Rolling-Buffer Biosignal Capture for Emotional Condition Visualization
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Solution Overview
Problem
Existing methods fail to efficiently capture and store human emotional conditions in a way that allows for real-time or post-hoc visualization and sharing of emotional experiences without interrupting the user's experience or requiring prediction of emotion events.
Innovation Solution
A method involving wearable biosensors that collect and store biosignal data in a rolling buffer, transforming it into emotional data, and generating visualizations that can be shared with others, allowing users to record and replay their emotional experiences.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If biosensors continuously collect and store biosignal data in a rolling buffer, then emotional experiences can be captured without interruption, but data storage requirements increase
Solution Approach 1:
The patent extracts only the relevant portions of continuous biosignal data by using trigger events to identify and save specific emotional episodes. Instead of storing all biosignal data continuously, the system extracts and stores only the segments surrounding triggered emotional events, significantly reducing storage requirements while maintaining capture reliability.
Solution Approach 2:
The system performs preliminary actions by continuously monitoring biosignals and maintaining a rolling buffer of recent data in memory before a trigger event occurs. This preliminary buffering allows the system to immediately capture and save the relevant emotional episode data when triggered, without needing to store all historical data permanently.
2Speed
If the system transforms biosignal data into emotional data in real-time, then emotional visualization is immediate, but processing complexity increases
Solution Approach 1:
The system applies partial action by transforming biosignal data into emotional data only for the specific time windows surrounding trigger events, rather than continuously transforming all biosignal data. This selective transformation reduces processing complexity while maintaining real-time visualization capability for relevant emotional episodes.
3Measurement precision
If the system captures biosignal data from multiple biosensors simultaneously, then emotional measurement precision improves, but device complexity increases
Solution Approach 1:
The patent merges data from multiple biosensors (heart rate, skin temperature, galvanic skin response, etc.) into a unified emotional state assessment. By combining multiple sensor inputs and processing them through a single emotion transformation algorithm, the system achieves high measurement precision while managing device complexity through data consolidation rather than separate processing chains.
Data Source
AI summary
One variation of a method for deriving and storing emotional conditions of humans includes: writing timeseries biosignal data, output by a set of biosensors in a local device coupled to a user, to a rolling buffer spanning a look-back duration; in response to a trigger event at a first time, retrieving a set of biosignal data, spanning a first period of time preceding the first time, from the rolling buffer; transforming the set of biosignal data into a timeseries of emotions exhibited by the user during the first period of time; generating a visualization of the timeseries of emotions; and rendering the visualization of the timeseries of emotions on a display.