Old people health risk prediction method and prediction system

Abstract

The invention relates to the technical field of human body monitoring, and provides an old people health risk prediction method and prediction system.When the old people health risk prediction system is used, the old people health risk prediction system is correctly assembled and installed in a residence of the old people, and sleep of the old people can be monitored; detecting environment data of the current sleep area, judging whether a human body exists in the current sleep area or not, and controlling the working state of the old people health risk prediction system according to a judgment result; acquiring chest tiny vibration, thoracic fluctuation and body movement trajectory data of the old in sleep by using an old people health risk prediction system; performing deep analysis on the acquired data by using an AI large model, and identifying two abnormal modes of apnea and arrhythmia; when an abnormal mode is detected, an early warning mechanism is triggered, family members or community workers are notified, and the method has the advantages of non-inductive detection, accurate early warning, personalized management, efficient response and the like.

Description

Technical Field

[0001] The present invention relates to the technical field of human body monitoring, and in particular to a method and system for predicting health risks of the elderly. Background Art

[0002] Cardiovascular and cerebrovascular diseases, respiratory disorders and other diseases are common health risks for the elderly, and these diseases are often sudden and difficult to detect and intervene in time.

[0003] As disclosed in application number: CN202420572314.8, a radar device for a sleep breathing monitor includes a main body, a mounting mechanism is provided inside the main body, and the mounting mechanism includes a mounting plate, a radar, an L-shaped mounting block and a threaded rod. The back of the mounting plate abuts against the front of the interior of the main body, the outer surface of the radar is engaged with the interior of the mounting plate, a through groove is provided inside the mounting plate, the outer surface of the L-shaped mounting block is slidably connected to the inner wall of the through groove, and the outer surface of the threaded rod is threadedly connected to the interior of the L-shaped mounting block.

[0004] However, in home-based elderly care settings, many elderly people lack access to effective sleep monitoring, making it difficult to detect and treat premature ejaculation in a timely manner. Traditional monitoring equipment is expensive and lacks comfort, leading to poor compliance among the elderly and making it difficult to effectively prevent health risks. Summary of the Invention

[0005] In order to solve the above problems, the present invention provides a method and system for predicting health risks of the elderly to solve the problem.

[0006] In order to achieve the above object, the present invention provides the following technical solutions:

[0007] A method for predicting health risks of the elderly comprises the following steps:

[0008] S1: The elderly health risk prediction system is correctly assembled and installed in the elderly’s residence and can monitor the elderly while they are sleeping;

[0009] S2: Detecting the environmental data of the current sleeping area and determining whether there is a human body in the current sleeping area and controlling the working state of the elderly health risk prediction system based on the determination result;

[0010] S3: Use the elderly health risk prediction system to collect data on chest micro-vibration, chest rise and fall, and body movement trajectory during sleep of the elderly;

[0011] S4: Use AI big models to conduct in-depth analysis of the collected data and identify two abnormal patterns: apnea and arrhythmia;

[0012] S5: When an abnormal pattern is detected, trigger the early warning mechanism to inform family members or community workers.

[0013] Preferably, the AI large model includes a convolutional neural network (CNN) and a long short-term memory network (LSTM).

[0014] Preferably, the early warning mechanism includes SMS notification, phone notification, and application push.

[0015] An elderly health risk prediction system, comprising:

[0016] A portable mounting bracket for mounting the elderly health risk prediction device body;

[0017] The elderly health risk prediction device body is provided with a data acquisition module, a data processing module and a warning module;

[0018] Data acquisition module: used for collecting key vital sign data such as chest fluctuation and heart rate during sleep of the elderly;

[0019] Data processing module: used for deep analysis of the collected data to identify abnormal patterns such as apnea and arrhythmia;

[0020] Warning module: used for triggering the early warning mechanism to inform family members or community workers when an abnormal pattern is detected.

[0021] Preferably, the portable mounting bracket comprises a mounting base, a reinforcing support frame, a rotating mounting seat and an elastic limiting component, the reinforcing support frame is fixedly installed at the lower end of the mounting base, the rotating mounting seat is rotatably arranged on the reinforcing support frame, a top screw is arranged at the rotating connection between the rotating mounting seat and the reinforcing support frame, and the elastic limiting component is arranged in the rotating mounting seat. The elastic limiting component cooperates with the rotating mounting seat to clamp the elderly health risk prediction device body.

[0022] Preferably, the rotating mounting seat is provided with a rotating shaft rotatably connected with the reinforcing support frame in the middle of the left and right sides, the rotating shaft is provided with a heat dissipation grille on one side, the rotating mounting seat is provided with a quick-release empty slot at the bottom, the rotating mounting seat is provided with a detection peephole at the front side, and the elastic limiting component is slidingly arranged at the rear part of the rotating mounting seat.

[0023] Preferably, the elastic limiting component comprises a sliding clamp plate, an anti-skid strip, a sliding rail, an elastic element and a cover plate, the sliding clamp plate is slidingly arranged at the rear part of the rotating mounting seat through the sliding rail, the sliding rail is fixedly connected with the rotating mounting seat, the elastic element is arranged between the sliding clamp plate and the back plate of the rotating mounting seat, the anti-skid strip is provided in multiple groups, the multiple groups of anti-skid strips are arranged at the front side of the sliding clamp plate, the cover plate is arranged above the elastic element, one side of the cover plate is fixedly connected with the upper part of the sliding clamp plate, and the other side of the cover plate is slidingly connected with the rotating mounting seat.

[0024] Preferably, the mounting bottom plate is provided with screw mounting holes at four corners, and the lower side of the mounting bottom plate is provided with double-sided adhesive.

[0025] The present application has the advantages that: the present application avoids interference with the sleep of the elderly by using a non-contact millimeter wave radar, improves the comfort and compliance of monitoring, can accurately identify abnormal patterns such as apnea and arrhythmia by using an AI large model, effectively prevents health risks such as cardiovascular and cerebrovascular diseases and respiratory disorders, can timely trigger an early warning mechanism by setting an elderly health risk prediction system, notify family members or community staff for intervention, effectively reduce the health risks of the elderly, and conveniently clamp and fix the elderly health risk prediction device body by setting a portable mounting bracket, and conveniently install it in a suitable position, thereby effectively monitoring the elderly. BRIEF DESCRIPTION OF DRAWINGS

[0026] The accompanying drawings, which form a part of the present application, are used to provide further understanding of the present application, and the illustrative embodiments of the present application and their description are used to explain the present application, and do not constitute improper limitations on the present application. In the drawings:

[0027] Figure 1 is a flowchart of the present application;

[0028] Figure 2 is a structural schematic diagram of the present application;

[0029] Figure 3 is a structural schematic diagram of the rotating mounting seat of the present application;

[0030] Figure 4 is a structural schematic diagram of the inside of the elastic limiting assembly of the present application;

[0031] Figure 5 is a structural schematic diagram of the outside of the elastic limiting assembly of the present application;

[0032] Figure 6 is a structural schematic diagram of the mounting bottom plate of the present application.

[0033] BRIEF DESCRIPTION OF DRAWINGS

[0034] 1, portable mounting bracket; 2, elderly health risk prediction device body; 3, mounting bottom plate; 4, reinforcing support frame; 5, rotating mounting seat; 6, elastic limiting assembly; 7, jackscrew; 51, rotating shaft; 52, heat dissipation grille; 53, quick release gap; 54, detection peephole; 61, sliding clamping plate; 62, anti-skid strip; 63, sliding rail; 64, elastic element; 65, cover plate; 31, screw mounting hole; 32, double-sided adhesive. DETAILED DESCRIPTION

[0035] It should be noted that the embodiments in the present application and the features in the embodiments can be combined with each other in the case of no conflict.

[0036] In the description of the present application, it should be understood that the orientation or positional relationship indicated by the terms "center", "longitudinal", "transverse", "upper", "lower", "front", "rear", "left", "right", "vertical", "horizontal", "top", "bottom", "inner", "outer" and the like is based on the orientation or positional relationship shown in the drawings, and is only for the convenience of describing the present application and simplifying the description, and does not indicate or imply that the device or element referred to must have a particular orientation, be constructed and operated in a particular orientation, and therefore cannot be understood as a limitation on the present application. In addition, the terms "first", "second" and the like are only for descriptive purposes and cannot be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Therefore, the features defined with "first", "second" and the like can explicitly or implicitly include one or more of the features. In the description of the present application, unless otherwise specified, the meaning of "a plurality of" is two or more.

[0037] In the description of the present application, it should be noted that unless otherwise explicitly specified and limited, the terms "mounting", "connection", "connection" should be understood broadly, for example, it can be fixedly connected, or it can be detachably connected, or integrally connected; it can be mechanically connected, or it can be electrically connected; it can be directly connected, or it can be indirectly connected through an intermediate medium, or it can be the communication inside two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood through specific circumstances.

[0038] Embodiment one, in combination Figure 1 It is explained that:

[0039] An old person health risk prediction method, comprising the following steps:

[0040] S1: Assemble and install the old person health risk prediction system correctly in the old person's residence and monitor the old person when sleeping;

[0041] S2: Detect the environmental data of the current sleeping area and judge whether there is a human body in the current sleeping area, and control the working state of the old person health risk prediction system according to the judgment result;

[0042] S3: Collect the chest micro-vibration, chest fluctuation and body movement trajectory data of the old person during sleep by using the old person health risk prediction system;

[0043] S4: Deeply analyze the collected data by using an AI large model to identify two abnormal modes of apnea and arrhythmia;

[0044] S5: When an abnormal pattern is detected, trigger an early warning mechanism to inform family members or community staff.

[0045] The AI large model includes a convolutional neural network and a long short-term memory network.

[0046] The convolutional neural network (CNN) is good at extracting spatial patterns from local features of images or time series (such as the waveform features of thoracic fluctuations), and the long short-term memory network (LSTM) is good at capturing long-term dependencies in time series (such as heart rate variation trends). Different sensor data (vibration, fluctuation, body movement, heart rate) can be input into the CNN-LSTM subnetwork, and then the outputs of each subnetwork can be weighted and fused or attention mechanism fused to obtain the final judgment result. Attention mechanism can be added after the LSTM layer, which can allow the model to automatically "focus" on the most critical time segments (such as the few minutes of apnea events) when analyzing data for an entire night, thereby improving the accuracy of the judgment.

[0047] The system can enter a "learning period" or "baseline establishment period" in the early stage after installation (such as 1-2 weeks), during which the system quietly collects the user's normal sleep data to establish the user's unique physiological feature baseline, and subsequent abnormality detection will be based on this personalized baseline, greatly reducing the false positive rate.

[0048] The early warning mechanism includes SMS notification, phone notification, and application program push.

[0049] The early warning mechanism can be divided into three levels: "attention", "warning", and "emergency".

[0050] Attention level: Detecting a slight or short-term abnormality, which can be pushed to the user himself or his family through the application, and is recommended for attention.

[0051] Warning level: Detecting a persistent or more serious abnormality (such as multiple apnea events), triggering SMS and phone notifications to inform family members.

[0052] Emergency level: Detecting a high-risk event (such as long apnea accompanied by rapid oxygen saturation decline, malignant arrhythmia), triggering the highest level of early warning (high-frequency phone calls, emergency contact App push), and considering whether to directly contact the community emergency center or family doctor.

[0053] Among them, the early warning information contains specific and readable content, such as "detecting about 5 apnea events in the user from 2:15 to 2:30 in the morning, with the lowest oxygen saturation falling to 85%". Subsequently, the APP adds user confirmation and feedback, allowing the user to feedback "I'm fine, false alarm" after receiving the early warning.

[0054] In addition, a visual sleep quality report can be generated periodically (e.g., weekly, monthly), including sleep duration, deep / shallow sleep distribution, apnea index, average heart rate, etc., so that users and family members can intuitively understand the changing trend of health status.

[0055] In the second embodiment, the first embodiment is combined with Figure 2 The application is described as follows:

[0056] The application relates to an old person health risk prediction system, which comprises the following components.

[0057] A portable mounting bracket 1 is used for mounting an old person health risk prediction device body 2.

[0058] The old person health risk prediction device body 2 is provided with a data acquisition module, a data processing module and a warning module.

[0059] The data acquisition module is used for acquiring key vital sign data such as chest fluctuation and heart rate during sleep of the old person.

[0060] The data processing module is used for deeply analyzing the acquired data and identifying abnormal modes such as apnea and arrhythmia.

[0061] The warning module is used for triggering a warning mechanism and notifying family members or community staff when the abnormal mode is detected.

[0062] The portable mounting bracket 1 comprises a mounting bottom plate 3, a reinforcing support frame 4, a rotating mounting seat 5 and an elastic limiting assembly 6. The reinforcing support frame 4 is fixedly installed at the lower end of the mounting bottom plate 3. The rotating mounting seat 5 is rotationally arranged on the reinforcing support frame 4. A jackscrew 7 is arranged at the rotating connection position of the rotating mounting seat 5 and the reinforcing support frame 4. The elastic limiting assembly 6 is arranged in the rotating mounting seat 5 and is used for clamping the old person health risk prediction device body 2 in cooperation with the rotating mounting seat 5.

[0063] In use, the mounting bottom plate 3 is first installed at a proper position. Then, the rotating mounting seat 5 can be rotated according to requirements, so that the non-contact millimeter wave radar has a better monitoring angle. Then, the rotating mounting seat 5 is fixed by using the jackscrew 7, and the installation of the assembly is completed.

[0064] In the third embodiment, the second embodiment is combined with Figure 3 The application is described as follows:

[0065] The rotating mounting seat 5 is provided with a rotating shaft 51 rotationally connected with the reinforcing support frame 4 at the middle part of the left and right sides. A heat dissipation grille 52 is arranged at one side of the rotating shaft 51. A quick-release empty slot 53 is arranged at the bottom of the rotating mounting seat 5. A detection peephole 54 is arranged at the front side of the rotating mounting seat 5. The elastic limiting assembly 6 is slidingly arranged at the rear part of the rotating mounting seat 5.

[0066] In this way, the rotating seat 5 is connected to the reinforcing support frame 4 through the rotating shaft 51, so that the detection module (such as a camera, a sensor, etc.) mounted thereon can rotate at a certain angle. The heat dissipation grille 52 provides a heat dissipation channel for the heat generating elements such as the main control board, the sensor, and the AI calculation unit inside the device. Since the health monitoring device needs to operate continuously for 7x24 hours, especially when performing AI deep analysis, the processor will generate a large amount of heat. If there is no effective heat dissipation, the device may be overheated, resulting in frequency reduction, shutdown, or even damage. By setting the dismounting slot 53, the old-age health risk prediction device body 2 can be quickly dismounted. By setting the detection peephole 54, the non-contact millimeter wave radar can work normally.

[0067] In the fourth embodiment, the above-mentioned Figure 4 and Figure 5 are combined to illustrate.

[0068] The elastic limiting assembly 6 comprises a sliding clamp plate 61, an anti-skid strip 62, a sliding rail 63, an elastic element 64, and a cover plate 65. The sliding clamp plate 61 is slidably arranged at the rear of the rotating seat 5 through the sliding rail 63, the sliding rail 63 is fixedly connected with the rotating seat 5, the elastic element 64 is arranged between the sliding clamp plate 61 and the back plate of the rotating seat 5, the anti-skid strip 62 is provided in multiple groups, the multiple groups of anti-skid strips 62 are arranged on the front side of the sliding clamp plate 61, the cover plate 65 is arranged above the elastic element 64, one side of the cover plate 65 is fixedly connected with the upper part of the sliding clamp plate 61, and the other side of the cover plate 65 is slidably connected with the rotating seat 5.

[0069] In this way, by setting the sliding clamp plate 61 and the sliding rail 63, smooth linear motion is achieved. By setting the elastic element 64, the sliding clamp plate 61 can clamp the old-age health risk prediction device body 2 in cooperation with the rotating seat 5. By setting the anti-skid strip 62, the old-age health risk prediction device body 2 can be prevented from falling out. By setting the cover plate 65, dust can be prevented from entering the elastic element 64, thereby prolonging the service life.

[0070] In the fifth embodiment, the above-mentioned Figure 6 are combined to illustrate.

[0071] The mounting bottom plate 3 is provided with screw mounting holes 31 at four corners, and the mounting bottom plate 3 is provided with double-sided adhesive tape 32 on the lower side. In this way, the diversified needs of different users and different environments can be met. Users can choose the most suitable installation method according to their specific circumstances. By setting the screw installation, the firmness of the installation is improved, and there is no need to worry about installation failure during long-term use. The double-sided adhesive tape 32 does not require tools and does not need to be drilled, and the operation is extremely simple. The installation can be completed in a few seconds without causing any physical damage to furniture or walls, thereby improving the user experience.

[0072] The working principle of the present application: first, correctly assemble and install the old people health risk prediction system in the old people's residence and can monitor the old people when sleeping; then detect the environmental data of the current sleeping area and judge whether there is a human body in the current sleeping area and control the working state of the old people health risk prediction system according to the judgment result; collect the chest micro-vibration, chest fluctuation and body movement trajectory data of the old people in sleep by using the old people health risk prediction system; use the AI large model to deeply analyze the collected data, identify two abnormal modes of apnea and arrhythmia; when detecting abnormal mode, trigger the early warning mechanism, notify the family members or community workers, the present application has the characteristics of non-sensing detection, accurate early warning, personalized management and efficient response.

[0073] For those skilled in the art, the present application is not limited to the details of the above exemplary embodiments, and can be implemented in other specific forms without departing from the spirit or essential characteristics of the present application; therefore, from any point of view, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the claims involved.

[0074] The above is only the preferred embodiment of the present application, and is not intended to limit the present application, any slight modification, equivalent replacement and improvement made according to the technical essence of the present application to the above embodiments should be included in the protection scope of the technical scheme of the present application.

Claims

1. A method of predicting health risks in the elderly, characterized by, The method comprises the following steps: S1: Assemble and install the old-age health risk prediction system in the old-age residence and monitor the sleep of the old-age person; S2: Detect the environmental data of the current sleep area and determine whether there is a human body in the current sleep area, and control the working state of the old-age health risk prediction system according to the determination result; S3: Collect the chest cavity micro-vibration, chest fluctuation and body movement trajectory data of the old-age person during sleep by using the old-age health risk prediction system; S4: Deeply analyze the collected data by using an AI large model to identify two abnormal modes of apnea and arrhythmia; S5: When the abnormal mode is detected, trigger the early warning mechanism and notify the family members or community workers.

2. The method of claim 1, wherein, The AI large model comprises a convolutional neural network (CNN) and a long short-term memory network (LSTM).

3. The method of claim 1, wherein the method further comprises: The early warning mechanism comprises short message notification, telephone notification and application program push mode.

4. A geriatric health risk prediction system characterized by, It comprises: A portable mounting bracket (1) for mounting an old-age health risk prediction device body (2); The old-age health risk prediction device body (2) is provided with a data acquisition module, a data processing module and a warning module; The data acquisition module is used for collecting the chest fluctuation and heart rate data of the old-age person during sleep; The data processing module is used for deeply analyzing the collected data to identify apnea and arrhythmia; The warning module triggers the early warning mechanism and notifies the family members or community workers when the abnormal mode is detected.

5. The geriatric health risk prediction system of claim 4, wherein, The portable mounting bracket (1) comprises a mounting bottom plate (3), a reinforcing support frame (4), a rotating mounting seat (5) and an elastic limiting assembly (6), the reinforcing support frame (4) is fixedly installed at the lower end of the mounting bottom plate (3), the rotating mounting seat (5) is rotatably arranged on the reinforcing support frame (4), a jackscrew (7) is arranged at the rotating connection between the rotating mounting seat (5) and the reinforcing support frame (4), the jackscrew (7) is used for fixing the rotating mounting seat (5), the elastic limiting assembly (6) is arranged in the rotating mounting seat (5), and the elastic limiting assembly (6) cooperates with the rotating mounting seat (5) to clamp the old-age health risk prediction device body (2).

6. A system for predicting health risks in the elderly according to claim 5, characterized in that, The rotating mounting seat (5) is provided with a rotating shaft (51) rotatably connected with the reinforcing support frame (4) in the middle of the left and right sides, one side of the rotating shaft (51) is provided with a heat dissipation grille (52), the bottom of the rotating mounting seat (5) is provided with a quick-release empty slot (53), the front side of the rotating mounting seat (5) is provided with a detection peep slot (54), and the elastic limiting assembly (6) is slidingly arranged at the rear part of the rotating mounting seat (5).

7. A system for predicting health risks in the elderly according to claim 6, characterized in that, The elastic limiting component (6) comprises a sliding clamp plate (61), an anti-skid strip (62), a sliding rail (63), an elastic element (64) and a cover plate (65), the sliding clamp plate (61) is slidably arranged at the rear of the rotary mounting seat (5) through the sliding rail (63), the sliding rail (63) is fixedly connected with the rotary mounting seat (5), the elastic element (64) is arranged between the sliding clamp plate (61) and the back plate of the rotary mounting seat (5), the anti-skid strip (62) is provided in multiple groups, the multiple groups of anti-skid strips (62) are arranged on the front side of the sliding clamp plate (61), the cover plate (65) is arranged above the elastic element (64), one side of the cover plate (65) is fixedly connected with the upper portion of the sliding clamp plate (61), and the other side of the cover plate (65) is slidably connected with the rotary mounting seat (5).

8. The geriatric health risk prediction system of claim 5, wherein, The mounting bottom plate (3) is provided with screw mounting holes (31) at four corners, and double-sided adhesive tapes (32) are arranged on the lower side of the mounting bottom plate (3).

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