Abnormal Movement Detection via Matrix Dimension Reduction
Find Innovative SolutionsGenerate Solutions
Solution Overview
Problem
Current methods for detecting abnormal movement in physical objects, such as those represented by periodic signals, face challenges in accurately identifying anomalies due to variations in waveform patterns and frequencies, particularly in complex biological signals like ECG signals.
Innovation Solution
A method involving the generation of a raw matrix from periodic signals through multiple analyses, followed by dimension reduction and comparison with benchmark patterns to determine the likelihood of abnormal movement, utilizing techniques like time-domain analysis, morphology analysis, and mutual information calculation to derive feature matrices for enhanced detection.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If multiple analysis methods are used to generate comprehensive feature matrices for detecting abnormal movement, then measurement precision is improved, but device complexity increases
Solution Approach 1:
The detection system is segmented into multiple independent analysis modules (time-domain analysis, frequency-domain analysis, morphology analysis) that each process the periodic signal independently. Each module generates specific feature arrays that are then integrated into a comprehensive feature matrix, allowing the system to maintain high detection accuracy while organizing complexity into manageable, modular components
Solution Approach 2:
Multiple analysis results from different modules are merged into a single integrated feature matrix through dimension reduction techniques. This combining process integrates the strengths of various analysis methods (time-domain, frequency-domain, morphology) to achieve comprehensive detection accuracy while reducing the overall data structure to a manageable form for comparison with benchmark patterns
2Productivity
If dimension reduction is performed on the raw matrix, then productivity is improved, but measurement precision may deteriorate
Solution Approach 1:
The dimension reduction process extracts only the most significant features and patterns from the comprehensive raw matrix, separating essential diagnostic information from redundant data. This extraction maintains the critical measurement precision needed for detecting abnormal movement while removing unnecessary computational burden, thereby improving productivity without sacrificing detection accuracy
Solution Approach 2:
The dimension reduction transforms the raw matrix by changing its parameters - reducing the number of dimensions while preserving the essential variance and information content. This parameter transformation allows the system to process data more efficiently (improving productivity) while maintaining sufficient information for accurate abnormal movement detection (preserving measurement precision)
Data Source
AI summary
The present application discloses a method of detecting abnormal movement of a physical object. A periodic signal is representative of the movement of the object. According to some embodiments, a raw matrix having a first array and a second array is generated, and then an integrated matrix is generated by performing a dimension reduction on the raw matrix. A likelihood of a predetermined type of abnormal movement of the physical object is determined by comparing the integrated matrix with a predetermined benchmark pattern. In some embodiments, the generation of the raw matrix includes performing a first analysis on a predetermined portion of the periodic signal to generate the first array and performing a second analysis different from the first analysis on the predetermined portion of the periodic signal to generate the second array.