Signal Threshold Identification via Windowed Approximation
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Solution Overview
Problem
Current digital signal processing methods struggle to effectively characterize and distinguish between different types of signals, particularly in applications like motion detection, where identifying specific patterns or gestures is crucial, due to limitations in accurately identifying thresholds and patterns within acceleration signals.
Innovation Solution
The method involves sampling signals, dividing them into windows, and associating each window with one of several values based on constant, incremental, or decremental changes, generating an approximation vector, determining threshold positions, and assigning threshold values to characterize the signal, which can be used to identify motion patterns by comparing sets of accelerations followed by decelerations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If traditional digital signal processing methods are used to characterize signals, then the processing is simple, but the ability to accurately identify thresholds and distinguish different signal patterns is insufficient
Solution Approach 1:
The signal is divided into multiple windows, and each window is processed independently to generate approximation values. This segmentation allows the system to analyze local signal characteristics more precisely without overwhelming computational complexity, as each window can be processed in parallel or sequentially with simple comparison operations.
Solution Approach 2:
The patent performs preliminary processing by dividing the signal into windows and generating approximation values before conducting the actual threshold identification. This preliminary organization of data into structured segments facilitates more accurate and efficient threshold detection in subsequent processing stages.
2Reliability
If more detailed signal analysis is performed to improve pattern recognition, then the identification accuracy improves, but the processing time increases
Solution Approach 1:
The patent applies a simplified approximation method that uses only three possible values (constant, incremental, decremental) for each window rather than analyzing the full continuous signal. This partial analysis captures the essential motion patterns while significantly reducing computational time, providing sufficient accuracy for gesture recognition applications.
Solution Approach 2:
The patent transforms the continuous signal into a discrete approximation vector with limited parameter values (constant, incremental, decremental). This parameter transformation simplifies the data structure and enables faster processing while maintaining the key characteristics needed for reliable motion pattern recognition.
3Productivity
If simple signal processing is used, then the processing speed is fast, but the ability to characterize and distinguish different signal types is limited
Solution Approach 1:
By segmenting the signal into windows and analyzing each segment's trend (constant, incremental, decremental), the patent preserves essential signal characteristics while enabling fast processing. Each segment's approximation value captures the local behavior without requiring detailed analysis of every sample point.
Solution Approach 2:
The patent changes the parameter representation from continuous amplitude values to discrete trend categories (constant, incremental, decremental). This parameter transformation maintains the essential dynamic characteristics of the signal while enabling rapid comparison and pattern recognition operations.
Data Source
AI summary
A signal is sampled and the samples are divided into windows. One of at least three values is associated with a respective window based on whether the samples in the window are relatively constant, incremental or decremental, to thereby generate an approximation vector. Locations of thresholds in the approximation vector are determined, to generate a threshold position vector. Threshold values are assigned to the threshold positions in the threshold position vector. The threshold values, taken alone or in combination with a threshold position vector, may be used to characterize the signal. A training sequence may be used to adapt the functionality to individual needs.