Camera-Based Hypoactive Delirium Detection with Behavioral Baselines
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Solution Overview
Problem
Hypoactive delirium is difficult to recognize early due to subtle symptoms, often leading to misdiagnosis and increased healthcare costs, and existing EEG-based methods are invasive and require human observation.
Innovation Solution
A camera-based system using multimodal audio and video data analysis with an AI model to detect early signs of hypoactive delirium by establishing a patient-specific behavioral baseline and identifying subtle anomalies undetectable by humans.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If EEG machines are used to monitor brain electrical activity, then delirium detection capability is improved, but invasiveness and contact requirements increase
Solution Approach 1:
The patent replaces the mechanical/invasive EEG system with an optical sensing system using cameras to capture visual behavior patterns. Instead of inserting electrodes into the brain, the system uses visual sensors to detect subtle movements, eye movements, and behavioral changes that indicate delirium risk, thereby eliminating the harmful invasive aspect while maintaining detection capability.
Solution Approach 2:
The patent introduces an intermediary AI processing system that analyzes video footage to detect delirium signs. The camera captures visual data, which is then processed by machine learning models to identify patterns indicative of delirium, serving as a non-invasive intermediary between observation and diagnosis.
2Measurement precision
If human observation is used to monitor delirium symptoms, then interpretability is maintained, but detection of subtle anomalies is insufficient
Solution Approach 1:
The patent replaces human visual observation with an automated computer vision system using cameras and AI algorithms. The system continuously captures video data and uses machine learning models to detect subtle behavioral anomalies that would be too faint for human observers to notice, thereby improving detection precision without requiring complex manual observation protocols.
Solution Approach 2:
The system provides continuous automated monitoring through cameras that operate without interruption, unlike intermittent human observations. The AI system continuously analyzes behavioral patterns over time, detecting subtle changes and trends that accumulate indicators of delirium risk, thereby improving detection precision through uninterrupted observation.
3Loss of time
If continuous monitoring is implemented, then early detection capability is improved, but monitoring duration and resource consumption increase
Solution Approach 1:
The patent implements continuous monitoring using cameras that operate throughout the day without interruption. The AI system continuously processes video data to detect early signs of delirium, enabling timely intervention. This continuous action eliminates detection delays while the automated nature of the system makes the monitoring duration manageable through efficient processing.
Solution Approach 2:
The AI system performs autonomous analysis of video data without requiring continuous human intervention. The machine learning models automatically detect patterns and trigger alerts when delirium signs are detected, allowing the system to self-monitor and self-alert, thereby reducing the practical burden of continuous monitoring while maintaining early detection capability.
Data Source
AI summary
According to at least one embodiment, a method of determining a predictive index indicating a level to which a human subject is at risk of developing hypoactive delirium includes: extracting first features of first audio content and first video content continuously capturing the human subject in a setting over a first period; establishing a behavioral baseline specific to the human subject based on the extracted first features; providing the established behavioral baseline to a neural network; extracting second features of second audio content and second video content continuously capturing the human subject in the setting over a second period subsequent to the first period; providing the extracted second features to the neural network for determining the predictive index based on the established behavioral baseline and the extracted second features; and outputting an alert based on the determined predictive index being above a threshold value.