Elderly patient behavior image monitoring and falling risk analysis system and method thereof

By integrating differential geometry theory and computer vision technology, a system for monitoring the behavior of elderly patients and analyzing fall risks was constructed. This system solves the problems of objectivity and real-time performance in fall risk assessment for elderly patients in existing technologies, and achieves high-precision fall risk warning and monitoring.

CN121570167APending Publication Date: 2026-02-27徐州仁慈医院
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Patent Information

Application Number
CN202511743648.2
Authority / Receiving Office
CN · China
Patent Type
Applications(China)
Current Assignee / Owner
Filing Date
2025-11-25
Publication Date
2026-02-27

AI Technical Summary

Technical Problem

Existing methods for assessing fall risk in elderly patients lack objectivity and real-time performance. Traditional methods rely on wearable devices, which are inconvenient to wear. Existing computer vision systems have limited accuracy in estimating 3D human pose under monocular cameras, making it difficult to distinguish between normal and abnormal behaviors. They also lack precise quantification of gait instability and accurate early warning mechanisms.

Method used

By combining differential geometry theory with computer vision technology, image data is acquired through a monocular camera to construct a posture manifold representation. High-precision 3D posture estimation is performed using differential geometry theory and Riemannian geometric constraints. Combined with geodesic curvature analysis and gait feature extraction, a risk assessment model is established and differentiated early warnings are triggered.

Benefits of technology

It achieves high-precision monitoring of elderly patients' behavior, detects potential fall risks 3.2 seconds in advance, has an early warning accuracy rate of over 85%, a false positive rate of less than 15%, and a false negative rate of less than 5%, meeting the needs of real-time monitoring.

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Abstract

The invention relates to the technical field of image monitoring, in particular to an elderly patient behavior image monitoring and falling risk analysis system and method, and the method comprises the steps: a front-end collection module collects a time sequence image of an activity area of an elderly patient; the posture detection module analyzes and extracts three-dimensional posture information of the elderly patient based on a differential geometry theory, and comprises a key point detection unit, a manifold mapping unit and a constraint optimization unit; the gait analysis module processes attitude parameters through a parameter extraction unit, a time sequence track unit and a curvature analysis unit; the risk assessment module calculates a fall risk level based on the gait feature data; according to the invention, an APMK-HCW algorithm is innovatively applied to realize high-precision 3D attitude estimation under a monocular camera, geodesic curvature analysis is introduced to quantify gait stability, and a personalized baseline assessment and adaptive optimization mechanism is adopted to realize early precise early warning of the falling risk of the elderly patient.
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