Wearable Multimodal Tagging for Real-Time Event Video Clips
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Solution Overview
Problem
Existing wearable devices lack the capability to efficiently process and analyze multimodal data from physiological and environmental parameters to automatically identify significant events and generate relevant tagging information for multimedia streams in real-time, limiting their functionality beyond basic fitness tracking.
Innovation Solution
Implementing multimodal multimedia processing on wearable devices using image sensors and machine learning models, such as large language models (LLMs), to analyze physiological and environmental parameters and generate tagging information for selected video clips based on triggering events, enabling real-time editing and highlighting of important moments.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Manufacturing precision
If wearable devices continuously capture and store multimedia streams with high resolution, then the quality and detail of recorded data is improved, but the device complexity and energy consumption increase significantly
Solution Approach 1:
The patent extracts and processes only the most relevant portions of multimedia data by identifying triggering events (such as falls, collisions, or abnormal physiological patterns) and generating tags only for those specific segments. This selective processing approach maintains high data quality for important events while significantly reducing the overall computational burden and device complexity requirements.
Solution Approach 2:
The system performs preliminary analysis of physiological and environmental parameters in real-time to detect triggering events before processing the full multimedia stream. By pre-identifying significant moments through sensor data analysis, the device can then focus computational resources only on those specific time segments, rather than continuously processing all multimedia data at high resolution.
2Measurement precision
If wearable devices process and analyze multiple physiological and environmental parameters simultaneously, then the accuracy of event detection is improved, but the energy consumption increases
Solution Approach 1:
The patent implements partial processing by analyzing multiple physiological parameters (heart rate, temperature, acceleration) and environmental parameters simultaneously only when triggering conditions are detected. During normal operation, the device uses lower-power monitoring modes with reduced processing intensity, activating full multi-parameter analysis only when necessary to detect significant events, thus balancing detection accuracy with energy conservation.
3Loss of information
If wearable devices generate and store detailed tagging information for all multimedia content, then the usefulness and analyzability of data is improved, but the storage requirements and processing time increase
Solution Approach 1:
The system extracts and generates tagging information only for specific segments of multimedia content that correspond to detected triggering events, rather than processing and tagging the entire continuous stream. This approach preserves all critical information about significant events while dramatically reducing the total processing time and storage requirements by excluding redundant data from normal, non-event periods.
Data Source
AI summary
A method of multimodal multimedia processing for at least one wearable device comprising an image sensor and at least one processor. The method includes in response to the image sensor being turned on, obtaining a first measurement of at least one of a physiological parameter of an individual or an environmental parameter captured by the at least one wearable device in a vicinity of the individual; determining whether a triggering event has occurred based on the first measurement, the triggering event associated with generating tagging information based on the first measurement using a machine learning model adapted to run on the at least one processor; and in response to determining that the triggering event has occurred, editing a selected clip from a multimedia stream currently being captured by the image sensor to include the tagging information generated based on the first measurement at a corresponding timestamp.