Stress Valence Estimation via Habit-Aware Physiological Triggering
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Solution Overview
Problem
Current methods for monitoring stress valence using sensor data from user devices are intrusive and inefficient, leading to high user burden and decreased feedback, as they often trigger ecological momentary assessments (EMAs) randomly or periodically, resulting in false triggers and reduced user acceptance.
Innovation Solution
A system that filters physiological arousal unrelated to stress, triggers EMAs only when significant, considers habit information to determine stressors, and transmits EMAs at appropriate times, using dynamic temporal backtracking to estimate stress valence by combining contextual and physiological data with EMA responses, thereby reducing interruptions and improving user acceptance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If EMAs are triggered randomly or periodically to monitor stress valence, then stress monitoring coverage is improved, but user burden increases and false triggers occur
Solution Approach 1:
The system changes the triggering parameters from random or periodic intervals to event-driven thresholds based on physiological arousal levels. By monitoring continuous physiological data and triggering EMAs only when arousal exceeds significant thresholds, the system maintains reliable stress monitoring coverage while reducing unnecessary triggers and user burden.
Solution Approach 2:
The system uses automatic physiological data processing and machine learning models to determine when stress episodes occur, eliminating the need for manual user initiation. The system serves itself by automatically detecting stress states through sensor data and autonomously triggering EMAs at appropriate moments without user intervention.
2Loss of information
If EMAs are triggered frequently to capture stress episodes, then data collection completeness is improved, but user acceptance decreases
Solution Approach 1:
The system applies partial action by triggering EMAs only for significant stress episodes rather than all potential stress moments. By using threshold-based filtering and habit information to distinguish meaningful stress events from normal fluctuations, the system collects sufficient data for accurate stress valence estimation while maintaining user acceptance through reduced interruption frequency.
Solution Approach 2:
The system incorporates feedback loops where EMA responses are used to refine and update the machine learning model. This continuous feedback improves the accuracy of stress episode detection over time, allowing the system to become more selective in triggering EMAs, thereby maintaining data completeness while reducing false triggers and improving user acceptance.
3Measurement precision
If physiological arousal data is used to trigger EMAs, then stress detection sensitivity is improved, but false triggers from non-stress arousal increase
Solution Approach 1:
The system segments the physiological arousal data by analyzing patterns over time windows and distinguishing between different types of arousal based on contextual factors. By dividing the continuous data stream into meaningful episodes and applying habit information to categorize them, the system maintains high sensitivity for detecting actual stress while filtering out false triggers from non-stress-related arousal.
Solution Approach 2:
The system introduces habit information and machine learning models as intermediaries between raw physiological arousal data and EMA triggering decisions. These intermediaries process and interpret the arousal data, using learned patterns to distinguish stress-related arousal from other causes, thereby maintaining detection sensitivity while reducing false trigger rates.
Data Source
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AI summary
Systems, methods, apparatuses, and computer program products for inferring the stress valence of an individual from sensor data are provided. One method includes collecting a first set of data from sensor(s) of user device(s), collecting a second set of data including a partial or complete ecological momentary assessment (EMA) response from a user, establishing a set of physiological time-series data for the user based on the first set of data and the second set of data, identifying habit information of the user based on the first set of data. Based on the first set of data and the habit information, detecting a potential trigger point for sending a subsequent EMA to the user, and, at the time of the potential trigger point, calculating an estimate of a stress valence value associated with the subsequent EMA for which an EMA response was at least partially not received.