Vision-Assist Device Environmental Classification and Parameter Adjustment
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Solution Overview
Problem
Blind or visually impaired individuals face difficulties navigating their environment due to the inability to detect objects and adapt device parameters appropriately, as existing aid devices often require inappropriate volume settings in different environments.
Innovation Solution
A vision-assist device equipped with sensors and a processor that classifies the environment based on detected objects and adjusts parameters such as audio volume and tactile feedback settings using object correlation algorithms and machine learning.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If the vision-assist device uses fixed parameters for providing information, then the device structure is simple, but the device cannot adapt to different environments effectively
Solution Approach 1:
The patent implements dynamic parameter adjustment by enabling the vision-assist device to automatically modify its operational parameters (such as audio volume, haptic feedback intensity, and notification frequency) based on real-time environment classification. The processor continuously monitors sensor data, classifies the current environment, and adjusts parameters accordingly, transforming a static device into a dynamic one that adapts to changing conditions without requiring manual user intervention.
Solution Approach 2:
The device performs self-adjustment of parameters through automated environment classification and parameter modification algorithms. The processor independently analyzes sensor data, determines the appropriate environment type, and modifies device parameters without external input from the user. This self-service mechanism eliminates the need for manual parameter tuning while maintaining optimal performance across different environments.
2Reliability
If the device provides information at high volume in all environments, then the information is always audible, but the device causes disturbance in quiet environments
Solution Approach 1:
The patent applies local quality by adjusting device parameters according to specific environment characteristics. Different environments (e.g., noisy retail stores vs. quiet libraries) receive tailored parameter settings rather than a uniform configuration. The processor classifies the current environment and applies appropriate parameter sets, ensuring that information delivery is optimized for each specific context while minimizing disturbance to the surrounding environment.
Solution Approach 2:
The system dynamically changes operational parameters based on environment classification. When the processor identifies a quiet environment, it automatically reduces audio volume and adjusts haptic feedback intensity. In noisy environments, the system increases volume and modifies notification patterns. This parameter adaptation ensures reliable information delivery while respecting environmental context and avoiding unnecessary disturbance.
3Adaptability or versatility
If the device adjusts parameters frequently based on environment changes, then the device adapts well to different settings, but the device consumes more energy
Solution Approach 1:
The patent implements periodic action by having the processor evaluate sensor data and reclassify environments at regular intervals rather than continuously. The system monitors for significant environmental changes and triggers parameter adjustments only when classification thresholds are met or at predetermined time intervals. This periodic evaluation approach maintains environmental adaptability while reducing unnecessary processing and energy consumption during stable conditions.
Data Source
AI summary
A vision-assist device may include one or more sensors configured to generate data corresponding to one or more objects present in an environment, and a processor communicatively coupled to the one or more sensors. The processor is programmed to identify one or more objects present in the environment based on the data generated by the one or more sensors, classify the environment based on the one or more identified objects, and modify at least one parameter of the vision-assist device based on the classification of the environment.