AI Navigation Assistance Using Multi-Modal Sensory Feedback
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Solution Overview
Problem
Existing navigation assistance technologies fail to effectively provide sensory signals to users in new environments, limiting their ability to locate objects based on historical interactions, which can be particularly challenging for visually impaired individuals.
Innovation Solution
A system that uses AI models to analyze sensor data from IoT sensors to identify user movements associated with specific objects, retrieving and providing auditory, olfactory, or haptic sensory data to guide the user to the object's location through wearable devices, leveraging cross-modal neuroplasticity principles.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional navigation assistance methods are used, then the system structure remains simple, but the navigation effectiveness for visually impaired users in new environments deteriorates
Solution Approach 1:
The system segments navigation assistance into multiple sensory modalities (auditory, olfactory, haptic) that can be independently activated. Each sensory channel is processed separately through dedicated sensors and AI models, allowing the system to provide comprehensive navigation support without overwhelming complexity by dividing the assistance function into manageable modular components
Solution Approach 2:
The system performs preliminary actions by collecting and storing sensory data about objects in the environment before the user encounters them. AI models pre-process this data to identify objects of interest and prepare appropriate sensory signals, so that when a user approaches or interacts with an object, the navigation assistance is already ready to be delivered immediately, improving responsiveness and effectiveness
2Measurement precision
If AI models analyze sensor data to identify user movements associated with specific objects, then navigation assistance accuracy improves, but processing time and computational resources increase
Solution Approach 1:
The AI models perform partial analysis by focusing only on the most relevant sensor data and movement patterns needed for object identification. Rather than analyzing all possible sensor inputs comprehensively, the system selectively processes key features of user movements and sensor readings that are most indicative of interaction with specific objects, achieving sufficient accuracy with reduced processing overhead
Solution Approach 2:
The system introduces intermediary processing layers between raw sensor data and final object identification. AI models act as intermediaries that translate complex sensor data and movement patterns into simplified object classifications, and intermediate results are cached and reused when similar patterns occur again, reducing redundant processing time while maintaining identification accuracy
3Reliability
If sensory data from historical user interactions is retrieved and provided to guide the user, then navigation assistance quality improves, but data privacy and security concerns increase
Solution Approach 1:
The system applies local quality by processing and storing sensory data in a decentralized manner. Rather than centralizing all user interaction data in a single repository, the system maintains local data stores on user devices and processes data locally when possible. Only anonymized or aggregated data is transmitted to remote servers, ensuring that sensitive navigation assistance data remains localized and protected while still enabling high-quality personalized navigation support
Data Source
AI summary
A processor may receive first sensor data associated with detected movement of a user in an environment. The processor may determine, using an AI model, based on the first sensor data, that the movement of the user is associated with a first object. The processor may retrieve sensory data associated with the first object from a repository. In some embodiments, the sensory data may include at least one of auditory, olfactory, and haptic data associated with historical user interactions with the first object. The processor may provide a first sensory signal to the user to indicate a location of the first object relative to a first location of the user.