Gait Signal Compression Using Characteristic Feature Extraction
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Solution Overview
Problem
Current methods for processing and recovering gait signals, which are used for health management and research, face challenges in efficiently compressing and decompressing these signals while maintaining their characteristic features, particularly for gait characteristics like start, end, peak, and valley points, which are essential for accurate analysis.
Innovation Solution
A signal processing method that involves compressing data signals by sampling and quantizing them based on gait characteristics, appending sampling information to the header, and using a controller to generate transmission data, along with a signal recovering method that decompresses and compensates for distortions using characteristic features like start, end, peak, and valley points.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Quantity of substance
If gait signals are compressed by sampling and quantizing, then data storage efficiency is improved, but signal characteristic features may be lost
Solution Approach 1:
The patent applies preliminary action by detecting and recording characteristic features (start point, end point, peak point, valley point) of gait signals before compression. This allows the compression algorithm to preserve these critical features while reducing overall data quantity, ensuring that essential gait characteristics are maintained in the compressed signal.
Solution Approach 2:
The patent uses parameter changes by adjusting quantization parameters and sampling rates based on the detected characteristic features. The compression process modifies signal parameters dynamically to maintain feature integrity, changing the representation of the signal while preserving its essential characteristics for accurate gait analysis.
2Measurement precision
If sampling rate is increased to preserve signal accuracy, then measurement precision is improved, but data transmission volume increases
Solution Approach 1:
The patent extracts only the essential characteristic features (start, end, peak, valley points) from the continuous gait signal rather than transmitting the entire high-resolution signal. This extraction approach maintains measurement precision for critical gait events while significantly reducing the data transmission volume required for accurate gait analysis.
Solution Approach 2:
The patent applies partial action by sampling and transmitting only the portions of the signal that contain characteristic features, rather than transmitting the complete high-resolution signal. This partial transmission approach provides sufficient accuracy for gait analysis while minimizing data transmission requirements.
Data Source
AI summary
A signal processing method including receiving a signal, compressing the signal through a sampling of the signal, and generating transmission data of the signal by matching at least one feature indicating characteristics of the signal.