Contextual Video Recording Threshold Adjustment
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Solution Overview
Problem
Users face frustration with excessive captured video data and insufficient recording of interesting events due to devices continuously recording or failing to initiate recording when the user is interested, as biometric data meanings vary by context.
Innovation Solution
A method and system that adjust video recording thresholds based on contextual attributes and physiological measurements to determine user interestedness, initiating or terminating recording when the derived interestedness score meets or exceeds the set threshold.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If devices continuously record video to ensure all interesting events are captured, then the quantity of captured video increases, but the amount of unnecessary recording and data storage increases
Solution Approach 1:
The system dynamically changes the recording threshold parameter based on contextual information. When context indicates high user interestedness, the threshold is lowered to trigger recording; when context indicates low interestedness, the threshold is raised to prevent unnecessary recording. This resolves the contradiction by making the recording decision adaptive rather than static.
Solution Approach 2:
The system uses the user's own contextual data (biometric measurements, calendar events, location) to automatically determine when recording should occur, eliminating the need for continuous manual monitoring or continuous recording. The system serves itself by using its own collected context to make intelligent recording decisions.
2Device complexity
If devices use fixed thresholds for triggering video recording based on biometric data, then the system complexity is reduced, but the accuracy of detecting user interestedness decreases
Solution Approach 1:
The system transitions from static fixed thresholds to dynamic contextual thresholds. The threshold value changes dynamically based on the user's current context (activity, location, calendar events, biometric state). This allows the system to maintain simplicity in its decision logic while achieving high precision through adaptive threshold adjustment.
Solution Approach 2:
The system performs preliminary analysis of contextual attributes (calendar events, location, biometric data) before setting the recording threshold. By preparing and analyzing context in advance, the system can quickly make accurate recording decisions without complex real-time processing during the actual triggering moment.
3Quantity of substance
If devices manually trigger video recording to reduce unnecessary recording, then the quantity of captured video decreases, but the user may forget or fail to initiate recording when interested
Solution Approach 1:
The system continuously monitors contextual attributes and provides feedback to the recording decision process. When biometric data indicates heightened interestedness and contextual analysis supports it (e.g., user is at a location of interest, has an upcoming event), the system triggers recording automatically. This closed-loop feedback system ensures reliable capture without requiring user intervention.
Solution Approach 2:
The system introduces contextual analysis as an intermediary between raw biometric data and recording decisions. Rather than directly triggering on biometric thresholds alone, the context module mediates by evaluating multiple factors (calendar, location, activity) to determine true user interestedness, reducing false positives while maintaining reliability.
Data Source
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AI summary
Disclosed herein are methods and systems for contextual adjustment of thresholds of user interestedness for triggering video recording. An embodiment takes the form of a method that includes identifying one or more current contextual attributes of a current context of a user. The method also includes setting a current value of a stored interestedness threshold based at least in part on the one or more identified current contextual attributes. The method also includes obtaining one or more current physiological measurements of the user. The method also includes deriving an interestedness score based at least in part on the one or more obtained current physiological measurements. The method also includes comparing the derived interestedness score to the current value of the stored interestedness threshold. The method also includes initiating video recording when the derived interestedness score exceeds the current value of the stored interestedness threshold.