Automated Addiction Support via Physiological Trigger Detection
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Solution Overview
Problem
Conventional methods for addressing addiction are insufficient in providing effective automated assistance, particularly in recognizing physiological and environmental triggers for addictive behaviors.
Innovation Solution
A method and system that programmatically captures physiological and accelerometer data to determine primary and secondary triggering events, automatically initiating communication with a designated support contact through a mobile app, using predefined thresholds to minimize false positives and ensure timely intervention.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If conventional methods are used to address addiction, then manual intervention is provided, but the system cannot effectively recognize physiological and environmental triggers for addictive behaviors
Solution Approach 1:
The system segments trigger recognition into two distinct levels: primary triggers detected through physiological data (heart rate, blood pressure, temperature) and secondary triggers detected through accelerometer data (movement patterns, shaking, falling). This segmentation allows the system to precisely identify different types of triggers while maintaining manageable complexity through modular detection mechanisms.
Solution Approach 2:
The mobile application serves multiple functions: it continuously monitors physiological data, processes accelerometer data, identifies primary and secondary triggers, determines appropriate positive user contacts, and initiates communications. This multi-functionality consolidates what would otherwise require multiple separate systems into a single integrated platform, improving trigger recognition without proportionally increasing complexity.
2Loss of time
If automated communication initiation is implemented, then timely intervention is provided, but false positives may increase
Solution Approach 1:
The system performs preliminary analysis by first detecting primary triggers through physiological data before proceeding to secondary trigger detection via accelerometer data. This preliminary action structure ensures that automated communications are only initiated when both primary and secondary triggers are confirmed, reducing false positives while maintaining rapid intervention response when genuine triggers are detected.
Solution Approach 2:
The system uses feedback loops where accelerometer data serves as validation feedback for physiological trigger detection. When a primary physiological trigger is detected, the system checks accelerometer data for corresponding secondary trigger patterns before finalizing the trigger identification and initiating communication. This feedback mechanism significantly reduces false positives while keeping the overall process automated and timely.
3Reliability
If multiple data thresholds are used to determine triggers, then false positives are reduced, but the system complexity increases
Solution Approach 1:
The system segments threshold evaluation into two distinct stages: primary physiological thresholds (heart rate, blood pressure, temperature) and secondary accelerometer thresholds (movement intensity, pattern recognition). Each stage has its own set of thresholds and evaluation criteria, making the complex multi-threshold system more manageable through modular organization while maintaining high detection accuracy.
Solution Approach 2:
The system evaluates primary physiological thresholds first as a preliminary filter, then proceeds to evaluate secondary accelerometer thresholds only when primary triggers are detected. This preliminary action approach reduces the overall computational burden by not continuously processing both data types at full complexity, while still maintaining reliable trigger detection through the two-stage threshold evaluation process.
Data Source
AI summary
Embodiments of the invention provide apparatuses, systems, and methods with the ability to programmatically capture different types of data and to determine whether the data satisfies one or more thresholds indicative of one or more triggering events, and responsive thereto, to automatically initiate a communication between a user and a positive user contact.