Signal Conversion via Feature Point Extraction
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Solution Overview
Problem
Existing wearable devices, such as walking assistance robots, face challenges in efficiently converting and reconstructing signals for data storage and transmission, leading to increased storage requirements and power consumption, which hinders their compactness and usability.
Innovation Solution
A method and system for converting and reconstructing signals by acquiring signal-analyzed data with feature points, transmitting and receiving this data along with reference data, and using it to reconstruct the original signal, allowing for reduced data size and lower power consumption.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the original signal is stored and transmitted directly, then the data完整性 is maintained, but the storage requirements and power consumption increase
Solution Approach 1:
The patent extracts only the essential feature points from the original signal rather than storing the complete signal. The feature point extraction unit identifies and extracts key characteristics (peak points, valley points, inflection points) that represent the essential information of the original signal, thereby reducing data volume and power consumption while maintaining data integrity
Solution Approach 2:
The patent creates a simplified copy of the original signal by representing it through feature points and reference data. Instead of storing the full signal waveform, the system stores a compact representation that can be reconstructed later, significantly reducing storage requirements and transmission power while preserving the essential signal characteristics
2Reliability
If the original signal is stored and transmitted directly, then the data完整性 is maintained, but the storage requirements and power consumption increase
Solution Approach 1:
The patent extracts only the essential feature points from the original signal rather than storing the complete signal. The feature point extraction unit identifies and extracts key characteristics (peak points, valley points, inflection points) that represent the essential information of the original signal, thereby reducing data volume and power consumption while maintaining data integrity
Solution Approach 2:
The patent segments the original signal into discrete feature points rather than storing the continuous waveform. By dividing the signal into key characteristic points and storing them separately with reference data, the system achieves significant data compression while maintaining the ability to reconstruct the original signal accurately
3Use of energy by moving object
If feature point extraction and reconstruction is implemented, then data size and power consumption are reduced, but the device complexity increases
Solution Approach 1:
The patent implements self-service through automatic feature point detection algorithms that identify key signal characteristics without requiring complex manual processing. The system automatically detects peak points, valley points, and inflection points based on predefined criteria, reducing the need for complex external processing while maintaining low power consumption and data size benefits
Data Source
AI summary
Disclosed herein is a method of converting and reconstructing a signal, including: at a data generator, acquiring signal-analyzed data from an original signal, wherein the signal-analyzed data includes at least one feature point acquired from the original signal; transmitting and receiving the signal-analyzed data and at least one reference data corresponding to the signal-analyzed data; and reconstructing the original signal based on the signal-analyzed data and the at least one reference data to acquire a reconstructed signal.