Wearable Memory Recovery Through Salient Feature Mapping
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Solution Overview
Problem
Current methods are inadequate in addressing memory loss and recovery, particularly in conditions such as Alzheimer's disease and other nervous system degenerative diseases, traumatic brain injury, and psychological amnesia.
Innovation Solution
A memory identification and recovery method utilizing wearable devices that collect data through various sensory inputs, perform salient feature extraction, build mapping relations, and store information in a database for retrieval using artificial intelligence and deep learning techniques.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional memory recovery methods are used, then simplicity is maintained, but effectiveness in addressing memory loss is insufficient
Solution Approach 1:
The system segments memory recovery into multiple specialized modules: data collection module (collecting information through sensors), salient feature extraction module (identifying key features), mapping relation building module (creating associations), and retrieval module (searching and recovering memories). Each module handles a specific aspect of the complex memory recovery process, improving effectiveness while managing complexity through functional decomposition.
Solution Approach 2:
The patent introduces a database as an intermediary storage layer between the recognition device and the user. The database stores collected data, extracted features, and built mapping relations, serving as a mediator that enables systematic processing and retrieval of memory information without requiring direct complex processing at each interaction point.
2Loss of information
If comprehensive data collection through multiple sensory inputs is implemented, then memory recovery completeness is improved, but data processing complexity increases
Solution Approach 1:
The system extracts only the salient features from comprehensive multi-sensory data through dedicated feature extraction processing. Instead of storing and processing all raw sensory data, the system identifies and extracts key distinguishing features (such as prominent visual elements, significant audio patterns, or important tactile characteristics) that are most relevant for memory identification and recovery.
Solution Approach 2:
The system performs preliminary data processing and feature extraction in advance, before the actual memory retrieval is needed. Data collection, feature extraction, and mapping relation building are conducted proactively during normal operation, so that when memory recovery is required, the system can quickly query pre-processed data rather than processing raw sensory inputs in real-time.
3Measurement precision
If automated feature extraction and mapping relation building are performed, then memory identification accuracy is improved, but processing time is increased
Solution Approach 1:
The system performs feature extraction and mapping relation building as preliminary actions during data collection phases, so that when memory retrieval is needed, the processing is already complete. The database stores pre-extracted features and pre-built mapping relations, enabling rapid query and retrieval without repeating the computationally intensive extraction and mapping processes.
Solution Approach 2:
The system dynamically adjusts the level of feature extraction and processing based on the specific retrieval needs. For simple queries, the system can use pre-extracted features directly; for more complex retrieval scenarios, it can perform additional feature extraction or refine mapping relations, optimizing the balance between accuracy and processing time based on actual requirements.
Data Source
AI summary
A memory identification and recovery device, based on recognition, includes a storing means stores memory data, an interaction means interacts with users, a feature marks generation means generates feature marks according the input from the interaction means, a search means searches memory data in the storing means, and a scene enhance means enhances the memory information of the user through utilizing the memory information of other users.