted in the drawings do not necessarily have to follow the specific or sequential order shown to achieve the desired result. In some embodiments, multitasking and parallel processing are also possible or may be advantageous.

[0117] The systems, devices, modules, or units described in the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, a computer can be, for example, a personal computer, laptop computer, cellular phone, camera phone, smartphone, personal digital assistant, media player, navigation device, email device, game console, tablet computer, wearable device, or any combination of these devices.

[0118] For ease of description, the above devices are described in terms of function, divided into various units. Of course, in implementing this specification, the functions of each unit can be implemented in one or more software and / or hardware components.

[0119] Those skilled in the art will understand that the embodiments of this specification can be provided as methods, systems, or computer program products. Therefore, the embodiments of this specification can take the form of a completely hardware embodiment, a completely software embodiment, or an embodiment combining software and hardware aspects. Furthermore, the embodiments of this specification can take the form of a computer program product implemented on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, etc.) containing computer-usable program code.

[0120] This specification is described with reference to flowchart illustrations and / or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of this specification. It will be understood that each block of the flowchart illustrations and / or block diagrams, and combinations of blocks in the flowchart illustrations and / or block diagrams, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, special-purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create a machine for implementing the flowchart illustrations and / or block diagrams. Figure 1 One or more processes and / or boxes Figure 1 A device that provides the functions specified in one or more boxes.

[0121] These computer program instructions may also be stored in a computer-readable storage medium that can direct a computer or other programmable data processing device to function in a particular manner, such that the instructions stored in the computer-readable storage medium produce an article of manufacture including instruction means, which are implemented in a process Figure 1 One or more processes and / or boxes Figure 1 The function specified in one or more boxes.

[0122] These computer program instructions may also be loaded onto a computer or other programmable data processing equipment to cause a series of operational steps to be performed on the computer or other programmable equipment to produce a computer-implemented process, thereby providing instructions that execute on the computer or other programmable equipment for implementing the process. Figure 1 One or more processes and / or boxes Figure 1 The steps of the function specified in one or more boxes.

[0123] In a typical configuration, a computing device includes one or more processors (CPU), input / output interfaces, network interfaces, and memory.

[0124] Memory may include non-persistent storage in computer-readable media, such as random access memory (RAM) and / or non-volatile memory, such as read-only memory (ROM) or flash RAM. Memory is an example of computer-readable media.

[0125] Computer-readable media includes both permanent and non-permanent, removable and non-removable media that can store information using any method or technology. Information can be computer-readable instructions, data structures, modules of programs, or other data. Examples of computer storage media include, but are not limited to, phase-change memory (PRAM), static random access memory (SRAM), dynamic random access memory (DRAM), other types of random access memory (RAM), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), flash memory or other memory technologies, CD-ROM, digital versatile optical disc (DVD) or other optical storage, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other non-transferable medium that can be used to store information accessible by a computing device. As defined herein, computer-readable media does not include transient computer-readable media, such as modulated data signals and carrier waves.

[0126] It should also be noted that the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one..." does not exclude the presence of other identical elements in the process, method, article, or apparatus that includes said element.

[0127] This specification can be described in the general context of computer-executable instructions that are executed by a computer, such as program modules. Generally, program modules include routines, programs, objects, components, data structures, etc., that perform a specific task or implement a specific abstract data type. This specification can also be practiced in distributed computing environments, where tasks are performed by remote processing devices connected via a communication network. In distributed computing environments, program modules can reside in local and remote computer storage media, including storage devices.

[0128] The various embodiments in this specification are described in a progressive manner. Similar or identical parts between embodiments can be referred to interchangeably. Each embodiment focuses on describing the differences from other embodiments. In particular, the system embodiments are basically similar to the method embodiments, so the description is relatively simple; relevant parts can be referred to the descriptions in the method embodiments.

[0129] Finally, it should be noted that the user health monitoring method and system based on a smart ring and multi-level early warning disclosed in the embodiments of the present invention are merely preferred embodiments of the present invention and are only used to illustrate the technical solutions of the present invention, not to limit it. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some of the technical features. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the spirit and scope of the technical solutions of the embodiments of the present invention.

Claims

1. A user health monitoring method based on a smart ring and multi-level early warning, characterized in that, The method includes: Acquire multiple sensor data from the target user through a smart ring; Each of the sensor data is preprocessed and feature extracted to obtain the corresponding physiological feature parameters; Based on the physiological characteristic parameters and the multi-level early warning judgment rules, the early warning level of the target user is determined; Based on the warning level, the warning information and warning terminal corresponding to the target user are determined, and the warning information is sent to all the warning terminals.

2. The user health monitoring method based on a smart ring and multi-level early warning as described in claim 1, characterized in that, The smart ring includes a ring body, a controller and multiple sensors disposed within the ring body; the ring body is made of biocompatible materials and is manufactured using a waterproof and dustproof process; the sensors are PPG sensors, bioimpedance sensors, temperature sensors or motion sensors.

3. The user health monitoring method based on a smart ring and multi-level early warning as described in claim 1, characterized in that, The sensing data includes photoplethysmography (PPG) data, skin impedance data, fingertip temperature data, or body activity data.

4. The user health monitoring method based on a smart ring and multi-level early warning according to claim 2, characterized in that, The smart ring's controller also contains computer code to perform the following steps: Based on preset threshold rules and / or duration rules, determine whether the sensing data belongs to an abnormal situation, and obtain the judgment result; When the judgment result is negative, the aggregated sensor data is uploaded to the cloud device according to a preset first time interval. When the determination result is yes, the sensor data is uploaded to the cloud device at a second time interval less than the first time interval; the cloud device is used to execute the user health monitoring method.

5. The user health monitoring method based on a smart ring and multi-level early warning according to claim 1, characterized in that, The physiological characteristic parameters are instantaneous heart rate parameters, blood oxygen saturation parameters, exercise amplitude parameters, or exercise posture angle parameters.

6. The user health monitoring method based on a smart ring and multi-level early warning according to claim 1, characterized in that, The step of determining the warning level of the target user based on the physiological characteristic parameters and multi-level warning judgment rules includes: Based on a parameter rule judgment model corresponding to multiple preset severity levels, all the physiological characteristic parameters are judged to obtain at least one preset severity level in which the parameter rules are satisfied. The highest severity level among at least one of the preset severity levels is determined as the warning level for the target user.

7. The user health monitoring method based on a smart ring and multi-level early warning according to claim 6, characterized in that, The preset severity level is a reminder level, a warning level, or an emergency level; the parameter rule judgment model is used to determine whether at least one of the physiological characteristic parameters meets the corresponding parameter threshold rule and / or parameter duration rule.

8. The user health monitoring method based on a smart ring and multi-level early warning according to claim 1, characterized in that, The step of determining the warning information and warning terminal corresponding to the target user based on the warning level includes: From the multiple contact terminals corresponding to the target user, terminals matching the warning level are selected to obtain the warning terminals; An early warning message is generated based on the user parameters corresponding to the target user, the sensor data, and the early warning level.

9. A user health monitoring system based on a smart ring and multi-level early warning, characterized in that, The system includes: The acquisition module is used to acquire multiple sensor data of the target user through the smart ring; The processing module is used to preprocess and extract features from each of the sensor data to obtain the corresponding physiological feature parameters; The judgment module is used to determine the warning level of the target user based on the physiological characteristic parameters and the multi-level warning judgment rules; The early warning module is used to determine the early warning information and early warning terminal corresponding to the target user according to the early warning level, and to send the early warning information to all the early warning terminals.

10. A user health monitoring system based on a smart ring and multi-level early warning, characterized in that, The system includes: Memory containing executable program code; A processor coupled to the memory; The processor calls the executable program code stored in the memory to execute the user health monitoring method based on a smart ring and multi-level early warning as described in any one of claims 1-8.

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