Wearable Device Event Detection for Generative AI Third-Person AR
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Solution Overview
Problem
Existing wearable devices lack the capability to provide third-person perspective content, limiting their ability to enhance user experience through augmented reality by incorporating external events and user interactions.
Innovation Solution
An electronic device equipped with a camera, sensor, and processor that identifies events through video and sensing data, generates descriptions, extracts prompts, and inputs them into a generative AI model to create third-person perspective content.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If wearable devices capture first-person perspective video and sensing data, then user experience is enhanced through direct immersion, but the ability to provide third-person perspective content for augmented reality is limited
Solution Approach 1:
The system inverts the traditional first-person capture approach by using generative AI to create third-person perspective content from first-person video and sensing data. Instead of directly capturing third-person views, the system processes first-person data through event identification, description generation, and AI-based third-person content synthesis, enabling AR applications to receive externally-oriented perspective information.
Solution Approach 2:
The system introduces multiple intermediary processing stages between first-person data capture and third-person content delivery: event identification module detects significant moments, description generation module creates narrative representations, and generative AI model synthesizes third-person perspective content. These intermediaries transform raw first-person data into structured third-person information suitable for AR display.
2Manufacturing precision
If the device processes video and sensing data through multiple stages (event identification, description generation, prompt extraction), then third-person perspective content quality is improved, but processing complexity increases
Solution Approach 1:
The processing pipeline is segmented into distinct functional modules: event identification module that detects significant moments in video/sensing data, description generation module that creates narrative descriptions of identified events, prompt extraction module that formulates input for generative AI, and content generation module that produces third-person perspective content. This segmentation allows each module to specialize in one aspect of the transformation process, improving overall precision while managing complexity through modular design.
3Measurement precision
If the device continuously captures video and sensing data to identify events, then event detection accuracy is improved, but energy consumption increases
Solution Approach 1:
Instead of continuous processing, the system employs periodic event identification triggered by detected events or time intervals. The event identification module operates periodically on captured video and sensing data, generating descriptions and prompts only when significant events are detected or at scheduled intervals. This periodic operation maintains event detection accuracy for important moments while significantly reducing energy consumption compared to continuous full-pipeline processing.
Data Source
AI summary
An electronic device according to an example embodiment includes a processor, memory, a camera for generating a video, and a sensor for obtaining sensing data related to a user. The electronic device generate the content by identifying a valid event based on the video and/or the sensing data, extracting a prompt for generating third-person perspective content corresponding to the event, and inputting the prompt into a generative artificial intelligence model.