Digital Assistant Readiness Detection Using Affective-Cognitive Load
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Solution Overview
Problem
Current digital assistants, such as advanced driver assistance systems, struggle to accurately assess a driver's cognitive and emotional state, particularly in complex driving situations, as they fail to account for distractions and stimuli beyond visual focus, leading to inadequate assistance and potential safety risks.
Innovation Solution
A computer-implemented method and device that computes a user's readiness state by combining direct measurements of cognitive load and emotional state, using sensors to gather biometric data and apply algorithms to determine an affective-cognitive load, which maps to the user's ability to handle tasks, enabling targeted assistance and notifications.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If a driver monitoring system uses only visual focus detection (camera-based eye tracking), then the system can determine whether the driver's eyes are focused on the road, but the system cannot account for other objects and stimuli that lessen the focus on successfully performing driving tasks
Solution Approach 1:
The patent combines multiple monitoring approaches: visual focus detection via camera with cognitive load assessment through pupillometry (pupil size and response measurements) and emotional state detection. This merging of multiple measurement modalities allows the system to capture both explicit visual focus and implicit cognitive-emotional states that affect driving performance.
Solution Approach 2:
The system introduces pupillometry as an intermediary measurement technique that bridges the gap between simple eye tracking and comprehensive cognitive assessment. By measuring pupil diameter changes and pupillary light reflex responses, the system indirectly assesses cognitive load and emotional arousal without requiring direct intrusion into the driver's cognitive processes.
2Ease of operation
If the ADAS provides assistance based on limited visual focus data, then the system can respond to basic driver state, but the assistance is inadequate for complex driving situations requiring accurate cognitive and emotional assessment
Solution Approach 1:
The system implements continuous feedback loops where pupillometry measurements and emotional state assessments are constantly updated and fed back to adjust the level and type of ADAS assistance provided. This allows the system to dynamically adapt to changing cognitive and emotional states of the driver, ensuring reliable assistance during complex driving situations.
Solution Approach 2:
The ADAS transitions from static, pre-programmed assistance levels to dynamic, real-time adjustment of assistance based on measured cognitive load and emotional state. The system continuously adapts its response characteristics to match the driver's current capacity, providing more assistance when cognitive resources are depleted and less when the driver is fully capable.
3Device complexity
If the system monitors only basic driving tasks, then the implementation is simpler, but the system fails to assess the user's ability to handle complex tasks in dynamic environments
Solution Approach 1:
The patent makes the monitoring system universal by designing it to handle multiple task complexities simultaneously. The same pupillometry and emotional detection infrastructure serves both simple driving tasks and complex dynamic situations, allowing the system to scale its assessment capabilities without requiring separate monitoring systems for different task levels.
Data Source
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AI summary
Embodiments of the present disclosure set forth a computer-implemented method comprising receiving, from at least one sensor, sensor data associated with an environment, computing, based on the sensor data, a cognitive load associated with a user within the environment, computing, based on the sensor data, an affective load associated with an emotional state of the user, determining, based on both the cognitive load at the affective load, an affective-cognitive load, determining, based on the affective-cognitive load, a user readiness state associated with the user, and causing one or more actions to occur based on the user readiness state.