Echolocation Sound Nodes for Visually Impaired Navigation
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Solution Overview
Problem
Individuals with no sight or limited sight face challenges in navigating three-dimensional spaces, both physically and virtually, due to the lack of effective auditory cues for spatial awareness.
Innovation Solution
A system that uses unsupervised machine learning to segment a digital representation of a three-dimensional space into depth planes, determines echo sound nodes based on object depth and surface area, and generates spatialized echolocation sounds from these nodes to assist users in navigating through the space.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If traditional navigation methods are used by visually impaired individuals, then they lack spatial awareness, but implementing auditory cue systems increases device complexity
Solution Approach 1:
The system segments the three-dimensional space into multiple depth planes using unsupervised machine learning algorithms. Each depth plane represents a specific distance range from the user, allowing the system to process and prioritize spatial information at different depths separately. This segmentation enables efficient generation of echolocation sounds that correspond to objects at various distances, improving spatial awareness without requiring overly complex processing of the entire scene simultaneously.
Solution Approach 2:
The system introduces echo sound nodes as intermediary elements that represent objects in the environment. These nodes are generated based on detected objects and their properties (depth, surface area), and they serve as mediators between the complex environmental data and the simplified auditory cues presented to the user. The nodes enable the system to translate complex spatial information into intuitive sound characteristics without directly exposing the user to the underlying complexity.
2Measurement precision
If detailed object information is processed to improve echolocation accuracy, then measurement precision increases, but computational requirements and processing time increase
Solution Approach 1:
The system applies partial action by focusing computational resources on processing objects that are most relevant to the user's navigation needs. Rather than processing all detected objects with equal detail, the system prioritizes objects based on their depth, surface area, and potential impact on navigation. This selective processing maintains measurement precision for critical objects while reducing overall computational burden and processing time.
Solution Approach 2:
The system changes parameters such as depth plane segmentation and object priority thresholds dynamically based on the user's context and environmental conditions. By adjusting these parameters, the system can optimize the balance between measurement precision and processing speed, allocating more computational resources when high precision is needed and reducing processing when speed is more critical.
Data Source
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AI summary
Described herein is a system for generating echolocation sounds to assist a user having no sight or limited sight to navigate a three-dimensional space (e.g., physical environment, computer gaming experience, and/or virtual reality experience). Input is received from a user to generate echolocation sounds to navigate a three-dimensional space. Based at least on the received input, a digital representation of the threedimensional space is segmented into one or more depth planes using an unsupervised machine learning algorithm. For each depth plane, object segments are determined for each object within the particular depth plane. Locations of a plurality of echo sound nodes are determined in accordance with the depth level and surface area of each object defined by the determined segments. The echolocation sounds comprising a spatialized sound from each echo sound node originating from the determined location are generated.