Multimodal Cognitive Status Detection Using Eye and Speech Patterns
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Solution Overview
Problem
Poor cognitive states in actors performing high stress and high stakes responsibilities can lead to unacceptable outcomes such as loss of life and assets, as existing technologies fail to effectively assess and monitor cognitive performance.
Innovation Solution
A system utilizing speech and eye sensors to detect parameters, process them through a processor, and correlate them to cognitive status using machine learning models, determining cognitive performance relative to task requirements.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If speech and eye sensors are used to detect parameters and correlate them to cognitive status using machine learning models, then cognitive performance can be effectively assessed and monitored in real-time, but device complexity increases
Solution Approach 1:
The system segments cognitive assessment into multiple independent sensing modalities (speech parameters and eye parameters) that are processed separately through machine learning models. Each sensor type captures specific cognitive indicators, and the results are integrated to form a comprehensive cognitive status determination, thereby improving reliability while managing complexity through modular processing
Solution Approach 2:
The system employs multi-functional sensors that can detect multiple parameters simultaneously - speech sensors capture both acoustic and spectral characteristics, while eye sensors monitor various ocular parameters. This multi-functionality allows comprehensive cognitive assessment using integrated hardware, reducing the need for separate specialized devices and managing system complexity
2Measurement precision
If multiple sensors and machine learning models are integrated to monitor cognitive status, then measurement precision of cognitive performance improves, but device complexity increases
Solution Approach 1:
Machine learning models serve as intermediary components that bridge the gap between raw sensor data and cognitive status determination. The models process complex multi-parameter inputs from speech and eye sensors, transforming them into precise cognitive performance metrics. This intermediary processing layer enables high measurement precision while abstracting the complexity from the user interface
Solution Approach 2:
The system monitors changes in multiple parameters simultaneously - speech parameters (acoustic characteristics, spectral features) and eye parameters (various ocular measurements). By tracking parameter changes over time and correlating them with cognitive status, the system achieves precise measurement of cognitive performance while using standardized parameter sets that can be processed efficiently
Data Source
AI summary
Aspects relate to systems and methods for determining actor status according to behavioral phenomena. An exemplary system includes an eye sensor configured to detect an eye parameter as a function of an eye phenomenon, a speech sensor configured to detect a speech parameter as a function of a speech phenomenon, and a processor in communication with the eye sensor and the speech sensor; the processor is configured to receive the eye parameter and the speech parameter, determine an eye pattern as a function of the eye parameter, determine a speech pattern as a function of the speech parameter, and correlate one or more of the eye pattern and the speech pattern to a cognitive status.