Emotional Connectivity Metrics via Physiological Synchrony
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Solution Overview
Problem
Current methods lack effective means to determine and enhance emotional fitness metrics and connectivity among individuals during group activities, which are crucial for overall well-being and resilience, especially in environments where social interaction is limited.
Innovation Solution
A system and method utilizing connected devices with biosensors to measure and synchronize physiological parameters like heart rate and breathing, calculating emotional connectivity and resilience metrics, and computing an emotional fitness metric by correlating these with cognitive appraisal, to provide insights and recommendations for improving emotional well-being.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If physiological measurements are collected from multiple users during group activities, then emotional connectivity and resilience metrics can be determined, but the complexity of the system increases due to need for synchronization and comparison of multiple data streams
Solution Approach 1:
The patent introduces a server as an intermediary component that receives physiological measurements from multiple user devices, performs time-synchronization, and computes emotional connectivity metrics. This centralizes the complex processing logic, allowing individual user devices to remain relatively simple while the server handles the sophisticated synchronization and comparison algorithms across multiple data streams.
Solution Approach 2:
The system divides the emotional fitness assessment into distinct measurable components: emotional connectivity metric (based on physiological synchrony between users), resilience metric (based on physiological variability), and cognitive appraisal metric. This segmentation allows each metric to be computed independently using specific physiological parameters, simplifying the overall measurement approach while maintaining comprehensive assessment capability.
2Measurement precision
If real-time synchronization of physiological signals is performed across multiple users, then accurate emotional connectivity metrics are achieved, but the processing time and computational resources increase
Solution Approach 1:
The system performs time-synchronization of physiological measurements as a preliminary step before computing emotional connectivity metrics. By pre-aligning the temporal references of multiple users' physiological data streams, the system eliminates the need for complex real-time synchronization during metric computation, reducing processing time while maintaining measurement accuracy.
Solution Approach 2:
The patent creates a normalized cross-correlation matrix that represents the synchrony relationships between all user pairings. This matrix serves as a computational copy or representation of the complex multi-user physiological interactions, allowing the emotional connectivity metric to be derived from this simplified structure rather than processing all raw physiological data streams directly.
3Measurement precision
If comprehensive physiological parameters are monitored to determine emotional fitness, then the accuracy of emotional health assessment is improved, but the quantity of data to be processed and stored increases
Solution Approach 1:
The system extracts and focuses on specific physiological parameters that are most relevant to emotional fitness assessment: heart rate variability, respiratory rate, and skin conductance. By selecting only these key parameters rather than monitoring all possible physiological signals, the system maintains high measurement precision for emotional fitness while significantly reducing the volume of data that needs to be collected, stored, and processed.
Solution Approach 2:
The patent computes emotional connectivity metrics by focusing on the synchrony of specific physiological parameters (heart rate, respiration) rather than analyzing all physiological data in detail. This partial action approach—concentrating computational resources on the most informative aspects of the physiological data—maintains assessment accuracy while reducing overall data processing requirements.
Data Source
AI summary
The disclosure provides methods and systems for determining an emotional fitness metrics for users. A physiological parameter of the user is measured during a group activity using at least one biosensor to acquire a measured signal. The measured signal of the user is compared to a measured signal for the physiological parameter of one or more additional users as measured when the one or more additional users perform the group activity. An emotional connectivity may be calculated based on a synchronicity of the measured signal of the user with the measured signal of the one or more additional users, as well as a cognitive appraisal metric, a resilience metric and an emotional fitness metric. Connectivity values for user pairings for a group activity can be computed based a synchronicity in a time-series correlation for the physiological parameters for permutations of the user pairings for the group activity.