Nonlinear Coherence Analysis for Peak Performance State Detection
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Solution Overview
Problem
Current methods for determining peak performance states in individuals are inaccurate due to their reliance on linear comparisons of complex and dynamic physiological and non-physiological signals, failing to accurately identify and characterize optimal performance states.
Innovation Solution
A system and method utilizing nonlinear analysis of input signals from sensors to determine coherence between physiological and non-physiological signals, employing a combination of manifold learning and support vector machine algorithms, with a feedback mechanism to notify the test subject of their current peak performance state.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If linear comparison methods are used to determine peak performance states, then the measurement process is simple and straightforward, but the accuracy of peak performance state identification deteriorates due to the complex and dynamic nature of physiological signals
Solution Approach 1:
The patent transforms the analysis from linear parameter comparison to nonlinear coherence analysis. Instead of comparing individual signal parameters directly, the system calculates coherence values between different physiological signals (EEG, ECG, EMG) across multiple frequencies, fundamentally changing the measurement parameters to capture dynamic relationships between signals.
Solution Approach 2:
The patent adds a new dimension of analysis by introducing coherence measurement between multiple physiological signals simultaneously. Rather than analyzing signals independently in one dimension, the system evaluates the interrelationships between EEG, ECG, and EMG signals across frequency domains, creating a multi-dimensional coherence matrix that captures complex physiological dynamics.
2Measurement precision
If traditional linear comparison algorithms are used, then the computational process is fast and efficient, but the ability to accurately characterize optimal performance states deteriorates
Solution Approach 1:
The patent performs preliminary coherence calculations across all signal pairs and frequencies before the actual performance assessment. By pre-computing the coherence matrix and identifying baseline relationships between physiological signals, the system reduces the computational burden during real-time performance evaluation, storing these results for rapid comparison against performance criteria.
Solution Approach 2:
The patent divides the complex physiological signal analysis into separate frequency components and signal pairs. Instead of analyzing all signals simultaneously at once, the system segments the analysis into discrete coherence calculations between specific signal pairs (EEG-ECG, ECG-EMG, EEG-EMG) across different frequency bands, making the computational process more manageable and efficient.
3Measurement precision
If pairwise comparison techniques are used for signal coherence analysis, then the computational approach is simple, but the accuracy of identifying optimal performance states deteriorates due to limited signal relationship characterization
Solution Approach 1:
The patent merges multiple pairwise coherence comparisons into a unified multi-signal coherence framework. By combining the coherence relationships between EEG-ECG, ECG-EMG, and EEG-EMG signals into an integrated analysis model, the system captures the overall physiological state more comprehensively than any single pairwise comparison could achieve alone.
Solution Approach 2:
The patent creates a composite coherence metric that integrates information from multiple physiological signal relationships. Similar to how composite materials combine different substances to achieve superior properties, the system combines coherence measurements from multiple signal pairs to create a comprehensive performance state indicator that leverages the strengths of each individual signal relationship.
Data Source
AI summary
A system and method for determining a peak performance state by coherency of input signals from a test subject. The system includes two sensors for receiving separate input signals from a test subject. The system also includes a processor that is in communication with the sensors. The system further includes a memory that stores baseline data and is connected to the processor. The method includes the steps of receiving input signals from two sensors, communicating the input signals to a processor, analyzing the input signals with the processor as a function of a nonlinear relationship to determine coherency, and comparing the coherency data to baseline data stored in the memory to determine the presence of a peak performance state.