User State Estimation via Noise Injection and Adaptive Learning
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Solution Overview
Problem
Current user state estimation algorithms suffer from low resolution, infrequent updates, and poor generalizability due to reliance on standard machine learning techniques, which struggle to accurately model human operator states in real-time settings, especially when variability in user states is high and data collection is limited.
Innovation Solution
A computer-based method that uses a model-based classifier and predictor to estimate user states in real-time with high resolution (0-100 scale) by integrating physiological, performance, situational, and self-reported measures, incorporating noise injection to enhance data frequency and multi-modal data sources for more accurate and frequent updates, and employing adaptive learning to adjust weights based on real-time data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If standard machine learning techniques are used to train algorithms to find optimal weights among a data set, then the algorithm can be trained to match a particular measure of human user state, but the user state estimation algorithms suffer from low resolution, infrequent updates, and poor generalizability
Solution Approach 1:
The system dynamically adjusts the update frequency and resolution of user state estimates based on real-time physiological data quality and task requirements. The model adapts its behavior from static machine learning predictions to dynamic continuous estimation, allowing high-resolution updates when data quality is high and adjusting resolution when data is limited, thus resolving the contradiction between precision and productivity.
Solution Approach 2:
The invention changes the fundamental parameters of user state estimation from discrete, infrequent predictions to continuous, high-resolution estimates. By transforming the output from static class labels to continuous physiological state values updated in real-time, the system achieves both high measurement precision and frequent updates simultaneously.
2Reliability
If the time window for physiological data is increased to reliably detect human operator state, then detection accuracy improves, but the update frequency decreases
Solution Approach 1:
The system maintains continuous physiological monitoring without requiring long data windows for each estimate. By continuously updating the physiological state model in real-time, the system achieves both high reliability and fast update rates, eliminating the trade-off between accuracy and speed that exists in traditional batch processing approaches.
Solution Approach 2:
The system performs preliminary physiological data collection and modeling continuously in the background, so that when a user state estimate is needed, the data is already processed and ready for immediate output. This preliminary action allows fast updates without sacrificing accuracy, as the continuous background processing maintains detection reliability.
3Measurement precision
If laboratory based task environment is created to vary human operator functional state across many levels, then high output resolution can be achieved, but the number of levels that are practical to create is very limited within reasonable time constraints
Solution Approach 1:
The system uses the operator's own physiological responses and self-reported state estimates during normal task performance to train the model. Instead of requiring external manipulation of task difficulty levels, the operator naturally varies their state through real-world tasks, providing rich training data without time constraints. This self-service approach achieves high resolution without the time loss of laboratory experimentation.
4Device complexity
If self-reported estimate of user state is assumed to remain constant through data collection trial, then simplification is achieved, but variability in operator state throughout each trial is not captured
Solution Approach 1:
The system continuously incorporates feedback from physiological measurements and self-reported state estimates to update the user state model in real-time. This feedback mechanism captures temporal variability in operator state without significantly increasing model complexity, as the updates follow a structured iterative process that refines estimates based on ongoing data collection.
Data Source
AI summary
Computer based systems and methods for estimating a user state are disclosed. In some embodiments, the methods comprise inputting a first input at an intermittent interval and a second input at a frequent interval into a user state estimation model to estimate the user state. In some embodiments, the first inputs are enhanced by injecting a noise input to create a plurality of enhanced first inputs whereby the plurality of enhance first inputs correspond to the plurality of second inputs at the frequent interval. In some embodiments, the first input comprises a self-reported input and the second inputs comprise a physiological input, a performance input or a situational input. In some embodiments, a machine learning algorithm creates the state estimation model. In some embodiments, the state estimation model estimates a future user state. In some embodiments, a computer based system for estimating a user state is provided.