Pension service supervision method and system based on image recognition AI model algorithm

By deploying a sign monitoring device in the bedside area, combining the body's hot-pressure zone and respiratory micromovement data, and using image recognition AI model to generate multimodal early warning information, it solves the problem of the inability to monitor breathing and heartbeat synchronously in the prior art, and realizes accurate assessment and early warning of the health risks of bedside people.

CN120511049APending Publication Date: 2025-08-19TIANJIN INNUO TECH GRP CO LTD
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Patent Information

Application Number
CN202510576543.6
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-05-06
Publication Date
2025-08-19

AI Technical Summary

Technical Problem

The existing technology cannot simultaneously monitor key vital signs such as breathing and heartbeat in bed-living scenarios, resulting in missing risk judgments such as respiratory arrest and cardiopulmonary abnormalities. The lack of fusion analysis of body pressure data and physiological signals, and the accuracy of early warning is limited.

Method used

By deploying the sign monitoring device, the heartbeat vibration signal and respiratory micromovement amplitude are collected, combined with the body's hot zone distribution data, the image recognition AI model is used to analyze multiple key areas of the body, generate retention risk index, and generate multimodal early warning information based on the physiological parameter abnormal library.

Benefits of technology

It realizes accurate monitoring of multi-dimensional health risks for bedridden personnel, reduces the incidence of pressure ulcers and limb stiffness, and improves nursing accuracy and response efficiency.

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Abstract

The invention provides a pension service supervision method and system based on an image recognition AI model algorithm. The method comprises the following steps: aiming at respiratory arrest, pressure sores and limb stiffness risks in a bedridden old-age care scene, firstly, establishing a physiological abnormality library containing parameters such as a respiratory rate threshold value and a body pressure critical value; a physical sign monitoring device is deployed to collect body pressure hot area distribution data of the bedridden person in real time, breathing interval abnormity, heartbeat attenuation and body pressure exceeding areas are analyzed through an AI model, and a retention risk index is generated. And when the monitoring data exceeds an abnormal library threshold value, triggering multi-modal early warning information, fusing risk area positioning, abnormal duration and temperature gradient change, outputting a multi-modal early warning report combining images and texts, and guiding the nursing personnel to perform targeted intervention. According to the technical scheme provided by the invention, real-time monitoring and multi-mode risk early warning of health hidden dangers in bed are realized, the occurrence rate of complications such as respiratory arrest and pressure sores is reduced, and the precision and response efficiency of nursing for the aged are improved.
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