Neurological Training Graph Navigation via Gesture Feedback
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Solution Overview
Problem
Existing meditation and therapeutic applications on mobile devices are functionally linear and disrupt user experience with pre-recorded audio, while user interaction through graphical user interfaces can break the meditation or therapeutic session.
Innovation Solution
A computer-implemented method using a graph data structure with nodes associated with logical statements and audible outputs, allowing user gesture inputs to navigate through the graph, with machine-learning algorithms to adapt the session based on user feedback, enabling interactive and dynamic neurological training.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If pre-recorded audio is played from start to finish without user interaction, then the therapeutic session maintains continuity and immersion, but the application cannot capture user feedback or adapt to user needs
Solution Approach 1:
The patent introduces gesture recognition as an intermediary mechanism that allows users to provide feedback without interacting with traditional GUI elements. Sensors detect gestures (e.g., hand movements in the air) that serve as mediators between the user's internal state and the application's response system, enabling feedback capture while maintaining therapeutic immersion.
Solution Approach 2:
The patent replaces the mechanical interaction system (touchscreen buttons, sliders, and other GUI elements) with a gesture-based recognition system. This substitution allows users to interact through natural hand movements detected by sensors, eliminating the need to visually engage with or physically touch the device interface during the therapeutic session.
2Loss of information
If graphical user interfaces are used for user interaction, then user feedback can be captured, but the meditation or therapeutic session is disrupted by requiring visual attention and manual interaction
Solution Approach 1:
Gestures serve as an intermediary that captures user feedback information without requiring direct interaction with the device interface. The sensor system detects hand movements in the air, translating these gestures into feedback data while keeping the user's visual attention focused on their internal experience rather than the device screen.
Solution Approach 2:
The patent extracts the feedback capture function from the visual GUI interface and relocates it to the sensor system that detects gestures in the air. This separation allows feedback collection to occur independently of the visual display system, eliminating the need for users to look at or interact with graphical elements during the session.
3Adaptability or versatility
If a linear audio playback system is used, then the application structure is simple and easy to implement, but the training experience lacks customization and adaptability to individual user needs
Solution Approach 1:
The patent transforms the static linear audio playback structure into a dynamic adaptive system. The audio content and session flow automatically adjust based on real-time gesture feedback, creating a dynamic training experience that adapts to each user's needs while maintaining a relatively simple underlying architecture through rule-based decision logic.
Solution Approach 2:
The patent implements a feedback loop where gesture inputs are continuously monitored and used to modify the audio playback and session progression. This feedback mechanism enables customization of the training experience without requiring complex manual configuration, as the system automatically adjusts based on detected user gestures and responses.
Data Source
AI summary
Provided are systems, methods, and devices for interactive neurological training. The method includes traversing a graph data structure based on a user profile for a user operating a device, the graph data structure including a plurality of nodes, each node of the plurality of nodes associated with at least one of a logical statement and an audible output, presenting at least one audible output to the user based on at least one node of the graph data structure, receiving a gesture input from the user through the device in response to the at least one audible output, determining a next node of the graph data structure from at least two different nodes connected to the at least one node, and presenting at least one further audible output to the user based on the next node of the graph data structure.