Camera-Based Patient State Detection in Hospital Rooms
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Solution Overview
Problem
Current methods for monitoring patient environments in healthcare settings, such as hospital rooms, rely on costly hardware sensors or inefficient human visual inspections, which are prone to errors and not scalable for remote patient monitoring.
Innovation Solution
A system utilizing a camera-equipped monitoring device with machine learning algorithms to detect changes in patient states and conditions within a room, such as bed rail positions and patient movements, without the need for specialized hardware sensors, and sends notifications to caregivers via a server.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If costly hardware sensors are used for monitoring patient environments, then measurement precision is improved, but device complexity and cost increase
Solution Approach 1:
The patent replaces physical hardware sensors with a camera-based optical system combined with machine learning algorithms. The camera captures visual data of the patient environment (bed position, patient movements), and AI models process these images to detect state changes, substituting mechanical sensing with optical sensing and computational analysis.
Solution Approach 2:
The system creates visual copies (images) of the patient environment using a camera, then analyzes these copies through machine learning to infer patient states. Instead of directly measuring physical parameters with sensors, the system captures visual representations and derives information from them through algorithmic processing.
2Device complexity
If human visual inspections are used for monitoring patients, then device complexity is reduced, but reliability and productivity worsen due to errors and inefficiency
Solution Approach 1:
The system enables automated self-monitoring of the patient environment. The camera continuously captures images, and the machine learning model automatically detects state changes without human intervention. The system serves itself by autonomously processing visual data and generating alerts, eliminating the need for manual inspection while maintaining high reliability.
Solution Approach 2:
The system establishes a feedback loop where the camera continuously monitors the environment, the AI model analyzes images in real-time, and alerts are generated immediately upon detecting state changes. This continuous feedback mechanism ensures reliable and timely detection of patient conditions, surpassing the reliability of periodic manual inspections.
3Ease of operation
If manual monitoring methods are used, then ease of operation is maintained, but productivity and response time worsen
Solution Approach 1:
The system performs preliminary detection and analysis automatically, preparing information in advance for caregivers. By continuously monitoring and pre-processing visual data, the system identifies potential issues before they become critical, allowing caregivers to respond proactively rather than reactively, thus improving productivity without complicating operation.
Data Source
AI summary
A system for monitoring a room of a patient includes a server and a monitoring device. The monitoring device is configured to obtain at a first time a first image of at least a part of the room; identify a first state of the patient based on first image; obtain at a second time a second image of the at least the part of the room; identify a second state of the patient based on the second image; and, in response to the first state being different from the second state, sending a first notification to the server. The server is configured to, in response to receiving the first notification, set a monitored condition of the patient to a first value.