Visual Prosthesis Video Processing for Safe Neural Stimulation
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Solution Overview
Problem
Visual prostheses implants face challenges in effectively converting visual images into stimulation inputs for blind individuals, particularly in ensuring safety and optimizing neural stimulation for individual patients.
Innovation Solution
A method and system that involves receiving and digitizing images, checking their integrity, filtering them, and converting them into stimulation inputs for visual prosthesis implants, using a Retinal Stimulation System with a Video Processing Unit, Psychophysical Test System, and telemetry engine to provide personalized stimulation based on patient-specific settings.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If video frames are processed through multiple stages (digitizing, integrity checking, filtering, conversion), then the safety and reliability of neural stimulation is improved, but the system complexity and processing time increase
Solution Approach 1:
The video processing system is divided into distinct functional modules: a video decoder for receiving and decoding video signals, a digitizer for converting analog to digital format, an integrity checker for verifying data validity, a filter processor for optimizing the video frames, and a telemetry engine for converting to stimulation inputs. This segmentation allows each module to perform its specific function reliably while maintaining overall system manageability through modular architecture.
Solution Approach 2:
The integrity checking step is performed before filtering and conversion to ensure that only valid video frame data proceeds through subsequent processing stages. This preliminary validation prevents propagation of errors through the system and ensures safety without requiring complex error handling in later stages.
2Productivity
If video frames are filtered and converted with patient-specific settings, then the effectiveness of neural stimulation is improved, but the processing time and computational resources increase
Solution Approach 1:
Patient-specific filter settings are predetermined and stored in the system before video processing begins. These settings include filter type selections and parameter values tailored to individual patient needs. By pre-configuring these settings, the system avoids real-time calculation delays while still providing personalized optimization of the filtering process for each patient.
Solution Approach 2:
The filter processor dynamically adjusts processing parameters based on the selected filter type and patient-specific settings. Different filter types (e.g., spatial filters, temporal filters) have different computational requirements, and the system optimizes processing by selecting appropriate parameter sets that balance effectiveness with processing speed for each patient's specific needs.
Data Source
AI summary
Stimulation inputs are provided to a visual prosthesis implant. The images captured by a video decoder are received and digitized to provide a plurality of video frames; integrity of the video frames is checked, the checked video frames are filtered, and the filtered video frames are converted to stimulation inputs. A similar system is also disclosed.