Query Assistant Using Brainwave Data for Paralyzed Users
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Solution Overview
Problem
Individuals with severe physical challenges face difficulties interacting with conventional devices and services due to limited physical abilities, as existing technologies like voice commands and motion sensing are inadequate for those who cannot speak or move effectively.
Innovation Solution
The development of query assistant software that uses brainwave data to enable guided interaction, employing machine learning models to process user monitoring data and update the interface based on intended physiological actions such as jaw movements or eye blinking, allowing for improved detection of user responses.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If voice commands are used to control devices, then interaction capability is improved for able-bodied users, but individuals with paralysis who cannot speak remain unable to interact
Solution Approach 1:
The patent replaces traditional mechanical input methods (voice commands, touchscreen, keyboard) with a neural-based detection system that monitors physiological signals such as brainwaves, eye movements, and muscle activity. This substitution enables paralyzed individuals to interact with devices through residual physiological signals that conventional interfaces cannot detect.
Solution Approach 2:
The system changes the detection parameters from acoustic (voice) or tactile (touchscreen) to physiological parameters including brainwave frequencies, eye movement trajectories, and muscle electrical activity. This parameter transformation allows the system to capture subtle physiological signals that indicate user intent despite physical paralysis.
2Ease of operation
If motion sensing technologies are used to detect finger or eye movements, then control capability is improved, but individuals with severe paralysis have very little capability to move various muscles making detection difficult
Solution Approach 1:
The patent introduces specialized sensors and signal processing algorithms as intermediaries between the user's residual physiological signals and the device control system. These intermediaries amplify and interpret subtle signals such as micro-eye movements or minimal muscle contractions that would otherwise be undetectable by conventional motion sensors.
Solution Approach 2:
The system replaces conventional motion detection mechanisms with physiological signal monitoring that can detect brainwaves and electrical muscle activity. This substitution enables detection of intent at the neural level, before any physical movement occurs, thereby overcoming the limitation of requiring visible muscle movement.
3Ease of operation
If touchscreen interfaces are used for device interaction, then ease of use is improved for able-bodied users, but individuals with paralysis in their hands find the device virtually inaccessible
Solution Approach 1:
The patent replaces the mechanical touchscreen interaction model with a physiological signal-based control model. Instead of requiring physical contact with the screen, the system detects brainwaves, eye movements, or muscle signals that indicate selection intent, thereby eliminating the need for hand movement while maintaining intuitive interaction.
Solution Approach 2:
The system creates a universal interface that can serve both able-bodied users and paralyzed individuals through the same physiological detection mechanism. The interface adapts to different user capabilities by interpreting physiological signals in context, making it simultaneously accessible to users with varying levels of physical function.
Data Source
AI summary
Systems and methods are configured to enable guided interaction with query assistant software using brainwave data. In various embodiments, a client device presents a query assistant user interface to a monitored end-user that describes a query associated with a plurality of response options and an intended physiological action for each response option. Accordingly, one or more user monitoring data objects associated with the monitored end-user are received that include user brainwave monitoring data objects. These user monitoring data objects are processed using one or more response designation machine learning models to generate response designators based on the user monitoring data objects that includes a physiological response designator describing a selected intended physiological action that is deemed to be related to the user monitoring data objects. Accordingly, a user response is then determined based on the response designators and the user interface may be updated based on the user response.