Voice interactive health monitoring system

Abstract

The invention provides a voice interactive health monitoring system. The voice interactive health monitoring system comprises a voice interaction module, a physiological data acquisition module, a data analysis center and an early warning feedback module, the voice interaction module is connected with at least one wearable medical device and used for receiving and analyzing a natural language instruction of a user, generating a device operation instruction according to the natural language instruction, sending the device operation instruction to the corresponding wearable medical device and feeding back an operation result to the user; the physiological data acquisition module is used for acquiring physiological index data of a user from at least one wearable medical device and sending the physiological index data to the data analysis center; the data analysis center is used for performing dynamic analysis and anomaly detection on the received physiological index data of the user based on a machine learning algorithm, and sending an analysis and detection result to the early warning feedback module; and the early warning feedback module is used for sending graded early warning information to the user or the medical staff according to the received analysis and detection result. According to the invention, the health monitoring service can be provided for the user in a simpler and more convenient manner.

Description

Technical Field

[0001] One or more embodiments of the present invention relate to network communication technology, and more particularly, to a voice interactive health monitoring system. Background Art

[0002] Currently, people's demand for health management is growing stronger, and how to monitor their health at home at all times has become a pressing issue. For example, chronic disease management is one of the core challenges in the current healthcare sector. According to statistics, over 70% of deaths worldwide are related to chronic diseases. Long-term patient compliance and consistent data monitoring directly impact treatment outcomes.

[0003] However, current health monitoring methods are complex and inconvenient for users. For example, the limited interaction methods rely on touch screens or buttons, which cannot meet the needs of patients with limited mobility; insufficient data integration: different devices generate data in different formats, making cross-platform comprehensive analysis difficult; and complex operation procedures lead to low patient compliance. Summary of the Invention

[0004] One or more embodiments of the present invention describe a voice interactive health monitoring system that can provide health monitoring services to users in a simpler manner.

[0005] According to a first aspect, a voice interactive health monitoring system is provided, which includes: a voice interaction module, a physiological data acquisition module, a data analysis center, and an early warning feedback module; wherein,

[0006] A voice interaction module, connected to at least one wearable medical device, configured to receive and parse a user's natural language instructions, generate device operation instructions based on the natural language instructions, send the device operation instructions to the corresponding wearable medical device, and provide feedback of the operation results to the user;

[0007] A physiological data acquisition module is connected to at least one wearable medical device, obtains the user's physiological indicator data from the at least one wearable medical device, and sends the physiological indicator data to a data analysis center;

[0008] The data analysis center is used to dynamically analyze and detect anomalies in the received user's physiological indicator data based on machine learning algorithms, and send the analysis and detection results to the early warning feedback module;

[0009] The early warning feedback module sends graded early warning information to users or medical staff based on the received analysis and detection results.

[0010] The physiological data acquisition module is used to acquire multi-source data in different data formats from at least two wearable medical devices.

[0011] and / or,

[0012] The physiological data acquisition module supports Bluetooth, Wi-Fi and NFC communication protocols.

[0013] The voice interaction module supports a dynamic binding mechanism between instructions and device operations, and generates device operation instructions from natural language instructions based on this dynamic binding mechanism; and supports a voice feedback function, broadcasting operation results or health advice to users through text-to-speech technology.

[0014] The data analysis center is configured to:

[0015] Build a data warehouse based on the cloud server, and when receiving multi-source data from the physiological data acquisition module, integrate the multi-source data and store them in a standardized manner;

[0016] and / or, using an LSTM time series model to analyze trends in the user's physiological indicator data and identify abnormal fluctuations;

[0017] and / or, using a dynamic threshold adjustment algorithm to set real-time warning thresholds based on the user's individual health baseline;

[0018] and / or, support incremental learning;

[0019] And / or, generate a visual health report and push it to the user via voice or APP.

[0020] The visual health report generated by the data analysis center includes at least one of a trend chart, a statistical summary, and personalized health advice.

[0021] The early warning feedback module is configured to:

[0022] Real-time monitoring and analysis of test results, triggering an early warning when abnormal values ​​are detected in the analysis and test results;

[0023] And / or, implement a multi-level warning mechanism, prompting users to self-check via voice for primary anomalies and simultaneously notifying medical staff for serious anomalies;

[0024] and / or, sending graded warning information to users or medical staff by at least one of voice reminders, text messages, mobile application push notifications, and emails;

[0025] And / or, connect with the hospital system to enable early warning information to be directly entered into the electronic medical record.

[0026] The data analysis center connects data with a third-party medical platform to achieve electronic medical record synchronization and remote diagnosis and treatment collaboration.

[0027] The voice interaction module is implemented based on the ASR model of deep learning.

[0028] The at least one wearable medical device includes: at least one of a smart bracelet, a wireless blood pressure monitor, and a Bluetooth blood glucose meter.

[0029] The physiological data acquisition module is used to obtain at least one of the user's blood pressure, blood sugar, heart rate, and blood oxygen data from at least one wearable medical device.

[0030] It can be seen that each embodiment of the present invention has at least the following beneficial effects:

[0031] (1) Improved operational convenience: Voice interaction simplifies the key operations of traditional devices. Tests show that the operation time of elderly users is reduced by 60%. It supports multi-language instructions to cover patient groups with different cultural backgrounds.

[0032] (2) Enhanced data real-time performance: Automated collection and analysis shortens data latency. Experiments show that the anomaly detection response time is less than 5 seconds. The dynamic threshold algorithm is 25% more accurate than the fixed threshold model (based on 1,000 sample verification).

[0033] (3) Optimizing patient compliance: In a 3-month clinical trial, the data entry completeness rate of diabetic patients increased from 55% to 92%; the voice reminder function increased medication compliance by 40%.

[0034] (4) Support personalized management: adjust the health baseline according to individual differences of patients to reduce false positives; visual reports help patients intuitively understand health trends.

[0035] (5) Reduce medical costs: Real-time warnings reduce the number of emergency room visits. Data from pilot hospitals show that the number of emergency visits related to chronic diseases has decreased by 30%; data integration capabilities reduce the burden of manual data entry for medical staff. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the following briefly introduces the drawings required for use in the embodiments or the description of the prior art. Obviously, the drawings described below are some embodiments of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying any creative work.

[0037] Figure 1 1 is a schematic diagram of the structure of a voice interactive health monitoring system in one embodiment of the present invention.

[0038] Figure 2 This is a flow chart of a method for implementing health monitoring based on a voice interactive health monitoring system in one embodiment of the present invention. DETAILED DESCRIPTION

[0039] The solution provided by the present invention is described below with reference to the accompanying drawings.

[0040] First, it should be noted that the terms used in the embodiments of the present invention are for the purpose of describing specific embodiments only and are not intended to limit the present invention. As used in the embodiments of the present invention and the appended claims, the singular forms "a," "the," and "the" are intended to include the plural forms, unless the context clearly indicates otherwise.

[0041] It should be understood that the term "and / or" as used herein is merely a description of the relationship between associated objects, indicating that three possible relationships exist. For example, "A and / or B" can represent: A exists alone, A and B exist simultaneously, or B exists alone. Furthermore, the character " / " in this document generally indicates that the associated objects are in an "or" relationship.

[0042] One embodiment of the present invention provides a voice interactive health monitoring system, see Figure 1 The system includes: a voice interaction module 101, a physiological data acquisition module 102, a data analysis center 103 and a warning feedback module 104; wherein,

[0043] The voice interaction module 101 is connected to at least one wearable medical device and is used to receive and interpret the user's natural language instructions, generate device operation instructions based on the natural language instructions, send the device operation instructions to the corresponding wearable medical device, and provide feedback of the operation results to the user;

[0044] The physiological data acquisition module 102 is connected to at least one wearable medical device, obtains the user's physiological indicator data from the at least one wearable medical device, and sends the physiological indicator data to the data analysis center 103;

[0045] The data analysis center 103 is used to perform dynamic analysis and anomaly detection on the received physiological indicator data of the user based on a machine learning algorithm, and send the analysis and detection results to the early warning feedback module;

[0046] The early warning feedback module 104 sends graded early warning information to the user or medical staff based on the received analysis and detection results.

[0047] Figure 1 The system of the present invention is a user (e.g., chronic disease patient) health monitoring system based on voice interaction and artificial intelligence. The system integrates technologies such as the Internet of Things, voice recognition, and machine learning, and is suitable for chronic disease management scenarios requiring long-term monitoring of physiological indicators (e.g., patients with hypertension, diabetes, and cardiovascular disease). The system of the present invention simplifies the operating process through voice commands, enabling automated collection, real-time analysis, and abnormality warnings of physiological data, thereby optimizing patient health management efficiency.

[0048] Specifically, the embodiment of the present invention realizes all-weather health monitoring and management of users, such as patients with chronic diseases, by integrating voice interaction technology, multi-source physiological data collection, cloud data analysis and intelligent early warning feedback functions. The voice interactive health monitoring system of the embodiment of the present invention includes a voice interaction module, a physiological data collection module, a data analysis center and an early warning feedback module, supports natural language command operation, can automatically collect physiological indicators such as blood pressure, blood sugar, heart rate, etc. of patients, and performs dynamic analysis based on machine learning algorithms. When data abnormalities are detected, the voice interactive health monitoring system notifies patients and medical staff in real time through voice prompts, text messages or mobile application push. The system of the embodiment of the present invention solves the problems of complex operation, low data integration efficiency, poor patient compliance in the prior art, and significantly improves the convenience, real-time and accuracy of users, such as chronic disease management, and is particularly suitable for elderly patients and chronic disease patients who need long-term monitoring.

[0049] Each module is described below.

[0050] First, for the voice interaction module 101:

[0051] Functional description:

[0052] It integrates a high-precision speech recognition engine (such as an ASR model based on deep learning) to support natural language command parsing (such as "measure today's blood sugar" and "view last week's blood pressure report"); has built-in multi-language support (Chinese, English, etc.) and dialect adaptation functions to improve user coverage; and combines noise suppression technology to ensure command recognition accuracy in complex environments.

[0053] Technical features:

[0054] Dynamic binding mechanism between commands and device operations, for example, the voice command "start measuring" triggers the start of the associated blood glucose meter;

[0055] The voice feedback function uses TTS (text-to-speech) technology to broadcast operation results or health advice to users.

[0056] Therefore, it can be obtained that in one embodiment of the system of the present invention, the voice interaction module 101 is configured to: support a dynamic binding mechanism between instructions and device operations, and generate device operation instructions from natural language instructions according to the dynamic binding mechanism; and support a voice feedback function, and broadcast operation results or health advice to users through text-to-speech technology.

[0057] In one embodiment of the system of the present invention, the voice interaction module 101 is implemented based on an ASR model of deep learning.

[0058] As can be seen, existing technologies suffer from a single interaction method. Relying on touchscreen or button operations, these technologies cannot meet the needs of patients with limited mobility. However, the present invention addresses this operational complexity issue by simplifying the device operation process through voice interaction module 101, reducing user learning curves. Natural language interaction enhances user experience and strengthens patients' willingness to use the device over the long term.

[0059] Next, for the physiological data acquisition module 102:

[0060] Functional description:

[0061] Connect to a variety of wearable medical devices (such as smart bracelets, wireless blood pressure monitors, Bluetooth blood glucose meters, etc.) to collect blood pressure, blood sugar, heart rate, blood oxygen and other data in real time;

[0062] Supports multiple communication protocols such as Bluetooth, Wi-Fi and NFC, and is compatible with the data formats of mainstream medical devices.

[0063] Technical features:

[0064] Automated scheduling of data collection, such as starting measurements at a set time daily or triggering them based on voice commands;

[0065] Local caching function temporarily stores data and automatically synchronizes it to the cloud when the network is interrupted.

[0066] Therefore, it can be obtained that in one embodiment of the system of the present invention, the physiological data acquisition module 102 is used to obtain multi-source data in different data formats from at least two wearable medical devices, such as obtaining multi-source data in three different data formats from a smart bracelet, a wireless blood pressure monitor, and a Bluetooth blood glucose meter.

[0067] In one embodiment of the system of the present invention, the at least one wearable medical device includes at least one of a smart bracelet, a wireless blood pressure monitor, and a Bluetooth blood glucose meter.

[0068] In one embodiment of the system of the present invention, the physiological data acquisition module 102 is used to obtain at least one of the user's blood pressure, blood sugar, heart rate, and blood oxygen data from at least one wearable medical device.

[0069] Next for Data Analysis Center 103:

[0070] Functional description:

[0071] Build a data warehouse based on cloud servers to integrate multi-source heterogeneous data and store them in a standardized manner;

[0072] Use machine learning algorithms (such as LSTM time series models) to analyze data trends and identify abnormal fluctuations;

[0073] Dynamic threshold adjustment function sets warning thresholds based on the patient's individual health baseline (such as historical mean ± standard deviation).

[0074] Technical features:

[0075] The anomaly detection model supports incremental learning, continuously optimizing prediction accuracy as data accumulates;

[0076] Generate visual health reports (such as line graphs and statistical tables) and push them to users via voice or APP.

[0077] Therefore, it can be concluded that, in an embodiment of the system of the present invention, the data analysis center 103 can be configured as follows:

[0078] Build a data warehouse based on the cloud server, and when receiving multi-source data from the physiological data acquisition module, integrate the multi-source data and store them in a standardized manner;

[0079] and / or, using an LSTM time series model to analyze trends in the user's physiological indicator data and identify abnormal fluctuations;

[0080] and / or, using a dynamic threshold adjustment algorithm to set real-time warning thresholds based on the user's individual health baseline;

[0081] and / or, support incremental learning;

[0082] And / or, generate a visual health report and push it to the user via voice or APP.

[0083] In one embodiment of the system of the present invention, the visual health report generated by the data analysis center 103 includes at least one of a trend chart, a statistical summary, and personalized health advice.

[0084] The existing technology suffers from insufficient data integration, as data generated by different devices are in different formats, making cross-platform comprehensive analysis difficult. In the system of the embodiment of the present invention, the data analysis center 103 solves the data silo problem, builds a unified data analysis center, integrates multi-source physiological data, and realizes real-time analysis.

[0085] In one embodiment of the present invention, the voice interactive health monitoring system (for example, through the data analysis center 103) is connected to a third-party medical platform for data to achieve electronic medical record synchronization and remote diagnosis and treatment collaboration.

[0086] Next, for the early warning feedback module 104:

[0087] Functional description:

[0088] Real-time monitoring of data analysis results, triggering an alert when abnormal values ​​are detected (such as blood sugar levels exceeding a preset threshold);

[0089] Multi-level warning mechanism: primary abnormalities will prompt patients to self-check through voice, and serious abnormalities will be notified to medical staff simultaneously;

[0090] Supports multiple feedback channels, including voice reminders, SMS, mobile app push and email.

[0091] Technical features:

[0092] Dynamically assign warning priorities and adjust notification strategies based on the severity of the abnormality and the patient's medical history;

[0093] Connect with the hospital HIS system to enable early warning information to be directly entered into the electronic medical record.

[0094] Therefore, in one embodiment of the system of the present invention, the early warning feedback module 104 is configured to:

[0095] Real-time monitoring and analysis of test results, triggering an early warning when abnormal values ​​are detected in the analysis and test results;

[0096] And / or, implement a multi-level warning mechanism, prompting users to self-check via voice for primary anomalies and simultaneously notifying medical staff for serious anomalies;

[0097] and / or, sending graded warning information to users or medical staff by at least one of voice reminders, text messages, mobile application push notifications, and emails;

[0098] And / or, connect with the hospital system to enable early warning information to be directly entered into the electronic medical record.

[0099] Figure 2 This is a flow chart of a method for implementing health monitoring based on a voice interactive health monitoring system in one embodiment of the present invention. Figure 2 ,include:

[0100] Step 201: The patient issues a voice command "measure current blood pressure". The voice interaction module acquires and analyzes the voice command, converts the voice command into a device operation instruction for the smart blood pressure monitor, and thus activates the smart blood pressure monitor.

[0101] Step 203: The physiological data acquisition module collects the blood pressure data measured by the smart blood pressure monitor and uploads it to the data analysis center in real time for comparison and analysis with historical records;

[0102] Step 205: If the data analysis center detects that the blood pressure value is continuously higher than the threshold, a suggestion "Please rest immediately and contact a doctor" is pushed through the early warning feedback module and the APP of the user terminal;

[0103] Step 207: The warning information is synchronized to the hospital management platform for medical staff to track and handle.

[0104] An embodiment of the present invention provides a computer-readable storage medium having a computer program stored thereon. When the computer program is executed in a computer, the computer is caused to execute the method in any one of the embodiments in the specification.

[0105] An embodiment of the present invention provides a computing device, including a memory and a processor, wherein the memory stores executable code, and when the processor executes the executable code, the method in any embodiment of the specification is implemented.

[0106] It should be understood that the structures illustrated in the embodiments of the present invention do not constitute specific limitations on the apparatus of the embodiments of the present invention. In other embodiments of the present invention, the apparatus may include more or fewer components than shown, or may combine or separate certain components, or arrange the components differently. The illustrated components may be implemented in hardware, software, or a combination of software and hardware.

[0107] The various embodiments of the present invention are described in a progressive manner. Similar portions between the various embodiments can be referred to in conjunction with each other. Each embodiment focuses on the differences between the other embodiments. In particular, the device embodiments are generally similar to the method embodiments, so their description is relatively simple. For relevant portions, refer to the description of the method embodiments.

[0108] Those skilled in the art will appreciate that in one or more of the above examples, the functions described in the present invention may be implemented using hardware, software, widgets, or any combination thereof. When implemented using software, these functions may be stored in a computer-readable medium or transmitted as one or more instructions or codes on a computer-readable medium.

[0109] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent replacements, improvements, etc. made on the basis of the technical solution of the present invention should be included in the scope of protection of the present invention.

Claims

1. Voice interactive health monitoring system, characterized by: The system includes: voice interaction module, physiological data acquisition module, data analysis center and early warning feedback module; among them, A voice interaction module, connected to at least one wearable medical device, configured to receive and parse a user's natural language instructions, generate device operation instructions based on the natural language instructions, send the device operation instructions to the corresponding wearable medical device, and provide feedback of the operation results to the user; A physiological data acquisition module is connected to at least one wearable medical device, obtains the user's physiological indicator data from the at least one wearable medical device, and sends the physiological indicator data to a data analysis center; The data analysis center is used to dynamically analyze and detect anomalies in the received user's physiological indicator data based on machine learning algorithms, and send the analysis and detection results to the early warning feedback module; The early warning feedback module sends graded early warning information to users or medical staff based on the received analysis and detection results.

2. The system according to claim 1, wherein: The physiological data acquisition module is used to acquire multi-source data in different data formats from at least two wearable medical devices. and / or, The physiological data acquisition module supports Bluetooth, Wi-Fi and NFC communication protocols.

3. The system according to claim 1, wherein: The voice interaction module supports a dynamic binding mechanism between instructions and device operations, and generates device operation instructions from natural language instructions based on this dynamic binding mechanism; and supports a voice feedback function, broadcasting operation results or health advice to users through text-to-speech technology.

4. The system according to claim 1, wherein: The data analysis center is configured to: Build a data warehouse based on the cloud server, and when receiving multi-source data from the physiological data acquisition module, integrate the multi-source data and store them in a standardized manner; and / or, using an LSTM time series model to analyze trends in the user's physiological indicator data and identify abnormal fluctuations; and / or, using a dynamic threshold adjustment algorithm to set real-time warning thresholds based on the user's individual health baseline; and / or, support incremental learning; And / or, generate a visual health report and push it to the user via voice or APP.

5. The system according to claim 4, characterized in that The visual health report generated by the data analysis center includes at least one of a trend chart, a statistical summary, and personalized health advice.

6. The system according to claim 1, wherein: The early warning feedback module is configured to: Real-time monitoring and analysis of test results, triggering an early warning when abnormal values ​​are detected in the analysis and test results; And / or, implement a multi-level warning mechanism, prompting users to self-check via voice for primary anomalies and simultaneously notifying medical staff for serious anomalies; and / or, improving at least one of voice reminders, text messages, mobile application push notifications, and emails to send graded warning information to users or medical staff; And / or, connect with the hospital system to enable early warning information to be directly entered into the electronic medical record.

7. The system according to claim 1, wherein: The data analysis center connects data with a third-party medical platform to achieve electronic medical record synchronization and remote diagnosis and treatment collaboration.

8. The system according to claim 1, wherein: The voice interaction module is implemented based on the ASR model of deep learning.

9. The system according to claim 1, wherein: The at least one wearable medical device includes: at least one of a smart bracelet, a wireless blood pressure monitor, and a Bluetooth blood glucose meter.

10. The system according to claim 1, wherein: The physiological data acquisition module is used to obtain at least one of the user's blood pressure, blood sugar, heart rate, and blood oxygen data from at least one wearable medical device.

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