Exercise Replay Capture Using Gaze-Based Object Selection
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Solution Overview
Problem
Existing systems fail to selectively capture and replay user-based activities in computer-generated environments, particularly in extended reality settings, by identifying and prioritizing relevant objects based on user interaction and preferences.
Innovation Solution
An electronic device captures images for a timelapse by detecting user gaze and interaction with objects in a physical environment, applying criteria such as gaze duration and user data to determine object relevance, and generating metadata for formulation of a compilation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of information
If all objects in the physical environment are captured for timelapse compilation, then the completeness of the activity record is improved, but the data processing load and storage requirements increase
Solution Approach 1:
The system extracts only the relevant objects from the complete set of captured images by applying selection criteria (user gaze duration, interaction events, object importance scores). This extraction process filters out unnecessary data while preserving the essential activity information, thereby reducing processing load without sacrificing record completeness.
Solution Approach 2:
Different regions or objects in the captured environment are treated differently based on their relevance. Objects that are frequently gazed at or interacted with are selected for inclusion in the timelapse compilation, while less important objects are excluded. This local differentiation optimizes the balance between information completeness and processing efficiency.
2Measurement precision
If the system captures and processes all visual data during user activities, then the accuracy of activity documentation is improved, but the energy consumption and processing time increase
Solution Approach 1:
The system performs preliminary filtering of captured images by evaluating objects against predefined criteria (gaze duration thresholds, interaction event detection, importance scoring) before final compilation. This preliminary action reduces the volume of data requiring intensive processing, thereby lowering energy consumption while maintaining documentation accuracy.
Solution Approach 2:
Instead of processing all captured images with equal intensity, the system applies partial processing only to objects that meet the selection criteria. Objects with high relevance scores undergo detailed analysis and inclusion, while others receive minimal or no processing, optimizing the energy-accuracy tradeoff.
3Loss of information
If the timelapse compilation includes all detected objects, then the comprehensiveness of the replay is improved, but the user engagement and personalization decrease
Solution Approach 1:
The system incorporates user feedback mechanisms where user gaze patterns, interaction events, and explicit preferences are continuously monitored and used to adjust object selection criteria. This feedback loop enables the system to personalize the timelapse compilation by prioritizing objects that are most relevant to the specific user's interests and behaviors, thereby enhancing engagement while maintaining comprehensiveness.
Solution Approach 2:
The object selection criteria are made dynamic rather than static. The system adjusts which objects are included in the timelapse based on real-time user behavior data, historical preferences, and contextual factors. This dynamic adaptation allows the replay to be personalized for each user while still maintaining comprehensive coverage of relevant activities.
Data Source
AI summary
In some examples, an electronic device detects initiation of an exercise activity associated with a user of the electronic device. In some examples, in response to detecting the initiation of the exercise activity, the electronic device activates an image compilation capture mode of operation in which one or more images for formulating a compilation of the one or more images corresponding to the exercise activity are captured, including capturing one or more portions of a physical environment. In some examples, while the image compilation capture mode of operation is active, the electronic device visually detects a first object in the one or more captured portions of the physical environment. In some examples, in accordance with a determination that the first object satisfies one or more criteria, the electronic device generates a first event associated with formulating a first compilation of one or more images corresponding to the exercise activity.