Eye Gesture Monitoring for ICU Cognitive State Detection
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Solution Overview
Problem
Hospitalized patients, particularly in intensive care units, face challenges in effective communication due to limited verbal abilities, which can contribute to ICU delirium, and existing systems for communication and cognitive state evaluation are inadequate.
Innovation Solution
A wearable device with a camera captures eye image data, processes it to classify eye gestures, and identifies cognitive states such as delirium, using machine learning and computer vision to transmit alerts or provide outputs based on predefined criteria, enabling communication and monitoring without the need for screens or calibration.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If a wearable device with camera is used to capture and process eye image data for cognitive state monitoring, then communication effectiveness and delirium detection capability are improved, but device complexity and data processing requirements increase
Solution Approach 1:
The patent replaces complex mechanical communication interfaces (buttons, switches, physical boards) with an optical system using a camera to capture eye gestures. This substitution enables patients with motor impairments to communicate through natural eye movements, improving reliability while the wearable form factor keeps device complexity manageable.
Solution Approach 2:
The system processes eye gesture data locally using embedded algorithms that classify gazes and blinks into meaningful commands. This self-service processing reduces the need for complex external processing systems, allowing the wearable device to autonomously interpret patient intentions and generate communications.
2Ease of operation
If eye gesture classification algorithms are implemented to identify cognitive states, then communication capability is improved, but computational requirements and processing time increase
Solution Approach 1:
The system implements a hierarchical processing approach where simple eye gestures (blinks, sustained gazes) are classified using lightweight algorithms that consume minimal energy. More complex cognitive state assessments use additional processing only when necessary, optimizing the balance between communication ease and energy consumption.
Solution Approach 2:
The camera captures eye images at optimized intervals rather than continuous high-frame-rate recording. The system periodically processes eye gesture data to identify cognitive states, reducing computational load and energy consumption while maintaining effective communication capability.
3Measurement precision
If the system processes multiple eye gestures and patterns to determine cognitive state, then monitoring accuracy is improved, but measurement complexity increases
Solution Approach 1:
The patent segments eye gesture analysis into distinct, manageable components: detecting individual eye positions, identifying blink patterns, classifying gaze directions, and determining cognitive states from gesture sequences. This segmentation reduces measurement complexity by breaking down complex patterns into discrete, analyzable units.
Solution Approach 2:
The system uses intermediate classification layers that translate raw eye gesture data into standardized gesture types before final cognitive state determination. This intermediary processing step simplifies the overall measurement complexity by creating a structured intermediate representation of eye movements.
Data Source
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AI summary
The present disclosure provides a system and method for monitoring the cognitive state of a patient based on eye image data. The patient monitoring system comprising a camera unit configured for recording images of an eye of the patient, and a data processing sub-system in data communication with the camera and being operable to (i) receive and process eye image data from said camera, (ii) classify said eye image data into gestures and identify such gestures indicative of the cognitive state of the patient, and (iii) transmit a signal communicating said cognitive state to a remote unit. The system may further comprise an actuator module and an output unit wherein said output may be an automated medical questionnaire.