Sensor Event Detection System for Social Media Tagging
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Solution Overview
Problem
Existing motion capture systems fail to efficiently detect and tag events using sensor data, leading to excessive data storage and processing requirements, inability to differentiate between event types, and lack of integration with social media for event confirmation and filtering.
Innovation Solution
A sensor and media event detection system that utilizes motion capture sensors and other sensors to confirm and differentiate events, integrates with social media for event tagging and filtering, and enables intelligent video synchronization and transfer, reducing storage needs by uploading only event-related data.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If all motion capture data is stored and uploaded, then complete data record is maintained, but storage requirements and upload times increase excessively
Solution Approach 1:
The system extracts and identifies only the relevant portions of motion capture data that correspond to detected events, separating them from the bulk of non-event data. This allows the system to maintain complete event data for reliability while excluding irrelevant data segments, thereby reducing overall storage volume and upload requirements.
Solution Approach 2:
The system performs preliminary event detection and data filtering during the data capture phase, identifying and tagging event-related segments before storage or upload. This preliminary classification enables subsequent efficient retrieval and transmission of only necessary data, reducing both storage needs and upload times while preserving complete event information.
2Measurement precision
If event detection sensitivity is increased to capture all events, then detection accuracy improves, but false positive events increase
Solution Approach 1:
The system implements feedback mechanisms where detected events are validated against multiple criteria including sensor data patterns, contextual information, and previously learned event characteristics. This feedback loop allows the system to maintain high detection sensitivity while filtering out false positives through iterative validation and correction.
Solution Approach 2:
The system dynamically adjusts detection parameters and thresholds based on contextual information and learned patterns from previous events. By changing parameters adaptively rather than using fixed thresholds, the system maintains high detection accuracy while reducing false positives through context-aware parameter optimization.
3Measurement precision
If multiple sensors are integrated to differentiate event types, then event classification accuracy improves, but device complexity increases
Solution Approach 1:
The system segments the complex task of event differentiation into distinct processing stages, with each sensor type handling specific aspects of event detection. This segmentation allows multiple sensors to be integrated systematically, with each contributing specialized measurements that together enable accurate event classification without overwhelming system complexity.
Solution Approach 2:
The system implements a unified event detection framework that processes data from multiple sensor types through a common analysis pipeline. This multi-functional approach allows the same processing architecture to handle data from various sensors, reducing overall system complexity while maintaining the ability to differentiate event types through combined sensor inputs.
4Reliability
If video synchronization is performed for all motion data, then complete video record is maintained, but storage and processing requirements increase
Solution Approach 1:
The system extracts and synchronizes video segments only for detected event portions, separating event-related video from non-event video. This extraction approach maintains complete video records for all events while excluding irrelevant video segments, thereby reducing overall video storage volume and processing requirements while preserving video completeness for event analysis.
Data Source
AI summary
Enables detection and tagging of events using sensor data combined with data from servers such as social media sites. Sensors may measure values such as motion, temperature, humidity, wind, pressure, elevation, light, sound, or heart rate. Sensor data and event tags may be utilized to curate text, images, video, sound and post the results to social networks, for example in a dedicated feed. Event tags generated by the system may represent for example activity types, players, performance levels, or scoring results. The system may analyze social media postings to confirm or augment event tags. Users may filter and analyze saved events based on the assigned tags. The system may create highlight and fail reels filtered by metrics and by tags.