Patient Fall Prediction via Camera-Based AI Analysis
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Solution Overview
Problem
Hospitals face challenges in monitoring patients at risk of falling or recovering from anesthesia due to manpower limitations, especially in areas where constant medical staff presence is difficult, and existing systems require additional equipment that is not universally applicable.
Innovation Solution
A system and method using an image-based recognizer with a camera to detect patient motions and predict the likelihood of falling or degree of anesthesia recovery by analyzing facial expressions, gaze, and skeletal movements, employing an artificial neural network for real-time, automatic detection capable of monitoring multiple patients simultaneously.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If medical staff are directly deployed on-site to monitor patients, then patient safety and monitoring quality are improved, but labor costs increase and staff fatigue increases
Solution Approach 1:
The patent replaces the mechanical system of human medical staff monitoring with an automated image recognition system using cameras and AI algorithms. The system captures images of patients, detects motions such as falling, and monitors anesthesia recovery status automatically, eliminating the need for constant human presence while maintaining monitoring quality.
Solution Approach 2:
The system enables self-monitoring of patients through automated image analysis. The image recognition system independently detects patient conditions, identifies falling events, and assesses anesthesia recovery without human intervention, allowing the monitoring function to serve itself rather than requiring continuous human operation.
2Productivity
If additional medical staff or extended working hours are added to enhance response ability, then patient monitoring capability is improved, but operational costs increase and staff fatigue increases
Solution Approach 1:
The patent substitutes human labor with an automated image recognition system that provides continuous monitoring without additional operational costs. The system processes images and detects patient conditions automatically, maintaining high response capability while eliminating the need to hire additional staff or extend working hours.
3Measurement precision
If vital signs are used to detect emergency situations, then detection accuracy is improved, but equipment requirements increase and applicability decreases
Solution Approach 1:
The patent replaces complex vital sign monitoring equipment with a simpler camera-based image recognition system. By analyzing facial expressions, eye movements, and body motions in images, the system achieves emergency detection capability without requiring additional medical devices, making it universally applicable to all patients.
Solution Approach 2:
The image recognition system serves multiple functions: detecting falling events, monitoring anesthesia recovery, and identifying emergency situations. This multi-functional approach eliminates the need for specialized equipment for each monitoring task, allowing a single camera system to replace multiple dedicated devices and improve universal applicability.
Data Source
AI summary
Disclosed are a system and a method for predicting the likelihood of falling or the degree of anesthesia recovery, the system including: at least one camera installed at a predetermined location in a hospital to capture an image; a motion detector configured to detect the motion of a patient in the image; a face recognizer configured to recognize the face of the patient in the image to determine the identity of the patient, and recognize the expression and gaze of the patient; and a monitor configured to predict the likelihood of falling or the degree of anesthesia recovery by using an action of the patient detected by the motion detector, the identity of the patient determined by the face recognizer, and the expression and gaze of the patient.