Memory Recovery System Using Saliency Detection
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Solution Overview
Problem
Current methods are inadequate in addressing memory loss and recovery, particularly in cases of Alzheimer's disease and other nervous system degenerative conditions, as they lack effective technologies for identifying and recovering memories.
Innovation Solution
A memory identification and recovery method utilizing wearable devices that employ saliency detection, automatic semantic image data, and shape segmentation to collect, process, and store memory information, enabling users to retrieve memories through a database search system that uses AI and deep learning for efficient recall.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional memory recovery methods are used, then simplicity is maintained, but memory retrieval effectiveness is insufficient
Solution Approach 1:
The memory recovery system is segmented into multiple functional modules: data collection module (acquiring information through wearable devices), feature extraction module (extracting salient features from collected data), memory encoding module (converting features into memory representations), storage module (storing in database), and retrieval module (searching and recovering memories). This segmentation allows each module to specialize in specific tasks, improving overall memory retrieval effectiveness while managing system complexity through modular design.
Solution Approach 2:
The system performs preliminary actions by continuously collecting and processing data in advance before memory loss occurs. Wearable devices constantly gather sensory information, the system pre-extracts features and encodes memories into the database proactively. This preliminary encoding ensures that when memory recovery is needed, the system can quickly retrieve pre-processed information rather than processing raw data in real-time, significantly improving retrieval effectiveness.
2Loss of information
If comprehensive data collection is performed, then memory information completeness is improved, but data processing time increases
Solution Approach 1:
The system extracts only the most salient and relevant features from the comprehensive collected data using saliency detection algorithms. Instead of processing all collected sensory information equally, the system identifies and extracts key features that are most important for memory formation and recovery. This selective extraction maintains memory information completeness by capturing essential details while significantly reducing the volume of data requiring further processing and storage.
Solution Approach 2:
The system performs preliminary feature extraction and salient feature identification immediately when data is collected, rather than waiting until retrieval time. This preliminary processing prepares the data in advance by organizing it into meaningful features and relationships, so that when memory recovery is needed, the system works with pre-processed feature representations rather than raw comprehensive data, reducing retrieval processing time.
3Measurement precision
If AI-based feature extraction is used, then memory recognition accuracy is improved, but computational resource consumption increases
Solution Approach 1:
The system extracts only the most discriminative and informative features using AI-based saliency detection, rather than processing and analyzing all collected data. By identifying and extracting only the salient features that contribute most to memory recognition accuracy, the system achieves high measurement precision while minimizing the computational resources required for feature processing and comparison during retrieval operations.
Data Source
AI summary
The present invention is adapted for recognition technology improvement, which provides a memory identification and recovery method based on recognition, including: S1. collecting the data information from the scene of activity through a recognition device; S2. conducting salient feature extraction to the data information collected from the scene and generating feature marks; S3. building mapping relations between the generated feature marks and the extracted data information, automatically generating memory information in the database, and storing the information in the database; S4. inputting related data information for searching; S5. selecting a corresponding method to search the generated memory information in the database based on the input data information; S6. determining if there is related data information in the memory data. The method can helps to enhance memory of the user, recover memory after forget it, recover effectively through recognition technology, improve memory, and retrieve memory quickly after memory loss, which is convenient and efficient.