Hospital Bed Sensor Fusion for Automated Patient Condition Inference
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Solution Overview
Problem
Existing hospital equipment lacks the ability to autonomously determine patient conditions, relying heavily on human caregivers, which is inefficient due to caregiver scarcity and patient overload.
Innovation Solution
A machine learning compute device integrated with hospital beds to analyze sensor data from load cells, pressure sensors, and video cameras to infer patient conditions, using models trained on actual patient data to provide accurate assessments without continuous human intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human caregivers manually assess patient conditions, then assessment accuracy is maintained through direct observation, but caregiver availability decreases due to patient overload and scarcity
Solution Approach 1:
The patient support apparatus autonomously monitors and assesses patient conditions using integrated sensors, load cells, and machine learning algorithms, enabling the system to serve itself without requiring continuous human caregiver intervention for routine assessments
Solution Approach 2:
The patent replaces the mechanical system of human visual inspection and manual assessment with electronic sensors, automated imaging systems, and machine learning-based analysis algorithms that continuously evaluate patient conditions
2Loss of information
If multiple sensors and monitoring devices are integrated into hospital equipment, then patient condition data collection is improved, but device complexity increases
Solution Approach 1:
The patent combines multiple previously separate monitoring functions (weight measurement via load cells, pressure distribution sensing, positioning detection, and visual assessment via cameras) into a single integrated patient support apparatus that performs comprehensive patient assessment through unified hardware and software systems
Solution Approach 2:
The patient support apparatus is designed as a multi-functional system that simultaneously performs support, positioning, weight measurement, pressure monitoring, and automated patient condition assessment, eliminating the need for separate dedicated devices for each function
3Loss of time
If continuous patient monitoring is implemented using automated systems, then caregiver time allocation is improved, but automation extent must increase significantly
Solution Approach 1:
The system implements continuous automated monitoring of patient conditions through constantly active sensors, load cells, and imaging systems that operate without interruption, providing ongoing assessment data to free caregiver time while maintaining uninterrupted surveillance
Solution Approach 2:
The machine learning system continuously receives feedback from sensor data, load cell measurements, and imaging systems to dynamically update patient condition assessments, enabling adaptive monitoring that improves accuracy over time while reducing the need for manual intervention
Data Source
AI summary
A machine learning compute device may include circuitry configured to obtain sensor data from a product associated with a patient. The circuitry may also be configured to obtain response variable data indicative of an actual condition of the patient associated with the sensor data. Additionally, the circuitry may be configured to train, based on the response variable data and the sensor data, an inference model to infer the actual condition of the patient from the sensor data.