Personalized Edge Device Navigation via AI Object Classification
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Solution Overview
Problem
Existing guidance devices for visually impaired individuals are limited in their ability to detect specific navigation obstacles and provide personalized feedback, as they often have static designs and operational performance that do not account for user-specific preferences or unique environmental conditions.
Innovation Solution
A guidance assistance device that uses AI and machine learning techniques, specifically reinforcement learning, to process video data from a camera, classify objects, and provide personalized feedback through haptic, auditory, or visual means, adapting to user preferences and environmental contexts.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If generic guidance devices are used, then device complexity is reduced, but adaptability to user-specific preferences and environmental conditions deteriorates
Solution Approach 1:
The guidance device dynamically adapts its operational parameters based on user feedback and environmental conditions. The device modifies feedback thresholds, notification types, and guidance parameters in real-time to match user preferences and changing environmental contexts, transforming a static device into a dynamic, adaptive system that personalizes assistance without requiring complete system redesign
Solution Approach 2:
The system implements continuous feedback loops where user responses to guidance notifications are captured and used to adjust future feedback delivery. The device learns from user reactions (acknowledgments, corrections, ignore patterns) and environmental data to refine its guidance strategy, enabling adaptability through iterative learning rather than complex pre-programming
2Measurement precision
If static design is used, then manufacturing precision is improved, but measurement precision of specific navigation obstacles deteriorates
Solution Approach 1:
The device transitions from static obstacle detection to dynamic classification, where detected objects are continuously categorized based on user-defined importance criteria. The system adapts its detection and classification parameters in real-time according to user feedback and environmental context, enabling precise identification of navigation-critical obstacles while maintaining operational stability
Solution Approach 2:
The system changes detection and classification parameters dynamically based on user preferences and environmental conditions. Different object types (pedestrians, vehicles, animals) receive different classification thresholds and feedback priorities, allowing the device to maintain stable core functionality while adapting measurement precision for specific obstacle categories
3Loss of information
If generic feedback is provided, then ease of operation is improved, but loss of information about user-specific preferences increases
Solution Approach 1:
The guidance device automatically learns and adapts to user preferences without requiring manual configuration. The system monitors user responses to feedback notifications and autonomously adjusts feedback parameters, thresholds, and delivery methods, eliminating the need for users to manually program their preferences while preserving personalized information
Solution Approach 2:
The system uses user feedback on notifications as learning data to refine future feedback delivery. By analyzing user responses (acknowledgments, corrections, timing patterns), the device automatically adjusts feedback intensity, timing, and type to match user preferences, reducing information loss while maintaining ease of operation
Data Source
AI summary
A method, system, and computer program product provide navigation guidance around various object types in a vicinity of a guidance assistance device for a user by analyzing received data regarding an environment around the guidance assistance device to identify one or more entities Ei and by applying an artificial intelligence machine learning analysis to group the one or more entities Ei into corresponding categories Cj and to determine a minimum spacing distance Dmin for each of the one or more entities Ei, wherein the minimum spacing distance Dmin is a minimum distance between the guidance assistance device and the entity Ei based on the categorization Ci specified by the user, and then providing feedback to the user when any of the one or more entities Ei is within than minimum spacing distance Dmin corresponding to said entity.