Biosignal Interaction Framework for Adaptive AR/VR User Selection
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Solution Overview
Problem
Individuals with limited mobility, impaired perception, or inexperience in interacting with augmented and virtual reality systems face challenges in effectively communicating and making choices in their environment without assistance.
Innovation Solution
An interaction framework that utilizes biosignal inputs, such as EEG, ECoG, ECG, EMG, and other sensors, to interpret user attention and context, enabling dynamic user interfaces that adapt to user intentions through a classifier and context estimator, integrating with generative AI to facilitate interaction with assistive devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional user interfaces are used, then system accessibility is limited, but implementation complexity remains low
Solution Approach 1:
The system integrates multiple input modalities (biosignals, eye tracking, head tracking, binary input, gestures) into a single unified interface framework that can serve diverse user needs. The classifier system processes different input types through a common architecture, enabling the system to accommodate users with varying abilities while maintaining a consistent interaction model across all input methods
Solution Approach 2:
The interaction framework acts as an intermediary layer between the user's biosignals and the assistive device controls. The framework includes a classifier that translates biosignal patterns into meaningful commands, and a context estimator that adapts the interface based on user state, thereby mediating between raw physiological data and actionable device control without requiring complex direct mapping
2Ease of operation
If biosignal-based interaction framework is implemented, then user agency is enhanced, but system complexity increases
Solution Approach 1:
The system continuously monitors user biosignals and automatically adjusts the interface configuration without requiring explicit user commands. The context estimator analyzes user state in real-time and dynamically modifies UI parameters, allowing the system to serve itself by adapting to user needs autonomously based on physiological feedback
Solution Approach 2:
The user interface configuration is made dynamic through continuous biosignal monitoring and context estimation. The system adjusts UI parameters such as selection targets, feedback modes, and interaction thresholds in real-time based on detected user state, transforming a static interface into an adaptive system that evolves with user needs during interaction
3Measurement precision
If dynamic UI configuration is used, then interaction accuracy improves, but processing requirements increase
Solution Approach 1:
The system implements partial adaptation by focusing computational resources on the most critical UI parameters that directly impact interaction accuracy, such as selection target positioning and feedback timing. Rather than optimizing all UI aspects simultaneously, the context estimator prioritizes adjustments to parameters with the greatest impact on user performance while maintaining other parameters at baseline levels
Data Source
AI summary
An interactive system is presented for interacting with a user through biosignals and sensor data. The system includes a user input system, an interaction framework, a dynamic user interface (UI), a processor, and a memory. The interaction framework processes biosignal inputs, sensor data, and current context data to provide a context estimation, which may be used to classify user attention and determine the state of selection targets. The dynamic UI receives the UI configuration and LLM/GenAI suggestions or instructions, and provides output to the user. The system can also include additional features, such as presenting selection targets in the idle state, analyzing biosignal inputs and context data to determine user attention, and performing actions based on selection states.