XR Stimulus Perception Feedback Using Multi-Channel Physiological Signals
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Solution Overview
Problem
Current extended reality (XR) systems lack the ability to accurately determine whether notifications or stimuli have been perceived by users without explicit interaction, leading to inefficiencies and disruptions due to false positives or negatives, which impact user productivity and well-being.
Innovation Solution
A method that uses multi-channel physiological signals, such as skin conductance, photoplethysmography, and electroencephalography, to determine perception by analyzing changes in these signals before and after stimulus presentation, weighted by user activity to improve accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If explicit user interaction is required to determine perception, then the system can confirm perception with high reliability, but user productivity decreases due to manual acknowledgment requirements
Solution Approach 1:
The system automatically detects user perception through physiological signals without requiring manual acknowledgment. The processor analyzes skin conductance, heart rate, and other biometric data to determine when the user has perceived the stimulus, allowing the system to self-verify perception and eliminate the need for explicit user interaction.
Solution Approach 2:
The system implements a closed-loop feedback mechanism where physiological signals provide real-time feedback about user perception state. The processor continuously monitors biometric data and adjusts notification delivery based on detected perception, creating a feedback loop that improves both reliability and user experience without manual input.
2Productivity
If physiological signals are used to detect perception, then user productivity improves by eliminating manual acknowledgment, but measurement precision may decrease due to signal corruption from user activities
Solution Approach 1:
The system segments the perception detection process into multiple independent physiological signal channels (skin conductance, heart rate, respiration). By dividing the detection task across multiple signal types, the system can identify perception through consensus or weighted combination of channels, reducing the impact of corruption in any single channel.
Solution Approach 2:
The system employs multiple physiological signal sources that serve the same perception detection function. Different biometric sensors (accelerometers, gyroscopes, skin conductance sensors, heart rate monitors) universally contribute to perception detection, allowing the system to maintain precision even when some signals are corrupted by user activities.
3Reliability
If notifications are repeated to ensure perception, then perception reliability improves, but user well-being deteriorates due to sensory and cognitive demands
Solution Approach 1:
The system performs preliminary detection of user perception state before delivering notifications. By analyzing physiological signals in advance, the system determines the optimal timing for notification delivery, ensuring high perception reliability without needing to repeat notifications. The processor detects when the user is in a receptive state and delivers stimuli at that optimal moment.
Solution Approach 2:
The notification delivery system dynamically adapts to real-time physiological state. Rather than using fixed repetition schedules, the system adjusts notification timing and characteristics based on detected perception states, delivering stimuli when physiological indicators suggest high attentiveness and avoiding repetition when the user is already engaged or stressed.
Data Source
AI summary
In example embodiments, a method includes obtaining a first measurement of at least a first physiological parameter of a user from a time before presentation of an information item to the user. The information item may be, for example, a notification, an advertisement, or an emergency alert, among other possibilities. A second measurement of the first physiological parameter is obtained from a time after a beginning of the presentation of the information item to the user (e.g. during the presentation). Based at least on a comparison between the first and second measurements, a determination is made of whether the user has perceived the information item. The determination may also be based on a corruption coefficient indicating an amount by which an activity of the user is likely to interfere with the first physiological parameter.