Selective Content Delivery via Wearable Attention Tracking
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Solution Overview
Problem
Users may miss parts of streaming or live content due to distraction, lack of attention, or cognitive state changes, leading to incomplete understanding of the media being consumed.
Innovation Solution
A system that uses wearable devices and IoT-enabled cameras to track a user's eye direction, attentiveness, and cognitive state, identifying when a user's attentiveness score falls below a threshold, and automatically delivers the missed content sections to ensure the user can follow the media seamlessly.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Loss of time
If streaming media is delivered in continuous packets without local storage, then download waiting time is reduced and no physical copies are stored locally, but lost or damaged packets cause delays in content display
Solution Approach 1:
The system performs preliminary actions by detecting user attention status in real-time and proactively identifying content that will be missed. It prepares and delivers missed content sections before the user actually needs them, using attention detection data to predict which content portions require supplementation. This allows the system to pre-fetch and prepare replacement content without waiting for packet loss to occur.
Solution Approach 2:
The system introduces an intermediary mechanism - an attention detection and content analysis system - that sits between the streaming content source and the user display. This intermediary monitors user attention, analyzes content importance, and selectively supplements missed content, thereby mediating between the continuous streaming packets and the user's actual content reception without requiring full local storage.
2Loss of information
If the system tracks user attentiveness and delivers missed content, then user understanding of media is improved, but device complexity increases due to wearable devices and real-time monitoring
Solution Approach 1:
The system applies multi-functionality by using a unified attention detection mechanism that serves multiple purposes: it monitors user engagement levels, identifies moments of distraction, determines content importance, and triggers selective content delivery. The same sensor data from wearable devices is processed to both detect attention loss and guide content supplementation decisions, reducing the need for separate specialized systems.
Solution Approach 2:
The system changes parameters by converting raw sensor data from wearable devices into an attentiveness score through signal processing and analysis. It transforms physical measurements (eye movement, head position, etc.) into a meaningful metric that can be thresholded and used to trigger content delivery decisions. This parameter transformation simplifies the complexity by creating a single actionable metric from multiple sensor inputs.
3Loss of information
If the system delivers missed content sections, then user engagement and comprehension are enhanced, but additional data transmission and processing requirements increase
Solution Approach 1:
The system applies local quality by selectively delivering only the specific content sections that were missed during user distraction, rather than retransmitting entire content segments. It identifies the precise temporal and spatial boundaries of missed content and delivers supplements localized to those specific gaps, minimizing unnecessary data transmission and energy consumption while maintaining content completeness.
Data Source
AI summary
A method, computer system, and computer program product for selectively delivering missed content to a user are provided. The embodiment may include identifying a user's eye direction, awareness, and attentiveness using a wearable device. The embodiment may also include determining whether the user is in a physical place where the user is viewing a live event. The embodiment may further include determining a minimum level of an attentiveness score for the user. The embodiment may also include identifying the attentiveness score of the user while watching any content when the user's attentive score is below a preconfigured threshold value. The embodiment may further include delivering content that the user missed based on the attentive score.