Wearable Scene Recognition and Feature Extraction for Memory Recovery
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Solution Overview
Problem
Current methods are inadequate in addressing memory loss and recovery support, particularly for conditions like 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 senses, 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 the process is simple and low-cost, but the effectiveness and speed of memory recovery is insufficient
Solution Approach 1:
The patent introduces an external recognition device as an intermediary between the user and the memory recovery process. This device collects scene data, extracts features, and stores them in a database, serving as a mediator that compensates for the user's memory loss without requiring complex internal brain stimulation or modification.
Solution Approach 2:
The system creates a digital copy of memory information by extracting features from scene data and storing them in a database. This copied memory information can then be retrieved and presented to the user, providing a backup that preserves memory without requiring complex biological intervention.
2Productivity
If automated recognition and data collection systems are implemented, then memory recovery speed and accuracy is improved, but the device complexity and data processing requirements increase
Solution Approach 1:
The patent segments the memory recovery process into distinct modules: data collection by the recognition device, feature extraction by the processing unit, database storage, and information retrieval. This segmentation allows each component to handle specific tasks independently, improving overall efficiency while managing complexity through modular architecture.
Solution Approach 2:
The system extracts salient features from the collected scene data using automated recognition algorithms. By taking out only the essential feature information rather than storing all raw data, the system reduces data processing complexity while maintaining high recovery speed and accuracy.
3Loss of information
If comprehensive data collection from multiple senses is performed, then the completeness of memory information is improved, but the data processing and storage requirements increase
Solution Approach 1:
The patent extracts only the salient features from the comprehensive multi-sense data collected by the recognition device. This extraction process filters out redundant information while preserving essential memory details, thereby maintaining information completeness without proportionally increasing data storage requirements.
Solution Approach 2:
The system applies different processing methods to different types of sensory data based on their local quality and importance. By prioritizing and processing the most salient features from each sense modality, the system maintains comprehensive memory information while optimizing data storage efficiency.
Data Source
AI summary
A memory identification and recovery method, based on recognition, includes storing memory data by a storing means, interacting with users by an interaction means, generating feature marks according the input from the interaction means by a feature marks generation means, searching memory data in the storing means by a search means, and enhancing the memory information of the user through utilizing the memory information of other users by a scene enhance means.