Stress Performance Training System Using GSR Data
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Solution Overview
Problem
Conventional systems for monitoring physiological data during stressful conditions fail to link this data with relevant performance indicators, lacking a technical solution for objectively training and evaluating user performance in high-stress scenarios.
Innovation Solution
A stress performance training system that includes sensor assemblies, client devices, trainer devices, and a stress management system, which collects and processes physiological data such as galvanic skin response (GSR) to generate performance scores, including a stress regulation score, and provides real-time feedback and alerts to improve user performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If conventional devices monitor physiological data during stressful conditions, then physiological monitoring is provided, but the monitored data is not linked to relevant performance indicators for high stress events
Solution Approach 1:
The patent combines multiple data streams (physiological monitoring data from wearables, performance indicator data from task completion, and contextual stressor data) into a unified evaluation framework. The system merges these previously separate data sources to create comprehensive performance assessments, directly resolving the contradiction by linking physiological data to performance indicators through integrated data processing and analysis mechanisms.
Solution Approach 2:
The patent introduces an intermediary processing layer that includes signal processing modules, feature extraction algorithms, and data correlation engines. This intermediary layer processes raw physiological data, contextualizes it with stressor information, and correlates it with performance indicators, thereby establishing the missing linkage without requiring direct complex integration of all data sources.
2Measurement precision
If the system processes and analyzes physiological data to generate performance scores, then objective evaluation of user performance is achieved, but data processing time and computational resources increase
Solution Approach 1:
The patent implements preliminary data processing actions by pre-processing physiological signals (filtering, baseline normalization, artifact removal) and pre-computing performance indicator thresholds before actual stress events occur. Historical data is used to establish baseline characteristics and predictive models in advance, enabling faster real-time evaluation during actual stressors while maintaining objective measurement precision.
Solution Approach 2:
The system applies partial processing by selectively analyzing only the most relevant physiological parameters (such as heart rate variability, skin conductance, or respiratory rate) rather than processing all available biological signals. The system identifies and processes only those physiological indicators most correlated with stress response and performance outcomes, reducing computational burden while maintaining evaluation objectivity.
3Productivity
If the system provides real-time feedback and alerts to users during stress events, then user performance improvement is enhanced, but system response time requirements increase
Solution Approach 1:
The patent implements multi-level feedback mechanisms that provide real-time physiological feedback to users through alerts and guidance cues during stress events. The system continuously monitors physiological data, compares it against performance thresholds, and delivers immediate feedback when stress regulation deviations are detected. This feedback loop enables users to adjust their stress response in real-time, enhancing performance improvement while the system maintains acceptable response times through efficient data processing pipelines.
Applied Scientific Principles
This section explains which scientific principles are used to turn an abstract innovation direction into a practical engineering solution.
Function Achieved in This Case
The system effectively evaluates and improves user performance in stressful conditions by providing objective performance scores and actionable feedback, enhancing the user's ability to regulate stress and perform better in high-stress events.
Implementation Method 1
the sensor assembly collects sensor data that describes in part physiological data (e.g., galvanic skin response (GSR) data) of the user
Data Source
AI summary
A stress performance training system may monitor sensor data that describes in part physiological data of a user collected over a time period. The sensor data includes galvanic skin response (GSR) data for the user. The system may determine a target period within the time period. The target period corresponds to an occurrence of the user experiencing a stressor. The system may pre-preprocess, for at least some of the time period including the target period, the GSR data to determine one or more performance scores of the user. The system may present the one or more performance scores for stress management. In some embodiments, the system may alert the user if a performance score of the one or more performance scores does not satisfy a threshold value. In some embodiments, the alert may include a recommended course of action for improving the performance score.